EP3769321A1 - Verfahren und system zur charakterisierung von mit mikroorganismen verbundenen schlafbezogenen zuständen - Google Patents

Verfahren und system zur charakterisierung von mit mikroorganismen verbundenen schlafbezogenen zuständen

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Publication number
EP3769321A1
EP3769321A1 EP18733484.2A EP18733484A EP3769321A1 EP 3769321 A1 EP3769321 A1 EP 3769321A1 EP 18733484 A EP18733484 A EP 18733484A EP 3769321 A1 EP3769321 A1 EP 3769321A1
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EP
European Patent Office
Prior art keywords
bacteroides
species
lactobacillus
actinomyces
streptococcus
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP18733484.2A
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English (en)
French (fr)
Inventor
Zachary APTE
Jessica RICHMAN
Daniel Almonacid
Inti Pedroso
Catalina Valdivia
Rodrigo Ortiz
Paz Tapia
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Psomagen Inc
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Psomagen Inc
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Publication date
Application filed by Psomagen Inc filed Critical Psomagen Inc
Publication of EP3769321A1 publication Critical patent/EP3769321A1/de
Pending legal-status Critical Current

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/30—Unsupervised data analysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • Provisional Application serial number 62/147,362 filed 14-APR-2015
  • U.S. Provisional Application serial number 62/146,855 filed 13-APR-2015
  • U.S Provisional Application serial number 62/206,654 filed 18-AUG-2015, which are each incorporated in its entirety herein by this reference.
  • the disclosure generally relates to genomics and microbiology.
  • a microbiome can include an ecological community of commensal, symbiotic, and pathogenic microorganisms that are associated with an organism. Characterization of the human microbiome is a complex process. The human microbiome includes over 10 times more microbial cells than human cells, but characterization of the human microbiome is still in nascent stages such as due to limitations in sample processing techniques, genetic analysis techniques, and resources for processing large amounts of data. Present knowledge has clearly established the role of microbiome associations with multiple health conditions, and has become an increasingly appreciated mediator of host genetic and environmental factors on human disease development. The microbiome is suspected to play at least a partial role in a number of health/disease-related states.
  • microbiome may mediate effects of environmental factors on human, plant, and/or animal health. Given the profound implications of the microbiome in affecting a user's health, efforts related to the characterization of the microbiome, the generation of insights from the characterization, and the generation of therapeutics configured to rectify states of dysbiosis should be pursued. Methods and systems for analyzing the microbiomes of humans and/or providing therapeutic measures based on gained insights have, however, left many questions unanswered.
  • FIGURE l is a flowchart representation of variations of an embodiment of a method
  • FIGURE 2 depicts embodiments of a method and system
  • FIGURE 3 depicts a variation of a process for generation of a characterization model in an embodiment of a method
  • FIGURE 4 depicts variations of mechanisms by which probiotic-based therapies operate in an embodiment of a method
  • FIGURE 5 depicts variations of sample processing in an embodiment of a method
  • FIGURE 6 depicts examples of notification provision
  • FIGURE 7 depicts a schematic representation of variations of an embodiment of the method
  • FIGURES 8A-8C depicts variations of performing characterization processes with models
  • FIGURE 9 depicts promoting a therapy in an embodiment of a method.
  • embodiments of a method 100 for characterizing one or more sleep-related conditions can include: determining a microorganism dataset (e.g., microorganism sequence dataset, microbiome composition diversity dataset such as based upon a microorganism sequence dataset, microbiome functional diversity dataset such as based upon a microorganism sequence dataset, etc.) associated with a set of subjects Sno; and/or performing a characterization process (e.g., pre-processing, feature determination, feature processing, sleep-related characterization model processing, etc.) associated with the one or more sleep-related conditions, based on the microorganism dataset (e.g., based on microbiome composition features and/or microbiome functional features derived from the microorganism dataset and associated with the one or more sleep-related conditions; etc.) S130, where performing the characterization process can additionally or alternatively include performing a sleep-related characterization process for the one or more sleep-related conditions S135, and/or determining one or
  • Embodiments of the method 100 can additionally or alternatively include one or more of: processing supplementary data associated with (e.g., informative of; describing; indicative of; correlated with, etc.) one or more sleep-related conditions S120; processing one or more biological samples associated with a user (e.g., subject, human, animal, patient; etc.) S150; determining, with one or more characterization processes, a sleep-related characterization for the user for one or more sleep-related conditions, based on a user microorganism dataset (e.g., user microorganism sequence dataset; user microbiome composition dataset; user microbiome function dataset; user microbiome features derived from the user microorganism dataset, where the user microbiome features can correspond to feature values for the microbiome features determined from one or more characterization processes; etc.) associated with a biological sample of the user S160; facilitating therapeutic intervention for the one or more sleep-related conditions for the user (e.g., based upon the sleep-related characterization and/or a
  • Embodiments of the method 100 and/or system 200 can function to characterize (e.g., assess, evaluate, diagnose, describe, etc.) one or more sleep-related conditions (e.g., characterizing the sleep-related conditions themselves, such as determining microbiome features correlated with and/or otherwise associated with the sleep-related conditions; characterizing one or more sleep-related conditions for one or more users, such as determining propensity metrics for the one or more sleep-related conditions for the one or more users; etc.).
  • one or more sleep-related conditions e.g., characterizing the sleep-related conditions themselves, such as determining microbiome features correlated with and/or otherwise associated with the sleep-related conditions; characterizing one or more sleep-related conditions for one or more users, such as determining propensity metrics for the one or more sleep-related conditions for the one or more users; etc.
  • the method 100 can include: determining a microorganism dataset associated with a set of subjects (e.g., including subjects with one or more sleep-related conditions, subjects without the one or more sleep-related conditions, etc.), based on microorganism nucleic acids from biological samples associated with the set of subjects, where the microorganism nucleic acids are associated with one or more sleep-related conditions; processing (e.g., collecting, etc.), for the set of subjects, supplementary data associated with the one or more sleep- related conditions; determining microbiome features (e.g., at least one of a set of microbiome composition features and a set of microbiome functional features, etc.) associated with the set of subjects, based on the microorganism dataset (and/or the supplementary data and/or other suitable data); generating a sleep-related characterization model (e.g., for determining sleep-related characterizations; a therapy model; etc.) based on the supplementary data and the microbiome features, where the sleep-related
  • the method 100 can include: collecting a biological sample from a user (e.g., via sample kit provision and collection, etc.), where the biological sample includes microorganism nucleic acids associated with one or more sleep-related conditions; determining a microorganism dataset associated with the user based on the microorganism nucleic acids of the biological sample (e.g., based on sample preparation and/or sequencing with the biological sample, etc.); determining user microbiome features (e.g., including at least one of user microbiome composition features and user microbiome functional features, based on the microorganism dataset, etc.), where the user microbiome features are associated with the one or more sleep-related conditions; determining a sleep-related characterization for a user for the one or more sleep-related conditions based on the user microbiome features; and facilitating therapeutic intervention in relation to a therapy for the user for facilitating improvement of the one or more sleep-related conditions, based on the sleep-related characterization.
  • a biological sample from a user (e.g
  • embodiments of the method 100 and/or system 200 can identify microbiome features and/or other suitable data associated with (e.g., positive correlated with, negatively correlated with, etc.) one or more sleep-related conditions, such as for use as biomarkers (e.g., for diagnostic processes, for treatment processes, etc.).
  • sleep-related characterization can be associated with at least one or more of user microbiome composition (e.g., microbiome composition diversity, etc.), microbiome function (e.g., microbiome functional diversity, etc.), and/or other suitable microbiome-related aspects.
  • microorganism features e.g., describing composition, function, and/or diversity of recognizable patterns, such as in relation to relative abundance of microorganisms that are present in a subject's microbiome, such as for subjects exhibiting one or more sleep-related conditions; etc.
  • microorganism datasets e.g., from which microbiome features can be derived, etc.
  • diagnostics, characterizations, therapeutic intervention facilitation, monitoring, and/or other suitable purposes such as by using bioinformatics pipelines, analytical techniques, and/or other suitable approaches described herein.
  • embodiments of the method 100 and/or system 200 can function to perform cross-condition analyses for a plurality of sleep- related conditions (e.g., performing characterization processes for a plurality of sleep- related conditions, such as determining correlation, covariance, comorbidity, and/or other suitable relationships between different sleep-related conditions, etc.), such as in the context of characterizing, diagnosing, and/or treating a user.
  • characterization processes for a plurality of sleep- related conditions, such as determining correlation, covariance, comorbidity, and/or other suitable relationships between different sleep-related conditions, etc.
  • embodiments can function to facilitate therapeutic intervention (e.g., therapy selection; therapy promotion and/or provision; therapy monitoring; therapy evaluation; etc.) for one or more sleep-related conditions, such as through promotion of associated therapies (e.g., in relation to specific physiological sites gut, skin, nose, mouth, genitals, other suitable physiological sites, other collection sites; therapies determined by therapy models; etc.).
  • therapeutic intervention e.g., therapy selection; therapy promotion and/or provision; therapy monitoring; therapy evaluation; etc.
  • therapies e.g., in relation to specific physiological sites gut, skin, nose, mouth, genitals, other suitable physiological sites, other collection sites; therapies determined by therapy models; etc.
  • embodiments can function to generate models (e.g., sleep-related characterization models such as for phenotypic prediction; therapy models such as for therapy determination; machine learning models such as for feature processing, etc.), such as models that can be used to characterize and/or diagnose users based on their microbiome (e.g., user microbiome features; as a clinical diagnostic; as a companion diagnostic, etc.), and/or that can be used to select and/or provide therapies for subjects in relation to one or more sleep-related conditions. Additionally or alternatively, embodiments can perform any suitable functionality described herein.
  • models e.g., sleep-related characterization models such as for phenotypic prediction; therapy models such as for therapy determination; machine learning models such as for feature processing, etc.
  • models e.g., sleep-related characterization models such as for phenotypic prediction; therapy models such as for therapy determination; machine learning models such as for feature processing, etc.
  • models e.g., sleep-related characterization models such as for phenotypic prediction
  • data from populations of subjects can be used to characterize subsequent users, such as for indicating microorganism-related states of health and/or areas of improvement, and/or to facilitate therapeutic intervention (e.g., promoting one or more therapies; facilitating modulation of the composition and/or functional diversity of a user's microbiome toward one or more of a set of desired equilibrium states, such as states correlated with improved health states associated with one or more sleep-related conditions; etc.), such as in relation to one or more sleep-related conditions.
  • Variations of the method 100 can further facilitate selection, monitoring (e.g., efficacy monitoring, etc.) and/or adjusting of therapies provided to a user, such as through collection and analysis (e.g., with sleep- related characterization models) of additional samples from a subject over time (e.g., throughout the course of a therapy regimen, through the extent of a user's experiences with sleep-related conditions; etc.), across collection sites, in addition or alternative to processing supplementary data over time (e.g., sleep-tracking data, etc.), such as for one or more sleep-related conditions.
  • data from populations, subgroups, individuals, and/or other suitable entities can be used by any suitable portions of the method 100 and/or system 200 for any suitable purpose.
  • Embodiments of the method 100 and/or system 200 can preferably determine and/or promote (e.g., provide; present; notify regarding; etc.) characterizations and/or therapies for one or more sleep-related conditions, and/or any suitable portions of the method 100 and/or system 200 can be performed in relation to sleep-related conditions.
  • characterizations and/or therapies for one or more sleep-related conditions, and/or any suitable portions of the method 100 and/or system 200 can be performed in relation to sleep-related conditions.
  • Sleep-related conditions can include any one or more of: insomnias (e.g., short sleeping, child insomnia, etc.), hypersomnias (e.g., narcolepsy, idiopathic hypersomnia, Kleine-Levin syndrome, insufficient sleep syndrome, long sleeping, idiopathic hypersomnia, etc.), sleep-related breathing disorders (e.g., sleep apnea, obstructive sleep apnea, snoring, central sleep apnea, child sleep apnea, infant sleep apnea, sleep-related groaning, catathrenia, hypopnea syndrome, etc.), circadian rhythm-related sleep disorders (e.g., delayed sleep-wake phase, advanced sleep-wake phase, irregular sleep-wake rhythm, non-24-hour sleep-wake rhythm, shift work sleep disorders, jet lag, etc.), parasomnias (e.g., sleepwalking, confusional arousals, sleep terror
  • sleep-related conditions can include one or more of: diseases, symptoms, causes (e.g., triggers, etc.), disorders, associated risk (e.g., propensity scores, etc.), associated severity, behaviors (e.g., physical activity behavior; alcohol consumption; smoking behaviors; stress-related characteristics; other psychological characteristics; sickness; social behaviors; caffeine consumption; alcohol consumption; sleep habits such as sleep time, wake time, naps, length, quality, sleep phases, consistence, variance and/or other sleep behaviors; other habits; diet-related behaviors; meditation and/or other relaxation behaviors; lifestyle conditions associated with sleep-related conditions; lifestyle conditions affecting sleep quality; lifestyle conditions informative of, correlated with, indicative of, facilitative of, and/or otherwise associated with diagnosis and/or therapeutic intervention for sleep-related conditions; behaviors affecting and/or otherwise associated with sleep and/or sleep-related conditions; etc.), environmental factors (e.g., location of sleep; bed, mattress, pillow, blanket, and/or other bedding and/or sleeping environment factors; lighting; other visual factors; noise; other audio
  • one or more sleep-related conditions can include a medical disorder affecting the sleep patterns of a human, animal, and/or other suitable entity.
  • one or more sleep-related conditions can interfere with normal physical, mental, social and/or emotional function.
  • one or more sleep-related conditions can be characterized by and/or diagnosed by medical interview, medical history, survey, sensor data, medical exams, data activities including and/or requiring monitoring individuals as they sleep, other supplementary data, and/or through any suitable techniques (e.g., techniques available for diagnosis for sleep-related conditions, etc.).
  • Embodiments of the method 100 and/or system 200 can be implemented for a single user, such as in relation to applying one or more sample handling processes and/or characterization processes for processing one or more biological samples (e.g., collected across one or more collection sites, etc.) from the user, for sleep-related characterization, facilitating therapeutic intervention, and/or for any other suitable purpose.
  • sample handling processes and/or characterization processes for processing one or more biological samples (e.g., collected across one or more collection sites, etc.) from the user, for sleep-related characterization, facilitating therapeutic intervention, and/or for any other suitable purpose.
  • embodiments can be implemented for a population of subjects (e.g., including the user, excluding the user), where the population of subjects can include subjects similar to and/or dissimilar to any other subjects for any suitable type of characteristics (e.g., in relation to sleep-related conditions, demographic characteristics, behaviors, microbiome composition and/or function, etc.); implemented for a subgroup of users (e.g., sharing characteristics, such as characteristics affecting sleep-related characterization and/or therapy determination; etc.); implemented for plants, animals, microorganisms, and/or any other suitable entities.
  • information derived from a set of subjects e.g., population of subjects, set of subjects, subgroup of users, etc.
  • an aggregate set of biological samples is preferably associated with and processed for a wide variety of subjects, such as including subjects of one or more of: different demographic characteristics (e.g., genders, ages, marital statuses, ethnicities, nationalities, socioeconomic statuses, sexual orientations, etc.), different sleep-related conditions (e.g., health and disease states; different genetic dispositions; etc.), different living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), different dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, caffeine consumption, etc.), different behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), and/or any other suitable characteristic (e.g., characteristics influencing, correlated with, and/or otherwise associated with microbiome composition and/or function, etc.).
  • different demographic characteristics e.g., genders,
  • the predictive power of processes implemented in portions of the method 100 and/or system 200 can increase, such as in relation to characterizing subsequent users (e.g., with varying characteristics, etc.) based upon their microbiomes (e.g., in relation to different collection sites for samples for the users, etc.).
  • portions of the method 100 and/or system 200 can be performed and/or configured in any suitable manner for any suitable entity or entities.
  • Data described herein can be associated with any suitable temporal indicators (e.g., seconds, minutes, hours, days, weeks, etc.) including one or more: temporal indicators indicating when the data was collected (e.g., temporal indicators indicating when a sample was collected; etc.), determined, transmitted, received, and/or otherwise processed; temporal indicators providing context to content described by the data (e.g., temporal indicators associated with sleep-related characterizations, such as where the sleep- related characterization describes the sleep-related conditions and/or user microbiome status at a particular time; etc.); changes in temporal indicators (e.g., changes in sleep- related characterizations over time, such as in response to receiving a therapy; latency between sample collection, sample analysis, provision of a sleep-related characterization or therapy to a user, and/or other suitable portions of the method 100; etc.); and/ or
  • parameters, metrics, inputs, outputs, and/or other suitable data can be associated with value types including: scores (e.g., sleep- related condition propensity scores; feature relevance scores; correlation scores, covariance scores, microbiome diversity scores, severity scores; etc.), individual values (e.g., individual sleep-related condition scores, such as condition propensity scores, for different collection sites, etc.), aggregate values, (e.g., overall scores based on individual microorganism-related scores for different collection sites, etc.), binary values (e.g., presence or absence of a microbiome feature; presence or absence of a sleep-related condition; etc.), relative values (e.g., relative taxonomic group abundance, relative microbiome function abundance, relative feature abundance, etc.), classifications (e.g., sleep-related condition classifications and/or diagnoses for users; feature classifications; behavior classifications; demographic characteristic classifications; etc.), confidence levels (e.g., associated with microorganism sequence datasets; with micro
  • Any suitable types of data described herein can be used as inputs (e.g., for different analytical techniques, models, and/or other suitable components described herein), generated as outputs (e.g., of different analytical techniques, models, etc.), and/or manipulated in any suitable manner for any suitable components associated with the method 100 and/or system 200.
  • One or more instances and/or portions of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., parallel data processing; concurrent cross-condition analysis; multiplex sample processing, such as multiplex amplification of microorganism nucleic acid fragments corresponding to target sequences associated with sleep-related conditions; performing sample processing and analysis for substantially concurrently evaluating a panel of sleep-related conditions; computationally determining microorganism datasets, microbiome features, and/or characterizing sleep-related conditions in parallel for a plurality of users; such as concurrently on different threads for parallel computing to improve system processing ability; etc.), in temporal relation (e.g., substantially concurrently with, in response to, serially, prior to, subsequent to, etc.) to a trigger event (e.g., performance of a portion of the method 100), and/or in any other suitable order at any suitable time and frequency by and/or using one or more instances of the system 200, components, and/or entities described
  • the method 100 can include generating a microorganism dataset based on processing microorganism nucleic acids of one or more biological samples with a bridge amplification substrate of a next generation sequencing platform (and/or other suitable sequencing system) of a sample handling system, and determining microbiome features and microbiome functional diversity features at computing devices operable to communicate with the next generation sequencing platform.
  • the method 100 and/or system 200 can be configured in any suitable manner.
  • Microbiome analysis can enable accurate and/or efficient characterization and/or therapy provision (e.g., according to portions of the method 100, etc.) for sleep- related conditions caused by, correlated with, and/or otherwise associated with microorganisms.
  • Specific examples of the technology can overcome several challenges faced by conventional approaches in characterizing a sleep-related conditions and/or facilitating therapeutic intervention.
  • conventional approaches can require patients to visit one or more care providers to receive a characterization and/or a therapy recommendation for a sleep-related condition (e.g., through diagnostic medical procedures such as in-hospital sleep-tracking; etc.), which can amount to inefficiencies and/or health-risks associated with the amount of time elapsed before diagnosis and/or treatment, with inconsistency in healthcare quality, and/or with other aspects of care provider visitation.
  • a characterization and/or a therapy recommendation for a sleep-related condition e.g., through diagnostic medical procedures such as in-hospital sleep-tracking; etc.
  • conventional genetic sequencing and analysis technologies for human genome sequencing can be incompatible and/or inefficient when applied to the microbiome (e.g., where the human microbiome can include over 10 times more microbial cells than human cells; where viable analytical techniques and the means of leveraging the analytical techniques can differ; where optimal sample processing techniques can differ, such as for reducing amplification bias; where different approaches to sleep-related characterizations can be employed; where the types of conditions and correlations can differ; where causes of the associated conditions and/or viable therapies for the associated conditions can differ; where sequence reference databases can differ; where the microbiome can vary across different body regions of the user such as at different collection sites; etc.).
  • sequencing technologies e.g., next-generation sequencing, associated technologies, etc.
  • technological issues e.g., data processing and analysis issues for the plethora of generated sequence data; issues with processing a plurality of biological samples in a multiplex manner; information display issues; therapy prediction issues; therapy provision issues, etc.
  • Specific examples of the method 100 and/or system 200 can confer technologically-rooted solutions to at least the challenges described above.
  • the technology can transform entities (e.g., users, biological samples, therapy facilitation systems including medical devices, etc.) into different states or things.
  • the technology can transform a biological sample into components able to be sequenced and analyzed to generate microorganism dataset and/or microbiome features usable for characterizing users in relation to one or more sleep-related conditions (e.g., such as through use of next-generation sequencing systems, multiplex amplification operations; etc.).
  • the technology can identify, promote (e.g., present, recommend, etc.), discourage, and/or provide therapies (e.g., personalized therapies based on a sleep-related characterization; etc.) and/or otherwise facilitate therapeutic intervention (e.g., facilitating modification of a user's microbiome composition, microbiome functionality, etc.), which can prevent and/or ameliorate one or more sleep-related conditions, thereby transforming the microbiome and/or health of the patient (e.g., improving a health state associated with a sleep-related condition; etc.).
  • therapies e.g., personalized therapies based on a sleep-related characterization; etc.
  • therapeutic intervention e.g., facilitating modification of a user's microbiome composition, microbiome functionality, etc.
  • the technology can transform microbiome composition and/or function at one or more different physiological sites of a user (e.g., one or more different collection sites, etc.), such as targeting and/or transforming microorganisms associated with a gut, nose, skin, mouth, and/or genitals microbiome.
  • the technology can control therapy facilitation systems (e.g., dietary systems; automated medication dispensers; behavior modification systems; diagnostic systems; disease therapy facilitation systems; etc.) to promote therapies (e.g., by generating control instructions for the therapy facilitation system to execute; etc.), thereby transforming the therapy facilitation system.
  • therapy facilitation systems e.g., dietary systems; automated medication dispensers; behavior modification systems; diagnostic systems; disease therapy facilitation systems; etc.
  • the technology can confer improvements in computer-related technology (e.g., improving computational efficiency in storing, retrieving, and/or processing microorganism-related data for sleep-related conditions; computational processing associated with biological sample processing, etc.) such as by facilitating computer performance of functions not previously performable.
  • the technology can apply a set of analytical techniques in a non-generic manner to non-generic microorganism datasets and/or microbiome features (e.g., that are recently able to be generated and/or are viable due to advances in sample processing techniques and/or sequencing technology, etc.) for improving sleep-related characterizations and/or facilitating therapeutic intervention for sleep-related conditions.
  • the technology can confer improvements in processing speed, sleep-related characterization, accuracy, microbiome-related therapy determination and promotion, and/or other suitable aspects in relation to sleep-related conditions.
  • the technology can leverage non-generic microorganism datasets to determine, select, and/or otherwise process microbiome features of particular relevance to one or more sleep-related conditions (e.g., processed microbiome features relevant to a sleep-related condition; cross-condition microbiome features with relevance to a plurality of sleep-related conditions, etc.), which can facilitate improvements in accuracy (e.g., by using the most relevant microbiome features; by leveraging tailored analytical techniques; etc.), processing speed (e.g., by selecting a subset of relevant microbiome features; by performing dimensionality reduction techniques; by leveraging tailored analytical techniques; etc.), and/or other computational improvements in relation to phenotypic prediction (e.g., indications of the sleep-related conditions, etc.), other suitable characterizations, therapeutic intervention facilitation, and/or other
  • the technology can apply feature-selection rules (e.g., microbiome feature-selection rules for composition, function; for supplemental features extracted from supplementary datasets; etc.) to select an optimized subset of features (e.g., microbiome functional features relevant to one or more sleep-related conditions; microbiome composition diversity features such as reference relative abundance features indicative of healthy, presence, absence, and/or other suitable ranges of taxonomic groups associated with sleep-related conditions; user relative abundance features that can be compared to reference relative abundance features correlated with sleep-related conditions and/or therapy responses; etc.) out of a vast potential pool of features (e.g., extractable from the plethora of microbiome data such as sequence data; identifiable by univariate statistical tests; etc.) for generating, applying, and/or otherwise facilitating characterization and/or therapies (e.g., through models, etc.).
  • feature-selection rules e.g., microbiome feature-selection rules for composition, function; for supplemental features extracted from supplementary
  • microbiomes e.g., human microbiomes, animal microbiomes, etc.
  • the potential size of microbiomes can translate into a plethora of data, giving rise to questions of how to process and analyze the vast array of data to generate actionable microbiome insights in relation to sleep-related conditions.
  • the feature-selection rules and/or other suitable computer-implementable rules can enable one or more of: shorter generation and execution times (e.g., for generating and/or applying models; for determining sleep-related characterizations and/or associated therapies; etc.); optimized sample processing techniques (e.g., improving transformation of microorganism nucleic acids from biological samples through using primer types, other biomolecules, and/or other sample processing components identified through computational analysis of taxonomic groups, sequences, and/or other suitable data associated with sleep-related conditions, such as while optimizing for improving specificity, reducing amplification bias, and/or other suitable parameters; etc.); model simplification facilitating efficient interpretation of results; reduction in overfitting; network effects associated with generating, storing, and applying sleep-related characterizations for a plurality of users over time in relation to sleep-related conditions (e.g., through collecting and processing an increasing amount of microbiome-related data associated with an increasing number of users to improve predictive power of the sleep-related characterizations and/or therapy determinations
  • specific examples of the technology can amount to an inventive distribution of functionality across a network including a sample handling system, a sleep-related characterization system, and a plurality of users, where the sample handling system can handle substantially concurrent processing of biological samples (e.g., in a multiplex manner) from the plurality of users, which can be leveraged by the sleep-related characterization system in generating personalized characterizations and/or therapies (e.g., customized to the user's microbiome such as in relation to the user's dietary behavior, probiotics-associated behavior, medical history, demographic characteristics, other behaviors, preferences, etc.) for sleep-related conditions.
  • personalized characterizations and/or therapies e.g., customized to the user's microbiome such as in relation to the user's dietary behavior, probiotics-associated behavior, medical history, demographic characteristics, other behaviors, preferences, etc.
  • specific examples of the technology can improve the technical fields of at least genomics, microbiology, microbiome-related computation, diagnostics, therapeutics, microbiome-related digital medicine, digital medicine generally, modeling, and/or other relevant fields.
  • the technology can model and/or characterize different sleep-related conditions, such as through computational identification of relevant microorganism features (e.g., which can act as biomarkers to be used in diagnoses, facilitating therapeutic intervention, etc.) for sleep-related conditions.
  • the technology can perform cross-condition analysis to identify and evaluate cross-condition microbiome features associated with (e.g., shared across, correlated across, etc.) a plurality of a sleep-related conditions (e.g., diseases, phenotypes, etc.).
  • a sleep-related conditions e.g., diseases, phenotypes, etc.
  • identification and characterization of microbiome features can facilitate improved health care practices (e.g., at the population and individual level, such as by facilitating diagnosis and therapeutic intervention, etc.), by reducing risk and prevalence of comorbid and/or multi-morbid sleep-related conditions (e.g., which can be associated with environmental factors, and thereby associated with the microbiome, etc.).
  • the technology can leverage specialized computing devices (e.g., devices associated with the sample handling system, such as next-generation sequencing systems; sleep-related characterization systems; therapy facilitation systems; etc.) in performing suitable portions associated with the method loo and/or system 200.
  • specialized computing devices e.g., devices associated with the sample handling system, such as next-generation sequencing systems; sleep-related characterization systems; therapy facilitation systems; etc.
  • embodiments of the system 200 can include any one or more of: a handling system (e.g., a sample handling system, etc.) 210 operable to collect and/or process biological samples (e.g., collected by users and included in containers including preprocessing reagents; etc.) from one or more users (e.g., a human subject, patient, animal subject, environmental ecosystem, care provider, etc.) for facilitating determination of a microorganism dataset (e.g., microorganism genetic sequences; microorganism sequence dataset; etc.); a sleep-related characterization system 220 operable to determine user microbiome features (e.g., microbiome composition features; microbiome functional features; diversity features; relative abundance ranges; such as based on a microorganism dataset and/or other suitable data; etc.), determine sleep-related characterizations (e.g., sleep-related condition characterizations, therapy- related characterization
  • a handling system e.g., a sample handling system, etc.
  • biological samples
  • the handling system 210 of the system 200 can function to receive and/or process (e.g., fragment, amplify, sequence, generate associated datasets, etc.) biological samples to transform microorganism nucleic acids and/or other components of the biological samples into data (e.g., genetic sequences that can be subsequently aligned and analyzed; microorganism datasets; etc.) for facilitating generation of sleep-related characterizations and/or therapeutic intervention.
  • the handling system 210 can additionally or alternatively function to provide sample kits 250 (e.g., including sample containers, instructions for collecting samples from one or more collection sites, etc.) to a plurality of users (e.g., in response to a purchase order for a sample kit 250), such as through a mail delivery system.
  • the handling system 210 can include one or more sequencing systems 215 (e.g., a next-generation sequencing systems, sequencing systems for targeted amplicon sequencing, metatranscriptomic sequencing, metagenomic sequencing, sequencing-by-synthesis techniques, capillary sequencing technique, Sanger sequencing, pyrosequencing techniques, nanopore sequencing techniques, etc.) for sequencing one or more biological samples (e.g., sequencing microorganism nucleic acids from the biological samples, etc.), such as in generating microorganism data (e.g., microorganism sequence data, other data for microorganism datasets, etc.).
  • sequencing systems 215 e.g., a next-generation sequencing systems, sequencing systems for targeted amplicon sequencing, metatranscriptomic sequencing, metagenomic sequencing, sequencing-by-synthesis techniques, capillary sequencing technique, Sanger sequencing, pyrosequencing techniques, nanopore sequencing techniques, etc.
  • biological samples e.g., sequencing microorganism nucleic acids from the biological samples, etc.
  • the handling system 210 can additionally or alternatively include a library preparation system operable to automatically prepare biological samples (e.g., fragment and amplify using primers compatible with genetic targets associated with the sleep-related condition) in a multiplex manner to be sequenced by a sequencing system; and/or any suitable components.
  • the handling system can perform any suitable sample processing techniques described herein. However, the handling system 210 and associated components can be configured in any suitable manner.
  • the sleep-related characterization system 220 of the system 200 can function to determine, analyze, characterize, and/or otherwise process microorganism datasets (e.g., based on processed biological samples leading to microorganism genetic sequences; alignments to reference sequences; etc.), microbiome features (e.g., individual variables; groups of variables; features relevant for phenotypic prediction, for statistical description; variables associated with a sample obtained from an individual; variables associated with sleep-related conditions; variables describing fully or partially, in relative or absolute quantities the sample's microbiome composition and/or functionality; etc.), models, and/or other suitable data for facilitating sleep-related characterization and/or therapeutic intervention.
  • microorganism datasets e.g., based on processed biological samples leading to microorganism genetic sequences; alignments to reference sequences; etc.
  • microbiome features e.g., individual variables; groups of variables; features relevant for phenotypic prediction, for statistical description; variables associated with a sample obtained from an individual; variables associated with sleep-
  • the sleep-related characterization system 220 can identify data associated with the information of the features that statistically describe the differences between samples associated with one or more sleep-related conditions (e.g., samples associated with presence, absence, risk of, propensity for, and/or other aspects related to sleep-related conditions etc.), such as where the differing analyses can provide complementing views into the features differentiating the different samples (e.g., differentiating the subgroups associated with presence or absence of a condition, etc.).
  • individual predictors, a specific biological process, and/or statistically inferred latent variables can provide complementary information at different levels of data complexity to facilitate varied downstream opportunities in relation to characterization, diagnosis, and/or treatment.
  • the sleep-related characterization system 220 process supplementary data for performing one or more characterization processes.
  • the sleep-related characterization system 220 can include, generate, apply, and/or otherwise process sleep-related characterization models, which can include any one or more of sleep-related condition models for characterizing one or more sleep-related conditions (e.g., determining propensity of one or more sleep- related conditions for one or more users, etc.), therapy models for determining therapies, and/or any other suitable models for any suitable purposes associated with the system 200 and/or method 100.
  • the sleep-related characterization system 220 can generate and/or apply a therapy model (e.g., based on cross-condition analyses, etc.) for identifying and/or characterizing a therapy used to treat one or more sleep-related conditions.
  • a therapy model e.g., based on cross-condition analyses, etc.
  • Different sleep-related characterization models e.g., different combinations of sleep-related characterization models; different models applying different analytical techniques; different inputs and/or output types; applied in different manners such as in relation to time and/or frequency; etc.
  • can be applied e.g., executed, selected, retrieves, stored, etc.
  • sleep-related conditions e.g., using different sleep-related characterization models depending on the sleep-related condition or conditions being characterized, such as where different sleep-related characterization models possess differing levels of suitability for processing data in relation to different sleep-related conditions and/or combinations of conditions, etc.
  • users e.g., different sleep-related characterization models based on different user data and/or characteristics, demographic characteristics, genetics, environmental factors, etc.
  • sleep-related characterizations e.g., different sleep-related characterization models for different types of characterizations, such as a therapy- related characterization versus a diagnosis-related characterization, such as for identifying relevant microbiome composition versus determining a
  • different sleep-related characterization models can be tailored to different types of inputs, outputs, sleep- related characterizations, sleep-related conditions (e.g., different phenotypic measures that need to be characterized), and/or any other suitable entities.
  • sleep- related characterization models can be tailored and/or used in any suitable manner for facilitating sleep-related characterization and/or therapeutic intervention.
  • Sleep-related characterization models, other models, other components of the system 200, and/or suitable portions of the method 100 can employ analytical techniques including any one or more of: univariate statistical tests, multivariate statistical tests, dimensionality reduction techniques, artificial intelligence approaches (e.g., machine learning approaches, etc.), performing pattern recognition on data (e.g., identifying correlations between sleep- related conditions and microbiome features; etc.), fusing data from multiple sources (e.g., generating characterization models based on microbiome data and/or supplementary data from a plurality of users associated with one or more sleep-related conditions, such as based on microbiome features extracted from the data; etc.), combination of values (e.g., averaging values, etc.), compression, conversion (e.g., digital-to-analog conversion, analog-to-digital conversion), performing statistical estimation on data (e.g.
  • analytical techniques including any one or more of: univariate statistical tests, multivariate statistical tests, dimensionality reduction techniques, artificial intelligence approaches (e
  • Artificial intelligence approaches can include any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, a deep learning algorithm (e.g., neural networks, a restricted Boltzmann machine, a deep belief network method, a convolutional neural network method, a recurrent neural network method, stacked auto-encoder method, etc.) reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance- based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net
  • the sleep-related characterization system 220 can preferably perform cross-condition analyses for a plurality of sleep-related conditions (e.g., generating multi-condition characterizations based on outputs of different sleep-related characterization models, such as multi-condition microbiome features; etc.).
  • the sleep-related characterization system can characterize relationships between sleep-related conditions based on microorganism data, microbiome features, and/or other suitable microbiome characteristics of users associated with (e.g., diagnosed with, characterized by, etc.) a plurality of sleep-related conditions.
  • cross-condition analyses can be performed based on characterizations for individual sleep-related conditions (e.g., outputs from sleep-related characterization models for individual sleep-related conditions, etc.).
  • Cross-condition analyses can include identification of condition-specific features (e.g., associated exclusively with a single sleep-related condition, etc.), multi-condition features (e.g., associated with two or more sleep-related conditions, etc.), and/or any other suitable types of features.
  • Cross-condition analyses can include determination of parameters informing correlation, concordance, and/or other similar parameters describing relationships between two or more sleep-related conditions, such as by evaluating different pairs of sleep-related conditions.
  • the sleep-related characterization system and/or other suitable components can be configured in any suitable manner to facilitate cross- condition analyses (e.g., applying analytical techniques for cross-condition analysis purposes; generating cross-condition characterizations, etc.).
  • the sleep-related characterization system 220 preferably includes a remote computing system (e.g., for applying sleep-related characterization models, etc.), but can additionally or alternatively include any suitable computing systems (e.g., local computing systems, user devices, handling system components, etc.). However, the sleep-related characterization system 220 can be configured in any suitable manner.
  • a remote computing system e.g., for applying sleep-related characterization models, etc.
  • any suitable computing systems e.g., local computing systems, user devices, handling system components, etc.
  • the sleep-related characterization system 220 can be configured in any suitable manner.
  • the therapy facilitation system 230 of the system 200 can function to facilitate therapeutic intervention (e.g., promote one or more therapies, etc.) for one or more sleep-related conditions (e.g., facilitating modulation of a user microbiome composition and functional diversity for improving a state of the user in relation to one or more sleep-related conditions, etc.).
  • the therapy facilitation system 230 can facilitate therapeutic intervention for any number of sleep-related conditions associated with any number of collection sites, such as based on multi-site characterizations, multi- condition characterizations, other characterizations, and/or any other suitable data.
  • the therapy facilitation system 230 can include any one or more of: a communications system (e.g., to communicate therapy recommendations, selections, discouragements, and/or other suitable therapy-related information to a computing device (e.g., user device and/or care provider device; mobile device; smart phone; desktop computer; at a website, web application, and/or mobile application accessed by the computing device; etc.); to enable telemedicine between a care provider and a subject in relation to a sleep- related condition; etc.), an application executable on a user device (e.g., indicating microbiome composition and/or functionality for a user; etc.), a medical device (e.g., a biological sampling device, such as for collecting samples from different collection sites; medication provision devices; surgical systems; etc.), a user device (e.g., biometric sensors), and/or any other suitable component.
  • a communications system e.g., to communicate therapy recommendations, selections, discouragements, and/or other suitable therapy-related information to a computing device (e
  • One or more therapy facilitation systems 230 can be controllable, communicable with, and/or otherwise associated with the sleep-related characterization system 220.
  • the sleep-related characterization system 220 can generate characterizations of one or more sleep-related conditions for the therapy facilitation system 230 to present (e.g., transmit, communicate, etc.) to a corresponding user (e.g., at an interface 240, etc.).
  • the therapy facilitation system 230 can update and/or otherwise modify an application and/or other software of a device (e.g., user smartphone) to promote a therapy (e.g., promoting, at a to-do list application, lifestyle changes for improving a user state associated with one or more sleep-related conditions, etc.).
  • the therapy facilitation system 230 can be configured in any other manner.
  • the system 200 can additionally or alternatively include an interface 240, which can function to improve presentation of microbiome characteristics, sleep-related condition information (e.g., propensity metrics; therapy recommendations; comparisons to other users; other characterizations; etc.).
  • the interface 240 can present sleep-related condition information including a microbiome composition (e.g., taxonomic groups; relative abundances; etc.), functional diversity (e.g., relative abundance of genes associated with particular functions, and propensity metrics for one or more sleep-related conditions, such as relative to user groups sharing a demographic characteristic (e.g., smokers, exercisers, users on different dietary regimens, consumers of probiotics, antibiotic users, groups undergoing particular therapies, etc.).
  • the interface 240 can be configured in any suitable manner.
  • a computing system e.g., a remote computing system, a user device, etc.
  • can implement portions and/or all of the sleep-related characterization system 220 e.g., apply a microbiome-related condition model to generate a characterization of sleep-related conditions for a user, etc.
  • the therapy facilitation system 230 e.g., facilitate therapeutic intervention through presenting insights associated with microbiome composition and/or function; presenting therapy recommendations and/or information; scheduling daily events at a calendar application of the smartphone to notify the user in relation to therapies for improving sleep-related, etc.
  • the functionality of the system 200 can be distributed in any suitable manner amongst any suitable system components.
  • the components of the system 200 can be configured in any suitable manner
  • Block S110 can include determining a microorganism dataset (e.g., microorganism sequence dataset, microbiome composition diversity dataset such as based upon a microorganism sequence dataset, microbiome functional diversity dataset such as based upon a microorganism sequence dataset, etc.) associated with a set of subjects S110.
  • Block S110 can function to process biological samples (e.g., an aggregate set of biological samples associated with a population of subjects, a subpopulation of subjects, a subgroup of subjects sharing a demographic characteristic and/or other suitable characteristics; a user biological sample; etc.), in order to determine compositional, functional, pharmacogenomics, and/or other suitable aspects associated with the corresponding microbiomes, such as in relation to one or more sleep-related conditions.
  • biological samples e.g., an aggregate set of biological samples associated with a population of subjects, a subpopulation of subjects, a subgroup of subjects sharing a demographic characteristic and/or other suitable characteristics; a user biological sample; etc.
  • Compositional and/or functional aspects can include one or more of aspects at the microorganism level (and/or other suitable granularity), including parameters related to distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups represented, etc.).
  • Compositional and/or functional aspects can also be represented in terms of operational taxonomic units (OTUs).
  • compositional and/or functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.).
  • compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.).
  • Outputs of Block Sno can thus be used to facilitate determination of microbiome features (e.g., generation of a microorganism sequence dataset usable for identifying microbiome features; etc.) for the characterization process of Block S130 and/or other suitable portions of the method 100 (e.g., where Block S110 can lead to outputs of microbiome composition datasets, microbiome functional datasets, and/or other suitable microorganism datasets from which microbiome features can be extracted, etc.), where the features can be microorganism-based (e.g., presence of a genus of bacteria), genetic- based (e.g., based upon representation of specific genetic regions and/or sequences), functional -based (e.g., presence of a specific catalytic activity), and/or any other suitable microbiome features.
  • microorganism-based e.g., presence of a genus of bacteria
  • genetic- based e.g., based upon representation of specific genetic regions and/or sequences
  • Block S110 can include assessment and/or processing based upon phylogenetic markers (e.g., for generating microorganism datasets, etc.) derived from bacteria and/or archaea in relation to gene families associated with one or more of: ribosomal protein S2, ribosomal protein S3, ribosomal protein S5, ribosomal protein S7, ribosomal protein S8, ribosomal protein S9, ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein Si5P/Si3e, ribosomal protein S17, ribosomal protein S19, ribosomal protein Li, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/Lie, ribosomal protein L5, ribosomal protein L6, ribosomal protein L10, ribo
  • markers can include target sequences (e.g., sequences associated with a microorganism taxonomic group; sequences associated with functional aspects; sequences correlated with sleep-related conditions; sequences indicative of user responsiveness to different therapies; sequences that are invariant across a population and/or any suitable set of subjects, such as to facilitate multiplex amplification using a primer type sharing a primer sequence; conserved sequences; sequences including mutations, polymorphisms; nucleotide sequences; amino acid sequences; etc.), proteins (e.g., serum proteins, antibodies, etc.), peptides, carbohydrates, lipids, other nucleic acids, whole cells, metabolites, natural products, genetic predisposition biomarkers, diagnostic biomarkers, prognostic biomarkers, predictive biomarkers, other molecular biomarkers, gene expression markers, imaging biomarkers, and/or other suitable markers.
  • markers can include any other suitable marker(s) associated with microbiome composition, microbiome functionality, and/or sleep
  • Characterizing the microbiome composition and/or functional aspects for each of the aggregate set of biological samples thus preferably includes a combination of sample processing techniques (e.g., wet laboratory techniques; as shown in FIGURE 5), including, but not limited to, amplicon sequencing (e.g., 16S, 18S, ITS), UMIs, 3 step PCR, Crispr, metagenomic approaches, metatranscriptomics, use of random primers, and computational techniques (e.g., utilizing tools of bioinformatics), to quantitatively and/or qualitatively characterize the microbiome and functional aspects associated with each biological sample from a subject or population of subjects.
  • sample processing techniques e.g., wet laboratory techniques; as shown in FIGURE 5
  • amplicon sequencing e.g., 16S, 18S, ITS
  • UMIs 3 step PCR
  • Crispr 3 step PCR
  • metagenomic approaches e.g., metatranscriptomics
  • metatranscriptomics e.g., metatranscriptomics
  • use of random primers
  • sample processing in Block S110 can include any one or more of: lysing a biological sample, disrupting membranes in cells of a biological sample, separation of undesired elements (e.g., RNA, proteins) from the biological sample, purification of nucleic acids (e.g., DNA) in a biological sample, amplification of nucleic acids from the biological sample, further purification of amplified nucleic acids of the biological sample, and sequencing of amplified nucleic acids of the biological sample.
  • undesired elements e.g., RNA, proteins
  • Block S110 can include: collecting biological samples from a set of users (e.g., biological samples collected by the user with a sampling kit including a sample container, etc.), where the biological samples include microorganism nucleic acids associated with the sleep-related condition (e.g., microorganism nucleic acids including target sequences correlated with a sleep-related condition; etc.).
  • Block Sno can include providing a set of sampling kits to a set of users, each sampling kit of the set of sampling kits including a sample container (e.g., including pre-processing reagents, such as lysing reagents; etc.) operable to receive a biological sample from a user of the set of users.
  • lysing a biological sample and/or disrupting membranes in cells of a biological sample preferably includes physical methods (e.g., bead beating, nitrogen decompression, homogenization, sonication), which omit certain reagents that produce bias in representation of certain bacterial groups upon sequencing.
  • lysing or disrupting in Block Sno can involve chemical methods (e.g., using a detergent, using a solvent, using a surfactant, etc.).
  • lysing or disrupting in Block Sno can involve biological methods.
  • separation of undesired elements can include removal of RNA using RNases and/or removal of proteins using proteases.
  • purification of nucleic acids can include one or more of: precipitation of nucleic acids from the biological samples (e.g., using alcohol-based precipitation methods), liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving use of binding moiety-bound particles (e.g., magnetic beads, buoyant beads, beads with size distributions, ultrasonically responsive beads, etc.) configured to bind nucleic acids and configured to release nucleic acids in the presence of an elution environment (e.g., having an elution solution, providing a pH shift, providing a temperature shift, etc.), and any other suitable purification techniques.
  • solvent-based precipitation methods e.g., liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving use of binding moiety-
  • amplification of purified nucleic acids can include one or more of: polymerase chain reaction (PCR)-based techniques (e.g., solid-phase PCR, RT- PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and any other suitable amplification technique.
  • PCR polymerase chain reaction
  • HDA helicase-dependent amplification
  • LAMP loop mediated isothermal amplification
  • NASBA nucleic acid sequence based amplification
  • SDA strand displacement amplification
  • RCA rolling circle amplification
  • LCR ligase chain reaction
  • the primers used are preferably selected to prevent or minimize amplification bias, as well as configured to amplify nucleic acid regions/sequences (e.g., of the 16S region, the 18S region, the ITS region, etc.) that are informative taxonomically, phylogenetically, for diagnostics, for formulations (e.g., for probiotic formulations), and/or for any other suitable purpose.
  • amplification bias e.g., a F27-R338 primer set for 16S RNA, a F515-R806 primer set for i6S RNA, etc.
  • Block Siio can additionally or alternatively include adaptor regions configured to cooperate with sequencing techniques involving complementary adaptors (e.g., Illumina Sequencing). Additionally or alternatively, Block Siio can implement any other step configured to facilitate processing (e.g., using a Nextera kit).
  • performing amplification and/or sample processing operations can be in a multiplex manner (e.g., for a single biological sample, for a plurality of biological samples across multiple users; etc.).
  • performing amplification can include normalization steps to balance libraries and detect all amplicons in a mixture independent of the amount of starting material, such as 3 step PCR, bead based normalization, and/or other suitable techniques.
  • sequencing of purified nucleic acids can include methods involving targeted amplicon sequencing, metatranscriptomic sequencing, and/or metagenomic sequencing, implementing techniques including one or more of: sequencing-by-synthesis techniques (e.g., Illumina sequencing), capillary sequencing techniques (e.g., Sanger sequencing), pyrosequencing techniques, and nanopore sequencing techniques (e.g., using an Oxford Nanopore technique).
  • sequencing-by-synthesis techniques e.g., Illumina sequencing
  • capillary sequencing techniques e.g., Sanger sequencing
  • pyrosequencing techniques e.g., using an Oxford Nanopore technique
  • amplification and sequencing of nucleic acids from biological samples of the set of biological samples includes: solid-phase PCR involving bridge amplification of DNA fragments of the biological samples on a substrate with oligo adapters, where amplification involves primers having a forward index sequence (e.g., corresponding to an Illumina forward index for MiSeq/NextSeq/HiSeq platforms), a forward barcode sequence, a transposase sequence (e.g., corresponding to a transposase binding site for MiSeq/NextSeq/HiSeq platforms), a linker (e.g., a zero, one, or two-base fragment configured to reduce homogeneity and improve sequence results), an additional random base, UMIs, a sequence for targeting a specific target region (e.g., 16S region, 18S region, ITS region), a reverse index sequence (e.g., corresponding to an Illumina reverse index for MiSeq/HiSeq platforms), a forward index sequence (e.
  • sequencing can include Illumina sequencing (e.g., with a HiSeq platform, with a MiSeq platform, with a NextSeq platform, etc.) using a sequencing-by-synthesis technique.
  • the method 100 can include: identifying one or more primer types compatible with one or more genetic targets associated with one or more sleep-related conditions (e.g., a biomarker of the one or more sleep-related conditions; positively correlated with; negatively correlated with; causative of; etc.); determining a microorganism dataset (e.g., microorganism sequence dataset; such as with a next-generation sequencing system; etc.) for one or more users (e.g., set of subjects) based on the one or more primer types (e.g., based on primers corresponding to the one or more primer types, and on the microorganism nucleic acids included in collected biological samples, etc.), such as through fragmenting the microorganism nucleic acids, and/or performing a singleplex
  • the biological samples can correspond to a set of collection sites including at least one of a gut site, a skin site, a nose site, a mouth site, and a genitals site
  • determining a microorganism dataset can include identifying a first primer type compatible with a first genetic target associated with one or more sleep- related conditions and a first collection site of the set of collection sites; identifying a second primer type compatible with a second genetic target associated with the one or more sleep-related conditions and a second collection site of the set of collection sites; and generating the microorganism dataset for the set of subjects based on the microorganism nucleic acids, the first primers corresponding to the first primer type, and second primers corresponding to the second primer type.
  • the first collection site type can include the gut site (e.g., which can be evaluated through stool samples, etc.), where determining the microorganism dataset can include determining at least one of a metagenomic library and a metatranscriptomic library based on a subset of the microorganism nucleic acids and the first primers, and where determining the at least one of the set of microbiome composition features and the set of microbiome functional features can include determining the at least one of the set of microbiome composition features and the set of microbiome functional features based on the at least one of the metagenomic library and the metatranscriptomic library.
  • processing metagenomic libraries and/or metatranscriptomic libraries e.g., for any suitable portions of the method 100 and/or system 200
  • processing metagenomic libraries and/or metatranscriptomic libraries can be performed in any suitable manner.
  • primers used in Block S110 and/or other suitable portions of the method 100 can include primers associated with protein genes (e.g., coding for conserved protein gene sequences across a plurality of taxa, such as to enable multiplex amplification for a plurality of targets and/or taxa; etc.).
  • Primers can additionally or alternatively be associated with sleep-related conditions (e.g., primers compatible with genetic targets including microorganism sequence biomarkers for microorganisms correlated with sleep-related conditions; etc.), microbiome composition features (e.g., identified primers compatible with a genetic target corresponding to microbiome composition features associated with a group of taxa correlated with a sleep-related condition; genetic sequences from which relative abundance features are derived etc.), functional diversity features, supplementary features, and/or other suitable features and/or data.
  • Primers and/or other suitable molecules, markers, and/or biological material described herein
  • can possess any suitable size e.g., sequence length, number of base pairs, conserved sequence length, variable region length, etc.).
  • any suitable number of primers can be used in sample processing for performing characterizations (e.g., sleep-related characterizations; etc.), improving sample processing (e.g., through reducing amplification bias, etc.), and/or for any suitable purposes.
  • the primers can be associated with any suitable number of targets, sequences, taxa, conditions, and/or other suitable aspects.
  • Primers used in Block S110 and/or other suitable portions of the method 100 can be selected through processes described in Block S110 (e.g., primer selection based on parameters used in generating the taxonomic database) and/or any other suitable portions of the method 100.
  • primers (and/or processes associated with primers) can include and/or be analogous to that described in U.S. App. No. 14/919,614, filed 21- OCT-2015, which is herein incorporated in its entirety by this reference.
  • identification and/or usage of primers can be configured in any suitable manner.
  • sample processing can include further purification of amplified nucleic acids (e.g., PCR products) prior to sequencing, which functions to remove excess amplification elements (e.g., primers, dNTPs, enzymes, salts, etc.).
  • additional purification can be facilitated using any one or more of: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and/or any other suitable purification technique.
  • computational processing in Block Sno can include any one or more of: identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing), and generating features associated with (e.g., derived from) compositional and/or functional aspects of the microbiome associated with a biological sample.
  • identification of microbiome-derived sequences e.g., as opposed to subject sequences and contaminants
  • alignment and mapping of microbiome-derived sequences e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing
  • generating features associated with e.g., derived from compositional and/or functional aspects of the microbiome associated with a biological sample.
  • Identification of microbiome-derived sequences can include mapping of sequence data from sample processing to a subject reference genome (e.g., provided by the Genome Reference Consortium), in order to remove subject genome-derived sequences. Unidentified sequences remaining after mapping of sequence data to the subject reference genome can then be further clustered into operational taxonomic units (OTUs) based upon sequence similarity and/or reference-based approaches (e.g., using VAMPS, using MG-RAST, using QIIME databases), aligned (e.g., using a genome hashing approach, using a Needleman-Wunsch algorithm, using a Smith-Waterman algorithm), and mapped to reference bacterial genomes (e.g., provided by the National Center for Biotechnology Information), using an alignment algorithm (e.g., Basic Local Alignment Search Tool, FPGA accelerated alignment tool, BWT-indexing with BWA, BWT-indexing with SOAP, BWT-indexing with Bowtie, etc.).
  • OTUs operational taxono
  • Mapping of unidentified sequences can additionally or alternatively include mapping to reference archaeal genomes, viral genomes and/or eukaryotic genomes. Furthermore, mapping of taxa can be performed in relation to existing databases, and/or in relation to custom-generated databases.
  • processing biological samples, generating a microorganism dataset, and/or other aspects associated with Block Sno can be performed in any suitable manner.
  • the method loo can additionally or alternatively include Block S120, which can include processing (e.g., receiving, collecting, transforming, determining supplementary features, ranking supplementary features, identifying correlations, etc.) supplementary data (e.g., one or more supplementary datasets, etc.) associated with (e.g., informative of; describing; indicative of; correlated with; etc.) one or more sleep- related conditions, one or more users, and/or other suitable entities.
  • processing e.g., receiving, collecting, transforming, determining supplementary features, ranking supplementary features, identifying correlations, etc.
  • supplementary data e.g., one or more supplementary datasets, etc.
  • supplementary data e.g., informative of; describing; indicative of; correlated with; etc.
  • Block S120 can function to process data for supplementing microorganism datasets, microbiome features (e.g., in relation to determining sleep-related characterizations and/or facilitating therapeutic intervention, etc.), and/or can function to supplement any suitable portion of the method 100 and/or system 200 (e.g., processing supplementary data for facilitating one or more characterization processes, such as in Block S130; such as for facilitating training, validating, generating, determining, applying and/or otherwise processing sleep-related characterization models, etc.).
  • processing supplementary data for facilitating one or more characterization processes such as in Block S130; such as for facilitating training, validating, generating, determining, applying and/or otherwise processing sleep-related characterization models, etc.
  • supplementary data can include at least one of survey-derived data, user data, site- specific data, and device data (and/or other suitable supplementary data), where the method 100 can include determining a set of supplementary features based on the at least one of the survey-derived data, the user data, the site-specific data, and the device data (and/or other suitable supplementary data); and generating one or more sleep- related characterization models based on the supplementary features, microbiome features, and/or other suitable data.
  • Supplementary data can include any one or more of: survey-derived data
  • site-specific data e.g., data informative of different collection sites, such as prior biological knowledge indicating correlations between microbiomes at specific collection sites and one or more sleep-related conditions; etc.
  • sleep-related condition data e.g., data informative of different sleep-related conditions, such as in relation to microbiome characteristics, therapies, users, etc.
  • device data e.g.., sensor data; contextual sensor data associated with sleep; wearable device data; medical device data; user device data such as mobile phone application data; web application data; etc.
  • user data e.g., user medical data current and historical medical data such as historical therapies, historical medical examination data; medical device-derived data; physiological data; data associated with medical tests; social media data; demographic data; family history data; behavior data describing behaviors; environmental factor data describing environmental factors; diet-related data such as data from food establishment check- ins, data from spectrophotometric
  • processing supplementary data can include processing survey-derived data, where the survey-derived data can provide physiological data, demographic data, behavior data, environmental factor data (e.g., describing environmental factors, etc.), other types of supplementary data, and/or any other suitable data.
  • Physiological data can include information related to physiological features (e.g., height, weight, body mass index, body fat percent, body hair level, medical history, etc.).
  • Demographic data can include information related to demographic characteristics (e.g., gender, age, ethnicity, marital status, number of siblings, socioeconomic status, sexual orientation, etc.).
  • Behavioral data can describe behaviors including one or more: health-associated states (e.g., health and disease states), dietary habits (e.g., alcohol consumption, caffeine consumption, omnivorous, vegetarian, vegan, sugar consumption, acid consumption, consumption of wheat, egg, soy, treenut, peanut, shellfish, food preferences, allergy characteristics, consumption and/or avoidance of other food items, etc.), behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, habit development, etc.), different levels of mobility (e.g., amount of exercise such as low, moderate, and/or extreme physical exercise activity; related to distance traveled within a given time period; indicated by mobility sensors such as motion and/or location sensors; etc.), different levels of sexual activity (e.g., related to numbers of partners and sexual orientation), and any other suitable behavioral data.
  • health-associated states e.g., health and disease states
  • dietary habits e.g., alcohol consumption, caffeine consumption, omnivorous, vegetarian, vegan, sugar consumption, acid consumption, consumption of wheat, egg,
  • Survey-derived data can include quantitative data, qualitative data, and/or other suitable types of survey-derived data, such as where qualitative data can be converted to quantitative data (e.g., using scales of severity, mapping of qualitative responses to quantified scores, etc.).
  • Processing survey-derived data can include facilitating collection of survey-derived data, such as including providing one or more surveys to one or more users, subjects, and/or other suitable entities.
  • Surveys can be provided in-person (e.g., in coordination with sample kit provision and/or reception of samples; etc.), electronically (e.g., during account setup; at an application executing at an electronic device of a subject, at a web application and/or website accessible through an internet connection; etc.), and/or in any other suitable manner.
  • processing supplementary data can include processing sensor data (e.g., sensors of sleep-related devices, wearable computing devices, mobile devices; biometric sensors associated with the user, such as biometric sensors of a user smart phone; etc.).
  • Sensor data can include any one or more of: physical activity- and/or physical action-related data (e.g., accelerometer data, gyroscope data, location sensor data such as GPS data, and/or other mobility sensor data from one or more devices such as a mobile device and/or wearable electronic device, etc.), sensor data describing environmental factors (e.g., temperature data, elevation data, climate data, light parameter data, pressure data, air quality data, etc.), biometric sensor data (e.g., heart rate sensor data; fingerprint sensor data; optical sensor data such as facial images and/or video; data recorded through sensors of a mobile device; data recorded through a wearable or other peripheral device; etc.), and/or any other suitable data associated with sensors.
  • physical activity- and/or physical action-related data e.g., accelerometer
  • sensor data can include data sampled at one or more: optical sensors (e.g., image sensors, light sensors, cameras, etc.), audio sensors (e.g., microphones, etc.), temperature sensors, volatile compound sensors, air quality sensors, weight sensors, humidity sensors, depth sensors, location sensors (GPS receivers; beacons; indoor positioning systems; compasses; etc.), motion sensors (e.g., accelerators, gyroscope, magnetometer, remote motion detection systems such as for monitoring motion of a user when sleeping, motion sensors integrated with a device worn by a user during sleep, etc.), biometric sensors (e.g., heart rate sensors such as for monitoring heart rate during a time period associated with user sleep; fingerprint sensors; facial recognition sensors; bio-impedance sensors, etc.), pressure sensors (e.g., integrated with a bedding- related component, such as for detecting user motion when sleeping on a bed, etc.), proximity sensors (e.g., for monitoring motion and/or other aspects of third-party objects associated with user sleep periods; etc.
  • optical sensors
  • supplementary data can include medical record data and/or clinical data. As such, portions of the supplementary dataset can be derived from one or more electronic health records (EHRs). Additionally or alternatively, supplementary data can include any other suitable diagnostic information (e.g., clinical diagnosis information). Any suitable supplementary data (e.g., in the form of extracted supplementary features, etc.) can be combined with and/or used with microbiome features and/or other suitable data for performing portions of the method loo (e.g., performing characterization processes, etc.) and/or system 200.
  • EHRs electronic health records
  • supplementary data can include any other suitable diagnostic information (e.g., clinical diagnosis information).
  • Any suitable supplementary data e.g., in the form of extracted supplementary features, etc.
  • microbiome features and/or other suitable data for performing portions of the method loo (e.g., performing characterization processes, etc.) and/or system 200.
  • supplementary data associated with e.g., derived from, etc.
  • a colonoscopy, biopsy, blood test, diagnostic imaging, other suitable diagnostic procedures, survey-related information, and/or any other suitable test can be used to supplement (e.g., for any suitable portions of the method 100 and/or system 200).
  • supplementary data can include therapy- related data including one or more of: therapy regimens, types of therapies, recommended therapies, therapies used by the user, therapy adherence, and/or other suitable data related to therapies.
  • supplementary data can include user adherence metrics (e.g., medication adherence, probiotic adherence, physical exercise adherence, dietary adherence, etc.) in relation one or more therapies (e.g., a recommended therapy, etc.).
  • processing supplementary data can be performed in any suitable manner.
  • Block S130 can include, performing a characterization process (e.g., preprocessing, feature generation, feature processing, multi-site characterization for a plurality of collection sites, cross-condition analysis for a plurality of sleep-related conditions, model generation, etc.) associated with one or more sleep-related conditions, such as based on a microorganism dataset (e.g., derived in Block S110, etc.) and/or other suitable data (e.g., supplementary dataset; etc.) S130.
  • a characterization process e.g., preprocessing, feature generation, feature processing, multi-site characterization for a plurality of collection sites, cross-condition analysis for a plurality of sleep-related conditions, model generation, etc.
  • Block S130 can function to identify, determine, extract, and/or otherwise process features and/or feature combinations that can be used to determine sleep-related characterizations for users or and sets of users, based upon their microbiome composition (e.g., microbiome composition diversity features, etc.), function (e.g., microbiome functional diversity features, etc.), and/or other suitable microbiome features (e.g., such as through the generation and application of a characterization model for determining sleep-related characterizations, etc.).
  • microbiome composition e.g., microbiome composition diversity features, etc.
  • function e.g., microbiome functional diversity features, etc.
  • other suitable microbiome features e.g., such as through the generation and application of a characterization model for determining sleep-related characterizations, etc.
  • the characterization process can be used as a diagnostic tool that can characterize a subject (e.g., in terms of behavioral traits, in terms of medical conditions, in terms of demographic characteristics, etc.) based upon their microbiome composition and/or functional features, in relation to one or more of their health condition states (e.g., sleep-related condition states), behavioral traits, medical conditions, demographic characteristics, and/or any other suitable traits.
  • characterizations can be used to determine, recommend, and/or provide therapies (e.g., personalized therapies, such as determined by way of a therapy model, etc.), and/or otherwise facilitate therapeutic intervention.
  • Performing a characterization process S130 can include pre-processing microorganism datasets, microbiome features, and/or other suitable data for facilitation of downstream processing (e.g., determining sleep-related characterizations, etc.).
  • performing a characterization process can include, filtering a microorganism dataset (e.g., filtering a microorganism sequence dataset, such as prior to applying a set of analytical techniques to determine the microbiome features, etc.), by at least one of: a) removing first sample data corresponding to first sample outliers of a set of biological samples (e.g., associated with one or more sleep-related conditions, etc.), such as where the first sample outliers are determined by at least one of principal component analysis, a dimensionality reduction technique, and a multivariate methodology; b) removing second sample data corresponding to second sample outliers of the set of biological samples, where the second sample outliers can determined based on corresponding data quality for the set of microbiome features (e.g., filter
  • Block S130 can use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize a subject as exhibiting features (e.g., where determining user microbiome features can include determining feature values for microbiome features identified by characterization processes as correlated with and/or otherwise associated with one or more sleep-related conditions, etc.) associated with one or more sleep-related conditions (e.g., features characteristic of a set of users with the one or more sleep-related conditions, etc.).
  • computational methods e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.
  • performing characterization processes can include determining one or more microbiome features associated with one or more sleep-related conditions (e.g., identifying microbiome features with greatest relevance to one or more sleep-related conditions; determining user microbiome features, such as presence, absence, and/or values of user microbiome features corresponding to the identified microbiome features associated with the one or more sleep-related conditions, etc.), such as through applying one or more analytical techniques.
  • determining one or more microbiome features associated with one or more sleep-related conditions e.g., identifying microbiome features with greatest relevance to one or more sleep-related conditions; determining user microbiome features, such as presence, absence, and/or values of user microbiome features corresponding to the identified microbiome features associated with the one or more sleep-related conditions, etc.
  • determining microbiome features can applying a set of analytical techniques including at least one of a univariate statistical test, a multivariate statistical test, a dimensionality reduction technique, and an artificial intelligence approach, such as based on a microorganism dataset (e.g., microorganism sequence dataset, etc.), and where the microbiome features configured to improve computing system-related functionality associated with the determining of the sleep-related characterization for the user (e.g., in relation to accuracy, reducing error, processing speed, scaling, etc.).
  • a set of analytical techniques including at least one of a univariate statistical test, a multivariate statistical test, a dimensionality reduction technique, and an artificial intelligence approach, such as based on a microorganism dataset (e.g., microorganism sequence dataset, etc.), and where the microbiome features configured to improve computing system-related functionality associated with the determining of the sleep-related characterization for the user (e.g., in relation to accuracy, reducing error, processing speed, scaling, etc
  • determining microbiome features can include applying a set of analytical techniques to determine at least one of presence of at least one of a microbiome composition diversity feature and a microbiome functional diversity feature, absence of the at least one of the microbiome composition diversity feature and the microbiome functional diversity feature, a relative abundance feature describing relative abundance of different taxonomic groups associated with the first sleep-related condition, a ratio feature describing a ratio between at least two microbiome features associated with the different taxonomic groups, an interaction feature describing an interaction between the different taxonomic groups, and a phylogenetic distance feature describing phylogenetic distance between the different taxonomic groups, such as based on the microorganism dataset, and where the set of analytical techniques can include at least one of a univariate statistical test, a multivariate statistical test, a dimensionality reduction technique, and an artificial intelligence approach.
  • generating features associated with e.g., derived from compositional and functional aspects of the microbiome associated with a biological sample can be performed.
  • generating features can include generating features based upon multilocus sequence typing (MSLT), in order to identify markers useful for characterization in subsequent blocks of the method 100.
  • generated features can include generating features that describe the presence or absence of certain taxonomic groups of microorganisms, and/or ratios between exhibited taxonomic groups of microorganisms.
  • generating features can include generating features describing one or more of: quantities of represented taxonomic groups, networks of represented taxonomic groups, correlations in representation of different taxonomic groups, interactions between different taxonomic groups, products produced by different taxonomic groups, interactions between products produced by different taxonomic groups, ratios between dead and alive microorganisms (e.g., for different represented taxonomic groups, based upon analysis of RNAs), phylogenetic distance (e.g., in terms of Kantorovich-Rubinstein distances, Wasserstein distances etc.), any other suitable taxonomic group-related feature(s), any other suitable genetic or functional aspect(s).
  • generating features can include generating features describing relative abundance of different microorganism groups, for instance, using a sparCC approach, using Genome Relative Abundance and Average size (GAAS) approach and/or using a Genome Relative Abundance using Mixture Model theory (GRAMMy) approach that uses sequence-similarity data to perform a maximum likelihood estimation of the relative abundance of one or more groups of microorganisms. Additionally or alternatively, generating features can include generating statistical measures of taxonomic variation, as derived from abundance metrics.
  • GAS Genome Relative Abundance and Average size
  • GRAMMy Genome Relative Abundance using Mixture Model theory
  • generating features can include generating features associated with (e.g., derived from) relative abundance factors (e.g., in relation to changes in abundance of a taxon, which affects abundance of other taxa). Additionally or alternatively, generating features can include generation of qualitative features describing presence of one or more taxonomic groups, in isolation and/or in combination. Additionally or alternatively, generating features can include generation of features related to genetic markers (e.g., representative 16S, 18S, and/or ITS sequences) characterizing microorganisms of the microbiome associated with a biological sample. Additionally or alternatively, generating features can include generation of features related to functional associations of specific genes and/or organisms having the specific genes.
  • genetic markers e.g., representative 16S, 18S, and/or ITS sequences
  • generating features can include generation of features related to pathogenicity of a taxon and/or products attributed to a taxon.
  • Block S130 can, however, include determination of any other suitable feature(s) derived from sequencing and mapping of nucleic acids of a biological sample.
  • the feature(s) can be combinatory (e.g. involving pairs, triplets), correlative (e.g., related to correlations between different features), and/or related to changes in features (e.g., temporal changes, changes across sample sites, etc., spatial changes, etc.).
  • determining microbiome features can be performed in any suitable manner.
  • performing a characterization process can include performing one or more multi-site analyses (e.g., with sleep-related characterization models; generating a multi-site characterization, etc.) associated with a plurality of collection sites.
  • multi-site analyses can be performed in any suitable manner.
  • performing a characterization process can include performing one or more cross-condition analyses (e.g., using sleep-related characterization models, etc.) for a plurality of sleep-related conditions.
  • performing cross-condition analyses can include determining a set of cross-condition features (e.g., as part of determining microbiome features, etc.) associated with a plurality of sleep-related conditions (e.g., a first sleep-related condition and a second sleep-related condition, etc.) based on one or more analytical techniques, where determining a sleep-related characterization can include determining the sleep-related characterization for a user for the plurality of sleep-related conditions (e.g., first and the second sleep-related conditions, etc.) based on one or more sleep-related characterization models, and where the set of cross-condition features is configured to improve the computing system-related functionality associated with the determining of the sleep-related characterization for the user for the plurality of sleep-related conditions.
  • determining a sleep-related characterization can include determining the sleep-related characterization for a user for the plurality of sleep-related conditions (e.g., first and the second sleep-related conditions, etc.) based on one or more sleep-related characterization models,
  • Performing cross-condition analyses can include determining cross- condition correlation metrics (e.g., correlation and/or covariance between data corresponding to different sleep-related conditions, etc.) and/or other suitable metrics associated with cross-condition analyses.
  • performing cross-condition analyses can be performed in any suitable manner.
  • characterization can be based upon features associated with (e.g., derived from) a statistical analysis (e.g., an analysis of probability distributions) of similarities and/or differences between a first group of subjects exhibiting a target state (e.g., a sleep-related condition state) and a second group of subjects not exhibiting the target state (e.g., a "normal" state).
  • a statistical analysis e.g., an analysis of probability distributions
  • KS Kolmogorov-Smirnov
  • permutation test e.g., a permutation test
  • Cramer-von Mises test e.g., any other statistical test (e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.), and/or other suitable analytical techniques
  • any other statistical test e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.
  • suitable analytical techniques e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.
  • suitable analytical techniques e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.
  • one or more such statistical hypothesis tests can be used to assess a set of features having varying degrees of abundance in a first group of subjects exhibiting a target state (e.g., a sick state) and a second group of subjects not
  • the set of features assessed can be constrained based upon percent abundance and/or any other suitable parameter pertaining to diversity in association with the first group of subjects and the second group of subjects, in order to increase or decrease confidence in the characterization.
  • a feature can be derived from a taxon of bacteria that is abundant in a certain percentage of subjects of the first group and subjects of the second group, where a relative abundance of the taxon between the first group of subjects and the second group of subjects can be determined from the KS test, with an indication of significance (e.g., in terms of p-value).
  • an output of Block S130 can include a normalized relative abundance value (e.g., 25% greater abundance of a taxon in subjects with a sleep-related condition vs. subjects without the sleep-related condition; in sick subjects vs. healthy subjects) with an indication of significance (e.g., a p-value of 0.0013).
  • Variations of feature generation can additionally or alternatively implement or be derived from functional features or metadata features (e.g., non-bacterial markers).
  • any suitable microbiome features can be derived based on statistical analyses (e.g., applied to a microorganism sequence dataset and/or other suitable microorganism dataset, etc.) including any one or more of: a prediction analysis, multi hypothesis testing, a random forest test, principal component analysis, and/or other suitable analytical techniques.
  • Block S130 can additionally or alternatively transform input data from at least one of the microbiome composition diversity dataset and microbiome functional diversity dataset into feature vectors that can be tested for efficacy in predicting characterizations of the population of subjects.
  • Data from the supplementary dataset can be used to provide indication of one or more characterizations of a set of characterizations, where the characterization process is trained with a training dataset of candidate features and candidate classifications to identify features and/or feature combinations that have high degrees (or low degrees) of predictive power in accurately predicting a classification.
  • refinement of the characterization process with the training dataset identifies feature sets (e.g., of subject features, of combinations of features) having high correlation with specific classifications of subjects.
  • feature vectors (and/or any suitable set of features) effective in predicting classifications of the characterization process can include features related to one or more of: microbiome diversity metrics (e.g., in relation to distribution across taxonomic groups, in relation to distribution across archaeal, bacterial, viral, and/or eukaryotic groups), presence of taxonomic groups in one's microbiome, representation of specific genetic sequences (e.g., 16S sequences) in one's microbiome, relative abundance of taxonomic groups in one's microbiome, microbiome resilience metrics (e.g., in response to a perturbation determined from the supplementary dataset), abundance of genes that encode proteins or RNAs with given functions (enzymes, transporters, proteins from the immune system, hormones, interference RNAs, etc.) and any other suitable features associated with (e.g., derived from) the microbiome diversity dataset and/or the supplementary dataset.
  • microbiome diversity metrics e.g., in relation to distribution across taxonomic groups
  • microbiome features can be associated with (e.g., include, correspond to, typify, etc.) at least one of: presence of a microbiome feature from the microbiome features (e.g., user microbiome features, etc.), absence of the microbiome features from the microbiome features, relative abundance of different taxonomic groups associated with the sleep-related condition; a ratio between at least two microbiome features associated with the different taxonomic groups, interactions between the different taxonomic groups, and phylogenetic distance between the different taxonomic groups.
  • a microbiome feature from the microbiome features e.g., user microbiome features, etc.
  • absence of the microbiome features from the microbiome features e.g., relative abundance of different taxonomic groups associated with the sleep-related condition
  • a ratio between at least two microbiome features associated with the different taxonomic groups, interactions between the different taxonomic groups, and phylogenetic distance between the different taxonomic groups e.
  • microbiome features can include one or more relative abundance characteristics associated with at least one of the microbiome composition diversity features (e.g., relative abundance associated with different taxa, etc.) and the microbiome functional diversity features (e.g., relative abundance of sequences corresponding to different functional features; etc.).
  • Relative abundance characteristics and/or other suitable microbiome features can be extracted and/or otherwise determined based on: a normalization, a feature vector derived from at least one of linear latent variable analysis and non-linear latent variable analysis, linear regression, non-linear regression, a kernel method, a feature embedding method, a machine learning method, a statistical inference method, and/or other suitable analytical techniques.
  • combinations of features can be used in a feature vector, where features can be grouped and/or weighted in providing a combined feature as part of a feature set.
  • one feature or feature set can include a weighted composite of the number of represented classes of bacteria in one's microbiome, presence of a specific genus of bacteria in one's microbiome, representation of a specific 16S sequence in one's microbiome, and relative abundance of a first phylum over a second phylum of bacteria.
  • the feature vectors can additionally or alternatively be determined in any other suitable manner.
  • the characterization process can be generated and trained according to a random forest predictor (RFP) algorithm that combines bagging (e.g., bootstrap aggregation) and selection of random sets of features from a training dataset to construct a set of decision trees, T, associated with the random sets of features.
  • RFP random forest predictor
  • N cases from the set of decision trees are sampled at random with replacement to create a subset of decision trees, and for each node, m prediction features are selected from all of the prediction features for assessment.
  • the prediction feature that provides the best split at the node (e.g., according to an objective function) is used to perform the split (e.g., as a bifurcation at the node, as a trifurcation at the node).
  • the strength of the characterization process, in identifying features that are strong in predicting classifications can be increased substantially.
  • measures to prevent bias e.g., sampling bias
  • account for an amount of bias can be included during processing, such as to increase robustness of the model.
  • Block S130 and/or other portions of the method 100 can include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process population-level data, but can additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a demographic characteristic-specific basis (e.g., subgroups sharing one or more demographic characteristics such as therapy regimens, dietary regimens, physical activity regimens, ethnicity, age, gender, weight, sleeping behaviors, etc.), condition- specific basis (e.g., subgroups exhibiting a specific sleep-related condition, a combination of sleep-related conditions, triggers for the sleep-related conditions, associated symptoms, etc.), a sample type-specific basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites; etc.), a user basis (e.g., different computer-implemented rules for different users; etc.) and/or any other suitable basis.
  • a demographic characteristic-specific basis e.g., subgroups sharing
  • Block S130 can include assigning users from the population of users to one or more subgroups; and applying different computer-implemented rules for determining features (e.g., the set of feature types used; the types of characterization models generated from the features; etc.) for the different subgroups.
  • features e.g., the set of feature types used; the types of characterization models generated from the features; etc.
  • applying computer-implemented rules can be performed in any suitable manner.
  • Block S130 can include processing (e.g., generating, training, updating, executing, storing, etc.) one or more sleep-related characterization models (e.g., sleep-related condition models, therapy models, etc.) for one or more sleep-related conditions (e.g., for outputting characterizations for users describing user microbiome characteristics in relation to sleep-related conditions; therapy models for outputting therapy determinations for one or more sleep-related conditions; etc.).
  • the characterization models preferably leverage microbiome features as inputs, and preferably output sleep-related characterizations and/or any suitable components thereof; but characterization models can use any suitable inputs to generate any suitable outputs.
  • Block S130 can include transforming the supplementary data, the microbiome composition diversity features, and the microbiome functional diversity features, other microbiome features, outputs of sleep- related characterization models, and/or other suitable data into one or more characterization models (e.g., training a sleep-related characterization model based on the supplementary data and microbiome features; etc.) for one or more sleep-related conditions.
  • characterization models e.g., training a sleep-related characterization model based on the supplementary data and microbiome features; etc.
  • the method 100 can include: determining a population microorganism sequence dataset (e.g., including microorganism sequence outputs for different users of the population; etc.) for a population of users associated with one or more sleep-related conditions, based on a set of samples from the population of users (e.g., and/or based on one or more primer types associated with the sleep-related condition; etc.); collecting a supplementary dataset associated with diagnosis of the one or more sleep-related conditions for the population of subjects; and generating the sleep-related characterization model based on the population microorganism sequence dataset and the supplementary dataset.
  • a population microorganism sequence dataset e.g., including microorganism sequence outputs for different users of the population; etc.
  • the method 100 can include determining a set of user microbiome features for the user based on a sample from the user, where the set of user microbiome features is associated with microbiome features associated with a set of subjects (e.g., microbiome features determined to be correlated with one or more sleep-related conditions, based on processing biological samples corresponding to a set of subjects associated with the one or more sleep-related conditions; a set microbiome composition features and the set of microbiome functional features; etc.); determining a sleep-related characterization, including determining a therapy for the user for the one or more sleep-related conditions based on a therapy model and the set of user microbiome features; providing the therapy (e.g., providing a recommendation for the therapy to the user at a computing device associated with the user, etc.) and/or otherwise facilitating therapeutic intervention.
  • a set of user microbiome features is associated with microbiome features associated with a set of subjects (e.g., microbiome features determined to be correlated with one or more sleep-
  • different sleep-related characterization models and/or other suitable models can be generated for different sleep-related conditions, different user demographic characteristics (e.g., based on age, gender, weight, height, ethnicity; etc.), different physiological sites (e.g., a gut site model, a nose site model, a skin site model, a mouth site model, a genitals site model, etc.), individual users, supplementary data (e.g., models incorporating prior knowledge of microbiome features, sleep-related conditions, and/or other suitable components; features associated with biometric sensor data and/or survey response data vs. models independent of supplementary data, etc.), and/or other suitable criteria.
  • different user demographic characteristics e.g., based on age, gender, weight, height, ethnicity; etc.
  • different physiological sites e.g., a gut site model, a nose site model, a skin site model, a mouth site model, a genitals site model, etc.
  • supplementary data e.g., models incorporating prior knowledge
  • determining sleep-related characterizations and/or any other suitable characterizations can include determining sleep-related characterizations in relation to specific physiological sites (e.g., gut, healthy gut, skin, nose, mouth, genitals, other suitable physiological sites, other sample collection sites, etc.), such as through any one or more of: determining a sleep-related characterization based on a sleep-related characterization model derived based on site-specific data (e.g., defining correlations between a sleep-related condition and microbiome features associated with one or more physiological sites); determining a sleep-related characterization based on a user biological sample collected at one or more physiological sites, and/or any other suitable site-related processes.
  • specific physiological sites e.g., gut, healthy gut, skin, nose, mouth, genitals, other suitable physiological sites, other sample collection sites, etc.
  • site-specific data e.g., defining correlations between a sleep-related condition and microbiome features associated with one or more physiological sites
  • machine learning approaches e.g., classifiers, deep learning algorithms, SVM, random forest
  • parameter optimization approaches e.g., Bayesian Parameter Optimization
  • validation approaches e.g., cross validation approaches
  • statistical tests e.g., univariate statistical techniques, multivariate statistical techniques, correlation analysis such as canonical correlation analysis, etc.
  • dimension reduction techniques e.g PCA
  • suitable analytical techniques e.g., described herein
  • site-related e.g., physiological site-related, etc.
  • characterizations e.g., using a one or more approaches for one or more sample collection sites, such as for each type of sample collection site, etc.
  • therapies and/or any other suitable outputs.
  • performing a characterization process can include applying at least one of: machine learning approaches, parameter optimization approaches, statistical tests, dimension reduction approaches, and/or other suitable approaches (e.g., where microbiome features such as a set of microbiome composition diversity features and/or a set of microbiome functional diversity features can be associated with microorganisms collected at least at one of a gut site, a skin site, a nose site, a mouth site, a genitals site, etc.).
  • characterization processes performed for a plurality of sample collection sites can be used to generate individual characterizations that can be combined to determine an aggregate characterization (e.g., an aggregate microbiome score, such as for one or more conditions described herein, etc.).
  • the method loo can include determining any suitable site-related (e.g., site-specific) outputs, and/or performing any suitable portions of the method loo (e.g., collecting samples, processing samples, determining therapies) with site-specificity and/or other site- relatedness in any suitable manner.
  • Characterization of the subject(s) can additionally or alternatively implement use of a high false positive test and/or a high false negative test to further analyze sensitivity of the characterization process in supporting analyses generated according to embodiments of the method 100.
  • performing one or more characterization processes S130 can be performed in any suitable manner.
  • Performing a characterization process S130 can include performing a sleep-related characterization process (e.g., determining a characterization for one or more sleep-related conditions; determining and/or applying one or more sleep-related characterization model; etc.) S135, such as for one or more users (e.g., for data corresponding to samples from a set of subjects for generating one or more sleep- related characterization models; for a single user for generating a sleep-related characterization for the user, such as through using one or more sleep-related characterization models; etc.) and/or for one or more sleep-related conditions.
  • a sleep-related characterization process e.g., determining a characterization for one or more sleep-related conditions; determining and/or applying one or more sleep-related characterization model; etc.
  • S135 such as for one or more users (e.g., for data corresponding to samples from a set of subjects for generating one or more sleep- related characterization models; for a single user for generating a sleep-related
  • performing a sleep-related characterization process can include determining microbiome features associated with one or more sleep-related conditions (e.g., a sleep order condition.
  • performing a sleep-related characterization process can include applying one or more analytical techniques (e.g., statistical analyses) to identify the sets of microbiome features (e.g., microbiome composition features, microbiome composition diversity features, microbiome functional features, microbiome functional diversity features, etc.) that have the highest correlations with one or more sleep-related conditions (e.g., features associated with a single sleep-related condition, cross-condition features associated with multiple sleep- related conditions and/or other suitable sleep-related conditions, etc.).
  • one or more sleep-related conditions e.g., features associated with a single sleep-related condition, cross-condition features associated with multiple sleep- related conditions and/or other suitable sleep-related conditions, etc.
  • performing a sleep-related characterization process can facilitate therapeutic intervention for one or more sleep-related conditions, such as through facilitating intervention associated with therapies having a positive effect on a state of one or more users in relation to the one or more sleep-related conditions.
  • performing a sleep-related characterization process e.g., determining features highest correlations to one or more sleep-related conditions, etc.
  • performing a sleep-related characterization process can be based upon a random forest predictor algorithm trained with a training dataset derived from a subset of the population of subjects (e.g., subjects having the one or more sleep-related conditions; subjects not having the one or more sleep-related conditions; etc.), and validated with a validation dataset derived from a subset of the population of subjects.
  • determining microbiome features and/or other suitable aspects associated with one or more sleep-related conditions can be performed in any suitable manner.
  • Microbiome features associated with one or more sleep-related conditions can include features associated with any combination of one or more of the following taxa (e.g., features describing abundance of; features describing relative abundance of; features describing functional aspects associated with; features derived from; features describing presence and/or absence of; etc.) described in Table 1 (e.g., in relation to a sleep-related condition of bad sleep quality, etc.) and/or Table 2 (e.g., in relation to a sleep-related condition of shift work, such as night time shift work with day time sleeping periods, etc.) and/or: Acetitomaculum (genus), Acidaminococcaceae (family), Acidaminococcus (genus), Acidaminococcus sp.
  • taxa e.g., features describing abundance of; features describing relative abundance of; features describing functional aspects associated with; features derived from; features describing presence and/or absence of; etc.
  • Table 1 e.g., in relation to a sleep-related condition of bad sleep quality
  • D21 (species), Actinobacteria (class), Actinobacteria (phylum), Actinomyces (genus), Actinomyces sp. ICM47 (species), Actinomyces sp. ICM54 (species), Actinomyces sp. S9 PR-21 (species), Akkermansia muciniphila (species), Alcaligenaceae (family), Alistipes indistinctus (species), Alistipes sp. 627 (species), Anaerococcus (genus), Anaerococcus hydrogenalis (species), Anaerococcus octavius (species), Anaerococcus sp.
  • NML 070203 (species), Atopobium (genus), Atopobium vaginae (species), Bacteroides clarus (species), Bacteroides coprocola (species), Bacteroides nordii (species), Bacteroides plebeius (species), Bacteroides sp. 2_2_4 (species), Bacteroides sp. CB57 (species), Bacteroides sp. DJF_Bo97 (species), Bacteroides sp. EBA5-17 (species), Bacteroides sp. SLCi-38 (species), Bacteroides sp.
  • XB12B Bacteroides stercorirosoris (species), Bifidobacteriaceae (family), Bifidobacteriales (order), Bifidobacterium (genus), Bifidobacterium biavatii (species), Bifidobacterium bifidum (species), Bifidobacterium choerinum (species), Bifidobacterium longum (species), Bifidobacterium merycicum (species), Bifidobacterium sp.
  • MSX5B (species), Bifidobacterium stercoris (species), Blautia glucerasea (species), Blautia hydrogenotrophica (species), Blautia sp. Ser8 (species), Blautia sp.
  • YHC-4 (species), Brevibacterium massiliense (species), Butyricicoccus (genus), Butyricicoccus pullicaecorum (species), Butyricimonas synergistica (species), Butyrivibrio (genus), Butyrivibrio crossotus (species), Campylobacter (genus), Campylobacter hominis (species), Campylobacter ureolyticus (species), Campylobacteraceae (family), Campylobacterales (order), Candidatus Soleaferrea (genus), Candidatus Stoquefichus (genus), Catabacter hongkongensis (species), Catenibacterium mitsuokai (species), Cellulosilyticum (genus), Collinsella aerofaciens (species), Collinsella intestinalis (species), Coprobacillus (genus), Coprobacillus sp.
  • D6 (species), Coprobacter (genus), Coprobacter fastidiosus (species), Corynebacterium sp. (species), Corynebacterium ulcerans (species), Cyanobacteria (phylum), Dermabacter (genus), Dermabacter hominis (species), Dermabacteraceae (family), Desulfovibrio desulfuricans (species), Desulfovibrio piger (species), Desulfovibrio sp.
  • C6I11 (species), Epsilonproteobacteria (class), Erysipelatoclostridium ramosum (species), Eubacteriaceae (family), Eubacterium (genus), Eubacterium callanderi (species), Eubacterium sp. SA11 (species), Facklamia sp.
  • 66c (species), Lactobacillus sp. Akhmroi (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-30 (species), Lactonifactor (genus), Lactonifactor longoviformis (species), Leptotrichiaceae (family), Leuconostoc (genus), Leuconostocaceae (family), Megamonas (genus), Megamonas funiformis (species), Megasphaera (genus), Megasphaera genomosp. Ci (species), Megasphaera sp. S6-MB2 (species), Megasphaera sp.
  • UPII 199-6 (species), Mobiluncus (genus), Mobiluncus mulieris (species), Moryella (genus), Negativicoccus (genus), Negativicoccus succinicivorans (species), Negativicutes (class), Oligella (genus), Oligella urethralis (species), Olsenella sp. 1183 (species), Oscillospiraceae (family), Pantoea (genus), Papillibacter (genus), Parabacteroides goldsteinii (species), Parabacteroides sp.
  • gpacoi8A (species), Phascolarctobacterium (genus), Phascolarctobacterium succinatutens (species), Phyllobacteriaceae (family), Phyllobacterium (genus), Porphyromonas uenonis (species), Prevotella bivia (species), Prevotella disiens (species), Propionibacteriaceae (family), Propionibacterium (genus), Proteobacteria (phylum), Pseudobutyrivibrio (genus), Pseudoclavibacter sp.
  • Timone (species), Rhizobiales (order), Roseburia (genus), Ruminococcaceae (family), Sarcina ventriculi (species), Selenomonadales order Shuttleworthia (genus), Sphingomonadaceae (family), Sphingomonadales (order), Stenotrophomonas (genus), Stenotrophomonas sp.
  • C-S-TSA3 species), Streptococcus agalactiae (species), Streptococcus gordonii (species), Streptococcus pasteurianus (species), Streptococcus peroris (species), Streptococcus sp.
  • BS35a (species), Streptococcus sp. oral taxon G59 (species), Sutterella (genus), Sutterella sp. YIT 12072 (species), Sutterella stercoricanis (species), Sutterella wadsworthensis (species), Terrisporobacter glycolicus (species), Thermoanaerobacteraceae (family), Thermoanaerobacterales (order), Turicibacter (genus), Turicibacter sanguinis (species), Varibaculum (genus), Varibaculum cambriense (species), Veillonella sp.
  • AS16 (species), Veillonellaceae (family), Weissella hellenica (species), Xanthomonadaceae (family), Xanthomonadales (order), Alistipes massiliensis (species), Butyricimonas virosa (species), Alistipes putredinis (species), Actinobacillus porcinus (species), Actinobacillus (genus), Butyricimonas (genus), Howardella ureilytica (species), Firmicutes (phylum), Clostridium (genus), Lentisphaeria (class), Anaeroplasmataceae (family), Pseudomonadaceae (family), Victivallaceae (family), Blautia (genus), Asteroleplasma (genus), Delftia (genus), Victivallis (genus), Peptostreptococcus (genus), Pseudomonas (genus), Alloprevotella (gen
  • CM60 (species), Porphyromonas sp. 2026 (species), Delftia sp. BN-SKY3 (species), Peptostreptococcus anaerobius (species), Citrobacter sp. BW4 (species), Alistipes sp. RMA 9912 (species), Bacteroides vulgatus (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-26 (species), Bifidobacterium kashiwanohense (species), Butyricimonas sp.
  • microbiome features associated with one or more sleep-related conditions can include features associated with one or more of the following taxa: Acetitomaculum (genus), Acidaminococcaceae (family), Acidaminococcus (genus), Acidaminococcus sp. D21 (species), Actinobacteria (class), Actinobacteria (phylum), Actinomyces (genus), Actinomyces sp. ICM47 (species), Actinomyces sp. ICM54 (species), Actinomyces sp.
  • S9 PR-21 (species), Akkermansia muciniphila (species), Alcaligenaceae (family), Alistipes indistinctus (species), Alistipes sp. 627 (species), Anaerococcus (genus), Anaerococcus hydrogenalis (species), Anaerococcus octavius (species), Anaerococcus sp. 8404299 (species), Anaerococcus sp.
  • Anaerococcus tetradius (species), Anaerococcus tetradius (species), Anaerofustis (genus), Anaerofustis stercorihominis (species), Anaeroplasma (genus), Anaerosporobacter (genus), Anaerostipes sp. iy-2 (species), Anaerostipes sp. 3_2_56FAA (species), Anaerotruncus colihominis (species), Anaerotruncus sp.
  • NML 070203 (species), Atopobium (genus), Atopobium vaginae (species), Bacteroides clarus (species), Bacteroides coprocola (species), Bacteroides nordii (species), Bacteroides plebeius (species), Bacteroides sp. 2_2_4 (species), Bacteroides sp. CB57 (species), Bacteroides sp. DJF_Bo97 (species), Bacteroides sp. EBA5-17 (species), Bacteroides sp. SLCi-38 (species), Bacteroides sp.
  • XB12B Bacteroides stercorirosoris (species), Bifidobacteriaceae (family), Bifidobacteriales (order), Bifidobacterium (genus), Bifidobacterium biavatii (species), Bifidobacterium bifidum (species), Bifidobacterium choerinum (species), Bifidobacterium longum (species), Bifidobacterium merycicum (species), Bifidobacterium sp.
  • MSX5B (species), Bifidobacterium stercoris (species), Blautia glucerasea (species), Blautia hydrogenotrophica (species), Blautia sp. Ser8 (species), Blautia sp.
  • YHC-4 (species), Brevibacterium massiliense (species), Butyricicoccus (genus), Butyricicoccus pullicaecorum (species), Butyricimonas synergistica (species), Butyrivibrio (genus), Butyrivibrio crossotus (species), Campylobacter (genus), Campylobacter hominis (species), Campylobacter ureolyticus (species), Campylobacteraceae (family), Campylobacterales (order), Candidatus Soleaferrea (genus), Candidatus Stoquefichus (genus), Catabacter hongkongensis (species), Catenibacterium mitsuokai (species), Cellulosilyticum (genus), Collinsella aerofaciens (species), Collinsella intestinalis (species), Coprobacillus (genus), Coprobacillus sp.
  • D6 (species), Coprobacter (genus), Coprobacter fastidiosus (species), Corynebacterium sp. (species), Corynebacterium ulcerans (species), Cyanobacteria (phylum), Dermabacter (genus), Dermabacter hominis (species), Dermabacteraceae (family), Desulfovibrio desulfuricans (species), Desulfovibrio piger (species), Desulfovibrio sp.
  • C6I11 (species), Epsilonproteobacteria (class), Erysipelatoclostridium ramosum (species), Eubacteriaceae (family), Eubacterium (genus), Eubacterium callanderi (species), Eubacterium sp. SA11 (species), Facklamia sp.
  • 66c (species), Lactobacillus sp. Akhmroi (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-30 (species), Lactonifactor (genus), Lactonifactor longoviformis (species), Leptotrichiaceae (family), Leuconostoc (genus), Leuconostocaceae (family), Megamonas (genus), Megamonas funiformis (species), Megasphaera (genus), Megasphaera genomosp. Ci (species), Megasphaera sp. S6-MB2 (species), Megasphaera sp.
  • UPII 199-6 (species), Mobiluncus (genus), Mobiluncus mulieris (species), Moryella (genus), Negativicoccus (genus), Negativicoccus succinicivorans (species), Negativicutes (class), Oligella (genus), Oligella urethralis (species), Olsenella sp. 1183 (species), Oscillospiraceae (family), Pantoea (genus), Papillibacter (genus), Parabacteroides goldsteinii (species), Parabacteroides sp.
  • gpacoi8A (species), Phascolarctobacterium (genus), Phascolarctobacterium succinatutens (species), Phyllobacteriaceae (family), Phyllobacterium (genus), Porphyromonas uenonis (species), Prevotella bivia (species), Prevotella disiens (species), Propionibacteriaceae (family), Propionibacterium (genus), Proteobacteria (phylum), Pseudobutyrivibrio (genus), Pseudoclavibacter sp.
  • Timone (species), Rhizobiales (order), Roseburia (genus), Ruminococcaceae (family), Sarcina ventriculi (species), Selenomonadales order Shuttleworthia (genus), Sphingomonadaceae (family), Sphingomonadales (order), Stenotrophomonas (genus), Stenotrophomonas sp.
  • C-S-TSA3 species), Streptococcus agalactiae (species), Streptococcus gordonii (species), Streptococcus pasteurianus (species), Streptococcus peroris (species), Streptococcus sp.
  • BS35a (species), Streptococcus sp. oral taxon G59 (species), Sutterella (genus), Sutterella sp. YIT 12072 (species), Sutterella stercoricanis (species), Sutterella wadsworthensis (species), Terrisporobacter glycolicus (species), Thermoanaerobacteraceae (family), Thermoanaerobacterales (order), Turicibacter (genus), Turicibacter sanguinis (species), Varibaculum (genus), Varibaculum cambriense (species), Veillonella sp. AS16 (species), Veillonellaceae (family), Weissella hellenica (species), Xanthomonadaceae (family), Xanthomonadales (order), and/or any other suitable taxa.
  • microbiome features associated with one or more sleep-related conditions can include features associated with one or more of the following taxa: Alistipes massiliensis (species), Butyricimonas virosa (species), Leuconostocaceae (family), Lactobacillus sp.
  • TAB-30 (species), Alistipes putredinis (species), Actinobacillus porcinus (species), Bifidobacterium stercoris (species), Actinobacillus (genus), Butyricimonas (genus), Howardella (genus), Catenibacterium mitsuokai (species), Howardella ureilytica (species), Firmicutes (phylum), Clostridium (genus), Lentisphaeria (class), Anaeroplasmataceae (family), Pseudomonadaceae (family), Victivallaceae (family), Blautia (genus), Asteroleplasma (genus), Delftia (genus), Victivallis (genus), Peptostreptococcus (genus), Pseudomonas (genus), Bifidobacterium (genus), Alloprevotella (genus), Catenibacterium (genus), Anaeroplasmatales (order
  • CM60 (species), Lactobacillus sp. Akhmroi (species), Porphyromonas sp. 2026 (species), Weissella hellenica (species), Delftia sp. BN-SKY3 (species), Peptostreptococcus anaerobius (species), Citrobacter sp. BW4 (species), Collinsella intestinalis (species), Alistipes sp. RMA 9912 (species), Bacteroides vulgatus (species), Lactobacillus sp. BL302 (species), Lactobacillus sp. TAB-26 (species), Bifidobacterium sp.
  • the method 100 can include determining a sleep-related characterization for the user for a first sleep-related condition and a second sleep- related condition based on a first set of composition features (e.g., including at least one or more of the microbiome features described above in relation to the first variation; including any suitable combination of microbiome features; etc.), a first sleep-related characterization model, a second set of composition features (e.g., including at least one or more of the microbiome features described above in relation to the second variation; including any suitable combination of microbiome features; etc.), and a second sleep- related characterization model, where the first sleep-related characterization model is associated with the first sleep-related condition (e.g., where the first sleep-related characterization model determines characterizations for the first sleep-related condition, etc.), and where the second sleep-related characterization model is associated with the second sleep-related condition (e.g., where the second sleep-related characterization model determines characterizations for the second sleep-related condition,
  • determining user microbiome features can include determining first user microbiome functional features associated with first functions from at least one of Cluster of Orthologous Groups (COG) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) database, where the first user microbiome functional features are associated with the first sleep-related condition; and determining second user microbiome functional features associated with second functions from at least one of the COG database and the KEGG database, where the second user microbiome functional features are associated with the second sleep- related condition, where determining the sleep-related characterization can include determining the sleep-related characterization for the user for the first sleep-related condition and the second sleep-related condition based on the first set of composition features, the first user microbiome functional features, the first sleep-related characterization model, the second set of composition features, the second user microbiome functional features, and the second sleep-related characterization model. Additionally or alternatively, any combinations of microbiome features can be used with any suitable number and types of sleep-related characterization models to determine sleep-related characterization for one or more
  • microbiome features associated with one or more sleep-related conditions can include microbiome functional features (e.g., features describing functions associated with one or more microorganisms, such as microorganisms classified under taxa described herein; features describing functional diversity; features describing presence, absence, abundance, and/or relative abundance; etc.) corresponding to functions from and/or otherwise associated with the Clusters of Orthologous Groups (COG) database, Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and/or any other suitable database available (e.g., databases with microorganism function data, etc.).
  • microbiome features can include any suitable microbiome functional features associated with any suitable microorganism function, human function, and/or other suitable functionality.
  • the method loo can include generating one or more sleep-related characterization models based on any suitable combination of microbiome features described above and/or herein (e.g., based on a set of microbiome composition features including features associated with at least one of the taxa described herein; and/or based on microbiome functional features described herein, such as corresponding to functions from databases described herein; etc.)
  • performing a characterization process for a user can include characterizing a user as having one or more sleep-related conditions, such as based upon detection of, values corresponding to, and/or other aspects related to microbiome features described herein (e.g., microbiome features described above, etc.), and such as in a manner that is an additional (e.g., supplemental to, complementary to, etc.) or alternative to typical approaches of diagnosis, other characterizations (e.g., treatment- related characterizations, etc.), treatment, monitoring, and/or other suitable approaches associated with sleep-related conditions.
  • the microbiome features e.g.
  • Performing a characterization process S130 can include Block S140, which can include determining one or more therapies (e.g., therapies configured to modulate microbiome composition, function, diversity, and/or other suitable aspects, such as for improving one or more aspects associated with sleep-related conditions, such as in users characterized based on one or more characterization processes; etc.).
  • Block S140 can function to identify, select, rank, prioritize, predict, discourage, and/or otherwise determine therapies (e.g., facilitate therapy determination, etc.).
  • Block S140 can include determining one or more of probiotic-based therapies, bacteriophage-based therapies, small molecule- based therapies, and/or other suitable therapies, such as therapies that can shift a subject's microbiome composition, function, diversity, and/or other characteristics (e.g., microbiomes at any suitable sites, etc.) toward a desired state (e.g., equilibrium state, etc.) in promotion of a user's health, for modifying a state of one or more sleep- related conditions, and/or for other suitable purposes.
  • a desired state e.g., equilibrium state, etc.
  • Therapies can include any one or more of: consumables (e.g., probiotic therapies, prebiotic therapies, medication, sleeping pills, melatonin supplements, allergy or cold medication, bacteriophage-based therapies, consumables for underlying conditions, small molecule therapies, etc.); device-related therapies (e.g., sleep-monitoring devices, such as a user device executing a sleep-monitoring application, sensor-based devices; dental guards; breathing devices; medical devices; implantable medical devices; sleep apnea devices such as continuous positive airway pressure devices, mandibular advancement devices, tongue retaining devices; stimulation devices such as electrostimulation devices, nerve stimulation devices; snoring prevention devices; catheters such as transtracheal catheters; nasal air filters; air quality devices such as air filtration devices; audio-based devices such as white noise machines; etc.); surgical operations (e.g., sleep apnea surgery; tonsillectomy; ade
  • types of therapies can include any one or more of: probiotic therapies, bacteriophage-based therapies, small molecule-based therapies, cognitive/behavioral therapies, physical rehabilitation therapies, clinical therapies, medication-based therapies, diet-related therapies, and/or any other suitable therapy designed to operate in any other suitable manner in promoting a user's health.
  • therapies can include one or more bacteriophage-based therapies (e.g., in the form of a consumable, in the form of a topical administration therapy, etc.), where one or more populations (e.g., in terms of colony forming units) of bacteriophages specific to a certain bacteria (or other microorganism) represented in the subject can be used to down-regulate or otherwise eliminate populations of the certain bacteria.
  • bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the subject.
  • bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
  • bacteriophage-based therapies can be used to modulate characteristics of microbiomes (e.g., microbiome composition, microbiome function, etc.) in any suitable manner, and/or can be used for any suitable purpose.
  • therapies can include one or more probiotic therapies and/or prebiotic therapies associated with any combination of at least one or more of (e.g., including any combination of one or more of, etc.) any suitable taxa described in Table ⁇ (e.g., in relation to therapies for a sleep-related condition of bad sleep quality, etc.) and/or Table 2 (e.g., in relation to therapies for a sleep-related condition for shift work, etc.) and/or: Anaerococcus sp. 8405254, Bacteroides nordii, Bacteroides sp. SLCi-38, Bifidobacterium merycicum, Blautia glucerasea, Blautia sp.
  • YHC-4 Butyrivibrio crossotus, Catabacter hongkongensis, Catenibacterium mitsuokai, Collinsella aerofaciens, Collinsella intestinalis, Desulfovibrio piger, Eubacterium sp. SA11, Fusobacterium ulcerans, Lactobacillus sp. TAB-30, Megamonas funiformis, Megasphaera sp. S6-MB2 , Olsenella sp. 1183, Phascolarctobacterium succinatutens, Streptococcus gordonii, Sutterella sp. YIT 12072, Sutterella wadsworthensis, Veillonella sp.
  • Anaerotruncus colihominis Varibaculum cambriense, Actinomyces sp. S9 PR-21, Desulfovibrio sp., Prevotella disiens, Mobiluncus mulieris, Lactobacillus rhamnosus, Bifidobacterium sp. MSX5B, Acidaminococcus sp. D21, Bifidobacterium bifidum, Bacteroides sp. EBA5-17, Anaerococcus hydrogenalis, Alistipes sp. 627, Negativicoccus succinicivorans, Anaerococcus sp.
  • Butyricimonas synergistica Actinomyces sp. ICM54, Turicibacter sanguinis, Blautia hydrogenotrophica, Parabacteroides goldsteinii, Bifidobacterium biavatii, Erysipelatoclostridium ramosum, Anaerofustis stercorihominis, Gardnerella vaginalis, Gordonibacter pamelaeae, Campylobacter hominis, Lactobacillus sp. BL302, Megasphaera sp. UPII 199-6, Peptoniphilus sp.
  • gpacoi8A Bifidobacterium stercoris, Butyricicoccus pullicaecorum
  • Megasphaera sp. S6-MB2 Corynebacterium sp., Dialister propionicifaciens, Anaerococcus tetradius, Eggerthella sp. HGAi, Peptoniphilus sp. 7-2, Terrisporobacter glycolicus, Peptoniphilus sp. 2002-2300004, Bacteroides sp. CB57, Streptococcus pasteurianus, Megasphaera genomosp. Ci, Holdemania filiformis, Coprobacillus sp.
  • NML 070203 Haemophilus parainfluenzae, Peptoniphilus coxii, Granulicatella adiacens, Campylobacter ureolyticus, Bifidobacterium longum, Bacteroides clarus, Bacteroides sp. XB12B, Streptococcus agalactiae, Kluyvera georgiana, Flavonifractor plautii, Paraprevotella clara, Stenotrophomonas sp. C-S-TSA3, Bacteroides sp. DJF_Bo97, Herbaspirillum seropedicae, Streptococcus sp.
  • RMA 9912 Bacteroides vulgatus, Lactobacillus sp. TAB-26, Bifidobacterium sp., Bifidobacterium kashiwanohense, Butyricimonas sp. JCM 18677, and/or any other suitable microorganisms associated with any suitable taxonomic groups (e.g., microorganisms from taxa described herein, such as in relation to microbiome features, etc.).
  • any suitable taxonomic groups e.g., microorganisms from taxa described herein, such as in relation to microbiome features, etc.
  • microorganisms associated with a given taxonomic group, and/or any suitable combination of microorganisms can be provided at dosages of o.i million to 10 billion CFU, and/or at any suitable amount (e.g., as determined from a therapy model that predicts positive adjustment of a patient's microbiome in response to the therapy, etc.).
  • a subject can be instructed to ingest capsules including the probiotic formulation according to a regimen tailored to one or more of his/her: physiology (e.g., body mass index, weight, height), demographic characteristics (e.g., gender, age), severity of dysbiosis, sensitivity to medications, and any other suitable factor.
  • candidate therapies of the therapy model can perform one or more of: blocking pathogen entry into an epithelial cell by providing a physical barrier (e.g., by way of colonization resistance), inducing formation of a mucous barrier by stimulation of goblet cells, enhance integrity of apical tight junctions between epithelial cells of a subject (e.g., by stimulating up regulation of zona-occludens 1, by preventing tight junction protein redistribution), producing antimicrobial factors, stimulating production of anti-inflammatory cytokines (e.g., by signaling of dendritic cells and induction of regulatory T-cells), triggering an immune response, and performing any other suitable function that adjusts a subject's microbiome away from a state of dysbiosis.
  • therapies can include medical-device based therapies (e.g., associated with human behavior modification, associated with treatment of disease-related conditions, etc.).
  • the therapy model is preferably based upon data from a large population of subjects, which can include the population of subjects from which the microbiome diversity datasets are derived in Block S110, where microbiome composition and/or functional features or states of health, prior exposure to and post exposure to a variety of therapeutic measures, are well characterized.
  • data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for subjects based upon different sleep-related characterizations.
  • support vector machines as a supervised machine learning algorithm, can be used to generate the therapy provision model.
  • any other suitable machine learning algorithm described above can facilitate generation of the therapy provision model.
  • the therapy model can be derived in relation to identification of a "normal" or baseline microbiome composition and/or functional features, as assessed from subjects of a population of subjects who are identified to be in good health.
  • therapies that modulate microbiome compositions and/or functional features toward those of subjects in good health can be generated in Block S140.
  • Block S140 can thus include identification of one or more baseline microbiome compositions and/or functional features (e.g., one baseline microbiome for each of a set of demographic characteristics), and potential therapy formulations and therapy regimens that can shift microbiomes of subjects who are in a state of dysbiosis toward one of the identified baseline microbiome compositions and/or functional features.
  • the therapy model can, however, be generated and/or refined in any other suitable manner.
  • Microorganism compositions associated with probiotic therapies and/or prebiotic therapies can include microorganisms that are culturable (e.g., able to be expanded to provide a scalable therapy) and/or non- lethal (e.g., non-lethal in their desired therapeutic dosages).
  • microorganism compositions can include a single type of microorganism that has an acute or moderated effect upon a subject's microbiome.
  • microorganism compositions can include balanced combinations of multiple types of microorganisms that are configured to cooperate with each other in driving a subject's microbiome toward a desired state.
  • a combination of multiple types of bacteria in a probiotic therapy can include a first bacteria type that generates products that are used by a second bacteria type that has a strong effect in positively affecting a subject's microbiome.
  • a combination of multiple types of bacteria in a probiotic therapy can include several bacteria types that produce proteins with the same functions that positively affect a subject's microbiome.
  • Probiotic and/or prebiotic compositions can be naturally or synthetically derived.
  • a probiotic composition can be naturally derived from fecal matter or other biological matter (e.g., of one or more subjects having a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model).
  • probiotic compositions can be synthetically derived (e.g., derived using a benchtop method) based upon a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model.
  • microorganism agents that can be used in probiotic therapies can include one or more of: yeast (e.g., Saccharomyces boulardii), gram-negative bacteria (e.g., E. coli Nissle), gram-positive bacteria (e.g., Bifidobacteria bifidum, Bifidobacteria infantis, Lactobacillus rhamnosus, Lactococcus lactis, Lactobacillus plantarum, Lactobacillus acidophilus, Lactobacillus casei, Bacillus polyfermenticus, etc.), and any other suitable type of microorganism agent.
  • yeast e.g., Saccharomyces boulardii
  • gram-negative bacteria e.g., E. coli Nissle
  • gram-positive bacteria e.g., Bifidobacteria bifidum, Bifidobacteria infantis, Lactobacillus rhamnosus
  • Block S140 can include executing, storing, retrieving, and/or otherwise processing one or more therapy models for determining one or more therapies.
  • Processing one or more therapy models is preferably based on microbiome features. For example, generating a therapy model can based on microbiome features associated with one or more sleep-related conditions, therapy-related aspects such as therapy efficacy in relation to microbiome characteristics, and/or other suitable data. Additionally or alternatively, processing therapy models can be based on any suitable data.
  • processing a therapy model can include determining one or more therapies for a user based on one or more therapy models, user microbiome features (e.g., inputting user microbiome feature values into the one or more therapy models, etc.), supplementary data (e.g., prior knowledge associated with therapies such as in relation to microorganism-related metabolization; user medical history; user demographic data, such as describing demographic characteristics; etc.), and/or any other suitable data.
  • processing therapy models can be based on any suitable data in any suitable manner.
  • Sleep-related characterization models can include one or more therapy models.
  • determining one or more sleep-related characterizations can include determining one or more therapies, such as based on one or more therapy models (e.g., applying one or more therapy models, etc.) and/or other suitable data (e.g., microbiome features such as user microbiome features, microorganism dataset such as user microorganism datasets, etc.).
  • determining one or more sleep- related characterizations can include determining a first sleep-related characterization for a user (e.g., describing propensity for one or more sleep-related conditions; etc.); and determining a second sleep-related characterization for the user based on the first sleep-related characterization (e.g., determining one or more therapies, such as for recommendation to a user, based on the propensity for one or more sleep-related conditions; etc.).
  • a sleep-related characterization can include both propensity-related data (e.g., diagnostic data; associated microbiome composition, function, diversity, and/or other characteristics; etc.) and therapy-related data (e.g., recommended therapies; potential therapies; etc.).
  • sleep-related characterizations can include any suitable data (e.g., any combination of data described herein, etc.).
  • Processing therapy models can include processing a plurality of therapy models.
  • different therapy models can be processed for different therapies (e.g., different models for different individual therapies; different models for different combinations and/or categories of therapies, such as a first therapy model for determining consumable therapies and a second therapy model for determining psychological-associated therapies; etc.).
  • different therapy models can be processed for different sleep-related conditions, (e.g., different models for different individual sleep-related conditions; different models for different combinations and/or categories of sleep-related conditions, such as a first therapy model for determining therapies for insomnias, and a second therapy model for determining therapies for hypersomnias, etc.).
  • processing a plurality of therapy models can be performed for (e.g., based on; processing different therapy models for; etc.) any suitable types of data and/or entities.
  • processing a plurality of therapy models can be performed in any suitable manner, and determining and/or applying one or more therapy models can be performed in any suitable manner.
  • the method loo can additionally or alternatively include Block S150, which can include processing one or more biological samples from a user (e.g., biological samples from different collection sites of the user, etc.).
  • Block S150 can function to facilitate generation of a microorganism dataset for a user, such as for use in deriving inputs for the characterization process (e.g., for generating a sleep-related characterization for the user, such as through applying one or more sleep-related characterization models, etc.).
  • Block S150 can include receiving, processing, and/or analyzing one or more biological samples from one or more users (e.g., multiple biological samples for the same user over time, different biological samples for different users, etc.).
  • noninvasive manners of sample reception can use any one or more of: a permeable substrate (e.g., a swab configured to wipe a region of a user's body, toilet paper, a sponge, etc.), a non-permeable substrate (e.g., a slide, tape, etc.) a container (e.g., vial, tube, bag, etc.) configured to receive a sample from a region of a user's body, and any other suitable sample-reception element.
  • a permeable substrate e.g., a swab configured to wipe a region of a user's body, toilet paper, a sponge, etc.
  • a non-permeable substrate e.g., a slide, tape, etc.
  • a container e.g., vial, tube, bag, etc.
  • the biological sample can be collected from one or more of the user's nose, skin, genitals, mouth, and gut (e.g., through stool samples, etc.) in a non-invasive manner (e.g., using a swab and a vial).
  • the biological sample can additionally or alternatively be received in a semi-invasive manner or an invasive manner.
  • invasive manners of sample reception can use any one or more of: a needle, a syringe, a biopsy element, a lance, and any other suitable instrument for collection of a sample in a semi-invasive or invasive manner.
  • samples can include blood samples, plasma/serum samples (e.g., to enable extraction of cell-free DNA), and tissue samples.
  • the biological sample can be taken from the body of the user without facilitation by another entity (e.g., a caretaker associated with a user, a health care professional, an automated or semi-automated sample collection apparatus, etc.), or can alternatively be taken from the body of the user with the assistance of another entity.
  • a sample-provision kit can be provided to the user.
  • the kit can include one or more swabs for sample acquisition, one or more containers configured to receive the swab(s) for storage, instructions for sample provision and setup of a user account, elements configured to associate the sample(s) with the user (e.g., barcode identifiers, tags, etc.), and a receptacle that allows the sample(s) from the user to be delivered to a sample processing operation (e.g., by a mail delivery system).
  • a sample processing operation e.g., by a mail delivery system.
  • the biological sample is extracted from the user with the help of another entity
  • one or more samples can be collected in a clinical or research setting from the user (e.g., during a clinical appointment). The biological sample can, however, be received from the user in any other suitable manner.
  • processing and analyzing biological samples from the user is preferably performed in a manner similar to that of one of the embodiments, variations, and/or examples of sample reception described in relation to Block Sno above, and/or any other suitable portions of the method loo and/or system 200.
  • reception and processing of the biological sample in Block S150 can be performed for the user using similar processes as those for receiving and processing biological samples used to perform the characterization processes of the method 100, such as in order to provide consistency of process.
  • biological sample reception and processing in Block S150 can additionally or alternatively be performed in any other suitable manner.
  • the method 100 can additionally or alternatively include Block S160, which can include determining, with one or more characterization processes (e.g., one or more characterization processes described in relation to Block S130, etc.), a sleep- related characterization for the user, such as based upon processing one or more microorganism dataset (e.g., user microorganism sequence dataset, microbiome composition dataset, microbiome functional diversity dataset; processing of the microorganism dataset to extract user microbiome features that can be used to determine the one or more sleep-related characterizations; etc.) derived from the biological sample of the user.
  • characterization processes e.g., one or more characterization processes described in relation to Block S130, etc.
  • a sleep- related characterization for the user such as based upon processing one or more microorganism dataset (e.g., user microorganism sequence dataset, microbiome composition dataset, microbiome functional diversity dataset; processing of the microorganism dataset to extract user microbiome features that can be used to determine the one or more sleep-related
  • Block S160 can function to characterize one or more sleep-related conditions for a user, such as through extracting features from microbiome-derived data of the user, and using the features as inputs into an embodiment, variation, or example of the characterization process described in Block S130 above (e.g., using the user microbiome feature values as inputs into a microbiome- related condition characterization model, etc.).
  • Block S160 can include generating a sleep-related characterization for the user based on user microbiome features and a sleep-related condition model (e.g., generated in Block S130).
  • Sleep- related characterizations can be for any number and/or combination of sleep-related conditions (e.g., a combination of sleep-related conditions, a single sleep-related condition, and/or other suitable sleep-related conditions; etc.), users, collection sites, and/or other suitable entities.
  • sleep-related conditions e.g., a combination of sleep-related conditions, a single sleep-related condition, and/or other suitable sleep-related conditions; etc.
  • Sleep-related characterizations can include one or more of: diagnoses (e.g., presence or absence of a sleep-related condition; etc.); risk (e.g., risk scores for developing and/or the presence of a sleep-related condition; information regarding sleep-related characterizations (e.g., symptoms, signs, triggers, associated conditions, etc.); comparisons (e.g., comparisons with other subgroups, populations, users, historic health statuses of the user such as historic microbiome compositions and/or functional diversities; comparisons associated with sleep-related conditions; etc.); therapy determinations; other suitable outputs associated with characterization processes; and/or any other suitable data.
  • diagnoses e.g., presence or absence of a sleep-related condition; etc.
  • risk e.g., risk scores for developing and/or the presence of a sleep-related condition
  • information regarding sleep-related characterizations e.g., symptoms, signs, triggers, associated conditions, etc.
  • comparisons e.g., comparisons with other sub
  • a sleep-related characterization can include a microbiome diversity score (e.g., in relation to microbiome composition, function, etc.) associated with (e.g., correlated with; negatively correlated with; positively correlated with; etc.) a microbiome diversity score correlated with one or more sleep-related conditions.
  • the sleep-related characterization can include microbiome diversity scores over time (e.g., calculated for a plurality of biological samples of the user collected over time), comparisons to microbiome diversity scores for other users, and/or any other suitable type of microbiome diversity score.
  • processing microbiome diversity scores e.g., determining microbiome diversity scores; using microbiome diversity scores to determine and/or provide therapies; etc.
  • processing microbiome diversity scores can be performed in any suitable manner.
  • Determining a sleep-related characterization in Block S160 preferably includes determining features and/or combinations of features associated with the microbiome composition and/or functional features of the user (e.g., determining feature values associated with the user, the feature values corresponding to microbiome features determined in Block S130, etc.), inputting the features into the characterization process, and receiving an output that characterizes the user as belonging to one or more of: a behavioral group, a gender group, a dietary group, a disease-state group, and any other suitable group capable of being identified by the characterization process.
  • Block S160 can additionally or alternatively include generation of and/or output of a confidence metric associated with the characterization of the user.
  • a confidence metric can be derived from the number of features used to generate the characterization, relative weights or rankings of features used to generate the characterization, measures of bias in the characterization process, and/or any other suitable parameter associated with aspects of the characterization process.
  • leveraging user microbiome features can be performed in any suitable manner to generate any suitable sleep-related characterizations.
  • features extracted from the microorganism dataset of the user can be supplemented with supplementary features (e.g., extracted from supplementary data collected for the user; such as survey-derived features, medical history-derived features, sensor data, etc.), where such data, the user microbiome data, and/or other suitable data can be used to further refine the characterization process of Block S130, Block S160, and/or other suitable portions of the method 100.
  • supplementary features e.g., extracted from supplementary data collected for the user; such as survey-derived features, medical history-derived features, sensor data, etc.
  • Determining a sleep-related characterization preferably includes extracting and applying user microbiome features (e.g., user microbiome composition diversity features; user microbiome functional diversity features; etc.) for the user (e.g., based on a user microorganism dataset), characterization models, and/or other suitable components, such as by employing processes described in Block S130, and/or by employing any suitable approaches described herein.
  • user microbiome features e.g., user microbiome composition diversity features; user microbiome functional diversity features; etc.
  • Block S160 can include presenting sleep-related characterizations (e.g., information extracted from the characterizations; as part of facilitating therapeutic intervention; etc.), such as at a web interface, a mobile application, and/or any other suitable interface, but presentation of information can be performed in any suitable manner.
  • sleep-related characterizations e.g., information extracted from the characterizations; as part of facilitating therapeutic intervention; etc.
  • presentation of information can be performed in any suitable manner.
  • the microorganism dataset of the user can additionally or alternatively be used in any other suitable manner to enhance the models of the method 100, and Block S160 can be performed in any suitable manner.
  • Block S170 can include facilitating therapeutic intervention (e.g., promoting therapies, providing therapies, facilitating provision of therapies, etc.) for one or more sleep-related conditions for one or more users (e.g., based upon a sleep- related characterization and/ or a therapy model).
  • Block S170 can function to recommend, promote, provide, and/or otherwise facilitate therapeutic intervention in relation to one or more therapies for a user, such as to shift the microbiome composition and/or functional diversity of a user toward a desired equilibrium state (and/or otherwise improving a state of the sleep-related condition, etc.) in relation to one or more sleep-related conditions.
  • Block S170 can include provision of a customized therapy to the user according to their microbiome composition and functional features, where the customized therapy can include a formulation of microorganisms configured to correct dysbiosis characteristic of users having the identified characterization.
  • outputs of Block S140 can be used to directly promote a customized therapy formulation and regimen (e.g., dosage, usage instructions) to the user based upon a trained therapy model.
  • therapy provision can include recommendation of available therapeutic measures configured to shift microbiome composition and/or functional features toward a desired state.
  • therapies can include any one or more of: consumables, topical therapies (e.g., lotions, ointments, antiseptics, etc.), medication (e.g., medications associated with any suitable medication type and/or dosage, etc.), bacteriophages, environmental treatments, behavioral modification (e.g., diet modification therapies, stress-reduction therapies, physical activity-related therapies, etc.), diagnostic procedures, other medical-related procedures, and/or any other suitable therapies associated with sleep-related conditions.
  • topical therapies e.g., lotions, ointments, antiseptics, etc.
  • medication e.g., medications associated with any suitable medication type and/or dosage, etc.
  • bacteriophages e.g., environmental treatments, behavioral modification (e.g., diet modification therapies, stress-reduction therapies, physical activity-related therapies, etc.), diagnostic procedures, other medical-related procedures, and/or any other suitable therapies associated with sleep-related conditions.
  • behavioral modification e.g., diet modification therapies, stress-reduction
  • Consumables can include any one or more of: food and/or beverage items (e.g., probiotic and/or prebiotic food and/or beverage items, etc.), nutritional supplements (e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, probiotics, etc.), consumable medications, and/or any other suitable therapeutic measure.
  • food and/or beverage items e.g., probiotic and/or prebiotic food and/or beverage items, etc.
  • nutritional supplements e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, probiotics, etc.
  • consumable medications e.g., any other suitable therapeutic measure.
  • a combination of commercially available probiotic supplements can include a suitable probiotic therapy for the user according to an output of the therapy model.
  • the method 100 can include determining a sleep-related condition risk for the user for the sleep-related condition based on a sleep- related condition model (e.g., and/or user microbiome features); and promoting a therapy to the user based on the sleep-related condition risk.
  • a sleep-related condition model e.g., and/or user microbiome features
  • facilitating therapeutic intervention can include promoting a diagnostic procedure (e.g., for facilitating detection of sleep-related conditions, which can motivate subsequent promotion of other therapies, such as for modulation of a user microbiome for improving a user health state associated with one or more sleep-related conditions; etc.).
  • Diagnostic procedures can include any one or more of: medical history analyses, imaging examinations, cell culture tests, antibody tests, skin prick testing, patch testing, blood testing, challenge testing, performing portions of the method 100, and/or any other suitable procedures for facilitating the detecting (e.g., observing, predicting, etc.) of sleep-related conditions.
  • diagnostic device-related information and/or other suitable diagnostic information can be processed as part of a supplementary dataset (e.g., in relation to Block S120, where such data can be used in determining and/or applying characterization models, therapy models, and/or other suitable models; etc.), and/or collected, used, and/or otherwise processed in relation to any suitable portions of the method 100 (e.g., administering diagnostic procedures for users for monitoring therapy efficacy in relation to Block S180; etc.)
  • Block S170 can include promoting a bacteriophage- based therapy.
  • one or more populations (e.g., in terms of colony forming units) of bacteriophages specific to a certain bacteria (or other microorganism) represented in the user can be used to down-regulate or otherwise eliminate populations of the certain bacteria.
  • bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the user.
  • bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
  • facilitating therapeutic intervention can include provision of notifications to a user regarding the recommended therapy, other forms of therapy, sleep-related characterizations, and/or other suitable data.
  • providing a therapy to a user can include providing therapy recommendations (e.g., substantially concurrently with providing information derived from a sleep-related characterization for a user; etc.) and/or other suitable therapy-related information (e.g., therapy efficacy; comparisons to other individual users, subgroups of users, and/or populations of users; therapy comparisons; historic therapies and/or associated therapy-related information; psychological therapy guides such as for cognitive behavioral therapy; etc.), such as through presenting notifications at a web interface (e.g., through a user account associated with and identifying a user; etc.).
  • Notifications can be provided to a user by way of an electronic device (e.g., personal computer, mobile device, tablet, wearable, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.) that executes an application, web interface, and/or messaging client configured for notification provision.
  • an electronic device e.g., personal computer, mobile device, tablet, wearable, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.
  • a web interface of a personal computer or laptop associated with a user can provide access, by the user, to a user account of the user, where the user account includes information regarding the user's sleep-related characterization, detailed characterization of aspects of the user's microbiome (e.g., in relation to correlations with sleep-related conditions; etc.), and/or notifications regarding suggested therapeutic measures (e.g., generated in Blocks S140 and/or S170, etc.).
  • an application executing at a personal electronic device can be configured to provide notifications (e.g., at a display, haptically, in an auditory manner, etc.) regarding therapy suggestions generated by the therapy model of Block S170.
  • Notifications and/or probiotic therapies can additionally or alternatively be provided directly through an entity associated with a user (e.g., a caretaker, a spouse, a significant other, a healthcare professional, etc.).
  • notifications can additionally or alternatively be provided to an entity (e.g., healthcare professional) associated with a user, such as where the entity is able to facilitate provision of the therapy (e.g., by way of prescription, by way of conducting a therapeutic session, through a digital telemedicine session using optical and/or audio sensors of a computing device, etc.).
  • entity e.g., healthcare professional
  • provision of the therapy e.g., by way of prescription, by way of conducting a therapeutic session, through a digital telemedicine session using optical and/or audio sensors of a computing device, etc.
  • Block S180 can include: monitoring effectiveness of one or more therapies and/or monitoring other suitable components (e.g., microbiome characteristics, etc.) for the user (e.g., based upon processing a series of biological samples from the user), over time.
  • Block S180 can function to gather additional data regarding positive effects, negative effects, and/or lack of effectiveness of one or more therapies (e.g., suggested by the therapy model for users of a given characterization, etc.) and/or monitoring microbiome characteristics (e.g., to assess microbiome composition and/or functional features for the user at a set of time points, etc.).
  • Monitoring of a user during the course of a therapy promoted by the therapy model can thus be used to generate a therapy-effectiveness model for each characterization provided by the characterization process of Block S130, and each recommended therapy measure provided in Blocks S140 and S170.
  • Block S180 the user can be prompted to provide additional biological samples, supplementary data, and/or other suitable data at one or more key time points of a therapy regimen that incorporates the therapy, and the additional biological sample(s) can be processed and analyzed (e.g., in a manner similar to that described in relation to Block S120) to generate metrics characterizing modulation of the user's microbiome composition and/or functional features.
  • metrics related to one or more of: a change in relative abundance of one or more taxonomic groups represented in the user's microbiome at an earlier time point, a change in representation of a specific taxonomic group of the user's microbiome, a ratio between abundance of a first taxonomic group of bacteria and abundance of a second taxonomic group of bacteria of the user's microbiome, a change in relative abundance of one or more functional families in a user's microbiome, and any other suitable metrics can be used to assess therapy effectiveness from changes in microbiome composition and/or functional features.
  • the method 100 can include receiving a post-therapy biological sample from the user; collecting a supplementary dataset from the user, where the supplementary dataset describes user adherence to a therapy (e.g., a determined and promoted therapy) and/or other suitable user characteristics (e.g., behaviors, conditions, etc.); generating a post-therapy sleep-related characterization of the first user in relation to the sleep-related condition based on the sleep-related characterization model and the post-therapy biological sample; and promoting an updated therapy to the user for the sleep-related condition based on the post -therapy sleep-related characterization (e.g., based on a comparison between the post-therapy sleep-related characterization and a pre-therapy sleep-related characterization; etc.) and/or the user adherence to the
  • a therapy e.g., a determined and promoted therapy
  • other suitable user characteristics e.g., behaviors, conditions, etc.
  • supplementary data describing user behavior associated with one or more sleep-related conditions; supplementary data describing a sleep-related condition such as observed symptoms; etc.
  • a post-therapy characterization e.g., degree of change from pre- to post- therapy in relation to the sleep-related condition; etc.
  • updated therapies e.g., determining an updated therapy based on effectiveness and/or adherence to the promoted therapy, etc.
  • the method loo can include collecting first sleep-tracking data (e.g., at least one of first survey-derived data and first device data) and/or other suitable supplementary, where the first sleep-tracking data is associated with sleep quality of the user; determining the sleep-related characterization for the user based on the user microbiome features and the first sleep-tracking data; facilitating therapeutic intervention based on the sleep-related characterization; collecting a post-therapy biological sample from the user; collecting second sleep-tracking data (e.g., including at least one of second survey-derived data and second device data; etc.) and/or other suitable supplementary data, where the second sleep-tracking data is associated with the sleep quality of the user; and determining a post-therapy sleep-related characterization for the user for the sleep-related condition based on the second sleep- tracking data and post-therapy microbiome features associated with the post -therapy biological sample.
  • first sleep-tracking data e.g., at least one of first survey-derived data and first device data
  • suitable supplementary where the
  • the method 100 can include facilitating therapeutic intervention in relation to an updated therapy (e.g., a modification of the therapy; a different therapy; etc.) for the user for improving the sleep-related condition, based on the post-therapy sleep-related characterization, such as where the updated therapy can include at least one of a consumable, a device-related therapy, a surgical operation, a psychological-associated therapy, a behavior modification therapy, and an environmental factor modification therapy.
  • an updated therapy e.g., a modification of the therapy; a different therapy; etc.
  • the updated therapy can include at least one of a consumable, a device-related therapy, a surgical operation, a psychological-associated therapy, a behavior modification therapy, and an environmental factor modification therapy.
  • determining the post- therapy sleep-related characterization can include determining a comparison between microbiome characteristics of the user and reference microbiome characteristics corresponding to a user subgroup sharing at least one of a behavior and an environmental factor (and/or other suitable characteristic) associated with the sleep- related condition, based on the post-therapy microbiome features, and where facilitating therapeutic intervention in relation to the updated therapy can include presenting the comparison to the user for facilitating at least one of the behavior modification therapy and the environmental factor modification therapy and/or other suitable therapies.
  • Block S180 can be performed in relation to additional biological samples, additional supplementary data, and/or other suitable additional data in any suitable manner.
  • Block S180 can be performed in any suitable manner.
  • the method loo can, however, include any other suitable blocks or steps configured to facilitate reception of biological samples from subjects, processing of biological samples from subjects, analyzing data derived from biological samples, and generating models that can be used to provide customized diagnostics and/or probiotic- based therapeutics according to specific microbiome compositions and/or functional features of subjects.
  • Embodiments of the system 200 and/or method 100 can include every combination and permutation of the various system components and the various method processes, including any variants (e.g., embodiments, variations, examples, specific examples, figures, etc.), where portions of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., in parallel), or in any other suitable order by and/or using one or more instances, elements, components of, and/or other aspects of the system 200 and/or other entities described herein.
  • any of the variants described herein e.g., embodiments, variations, examples, specific examples, figures, etc.
  • any portion of the variants described herein can be additionally or alternatively combined, aggregated, excluded, used, and/or otherwise applied.
  • the system 200 and/or method 100 and/or variants thereof can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions are preferably executed by computer-executable components preferably integrated with the system.
  • the computer-readable medium can be stored on any suitable computer- readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device.
  • the computer-executable component is preferably a general or application specific processor, but any suitable dedicated hardware or hardware/firmware combination device can alternatively or additionally execute the instructions.
  • Taxa associated with individuals with sleep-related condition of bad sleep quality where the first through fourth columns are a set corresponding to each other, the fifth through eighth columns are a set corresponding to each other, and the ninth through twelfth columns are a set corresponding to each other.
  • Column header “s” corresponds to site ("g” corresponds to gut, “ge” corresponds to genitals”, “n” corresponds to nose, “s” corresponds to skin, “m” corresponds to mouth”); column header “n” corresponds to taxon name; column header “r” corresponds to taxon rank ("f” corresponds to family, “g” corresponds to genus, “s” corresponds to species, “p” corresponds to phylum, “c” corresponds to class, "0” corresponds to order); column header “c” corresponds to correlation ("+” corresponds to correlation and/or other association with control group individuals (good sleep quality); “-” corresponds to correlation and/or other association with condition group individuals (bad sleep quality)). s n r c s n r c s n r c
  • Bifidobacterium sp. canine oral g Desulfovibrio sp. s g pullorum s n taxon 147 s + g Bacteroidetes p + g Leucono sto caceae f + n Murdochiella g +
  • Deltaproteobacte Fusobacterium Prevotella sp. S4- g ria c + g equinum s + n 10 s +
  • thermophilus s + g Caldicoprobacter g + n Victivallaceae f
  • Stenotrophomon g as sp. I_63- e Acinetobacter g m Rikenellaceae f + n LFP1A9B1 s + g Blastocatella e Moraxella g + m Shuttleworthia g n fasti diosa s + g Acinetobacter sp.
  • Rhizobiaceae f m mucilaginosa s n h ' h-25 s g Facklamia Frigoribacterium e languida s + m Abiotrophia g + n sp. 181 s + g Granulicatella Porphyrobacter e Slackia g + m adiacens s n sp. NMC22 s + g Abiotrophia Microvirga sp.
  • g canine oral taxon e Mogibacterium g m Kocuria g + n 015 s + g Propionibacteriu Tepidimonas sp.
  • Rhodospirillales o m Rhodobacterales o s Hespellia g g Sphingomonadal
  • Cloacibacillus g m oral taxon 168 s s Aerococcus g + g Cloacibacillus Streptococcus sp.

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