Abstract: Existing techniques fail to provide a method to cumulate effects of interactions between groups of gut-associated microbes to predict efficiency of a probiotic organism in an individual. The present disclosure collects a test biological sample from the subject requiring personalization and extracts DNA from test biological sample and information specific to dietary preferences of the subject. Organisms from probiotic organisms dataset are obtained and a plurality of genome scale metabolic models are created for microbes comprised in gut microbiota of subject and obtained probiotic organisms. Metabolic simulations are performed to ascertain monoculture and co-culture growth of every pair of organisms comprised in gut microbiota of subject and obtained probiotic organisms. Sustainability is computed for evaluating capability of each organism to proliferate within gut. Net-effect is computed by quantifying an overall influence of each probiotic organism.

Abstract: The taxonomic resolution obtained with conventional sequencing methods like Sanger (longer read lengths) takes a huge amount of time. While, NGS technologies (shorter read lengths) involves a lot of cost in sequencing. In addition to that the accuracy and depth of taxonomic classification is also less. A method and system for improving accuracy of amplicon sequencing based taxonomic profiling of microbial communities has been provided. The proposed strategy relies on obtaining taxonomic abundance profiles of a microbial community from two paired-end sequencing experiments, each of which targets different pair-wise combinations of non-contiguous (or contiguous) V-regions. The two taxonomic profiles are then combined based on (pre-estimated) accuracies of the individual V-regions (targeted in the experiments) in resolving each of the taxonomic groups under consideration.

Abstract: The present disclosure is related to method and system for identifying and utilizing frugal markers for classification of biological sample. Discovering an optimal and/or frugal set of features/biomarkers form a large set of features measured through high-throughput screening techniques, which can characterize a disease/anomaly with sufficient accuracy, still remains a challenge. According to the present disclosure, given a set of measurements of multiple features characterizing biological samples obtained from disease cases and healthy controls, a classification model combining the measured values of a small subset of the features is computed. The classification model is then used for classifying between disease cases and healthy controls.

Abstract: The present disclosure is related to a method and system for stratification of subjects as one of responders or non-responders to a therapy. It is imperative to critically evaluate the baseline/initial microbiome structure and composition of individuals and stratifying them before prescribing any microbiome-based drug/dietary interventions. The method identifies a panel of biological features/indicators/markers/signatures that can accurately stratify/classify/group individuals into responders and non-responders (for a given microbiome-based drug/therapy) based upon the differences in the metabolic functions of the gut microbial communities between the baseline gut microbiome profile (i.e. before the administration of an intervention) and after treatment gut microbiome profile (i.e. after the administration of the intervention).

Abstract: A system and method for identification and classification of operational taxonomic units (OTUs) in a metagenomic sample using short read amplicon sequences has been described. The disclosure enables accurate identification of OTU in a metagenomic sample and provides a framework for easy cross comparison of microbiome community structures sampled across different disconnected metagenomic studies. Instead of using a reference database consisting of full-length marker genes directly for taxonomic classification or OTU-picking, the present disclosure creates customized OTU databases for different hyper-variable regions of a marker gene. These databases consist of reference OTUs obtained through independent clustering of sequences pertaining to different selected hyper-variable regions of the marker gene. In another embodiment, mapping back is also provided facilitating cross comparison between results obtained from different studies that may have utilized different hyper-variable regions.

Abstract: The present invention relates to a process for the synthesis of B-site doped ABX3 perovskite nanocrystals. The process comprises the steps of loading non-halide precursors of A, B, and dopant in three neck flasks along with long chain acid and olefin, purging of the reaction mixture under heating, and finally injection of alkylammonium chloride stock solution in the reaction mixture at a desired temperature. Introducing transition metal such as Mn and other rare earths, etc., as dopants in the perovskite nanocrystals to induce a new optical window.

Abstract: This disclosure relates generally to the field of taxonomic profiling of microbial organisms such as bacteria, and, more particularly, to system and method for simultaneous interpretation of taxonomic distribution and replication rates of microbes constituting microbial communities. The present disclosure extracts bacterial genomic DNA from a plurality of bacterial organisms comprised in collected microbiome sample. Maps the plurality of the DNA sequence fragment reads to a precomputed reference sequence database of a plurality of all available completely sequenced bacterial genomes. Based on the mapping, the read coverage is measured at the genomic locations of the phylogenetic marker genes, wherein measured read coverage is used for interpretation of taxonomic distribution of the plurality of bacterial organisms. A plurality of slopes is obtained by fitting a linear function.

Abstract: State of art techniques hardly provide data balancing for multi-label multi-class data. Embodiments of the present disclosure provide a method and system for identifying cardiac abnormality in multi-lead ECGs using a Hybrid Neural Network (HNN) with fulcrum based data re-balancing for data comprising multiclass-multilabel cardiac abnormalities. The fulcrum based dataset re-balancing disclosed enables maintaining natural balance of the data, control the re-sample volume, and still support the lowly represented classes there by aiding proper training of the DL architecture. The HNN disclosed by the method utilizes a hybrid approach of pure CNN, a tuned-down version of ResNet, and a set of handcrafted features from a raw ECG signal that are concatenated prior to predicting the multiclass output for the ECG signal. The number of features is flexible and enables adding additional domain-specific features as needed.

Abstract: Sepsis is one of the most prevalent causes of mortality in Intensive Care Units (ICUs) and delayed treatment is associated with increase in death and financial burden. There is no single laboratory test or clinical sign that by itself can be considered diagnostic of sepsis. The present disclosure provides discriminating domain specific continuous and categorical features that can reliably classify a subject being monitored into a sepsis class or a normal class. A combination of physiological parameters, laboratory parameters and demographic details are used to extract the discriminating features. Even though the parameters may be sporadic in nature, the systems and methods of the present disclosure make use of a sliding time window to generate continuous features that capture the trend in the sporadic data; and a binning approach to generate categorical features to discriminate deviation from the normal class and facilitate timely treatment.

Abstract: Analysis of the flow of fluxes through the metabolic network of a cell type is useful in gaining knowledge about cellular physiology. Such knowledge can be used in understanding host's interactions with pathogens, drug response etc. However there is dearth of techniques that can incorporate metabolomics data into genome scale metabolic model (GEM) for FBA. A method and system for analyzing metabolic state of a cell at a genome scale by measuring concentrations of a one or more metabolites has been disclosed. The method is utilizing intracellular and/or extracellular metabolite concentrations for constraining reaction fluxes in FBA by incorporating it as part of stoichiometric constraint to metabolic model. The method is used to predict the change in flux flow through all reactions in an organism/cell type under different experimental conditions. The method enables constraining the flow of fluxes through reactions while performing FBA of GEMs using measured metabolite concentrations.

Abstract: Prediabetes is an intermediary physiological condition (in between healthy and diabetic states) which may be reversed through timely intervention. A system and method for assessing the risk of prediabetes in a person has been provided. The system 100 is configured to assess individuals to check the absence or presence of prediabetic symptoms, by quantifying the abundance of sensory proteins in their microbiome. The invention relates to a defined methodology that involves assessment and categorization of the person into healthy and prediabetic based on the abundance of sensory proteins in the sample collected from the faeces of the person. The systems and methods further describe microbiota based therapeutics for treatment/management of prediabetes through generating a therapeutic model and administering a consortium of healthy microbes which could modulate the disease microbiome composition towards a healthy equilibrium.

Abstract: Colorectal cancer is a severe disease, if not assessed properly, it may lead to the death of an individual. A system and method for assessing the risk of colorectal cancer has been provided. The system is configured to assess individuals to check the risk of presence of colorectal cancer (CRC) and/or adenomatous (colonic/rectal) polyps, by quantifying the abundance of sensory proteins in their gut microbiome. The system further categorizes the person into one of healthy, adenoma and cancerous categories based on the nature and abundance of sensory proteins in the gut microbiome. The system further describes microbiota based therapeutics for treatment of the person with colorectal adenoma and/or cancer through administration of at least one of a consortium of healthy microbes, antibiotic drugs and pre-/pro-/syn-/post-biotic compounds or fecal microbiome transplant which could modulate the disease microbiome composition towards a healthy equilibrium.

Abstract: Monitoring the quality of sleep of an individual is essential for ensuring one's overall well-being. The existing methods for non-apnea sleep arousal detection are manual. A system and method for the non-apnea sleep arousal detection has been provided. The method uses a feature engineering based binary classification approach for distinguishing non-apnea arousal and non-arousal. A training data set is prepared using a plurality of physiological signals. A plurality of features are derived from the training data set. Out of those only a set of features are selected for training a plurality of random forest classifier models. A test sample is then provided to the plurality of random forest classifier models in the instances of fixed duration. This results in generation of prediction probabilities for each instances. The prediction probabilities are then used to predict the probabilities of non-apnea sleep arousal in the test sample.

Abstract: A system and method for classifying the phonocardiogram (PCG) signal quality has been described. The system is configured to identify the quality of the PCG signal recording and accepting only diagnosable quality recordings for further cardiac analysis. The system includes the derivation of plurality features of the PCG signal from the training dataset. The extracted features are preprocessed and are then ranked using mRMR algorithm. Based on the ranking the irrelevant and redundant features are rejected if their mRMR strength is less. A training model is generated using the relevant set of features. The PCG signal of the person under test is captured using a digital stethoscope and a smartphone. The PCG signal is preprocessed and only the relevant set of features are extracted. And finally the PCG signal is classified into diagnosable or non-diagnosable using the relevant set of features and a random forest classifier.

Abstract: A method for measuring blood pressure of a subject is described herein. In an implementation, the method includes obtaining a plurality of photoplethysmogram (PPG) features associated with the subject. The method further includes ascertaining one or more latent parameters associated with the subject based on the plurality of PPG features and a reference model, wherein the reference model indicates a correlation between the plurality of PPG features and the one or more latent parameters. Further, blood pressure of the subject is determined based on the one or more latent parameters and the plurality of PPG features.

Abstract: A method and system for clustering users using cognitive stress report for classifying stress levels is provided. Detection and monitoring of cognitive stress experienced by users while performing a task is very crucial. The method includes receiving, user evaluated cognitive stress reports and the physiological signals of the user during the performance of the task. A normalized cognitive report is generated from the user evaluated cognitive stress report by computing mode and range value. The normalized cognitive stress reports of the users are used to cluster the users into a primary cluster and a secondary cluster. Feature sets are extracted from the physiological signals of the said users associated with the primary cluster. Using the said feature sets a classifier model is trained to classify the cognitive stress levels of the users as stressful class or stressless class.

Abstract: A system and method for estimating blood pressure (BP) using photoplethysmogram (PPG) has been explained. The PPG is captured from a PPG sensor (102). For preparing training model, a pulse oximeter is used for capturing PPG. For testing, a smartphone camera is used for capturing the PPG signal. A plurality of features are extracted from the preprocessed PPG signal. A BP distribution is then generated using the plurality of features and the training model. The BP distribution is part of a set of BP distributions generated from different subjects. Finally, a post-processing methodology have been used to reject inconsistent data out of the set of BP distributions and BP value is estimated only for the remaining BP distributions and a statistical average is provided as the blood pressure estimate.

Abstract: Sepsis is one of the most prevalent causes of mortality in Intensive Care Units (ICUs) and delayed treatment is associated with increase in death and financial burden. There is no single laboratory test or clinical sign that by itself can be considered diagnostic of sepsis. The present disclosure provides discriminating domain specific continuous and categorical features that can reliably classify a subject being monitored into a sepsis class or a normal class. A combination of physiological parameters, laboratory parameters and demographic details are used to extract the discriminating features. Even though the parameters may be sporadic in nature, the systems and methods of the present disclosure make use of a sliding time window to generate continuous features that capture the trend in the sporadic data; and a binning approach to generate categorical features to discriminate deviation from the normal class and facilitate timely treatment.

Abstract: Systems and methods to enable representation of sequence as well as structural information of an RNA molecule in the form of a single encoded string are described. The encoding steps are based on identifying one or more contiguous stretches of ribonucleotide bases having similar structural attributes and base-pairing patterns. In the encoded string, each of the identified contiguous structure stretches is represented by a single character that indicates the corresponding structural attribute. Appending these structural characters to the corresponding contiguous ribonucleotide character stretches, and subsequently eliminating redundant ribonucleotide characters based on standard base-pairing rules results in generating the final encoded string. Such concomitant representation of sequence and structural information of a given RNA molecule in a single encoded string enables efficient storage and easy dissemination of RNA data.

Abstract: A method and system for detection of coronary artery disease (CAD) in a person using a fusion approach has been described. The invention the detection of CAD in the person by capturing of a plurality of physiological signals such as phonocardiogram (PCG), photoplethysmograph (PPG), ECG, galvanic skin response (GSR) etc. from the person. A plurality of features are extracted from the physiological signals. The person is then classified as CAD or normal using the each of the features independently. The classification is done based on supervised machine learning technique. The output of the classification is then fused and used for the detection of the CAD in the person using a predefined criteria.