Embodiments of the present disclosure provide a method and Internet of Things system for deploying nucleic acid detection points in a smart city. This method is executed based on a management platform of the Internet of Things system for deploying nucleic acid detection points in a smart city, comprising: predicting nucleic acid detection person-time in a preset future period in at least one area in multiple areas based on epidemic information and environmental information in multiple areas; determining a deployment plan of the nucleic acid detection points based on the predicted nucleic acid detection person-time.

Embodiments of the present disclosure provide a method and Internet of Things system for deploying nucleic acid detection points in a smart city. This method is executed based on a management platform of the Internet of Things system for deploying nucleic acid detection points in a smart city, comprising: predicting nucleic acid detection person-time in a preset future period in at least one area in multiple areas based on epidemic information and environmental information in multiple areas; determining a deployment plan of the nucleic acid detection points based on the predicted nucleic acid detection person-time.

Systems and methods of the present disclosure enable automated analyses of a biological sample by receiving signal profiles of each allele of a set of cells in the sample. Cell vectors are generated by concatenating allele vectors derived from the signal profiles of each cell. A cluster model is utilized to generate clusters of the signal profiles based on the cell vectors to represent contributors. A first probability of observing the cluster given a target contributor donated their DNA and a second probability of observing the cluster given a random contributor donated are determined by comparing the target signal profile to each cluster. A likelihood ratio is determined from a ratio of the first and second probabilities, and the likelihood ratio is averaged across all clustered to output a probability of the target contributor having contributed to the sample.

Systems and methods of the present disclosure enable automated analyses of a biological sample by receiving signal profiles of each allele of a set of cells in the sample. Cell vectors are generated by concatenating allele vectors derived from the signal profiles of each cell. A cluster model is utilized to generate clusters of the signal profiles based on the cell vectors to represent contributors. A first probability of observing the cluster given a target contributor donated their DNA and a second probability of observing the cluster given a random contributor donated are determined by comparing the target signal profile to each cluster. A likelihood ratio is determined from a ratio of the first and second probabilities, and the likelihood ratio is averaged across all clustered to output a probability of the target contributor having contributed to the sample.

The methods, systems, and compositions provided herein allow improved methods for identifying cancer by measuring normalized truncated average sequencing depth from a mitochondrial chromosome in a population of samples in order to improve identification of cancer samples.

The methods, systems, and compositions provided herein allow improved methods for identifying cancer by measuring normalized truncated average sequencing depth from a mitochondrial chromosome in a population of samples in order to improve identification of cancer samples.

Systems and methods are disclosed for determining blood flow characteristics of a patient. One method includes: receiving, in an electronic storage medium, patient-specific image data of at least a portion of vasculature of the patient having geometric features at one or more points; generating a patient-specific reduced order model from the received image data, the patient-specific reduced order model comprising estimates of impedance values and a simplification of the geometric features at the one or more points of the vasculature of the patient; creating a feature vector comprising the estimates of impedance values and geometric features for each of the one or more points of the patient-specific reduced order model; and determining blood flow characteristics at the one or more points of the patient-specific reduced order model using a machine learning algorithm trained to predict blood flow characteristics based on the created feature vectors at the one or more points.

The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.

The invention includes methods and systems for identifying targets for therapeutic intervention for various diseases and conditions; and provides specific materials and methods for treatment of specific diseases and conditions.

A method of building an abnormality quantifier comprising: generating at least one selected first dataset comprising measurements of a normal population or sample and at least one second selected dataset comprising measurements of an abnormal population or sample; generating an image or map by imagizing the datasets; identifying a normality zone within the image or map using the first dataset; identifying an abnormality zone within the image or map using the second dataset; determining a definition of abnormality based on a comparison of the normality zone and the abnormality zone; receiving or accessing at least one third dataset comprising measurements of a both known normal and abnormal population or sample; testing the performance of the initially defined abnormality against one or more preset performance criteria; and outputting an abnormality quantifier when optimal performance has been reached.