Abstract: Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.

Abstract: Techniques regarding treating one or more microbe infections with combination therapy are provided. For example, one or more embodiments described herein can comprise a method, which can comprise enhancing an antimicrobial activity of an antibiotic by a combination therapy. The combination therapy can comprise the antibiotic and a polycarbonate polymer functionalized with a guanidinium functional group.

Abstract: Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Abstract: A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.

Abstract: A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.

Abstract: A method for application of a deep learning neural network (NN) for predicting the probability distribution of a biological phenotype does not require any assumption or prior knowledge of the probability distributions. The NN may be a recurrent neural network (RNN) or a long short-term memory (LSTM) network. The NN includes a loss function, which is trained on limited observations, as low as one observation, which is obtained from a large data set related to a biological system. The NN with the trained loss function is capable of calculating if readings that are outside of the mean for the data set are inherent to the biological system or are outlier readings. The output of the method is a continuous probability distribution of the biological phenotypes for each input parameter or set of parameters from the biological data set.

Abstract: Mechanisms are provided to adapt computer modeling of an infectious disease based on noisy data and perform hyperlocal prediction of infectious disease dynamics and risks. Case report data is received and a trained background noise computer model is applied to generate first prediction results predicting infectious disease dynamics. The trained background noise computer model is trained to model infectious disease dynamics assuming that there is no community spread of the infectious disease. A first error measure of the first prediction results is determined and, in response to the first error measure being lower than a threshold value, the first prediction results are selected to output as predicted infectious disease dynamics. In response to the first error measure being equal/greater than the threshold value, second prediction results are selected. The second prediction results are generated by applying a trained infectious disease computer model to the received case report data.

Abstract: Mechanisms are provided for performing automated monitoring and retraining of infectious disease computer models. A trained infectious disease computer model is executed on case report data for a target region to generate prediction results predicting a state of an infectious disease spread within the target region for a given time. The prediction results generated by the trained infectious disease computer model are automatically compared to ground truth data to determine a deviation between the prediction results and the ground truth data. The ground truth data comprises at least one of actual case report data collected and reported by source computing systems for the given time, or a previous prediction result generated by the trained infectious disease computer model. Statistical test(s) are applied to the deviation to determine if it is statistically significant, and if so, re-training of the trained infectious disease computer model is automatically initiated.

Abstract: Mechanisms are provided to hypothetical scenario evaluations with regard to infectious disease dynamics based on similar regions. A user definition of a hypothetical scenario for a target region is received which specifies scenario features affecting an infectious disease spread amongst a population within the target region. Other predefined regions, in the set of predefined regions, having similar region characteristics to the target region are identified and attributes of the other predefined regions corresponding to the scenario features are identified. Modified model parameter(s) for an infectious disease computer model are derived based on the identified attributes. An instance of the infectious disease computer model is configured with the modified model parameter(s) and the instance is executed on case report data for the target region to generate a prediction for an infectious disease spread in the target region according to the hypothetical scenario, which is then output.

Abstract: A method, a computer system, and a computer program product are provided for federated learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The cluster information may include centroid information per cluster. The aggregator may include a processor. The aggregator may integrate the cluster information to define data classes for machine learning classification. The integrating may include computing a respective distance between centroids of the clusters in order to determine a total number of the data classes. The aggregator may send a deep learning model that includes an output layer that has a total number of nodes equal to the total number of the data classes. The deep learning model may be for the distributed computing devices to perform machine learning classification in federated learning.

Abstract: Mechanisms are provided to perform automatic case intervention detection in infectious disease case reports and for configuring an infectious disease computer model based on the automatic intervention detection. Case report data is received and a time ordered curve of the case report data is generated. One or more inflection points in the time ordered curve are identified. The one or more inflection points in the time ordered curve are correlated with one or more intervention entries specified in time stamped infectious disease intervention data, the one or more intervention entries specifying interventions implemented by authorities to control spread of the infectious disease. One or more model parameters of an infectious disease computer model are configured based on results of correlating the one or more inflection points with the one or more intervention entries.

Abstract: Mechanisms are provided for hyperlocal prediction of epidemic dynamics and risks. Regional machine learning training is performed on an infectious disease computer model at least by: receiving first case report data; pre-processing the first case report data to remove noise at least by applying a smoothening algorithm to form first smoothed data; aggregating the first smoothed data into regional data, wherein aggregating the first smoothed data comprises correlating the first smoothed data to a target region corresponding to a population; and training the model using the regional data. The trained model is executed on new second case report data for the target region and automatic monitoring of performance of the model is performed according to a prediction accuracy of the model. In response to the prediction accuracy being below a predetermined threshold, automatic retraining is initiated.

Abstract: Mechanisms are provided for compartmental epidemiological computer modeling based on mobility data. Machine learning training of an isolation rate prediction computer model is performed to generate a trained isolation rate prediction model that predicts an isolation rate of a biological population. Isolation data is received which comprises data indicating a measure of mobility of the biological population. The trained isolation rate prediction model is executed on input features extracted from the isolation data to generate a predicted isolation rate. A compartmental epidemiological computer model, comprising a plurality of compartments representing states of a population with regard to an infectious disease, is executed to simulate a progression of the infectious disease and flows of portions of the population from between compartments in the compartmental epidemiological computer model are controlled based on the predicted isolation rate.

Abstract: Provided is a deep learning algorithm that analyzes fragments of biological sequences. The input for the deep learning algorithm is a biological sequence fragment of unknown origin and the output is the closest known biological genome that could share phenotypic properties with the biological species of unknown origin. The workflow thus has application for genomic classification, identification of mutations within known genomes, and the identification of the closest class for unknown species.

Abstract: A method, a computer system, and a computer program product are provided for federated learning enhanced with semantic learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The aggregator may integrate the cluster information to define classes. The integrating may include identifying any redundant clusters amongst the identified clusters. A number of the classes may correspond to a total number of the clusters from the distributed computing devices reduced by any redundant clusters. A deep learning model may be sent from the aggregator to the distributed computing devices. The deep learning model may include an output layer having nodes that may correspond to the defined classes. The aggregator may receive results of federated learning performed by the distributed computing devices. The federated learning may train the deep learning model.

Abstract: Provided is a data-driven deep-learning based algorithm for synthetic biology applications that makes no assumptions and/or hypotheses on genotype-phenotype interactions. deep-learning based algorithm trains a neural network with morphological features from single genetic modifications and tests the neural network with morphological features from multiple genetic modifications. The trained and tested neural network uses a link between the morphological features caused by the single and multiple gene modifications as input and outputs a genotype-phenotype mapping highlighting perturbation subspaces. The genotype-phenotype mapping is used to select one or more genetic insults as a starting point to engineer cells in synthetic biology applications.

Abstract: Volatile organic compounds classification by receiving test data associated with detecting volatile organic compounds (VOCs), analyzing the test data according to a set of data features associated with known VOCs, determining a match between each feature of the test data and a corresponding feature of the set of data features, yielding a set of matches, defining a first degree of anomaly for the test data according to the set of matches, and classifying the test data according to the first degree of anomaly.

Abstract: A method, a computer system, and a computer program product are provided for federated learning enhanced with semantic learning. An aggregator may receive cluster information from distributed computing devices. The cluster information may relate to identified clusters in sample data of the distributed computing devices. The aggregator may integrate the cluster information to define classes. The integrating may include identifying any redundant clusters amongst the identified clusters. A number of the classes may correspond to a total number of the clusters from the distributed computing devices reduced by any redundant clusters. A deep learning model may be sent from the aggregator to the distributed computing devices. The deep learning model may include an output layer having nodes that may correspond to the defined classes. The aggregator may receive results of federated learning performed by the distributed computing devices. The federated learning may train the deep learning model.

Abstract: A system and method that identify and classify unknown microorganisms and/or known microorganisms with anomalies are provided. The system and method comprise processing images of microorganisms from an aquatic environment; extracting features from the processed images; an unsupervised partitioning algorithm for identifying and classifying known microorganisms in the aquatic environment based upon the extracted features; and a supervised classifier neural network that is trained with the unsupervised partitioning algorithm and identifies and classifies unknown microorganisms and/or known microorganisms with anomalies.

Abstract: Color signals to be displayed on a colored display surface and having a first gamut in a color space, are subjected to radiometric compensation. An embodiment includes displaying on the colored surface a set of control points of a known color, acquiring via a camera the control points as displayed on the colored surface and evaluating at least one second color gamut of the control points displayed on the colored surface. The second color gamut(s) is/are misaligned with respect to the first color gamut due to the display surface being a colored surface. The method may also include evaluating as an intersection gamut, the misalignment of the second color gamut(s) with respect to the first color gamut, calculating the color transformation operator(s) as a function of the misalignment evaluated, and applying the color transformation operator(s) to the color signals for display on the colored display surface.