Abstract: A method of determining the infection risk probability for a patient, including: encoding physiological data of the patient into a first synthetic image; encoding environmental data of the patient into a second synthetic image; determining an intrinsic probability of infection for the patient based upon the first synthetic image and the second synthetic image using a machine learning model; generating a graphical model based upon the patient and other patients based upon similarity scores between the patient and the other patients; and determining the infection risk probability for the patient based upon the graphical model and the intrinsic probability of infection for the patient and the other patients.

Abstract: Various embodiments of the inventions of the present disclosure provide a combination of feature-based approach and deep learning approach for distinguishing between normal heart sounds and abnormal heart sounds. A feature-based classifier (60) is applied to a phonocardiogram (PCG) signal to obtain a feature-based abnormality classification of the heart sounds represented by the PCG signal and a deep learning classifier (70) is also applied to the PCG signal to obtain a deep learning abnormality classification of the heart sounds represented by the PCG signal. A final decision analyzer (80) is applied to the feature-based abnormality classification and the deep learning abnormality classification of the heart sounds represented by the PCG signal to determine a final abnormality classification decision of the PCG signal.

Abstract: Techniques described herein relate to training and applying predictive models using discretized physiological sensor data. In various embodiments, a continuous stream of samples measured by a physiological sensor may be discretized into a training sequence of quantized beats. A training sequence of vectors determined based on the training sequence of quantized beats and an embedding matrix may be associated with labels indicative of medical conditions, and applied as input across a neural network to generate corresponding instances of training output. Based on a comparison of each instance of training output with a respective label, the neural network and the embedding matrix may be trained and used to predict medical conditions from unlabeled continuous streams of physiological sensor samples. In some embodiments, the trained embedding matrix may be visualized to identify correlations between medical conditions and physiological signs.

Abstract: Techniques disclosed herein relate to learning and applying contextual patient similarities. Multiple template similarity functions may be provided. Each template similarity function may compare a respective subset of features of a query entity feature vector with a corresponding subset of features of a candidate entity feature vector. A composite similarity function may be provided as a weighted combination of respective outputs of the template similarity functions. A plurality of labeled entity vectors may be provided as context training data. An approximation function may be applied to approximate a first context label for each respective labeled entity vector. A first context specific composite similarity function may be trained based on the composite similarity function by learning first context weights for the template similarity functions using a first loss function based on output of application of the approximation function to the first context training data.

Abstract: Techniques disclosed herein relate to identifying individuals in digital images. In some embodiments, a digital image(s) that captures a scene containing one or more people may be acquired. The single digital image may be applied as input across a single machine learning model. In some implementations, the single machine learning model may be trained to perform a non-facial feature recognition task and a face-related recognition task. Output may be generated over the single machine learning model based on the input. The output may include first data indicative of non-facial features of a given person of the one or more people and second data indicative of at least a location of a face of the given person in the digital image relative to the non-facial features. In various embodiments, the given person may be identified based at least in part on the output.

Abstract: An expression system, including a host cell, a synthetic spider silk polypeptide-encoding nucleotide sequence, at least one synthetic tRNA molecule-encoding nucleotide sequence or a synthetic serine hydroxymethyl transferase (SHMT)-encoding nucleotide sequence.

Abstract: Methods and systems for delivering voltage limited neurostimulation to a patient. In one aspect, a method includes initiating a flow of electrical current through a first electrode and a second electrode coupled to the patient and increasing the flow of electrical current toward a target value by increasing a voltage across the first electrode and second electrode. Prior to reaching the target value of electrical current, the method includes preventing the voltage across the first electrode and second electrode from increasing beyond a first predetermined limit; and subsequently, maintaining the voltage across the first electrode and second electrode at or within a predetermined range that does not exceed the first predetermined limit. The amplitude of the electrical current continues to increase toward the target value during at least part of a time when the voltage across the first electrode and the second electrode is maintained within the predetermined range.

Abstract: An expression system, including a host cell, a synthetic spider silk polypeptide-encoding nucleotide sequence, at least one synthetic tRNA molecule-encoding nucleotide sequence or a synthetic serine hydroxymethyl transferase (SHMT)-encoding nucleotide sequence.

Abstract: A system and method for harvesting and processing algae, the system and method including harvesting algae by mechanical or chemical system and processing the harvested algae to produce at least one of biodiesel, biosolvents, bioplastics, biogas, or fertilizer.

Abstract: A method of producing bioplastics from algae, the method including processing algae to yield an aqueous phase containing glycerol, and fermenting the aqueous phase with a bioplastic-producing bacteria to yield bioplastics.

Abstract: Methods and systems for delivering voltage limited neurostimulation to a patient. In one aspect, a method includes initiating a flow of electrical current through a first electrode and a second electrode coupled to the patient and increasing the flow of electrical current toward a target value by increasing a voltage across the first electrode and second electrode. Prior to reaching the target value of electrical current, the method includes preventing the voltage across the first electrode and second electrode from increasing beyond a first predetermined limit; and subsequently, maintaining the voltage across the first electrode and second electrode at or within a predetermined range that does not exceed the first predetermined limit. The amplitude of the electrical current continues to increase toward the target value during at least part of a time when the voltage across the first electrode and the second electrode is maintained within the predetermined range.