Described herein are antibodies, immunogenic fragments and compositions thereof targeting the killer-cell immunoglobulin-like receptor protein KIR3DL3, as well as methods of using the same for the treatment of human diseases including cancer. In certain embodiments, the disclosure relates to an antibody or an immunogenic fragment thereof that specifically binds to KIR3DL3 protein, wherein the antibody or the immunogenic fragment thereof specifically binds to a KIR3DL3 epitope comprising the whole extracellular domain or a portion thereof.

An optical based method determines the most clinically effective antimicrobial agent treatment for a subject afflicted with a microbial infection, including those subjects that have developed resistance to said microbial agents. The provided method is based on the ability to discriminate between live and dead microbes growing in a culture medium.

A theoretical reserve evaluation method for ocean current energy includes steps of: 1) selecting a target region, and extracting a coordinate range of the target region; 2) obtaining a seabed water depth of the target region; 3) obtaining hydrological data of flow velocities and seawater densities of a target region space; 4) calculating a theoretical reserve of the ocean current energy per unit area of the target region according to the hydrological data, 5) calculating an area of the target region; and 6) calculating the theoretical reserves of the ocean current energy within a spatial range of the target region according to the hydrological data of the flow velocities and the seawater densities obtained in the step 3), the seabed water depth of the target region obtained in the step 2), and the area of the target region obtained in the step 5).

A method and system for developing a predictive model for diagnosis of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) in a human are disclosed. The method comprises receiving immune system data for each member of a population comprising healthy humans and humans with ME/CFS; extracting a set of features from the immune system data; and training a machine learning algorithm using the set of features to classify a human as healthy or having ME/CFS to obtain a predictive model. The system comprises a processor; and a memory storing computer executable instructions, which when executed by the processor cause the processor to perform operations of said method.

The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

A waveguide tube connecting member includes a first waveguide tube having a first waveguide path and a flange. The flange has a flange end surface extending from a first opening end of the first waveguide path toward an outer side in a tube radial direction, and a second flange outer peripheral surface which is a part of a first flange outer peripheral surface. The second flange outer peripheral surface is a surface formed in a shape in which a part of the flange has a cavity. An electric length from the first opening end of the flange end surface to the second flange outer peripheral surface along the tube radial direction is (2×N+1)/4 times a wavelength.

Presented herein are systems and methods for prediction of protein interfaces for binding to target molecules. In certain embodiments, technologies described herein utilize graph-based neural networks to predict portions of protein/peptide structures that are located at an interface of custom biologic (e.g., a protein and/or peptide) that is being designed for binding to a target molecule, such as another protein or peptide. In certain embodiments, graph-based neural network models described herein may receive, as input, a representation (e.g., a graph representation) of a complex comprising a target and a partially-defined custom biologic. Portions of the partially-defined custom biologic may be known, while other portions, such an amino acid sequence and/or particular amino acid types at certain locations of an interface, are unknown and/or to be customized for binding to a particular target. A graph-based neural network model as described herein may then, based on the received input, generate predictions of likely acid sequences and/or types of particular amino acids at the unknown portions. These predictions can then be used to determine (e.g., fill in) amino acid sequences and/or structures to complete the custom biologic.

A wearable infusion pump assembly. The wearable infusion pump assembly includes a reservoir for receiving an infusible fluid and a fluid delivery system configured to deliver the infusible fluid from the reservoir to an external infusion set. The fluid delivery system includes a controller, a pump assembly for extracting a quantity of infusible fluid from the reservoir and providing the quantity of infusible fluid to the external infusion set, the pump assembly comprising a pump plunger, the pump plunger having distance of travel, the distance of travel having a starting position and an ending position, at least one optical sensor assembly for sensing the starting position and ending position of the pump plunger distance of travel and sending sensor output to the controller, and a first valve assembly configured to selectively isolate the pump assembly from the reservoir, wherein the controller receives the sensor output and determines the total displacement of the pump plunger.

A method for stream-processing biomedical data includes receiving, by a file system on a computing device, a first request for access to at least a first portion of a file stored on a remotely located storage device. The method includes receiving, by the file system, a second request for access to at least a second portion of the file. The method includes determining, by a pre-fetching component executing on the computing device, whether the first request and the second request are associated with a sequential read operation. The method includes automatically retrieving, by the pre-fetching component, a third portion of the requested file, before receiving a third request for access to least the third portion of the file, based on a determination that the first request and the second request are associated with the sequential read operation.