Abstract: A method for identifying a liver in a CT image of a patient is provided. The method includes applying a liver model to the CT image. The method further includes extracting an internal liver region and an external liver region from the CT image based on the applied liver model. The method also includes performing a graph cut algorithm on the CT image based on the internal liver region and the external liver region to produce a liver image. The performing of the graph cut algorithm on the CT image to produce the liver image may be further based on an internal heart and/or kidney region and an external heart and/or kidney region. A non-transitory computer-readable storage medium encoded with a program is provided.

Abstract: This disclosure relates to novel SARS-CoV-2 targeting sequences. Novel SARS-CoV-2 targeting oligonucleotides for the treatment of SARS-CoV-2 infection are also provided.

Abstract: This disclosure relates to novel IFN-? signaling pathway target gene targeting sequences. Novel IFNGR1, JAK1, JAK2, and STAT1 targeting oligonucleotides for the treatment of vitiligo are also provided.

Abstract: Various embodiments of the teachings herein include a method for training a model configured to monitor a working status of equipment based on sensor data. An example method includes: training a model using a training data set including historical sensor data gathered only when the equipment is under normal working conditions; testing the model with sensor data causing a false alarm, sensor data of the equipment's historical confirmed failure, and sensor data within pre-defined recent time period when the equipment is under normal working conditions; and activating the model if the model passes test, otherwise rejecting the model.

Abstract: Residual semi-recurrent neural networks (RSNN) can be configured to receive both time invariant input and time variant input data to generate one or more time series predictions. The time invariant input can be processed by a multilayer perceptron of the RSNN. The output of the multilayer perceptron can be used as an initial state for a recurrent neural network unit of the RSNN. The recurrent neural network unit can also receive time invariant input, and process the time invariant input with the time invariant input to generate an output. The outputs of the multilayer perceptron and the recurrent neural network unit can be combined to generate the one or more time series predictions.

Abstract: Residual semi-recurrent neural networks (RSNN) can be configured to receive both time invariant input and time variant input data to generate one or more time series predictions. The time invariant input can be processed by a multilayer perceptron of the RSNN. The output of the multilayer perceptron can be used as an initial state for a recurrent neural network unit of the RSNN. The recurrent neural network unit can also receive time invariant input, and process the time invariant input with the time invariant input to generate an output. The outputs of the multilayer perceptron and the recurrent neural network unit can be combined to generate the one or more time series predictions.

Abstract: Data processing systems for predicting one or more responses to a chemical substance based on biological images. At least some of the data processing systems include at least one processor configured to execute at least one artificial neural network trained to predict one or more responses to a chemical substance based on biological images. When the at least one processor is executing computer-executable instructions, the at least one processor is configured to carry out operations including processing spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to determine one or more responses of a patient to the chemical substance.

Abstract: A radio frequency coupling structure comprising (1) a substrate that forms a top side of a waveguide, (2) a first conductive layer disposed on a bottom side of the substrate, (3) a second conductive layer incorporated within the substrate, (4) a through via that is communicatively coupled to the first conductive layer and extends through an opening in the second conductive layer toward a top side of the substrate, and/or (5) a ring slot formed around the through via in the first conductive layer. Various other apparatuses, systems, and methods are also disclosed.

Abstract: Provided in accordance with the present disclosure are systems and methods for identifying a percutaneous tool in image data. An exemplary method includes receiving image data of at least a portion of a patient's body, identifying an entry point of a percutaneous tool through the patient's skin in the image data, analyzing a portion of the image data including the entry point of the percutaneous tool through that patient's skin to identify a portion of the percutaneous tool inserted through the patient's skin, determining a trajectory of the percutaneous tool based on the identified portion of the percutaneous tool inserted through the patient's skin, identifying a remaining portion of the percutaneous tool in the image data based on the identified entry point and the determined trajectory of the percutaneous tool, and displaying the identified portions of the percutaneous tool on the image data.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for estimating treatment effect of medical treatments.

Abstract: Residual semi-recurrent neural networks (RSNN) can be configured to receive both time invariant input and time variant input data to generate one or more time series predictions. The time invariant input can be processed by a multilayer perceptron of the RSNN. The output of the multilayer perceptron can be used as an initial state for a recurrent neural network unit of the RSNN. The recurrent neural network unit can also receive time invariant input, and process the time invariant input with the time invariant input to generate an output. The outputs of the multilayer perceptron and the recurrent neural network unit can be combined to generate the one or more time series predictions.

Abstract: Implementations of the present disclosure include methods, systems, and computer-readable storage mediums for estimating treatment effect of medical treatments.

Abstract: A steerable antenna comprising (1) a lower waveguide configured to direct radio frequency signals in a specific direction, (2) an upper waveguide positioned substantially parallel to the lower waveguide, wherein the upper waveguide is configured to direct the radio frequency signals in another direction substantially opposite to the specific direction, and (3) a plate coupled between the lower waveguide and the upper waveguide, wherein the plate includes one or more coupling elements that facilitate transferring the radio frequency signals between the lower waveguide to the upper waveguide. Various other apparatuses, systems, and methods are also disclosed.

Abstract: One or more methods of predicting expression levels. At least one of the methods includes preprocessing image data representing at least one biological image of a patient to generate preprocessed image data representing at least one preprocessed biological image of the patient; and applying a trained machine learning model to the preprocessed image data to predict, based at least partially on the at least one preprocessed biological image, an expression level of a biological indicator.

Abstract: Provided in accordance with the present disclosure are systems and methods for identifying a percutaneous tool in image data. An exemplary method includes receiving image data of at least a portion of a patient's body, identifying an entry point of a percutaneous tool through the patient's skin in the image data, analyzing a portion of the image data including the entry point of the percutaneous tool through that patient's skin to identify a portion of the percutaneous tool inserted through the patient's skin, determining a trajectory of the percutaneous tool based on the identified portion of the percutaneous tool inserted through the patient's skin, identifying a remaining portion of the percutaneous tool in the image data based on the identified entry point and the determined trajectory of the percutaneous tool, and displaying the identified portions of the percutaneous tool on the image data.

Abstract: Residual semi-recurrent neural networks (RSNN) can be configured to receive both time invariant input and time variant input data to generate one or more time series predictions. The time invariant input can be processed by a multilayer perceptron of the RSNN. The output of the multilayer perceptron can be used as an initial state for a recurrent neural network unit of the RSNN. The recurrent neural network unit can also receive time invariant input, and process the time invariant input with the time invariant input to generate an output. The outputs of the multilayer perceptron and the recurrent neural network unit can be combined to generate the one or more time series predictions.

Abstract: This disclosure relates to novel SARS-CoV-2 targeting sequences. Novel SARS-CoV-2 targeting oligonucleotides for the treatment of SARS-CoV-2 infection are also provided.

Abstract: Techniques of displaying comments relative to video frames are described herein. The disclosed techniques include obtaining page data comprising a video file and rendering the page data to play a video comprising a plurality of frames; obtaining a comment file comprising a plurality of comments on the video; displaying the plurality of comments relative to the plurality of frames while playing the video; detecting a computer performance parameter during rendering the page data; and reducing a density of displaying comments in response to determining that the computer performance parameter is less than a predetermined value.

Abstract: A method for stability analysis of a combustion chamber of a gas turbine engine based on image sequence analysis belongs to the field of fault prediction and health management of aeroengine. Firstly, flow field data inside a combustion chamber of a gas turbine engine is acquired. Secondly, flow field images of the combustion chamber are preprocessed to respectively obtain a discrimination model data set and a prediction model data set. Then, a 3DWaveNet model is constructed as a generation network of a prediction model. A discrimination network of the module is constructed. The generation network and the discrimination network are combined to form the prediction model. Finally, a discrimination model is constructed according to the discrimination model data set; the training set in the discrimination model data set is used for training, and the test set is used for assessment.

Abstract: Embodiments of the present disclosure include systems, methods, and non-transitory computer-readable storage media for automated determination of a patient tumor-immune phenotype from immunofluorescence (IF) image analysis of pathology slides based on a tumor infiltrating lymphocytes score, a non-tumor infiltrating lymphocytes score, and a non-tumor infiltrating lymphocytes at tumor margin score.