Abstract: The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.

Abstract: The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.

Abstract: The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Abstract: The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Abstract: An exemplary method for determining a sampling protocol for sampling tissue cores for a tissue microarray includes obtaining an initial plurality of tissue cores from an image of a tissue slide; selecting a first subset of the initial plurality of tissue cores based on a first candidate sampling protocol; inputting the first subset of the plurality of tissue cores into a machine learning model; evaluating the first candidate sampling protocol by evaluating a first output of the machine learning model; selecting a second subset of the initial plurality of tissue cores based on a second candidate sampling protocol; inputting the second subset of the plurality of tissue cores into the machine learning model; evaluating the second candidate sampling protocol by evaluating a second output of the machine learning model; and determining the sampling protocol based on the evaluation of the first candidate sampling protocol and the second candidate sampling protocol.

Abstract: A method for filtering out artifacts from a microscopic image of a tissue includes determining a plurality of frequency values corresponding to a plurality of pixels in the microscopic image of the tissue; grouping the plurality of pixels into a plurality of pixel clusters based on the plurality of frequency values corresponding to the plurality of pixels; identifying, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the microscopic image; and filtering the microscopic image by removing one or more regions in the microscopic image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.

Abstract: An exemplary method for determining a sampling protocol for sampling tissue cores for a tissue microarray includes obtaining an initial plurality of tissue cores from an image of a tissue slide; selecting a first subset of the initial plurality of tissue cores based on a first candidate sampling protocol; inputting the first subset of the plurality of tissue cores into a machine learning model; evaluating the first candidate sampling protocol by evaluating a first output of the machine learning model; selecting a second subset of the initial plurality of tissue cores based on a second candidate sampling protocol; inputting the second subset of the plurality of tissue cores into the machine learning model; evaluating the second candidate sampling protocol by evaluating a second output of the machine learning model; and determining the sampling protocol based on the evaluation of the first candidate sampling protocol and the second candidate sampling protocol.

Abstract: A method for filtering out artifacts from a microscopic image of a tissue includes determining a plurality of frequency values corresponding to a plurality of pixels in the microscopic image of the tissue; grouping the plurality of pixels into a plurality of pixel clusters based on the plurality of frequency values corresponding to the plurality of pixels; identifying, from the plurality of pixel clusters, one or more pixel clusters corresponding to one or more artifacts in the microscopic image; and filtering the microscopic image by removing one or more regions in the microscopic image corresponding to the one or more pixel clusters corresponding to the one or more artifacts.

Abstract: The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Abstract: The present disclosure relates generally to machine learning techniques, and more specifically to machine learning techniques for generating synthetic spatial omics data based on histopathology image data. An exemplary system for generating synthetic spatial omics images comprises: one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for: receiving a histopathology image depicting a diseased region of interest of an input tissue sample; and generating a synthetic spatial omics image depicting one or more stained structures of interest within the diseased region of interest by inputting the histopathology image into a generator of a trained generative adversarial network (GAN) model.

Abstract: An exemplary method for predicting one or more adipose depots for a patient includes receiving one or more Dual-energy X-ray Absorptiometry (DEXA) scans comprising at least a portion of a torso of the patient; providing at least one or more portions of the one or more DEXA scans to a trained machine-learning model, wherein the machine-learning model is trained using a training dataset comprising: a plurality of training DEXA scans of a plurality of subjects and a plurality of corresponding Magnetic Resonance Imaging (MRI)-image-based adiposity scores of the plurality of subjects; and predicting the one or more adipose depots for the patient utilizing the trained machine-learning model.

Abstract: The present disclosure relates generally to biomarker discovery and patient stratification, and more specifically to machine learning techniques for discovering relevant biomarkers using data collected as part of the standard-of-care (SoC), which can be used to identify a relevant patient population for a therapeutic with a known mechanism of action (MoA). An exemplary method for predicting activity of a molecular analyte of a patient comprises: training a first module of a machine learning model based on a plurality of medical images of a first cohort; training a second module of the machine learning model based on one or more molecular analyte data sets obtained from a second cohort; receiving a medical image from the patient; and predicting, using the trained first and second modules of the machine learning model, the activity of the molecular analyte from the medical image of the patient.

Abstract: A diagnostic apparatus for analysing a sample to diagnose disease, the apparatus comprising: a separating element for separating gas derived from the sample into component parts; a sensor arrangement coupled to the separating element such that a component part of the gas is directed towards the sensor arrangement, the sensor arrangement being configured to detect compounds which may be indicative of disease; and a processing element coupled to an output of the sensor arrangement, the processing element being configured to process a signal output by the sensor arrangement to provide a diagnosis.

Abstract: A diagnostic apparatus for analysing a sample to diagnose disease, the apparatus comprising: a separating element for separating gas derived from the sample into component parts; a sensor arrangement coupled to the separating element such that a component part of the gas is directed towards the sensor arrangement, the sensor arrangement being configured to detect compounds which may be indicative of disease; and a processing element coupled to an output of the sensor arrangement, the processing element being configured to process a signal output by the sensor arrangement to provide a diagnosis.

Abstract: A personnel protection apparatus (22) for protecting personnel involved in fighting a fire comprising a flexible multi-layer wall member (32) mounted at an elevated position on a fire fighting truck (10), the wall member (32) being arranged to be stored in contracted condition within the vehicle (10) when not required for use and being arranged to be deployed from the vehicle (10), externally thereof so as to provide a protective enclosure which extends from the vehicle (10) to ground level for personnel.

Abstract: A diagnostic apparatus (10) for analysing a sample to diagnose disease, the apparatus (10) comprising: a separating element (16) for separating gas derived from the sample into component parts; a sensor arrangement (18) coupled to the separating element (16) such that a component part of the gas is directed towards the sensor arrangement (18), the sensor arrangement (18) being configured to detect compounds which may be indicative of disease; and a processing element (20) coupled to an output of the sensor arrangement (18), the processing element (20) being configured to process a signal output by the sensor arrangement (18) to provide a diagnosis.

Abstract: A method of determining the cause of disease is described, which method uses the detection of “signature” or “fingerprint” volatile compounds in an emission, especially flatus, from a patient.