The invention relates to a method of neighbor influence compensation between a plurality of objects in at least one digital image, wherein the at least one digital image contains image information about a plurality of objects. Each of the plurality of objects is configured to receive at least one molecule comprising genetic information, wherein the at least one molecule is configured to receive a fluorescent compound, and the at least one digital image is taken by an optical imaging system during emission of electromagnetic radiation of the fluorescent compounds received by the at least one molecules.

Systems and methods for identifying molecules that are biologically active against a disease, where the method can comprise culturing a first mammalian cell population under organoid formation conditions in the presence of a test molecule to obtain a first organoid, wherein the first mammalian cell population, when cultured under the organoid formation conditions in the absence of the test molecule, results in an organoid with a disease phenotype; imaging the first organoid following exposure to the test molecule; analyzing one or more images of the first organoid using a neural network that has been trained to assign a probability score of disease or non-disease ranging between 0% and 100%; assigning the first organoid a probability score ranging between 0% and 100%; wherein the test molecule is biologically active against the disease if the probability score of the first organoid is greater than a cutoff probability score of non-disease or lower than a cutoff probability score of disease.

A method of generating a plurality of spatially co-registered data elements, each spatially co-registered data element being associated with and generated from a pair of co-registered tissue sections obtained from adjacent positions of a core taken from a tissue sample and including an image data section and a genomic data section. The method includes, for each pair of co-registered tissue sections: (i) obtaining and storing as part of a data element a plurality of multi to hyperplexed images from the imaging data section of the co-registered tissue section, (ii) generating and storing as part of the data element image data from the plurality of multi to hyperplexed images, and (iii) generating and storing as part of the data element genomic data from the genomic data section of the associated co-registered tissue section.

Compositions and methods for improved cell-based methods of characterizing botulinum neurotoxins are provided. Cells utilized in these methods include a reporting construct that is cleaved following uptake and processing of botulinum neurotoxin by the cell, resulting in proteolysis of the portion of the reporting construct that is released following cleavage. The released portion includes a fluorophore and amino acid substitutions or sequences that enhance the rate of proteolysis. A pair of reporting constructs can be utilized in which one member of the pair is modified to resist cleavage by the botulinum neurotoxin while co-localizing with the remaining member of the pair.

The subject disclosure presents systems and computer-implemented methods for assessing a risk of cancer recurrence in a patient based on a holistic integration of large amounts of prognostic information for said patient into a single comparative prognostic dataset. A risk classification system may be trained using the large amounts of information from a cohort of training slides from several patients, along with survival data for said patients. For example, a machine-learning-based binary classifier in the risk classification system may be trained using a set of granular image features computed from a plurality of slides corresponding to several cancer patients whose survival information is known and input into the system. The trained classifier may be used to classify image features from one or more test patients into a low-risk or high-risk group.

A particle detection method detects presence and location of particles on a target using measured signals from a plurality of structured illumination patterns. The particle detection method uses measured signals obtained by illuminating the target with structured illumination patterns to detect particles. Specifically, the degree of variation in these measured signals in raw images is calculated to determine whether a particle is present on the target at a particular area of interest.

The invention relates to a method of neighbor influence compensation between a plurality of objects in at least one digital image, wherein the at least one digital image contains image information about a plurality of objects. Each of the plurality of objects is configured to receive at least one molecule comprising genetic information, wherein the at least one molecule is configured to receive a fluorescent compound, and the at least one digital image is taken by an optical imaging system during emission of electromagnetic radiation of the fluorescent compounds received by the at least one molecules.

In various embodiments, an experiment analysis application detects executional artifacts in experiments involving microwell plates. The experiment analysis application computes one or more sets of spatial features based on one or more heat maps associated with a microwell plate. The experiment analysis application then aggregates the set(s) of spatial features to generate a feature vector. The experiment analysis application inputs the feature vector into a trained classifier. In response, the trained classifier generates a label indicating that the microwell plate is associated with a first executional artifact.

The disclosure relates to methods and systems for identifying chromosomal structural variants in a subject using chromosomal conformational capture data, relating the chromosomal structural variants to diseases or disorders, and methods of treating same.

The subject invention pertains to methods and systems for classifying leukocytes using artificial intelligence called AIRFIHA (artificial-intelligence enabled reagent-free imaging hematology analyzer) that can accurately classify subpopulations of leukocytes in a label-free manner. AIRFIHA can not only subtype lymphocytes into B and T cell but is capable of sorting different types of T cells subtypes. AIRFIHA is realized through training a two-step neural network using label-free images of separated leukocytes acquired from a custom-built quantitative phase microscope. Owing to its easy operation, low cost, and strong discerning capability of complex leukocyte subpopulations, AIRFIHA is clinically translatable and can also be deployed in resource-limited settings.