For analyzing a sample containing particles of at least two categories, such as a sample containing blood cells, a particle counter subject to a detection limit is coupled with an analyzer capable of discerning particle number ratios, such as a visual analyzer, and a processor. A first category of particles can be present beyond detection range limits while a second category of particles is present within respective detection range limits. The concentration of the second category of particles is determined by the particle counter. A ratio of counts of the first category to the second category is determined on the analyzer. The concentration of particles in the first category is calculated on the processor based on the ratio and the count or concentration of particles in the second category.

An automatic focus system for an optical microscope that facilitates faster focusing by using at least two offset focusing cameras. Each offset focusing camera can be positioned on a different side of an image forming conjugate plane so that their sharpness curves intersect at the image forming conjugate plane. Focus of a specimen can be adjusted by using sharpness values determined from images taken by the offset focusing cameras.

A system for characterizing at least one particle from a fluid sample is disclosed. The system includes a filter disposed upstream of an outlet, and a luminaire configured to illuminate the at least one particle at an oblique angle. An imaging device is configured to capture and process images of the illuminated at least one particle as it rests on the filter for characterizing the at least one particle. A system for characterizing at least one particle using bright field illumination is also disclosed. A method for characterizing particulates in a fluid sample using at least one of oblique angle and bright field illumination is also disclosed.

A system for selective microcapsule extraction includes a non-planar core-shell microfluidic device. The non-planar core-shell microfluidic device generates microcapsules defining a core-shell configuration. A subset of the microcapsules contain aggregates, tissues, or at least one cell. A camera captures images of the microcapsules. A detection module includes a processor and a memory. The memory includes instructions that when executed by the processor causes the detection module to provide the images of the microcapsules as an input to a machine learning model. The machine learning model identifies microcapsules containing aggregates, tissues, or at least one cell. A force generator generates a force to extract the microcapsules. A microcontroller selectively activates the force generator to generate the force when the detection module identifies a microcapsule containing aggregates, tissues, or at least one cell to extract the microcapsule.

Methods for classifying fluorescent flow cytometer data are provided. In some instances, methods include processing the flow cytometer data with a supervised algorithm configured to cluster the fluorescent flow cytometer data into distinct populations according to the relationship of data points to relevant threshold values. In embodiments, methods include determining a measure of spillover spreading by calculating spillover spreading coefficients and combining them in a spillover spreading matrix. In some embodiments, populations of fluorescent flow cytometer data are adjusted to subtract the magnitude of spillover spreading. In embodiments, spillover spreading adjusted populations are partitioned after potential partitions are evaluated relative to the threshold values. In embodiments, partitioned populations of fluorescent flow cytometer data are classified (i.e., phenotyped) according to a hierarchy. Systems and computer-readable media for classifying fluorescent flow cytometer data are also provided.

A portable air quality monitoring device is disclosed that can identify the type of particles in the air. This device takes images of particles in the air and compares them with a library of particles in its memory to identify the type of particles. The device has a housing that draws ambient air into the system and takes microscopic images of the flowing particles and droplets using flash photography. The device can be stand alone or can connect to the back of a mobile phone and use the mobile phone camera and light. People can upload their local air quality data online for all to see the local air quality.

Aspects of the present disclosure include a method for sorting cells of a sample based on an image of a cell in a flow stream. Methods according to certain embodiments include detecting light from a sample having cells in a flow stream, generating an image mask of a cell from the sample and sorting the cell based on the generated image mask. Systems having a processor with memory operably coupled to the processor having instructions stored thereon, which when executed by the processor, cause the processor to generate an image mask of a cell in a sample in a flow stream and to sort the cell based on the generated image mask are also described. Integrated circuit devices (e.g., field programmable gate arrays) having programming for generating an image mask and for determining one or more features of the cell are also provided.

A fluorescence image analyzer has an imaging unit for capturing a first image containing at least a part of a region of a cell as an imaging target for a plurality of cells in a sample in which a target site on a chromosome is labeled with a fluorescent dye, and a second image including fluorescence generated from a fluorescent dye labeling the target site of the cell of the first image. The processing unit selects a plurality of test cells having specific morphological characteristics to be tested from a plurality of cells based on at least the first image, and extracts the bright spots of fluorescence generated from the fluorescent dye. The processing unit identifies cells with chromosomal abnormalities and/or cells without chromosomal abnormalities based on the extracted bright spots, and generates information related to the ratio of cells with chromosomal abnormalities relative to the test cells.

Embodiments of the present invention provide predictions from semen qualities (7) embryo characteristics (9), qualities (11), intracellular qualities (13), extracellular qualities (14), or the like of which a computational device prediction models automated computational transformation algorithm (3) may be applied to create a prediction model transformed data (4) perhaps to generate a prediction models completed prediction output which may be used to predict parameters (6) such as fertility-related parameters, fertility of an animal, embryo success rate, or the like.

A classification training method for training classifiers adapted to identify specific mutations associated with different cancer including identifying driver mutations. First cells from mutation cell lines derived from conditions having the number of driver mutations are acquired and 3D image feature data from the number of first cells is identified. 3D cell imaging data from the number of first cells and from other malignant cells is generated, where cell imaging data includes a number of first individual cell images. A second set of 3D cell imaging data is generated from a set of normal cells where the number of driver mutations are expected to occur, where the second set of cell imaging data includes second individual cell images. Supervised learning is conducted based on cell line status as ground truth to generate a classifier.