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.

The invention relates to a method for determining the absolute value of the flow velocity (v) of a particle-transporting medium. At least two measurement laser beams (L_i) with linearly independent, non-orthogonal measurement directions (b_i) are emitted. The measurement laser beams (L_i) scattered at particles are detected and one measurement signal (m_i) is generated in each case for each measurement laser beam (L_i). The measurement signals (m_i) are evaluated, wherein absolute values of velocity components (v_i) are ascertained as projections of the flow velocity (v) on the respective measurement directions (b_i), wherein a solid angle region is ascertained for the prevalent direction of the flow velocity (v) and signs assigned to this solid angle region are chosen for the individual velocity components (v_i), and wherein the absolute value of the flow velocity (v) is determined using the ascertained absolute values of the velocity components (v_i) and using the chosen signs for the velocity components (v_i).

Aspects of the present disclosure include methods for spectrally resolving light from fluorophores having overlapping fluorescence spectra in a sample. Methods according to certain embodiments include detecting light with a light detection system from a sample having a plurality of fluorophores having overlapping fluorescence spectra and spectrally resolving light from each fluorophore in the sample. In some embodiments, methods include estimating the abundance of one or more of the fluorophores in the sample, such as on a particle. In certain instances, methods include identifying the particle in the sample based on the abundance of each fluorophore and sorting the particle. Methods according to some embodiments includes spectrally resolving the light from each fluorophore by calculating a spectral unmixing matrix for the fluorescence spectra of each fluorophore. Systems and integrated circuit devices (e.g., a field programmable gate array) for practicing the subject methods are also provided.

A method of automated measurement of motility and morphology parameters of the same single motile sperm. Automated motility and morphology measurements of the same single sperm are performed under different microscope magnifications. The same single motile sperm is automatically positioned and kept inside microscope field of view and in focus after magnification switch. A method of automated non-invasive measurement of sperm morphology parameters under high magnification of imaging. Sperm morphology parameters including subcellular structures are automatically measured without invasive sample staining. A method of automatically selecting sperms with normal motility and morphology and DNA integrity for infertility treatment.

A gas detection device manufactured by a semiconductor process includes a substrate, a microelectromechanical element, a light-emitting element, a particle-sensing element, a gas-sensing element, a driving-chip element and an encapsulation layer. The driving-chip element controls driving operations of the microelectromechanical element, the light-emitting element, the particle-sensing element and the gas-sensing element, respectively. When the microelectromechanical element is enabled to actuate transportation of gas, the gas is introduced into the gas detection device through an inlet aperture of the substrate. Scattered light spots generated by the light beam of the light-emitting element irradiating on suspended particles contained in the gas are received by the particle-sensing element to generate a detection datum of the suspended particles. The gas-sensing element detects the gas passing through and generates a detection datum of hazardous gas contained in the gas. Finally, the gas is discharged from an outlet aperture of the encapsulation layer.

A particle detection apparatus is provided that includes: a plurality of optical detectors configured to detect light from a particle, wherein at least one of optical detectors has an applied voltage coefficient different from other optical detectors; and a processor including a processing device and a memory storing instructions that, when executed by the processing device, cause the processor to: correct optical data obtained from the particle in accordance with differentiation between applied voltage coefficients of the plurality of optical detectors and a predetermined applied voltage coefficient.

A calibration apparatus comprises estimation circuitry configured to estimate, based on a calibration factor, an estimated number of cells of a first type in a dyed biological sample containing an unknown number of cells. Determination circuitry determines the actual number of cells of the first type in the dyed biological sample. Processing circuitry adjusts the calibration factor. The estimation circuitry is configured with the processing circuitry to estimate the estimated number of the cells of the first type in the dyed biological sample one or more times, based on a different value of the calibration factor for each of the one or more times, until the estimated number of the cells of the first type approaches the actual number of cells of the first type.

There is provided a cell analysis apparatus that comprises image capture circuitry for capturing a brightfield image of a cell using brightfield imaging. The cell has been dyed by a functional dye that indicates, during fluorescence imaging and during brightfield imaging, whether the cell has a given characteristic. A model derived by machine learning is stored and used in combination with the brightfield image to determine whether the cell has the given characteristic. There is also provided a method for creating a cell categorisation model, comprising applying a functional dye to one or more samples comprising a plurality of cells. The functional dye indicates during fluorescence imaging and during brightfield imaging whether each of the cells has a given characteristic. A brightfield image and a corresponding fluorescence image for each of the plurality of cells to which the dye has been applied are captured and a machine learning process is used to generate a model that predicts whether a cell has the given characteristic from a brightfield image. The model is generated by using the brightfield image and the corresponding fluorescence image of each of the plurality of cells as training data.

A particle analysis method and apparatus, including a spectrometry-based analysis of a fluid sample (1), comprises the steps of creating a sample light beam S and a probe light beam P with a light source device (10) and periodically varying a relative phase between the sample and probe light beams S, P with a phase modulator device (20), irradiating the fluid sample (1) with the sample light beam S, detecting the sample and probe light beams S, P with a detector device (40), and providing a spectral response of the at least one particle (3), wherein the light source device (10) comprises at least one broadband source, which has an emission spectrum covering a mid-infrared MIR frequency range, and the phase modulator device (20) varies the relative phase with a scanning period equal to or below the irradiation period of irradiating the at least one particle (3, 4).

A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell.