A method and a system for determining an indicator of processing quality of an agricultural harvested material using a mobile device is disclosed. A computing unit analyzes image data of a prepared sample of harvested material containing grain components and non-grain components in an analytical routine to determine the indicator of the processing quality of the agricultural harvested material. Further, the computing unit uses a trained machine learning model in the analytical routine to perform at least one step of determining the indicator of the processing quality of the agricultural harvested material and that the computing unit adjusts at least one machine parameter of the forage harvester based on the indicator of processing quality.

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.

The present invention extends to methods, systems, and computer program products for estimating oocyte quality. A machine learning algorithm accesses oocyte training data for a mammalian species (e.g., humans) and trains a neural network to estimate oocyte quality for the mammalian species based on the oocyte training data. The neural network accesses a microscopic image of an oocyte and identifies oocyte features of the oocyte. Based on the identified oocyte features, the neural network estimates oocyte quality, including: (a) predicting a probability of a corresponding embryo maintaining sufficient developmental competence until a specified time after fertilization and (b) predicting another probability of the corresponding embryo reaching a specific embryonic stage after fertilization. An oocyte is selected, from among a plurality of human oocytes including the human oocyte, for a potential recipient based at least in part on the oocyte quality, including based on the probability and the other probability.

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.

A liquid droplet and solid particle sensing device is provided that can measure the average droplet size in a spray. The present device uses a swirling flow to draw a particulate or a spry into the device for sizing and counting. The swirling flow is configured to keep all the particles away from the walls of the device and to concentrate them at the center of a flow channel to pass through the center of a light beam for high sensitivity and repeatability of the measurement.

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.

Aspects of the present disclosure include systems for detecting light from a particle by birefringent interferometry. Systems according to certain embodiments include a light source configured to irradiate a particle propagating through a flow stream, a light detection system that includes a birefringent polarizing interferometer configured to generate interfering polarized beams of light, a light adjustment component configured to continuously convey light from the irradiated particle across different positions on the birefringent polarizing interferometer as the particle is propagated through the flow stream, a photodetector configured to detect interference patterns of the interfering polarized beams of light generated by the birefringent polarizing interferometer from light collected from the irradiated particle and generate a photodetector signal pulse in response to each detected interference pattern. Systems also include a processor for transforming the photodetector signal pulses into spectral data signals. Methods for detecting light with the subject systems are also described. Kits having one or more components for detecting light according to the subject methods are also provided.

Provided herein are embodiments of methods and systems for screening cellular, subcellular, and multicellular structures. In one embodiment, a method for screening is provided comprising the steps of introducing a plurality of cellular, subcellular, or multicellular structures, or a combination thereof, to an imaging system, wherein one or more structures of the plurality comprise one or more taggable markers; imaging the plurality of structures using the imaging system; identifying one or more target structures among the plurality of structures based on one or more properties of the target structures; tagging the target structures to produce tagged target structures, wherein each target structure is selectively illuminated by an excitation light, thereby causing one or more taggable markers within the target structure to be phototransformed to produce one or more phototransformed taggable markers within the target structure; and isolating one or more tagged target structures from the plurality of structures.

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.

To provide an adjusting method of a positional relationship between a flow path position and a light irradiation position.

The present technology provides a position adjusting method provided with an imaging step of imaging, while moving a flow path through which a microparticle is able to flow in an optical axis direction, the flow path in a plurality of positions in the optical axis direction, a movement step of moving the flow path in the optical axis direction on the basis of a focus index for each of a plurality of images acquired at the imaging step, and an adjustment step of specifying a feature position of the flow path from an image of the flow path in a position after movement at the movement step, and adjusting a positional relationship between the feature position and a reference position in a direction perpendicular to the optical axis direction.