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.

Systems and methods for monitoring air particulate matter are provided herein that capture particles from the air for analysis. Particles are captured using electrostatic and/or mechanical means to deflect particles toward a substrate. Electrostatic precipitation causes charged carriers to deflect towards a charged substrate. Filtration-based means employ filters and/or fibers to capture particles from air flowing therethrough. A sensor such as a camera is used to read the captured particles. An illumination source directs light towards the substrate, causing the particles to scatter light, which the sensor can detect and derive information or imaging therefrom, which can also be used for further particle or pollution analyses. The substrate can be replenished using electrostatic techniques such as reverse electrostatic force, or mechanical means such as cleaning using a brush or replacing a tape substrate. Dynamic PM monitoring detects and makes adjustments such as those related to air volume, imaging characteristics and substrate replenishment.

With a water and debris mixture, a method of quantifying debris content may include obtaining at least one image of a sample of the water and debris mixture. The image may be analyzed to quantify the debris content.

There is provided a cell evaluation device including an acquisition unit that acquires a cell image in which a nerve cell having a cell body, an axon, and a dendrite is shown; a first specifying unit that specifies the axon in the cell image; a second specifying unit that specifies spines formed on the dendrite in the cell image; a selection unit that selects a synapse-estimated spine, which is estimated to constitute a synapse with the axon and is close to the axon specified in the first specifying unit, from the spines specified in the second specifying unit; and a display control unit that performs control to display the synapse-estimated spine in a display form different from that of other spines in the cell image.

A device, system and method for the detection and screening of plastic microparticles in a sample is disclosed. A nanoporous silicon nitride membrane is used to entrap plastic microparticles contained in the sample. The sample may be a water sample, an air sample, or other liquid or gas sample. The entrapped plastic microparticles are then heated or otherwise processed on the nanoporous silicon nitride membrane. An imaging system observes the nanoporous silicon nitride membrane with tic entrapped plastic microparticles to determine the type and quantity of the various plastic microparticles that are entrapped on the membrane.

The present disclosure provides systems and methods for sorting a cell. The system may comprise a flow channel configured to transport a cell through the channel. The system may comprise an imaging device configured to capture an image of the cell from a plurality of different angles as the cell is transported through the flow channel. The system may comprise a processor configured to analyze the image using a deep learning algorithm to enable sorting of the cell.

An optical method of characterizing an object comprises providing an object to be characterized, the object having at least one nanoscale feature; illuminating the object with coherent plane wave optical radiation having a wavelength larger than the nanoscale feature; capturing a diffraction intensity pattern of the radiation which is scattered by the object; supplying the diffraction intensity pattern to a neural network trained with a training set of diffraction intensity patterns corresponding to other objects with a same nanoscale feature as the object to be characterized, the neural network configured to recover information about the object from the diffraction intensity pattern; and making a characterization of the object based on the recovered information.

The present disclosure is directed, among other things, to automated systems and methods for analyzing, storing, and/or retrieving information associated with biological objects having irregular shapes. In some embodiments, the systems and methods partition an input image into a plurality of sub-regions based on localized colors, textures, and/or intensities in the input image, wherein each sub-region represents biologically meaningful data.

The present invention discloses a three-dimensional diffraction tomography microscopy imaging method based on LED array coded illumination. Firstly, acquiring the raw intensity images, three sets of intensity image stacks are acquired at different out-of-focus positions by moving the stage or using electrically tunable lens. And then, after acquiring the intensity image stacks of the object to be measured at different out-of-focus positions, the three-dimensional phase transfer function of the microscopy imaging system with arbitrary shape illumination is derived. Further, the three-dimensional phase transfer function of the microscopic system under circular and annular illumination with different coherence coefficients is obtained as well, and the three-dimensional quantitative refractive index is reconstructed by inverse Fourier transform of the three-dimensional scattering potential function. The scattering potential function is converted into the refractive index distribution. Thus, the quantitative three-dimensional refractive index distribution of the test object is obtained. The invention realizes high-resolution and high signal-to-noise ratio 3D diffraction tomography microscopic imaging of cells, tiny biological tissues and other samples.

A system and method for determining a trajectory parameter of particles, comprising receiving a plurality of particles at a microfluidic channel, applying a force to each particle of the microfluidic channel, acquiring a dataset of each particle, measuring a trajectory of the particle, and determining a trajectory parameter of the particles.