Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positons within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: Systems and methods for detecting objects in a holographic image are provided. The techniques include obtaining a holographic image having one or more objects depicted therein. A set of object templates is obtained. The set of object templates represents objects to be detected in the holographic image. One or more objects are detected in the holographic image using the set of object templates by iteratively computing a phase (?) of the optical wavefront at the hologram plane, background illumination (?). and encoding coefficients (A) for the set of object templates, until converged.

Abstract: The present invention presents a sequence of the AAV/UPR-plus virus, a genetic treatment method and its use in the treatment of neurodegenerative diseases, such as Parkinson's and Huntington's diseases, among others, as presented in the in vitro studies shown in FIG. 14/17.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positions within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positons within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: A system for lens-free imaging includes a processor in communication with a lens-free image sensor. The processor is programmed to operate the image sensor to obtain a hologram ??. The processor is further programmed to generate, from the hologram, a reconstructed image X and phase W at a focal depth z using an assumption of sparsity.

Abstract: The present invention presents a sequence of the AAV/UPR-plus virus, a genetic treatment method and its use in the treatment of neurodegenerative diseases, such as Parkinson's and Huntington's diseases, among others, as presented in the in vitro studies shown in FIG. 14/17.

Abstract: A method is disclosed for classifying and/or counting objects (for example, cells) in an image that contains a mixture of several types of objects. Prior statistical information about the object mixtures (class proportion data) is used to improve classification results. The present technique may use a generative model for images containing mixtures of object types to derive a method for classifying and/or counting cells utilizing both class proportion data and classified object templates. The generative model describes an image as the sum of many images with a single cell, where the class of each cell is selected from some statistical distribution. Embodiments of the present techniques have been successfully used to classify white blood cells in images of lysed blood from both normal and abnormal blood donors.

Abstract: Systems and methods are provided for counting particles in a fluid flow. In an aspect, coordinates of particles are obtained from video data of particles in a fluid, the video data made up of a sequence of image frames. The particle positions are linked in each pair of consecutive image frames of the video data. The linked particle positions are used to calculate particle trajectories through sequential image frames of the video data, and the particles are counted based on the particle trajectory. In another aspect, the particle positions within each image frame are transformed to estimated positions within a common coordinate frame. The estimated particle positions of a particle are grouped into a cluster center, and the particle count is calculated based on the cluster centers.

Abstract: A system for detecting objects in a specimen includes a chamber for holding at least a portion of the specimen. The system also includes a lens-free image sensor for obtaining a holographic image of the portion of the specimen in the chamber. The system further includes a processor in communication with the image sensor, the processor programmed to obtain a holographic image having one or more objects depicted therein. The processor is further programmed to obtain at least one object template representing the object to be detected, and to detect at least one object in the holographic image.

Abstract: A system for lens-free imaging includes a processor in communication with a lens-free image sensor. The processor is programmed to operate the image sensor to obtain a hologram ??. The processor is further programmed to generate, from the hologram, a reconstructed image X and phase W at a focal depth z using an assumption of sparsity.

Abstract: The present invention provides a system and method for structured low-rank matrix factorization of data. The system and method involve solving an optimization problem that is not convex, but theoretical results should that a rank-deficient local minimum gives a global minimum. The system and method also involve an optimization strategy that is highly parallelizable and can be performed using a highly reduced set of variables. The present invention can be used for many large scale problems, with examples in biomedical video segmentation and hyperspectral compressed recovery.

Abstract: The present disclosure provides a system for lens-free imaging that includes a processor in communication with a lens-free imaging sensor. The processor is programmed to operate the imaging sensor to obtain a holographic image and to extract, from the holographic image, a plurality of patches, wherein the plurality of patches is a set of all fixed-size patches of the holographic image. The processor is also programmed to generate a dictionary D comprising a plurality of atoms, wherein the dictionary is generated by solving min ? , D ? ? i = 1 N ? E ? ( x i , D , ? i ) + ? ? ? R ? ( ? i ) , where N is the number of patches in the plurality of patches, xi is the ith patch of the plurality of patches, ?i represents the coefficients encoding the ith patch, E(xi, D, ?i) is a function measuring the squared error of the approximation of xi by the weighted combination of dictionary elements, and ?R (?i) is sparsity term.

Abstract: The present invention provides a system and method for structured low-rank matrix factorization of data. The system and method involve solving an optimization problem that is not convex, but theoretical results should that a rank-deficient local minimum gives a global minimum. The system and method also involve an optimization strategy that is highly parallelizable and can be performed using a highly reduced set of variables. The present invention can be used for many large scale problems, with examples in biomedical video segmentation and hyperspectral compressed recovery.

Abstract: A method for segmenting a digital image includes initializing object and background seed nodes in an image, where the image is represented as a graph G=(V, E) whose nodes i?V correspond to image points and whose edges e?E connect adjacent points, where set M?V contains locations of nodes marked as seeds, set U?V contains locations of unmarked nodes, set O?M contains locations of object seed nodes, and set B?M contains locations of background seed nodes, assigning to each seed node a membership value such that ?i?O,xi=1 and ?i?B,xi=0, where each node i?V is associated with a membership xi?[0,1], and finding a membership vector x?, whose ith entry is given by xi that minimizes E p ? ( x ) = ? eij ? E ? w ij ? ? x i - x j ? pij , where each edge eij?E connecting nodes i and j in V is associated with a weight wij and an exponent pij, and ?eij?E,1?pij<?, such that xi=1 if i?O and xi=0 if i?B.

Abstract: A method for segmenting a digital image includes initializing object and background seed nodes in an image, where the image is represented as a graph G=(V, E) whose nodes i?V correspond to image points and whose edges e?E connect adjacent points, where set M?V contains locations of nodes marked as seeds, set U?V contains locations of unmarked nodes, set O?M contains locations of object seed nodes, and set B?M contains locations of background seed nodes, assigning to each seed node a membership value such that ?i?O,xi=1 and ?i?B, xi=0, where each node i?V is associated with a membership xi?[0,1], and finding a membership vector x?, whose ith entry is given by xi that minimizes E p ? ( x ) = ? eij ? E ? w ij ? ? x i - x j ? pij , where each edge eij?E connecting nodes i and j in V is associated with a weight wij and an exponent pij, and ?eij?E,1?pij<?, such that xi=1 if i?O and xi=0 if i?B.