A method for constructing a sequencing template based on an image, a device, and a system. The image includes first, second, third and fourth images of one same field of view corresponding to base extensions of A, T/U, G, and C respectively; the first, second, third and fourth images respectively include images M1 and M2, images N1 and N2, images P1 and P2, and images Q1 and Q2; the method includes combining any two of the images M1, M2, N1, N2, P1, P2, Q1, and Q2 to perform bright spot matching, and enabling such images to participate in the combination for at least one time to obtain a plurality of combined images including first coincident bright spots, and merging the first coincident bright spots on the plurality of combined images to obtain a bright spot set corresponding to the sequencing template.

Various embodiments of the disclosure relate to systems and methods for aligning a sequence read to a graph reference. In one embodiment, the method comprises selecting a first node from a graph reference, the graph reference comprising a plurality of nodes connected by a plurality of directed edges, at least one node of the plurality of nodes having a nucleotide sequence. The method further comprises traversing the graph reference according to a depth-first search, and comparing a sequence read to nucleotide sequences generated from the traversal of the graph reference. The traversal of the graph is then modified in response to a determination that each and every node associated with a given nucleotide sequence was previously evaluated.

This disclosure pertains to a testing method for a target pathogen. The method uses biosensors with particular fluorescence characteristics, such that when the biosensor binds to a target pathogen, a fluorophore may emit light if excited. The biosensor may be an aptamer-based biosensor with a fluorophore reporter and a quencher. The excitation of the fluorophore and the detection of fluorescence may be made through the use of a flashlight source and a camera from a mobile device, such as a smartphone.

Systems and methods are disclosed for processing an electronic image corresponding to a specimen and automatically prioritizing processing of the electronic image. One method includes receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample of a patient; computing, using a machine learning system, a prioritization value of the target electronic image, the machine learning system having been generated by processing a plurality of training images, each training image comprising an image of human tissue and a label characterizing at least one of a slide morphology, a diagnostic value, a pathologist review outcome, and/or an analytic difficulty; and outputting a sequence of digitized pathology images, wherein a placement of the target electronic image in the sequence is based on the prioritization value of the target electronic image.

Apparatus and methods are described for use with a digital camera that is configured to acquire images of a bodily sample. Two or more stains are configured to stain the bodily sample. A computer processor drives the digital camera to acquire, for each of a plurality of imaging fields of the bodily sample, two or more digital images, at least one of the images being acquired under brightfield lighting conditions, and at least one of the images being acquired under fluorescent lighting conditions. The computer processor performs image processing on the digital images, by extracting visual classification features from the digital images and analyzing the extracted visual classification features. The computer processor outputs a result of the image processing that includes an indication of one or more entities that are contained within the sample. Other applications are also described.

Systems and methods for test object classification are provided in which the test object is docked with a target object in a plurality of different poses to form voxel maps. The maps are vectorized and fed into a convolutional neural network comprising an input layer, a plurality of individually weighted convolutional layers, and an output scorer. The convolutional layers include initial and final layers. Responsive to vectorized input, the input layer feeds values into the initial convolutional layer. Each respective convolutional layer, other than the final convolutional layer, feeds intermediate values as a function of the weights and input values of the respective layer into another of the convolutional layers. The final convolutional layer feeds values into one or more fully connected layers as a function of the final layer weights and input values. The one or more full connected layers feed values into the scorer which scores each input vector to thereby classify the test object.

The present invention relates to digital pathology. In order to facilitate analyzing a tissue microarray, an apparatus is provided for tissue examination. The apparatus comprises a data input (102), a tissue microarray analyzing unit (104), and an output (106). The data input is configured to receive a reference image of a reference slice obtained from a tissue sample block; and to receive a microarray image of a microarray slice comprising at least one tissue core obtained from at least the tissue sample block. The tissue microarray analyzing unit is configured to register tissue core images of at least one tissue core with the reference image based on a spatial arrangement of the respective tissue core within the tissue sample block. The output is configured to provide a registered result obtained from the tissue microarray analyzing unit for further analyzing purposes.

Various embodiments of the disclosure relate to systems and methods for aligning a sequence read to a graph reference. In one embodiment, the method comprises selecting a first node from a graph reference, the graph reference comprising a plurality of nodes connected by a plurality of directed edges, at least one node of the plurality of nodes having a nucleotide sequence. The method further comprises traversing the graph reference according to a depth-first search, and comparing a sequence read to nucleotide sequences generated from the traversal of the graph reference. The traversal of the graph is then modified in response to a determination that each and every node associated with a given nucleotide sequence was previously evaluated.

A device comprising a printer configured to apply a code of printed content on a substrate of a product based on a printer technology type, the code having a plurality of digits. The device includes an optical code detector, executed by one or more processors, to detect the code in a received image of the product printed by the printer by optically recognizing characters in the received image using a trained optical character recognition (OCR) algorithm for the printer technology type. The OCR algorithm is trained to identify each digit of the plurality of digits of the code in a region of interest (ROI) based on at least one product parameter to which the printed content is directly applied and the printer technology type. A system and method are also provided.

Methods and systems for image analysis are provided, and in particular for identifying a set of base-calling locations in a flow cell for DNA sequencing. These include capturing flow cell images after each sequencing step performed on the flow cell, and identifying candidate cluster centers in at least one of the flow cell images. Intensities are determined for each candidate cluster center in a set of flow cell images. Purities are determined for each candidate cluster center based on the intensities. Each candidate cluster center with a purity greater than the purity of the surrounding candidate cluster centers within a distance threshold is added to a template set of base-calling locations.