Abstract: One type of tissue-based assay, the companion diagnostic (“CDx”) allows for the identification of individuals within a larger patient population who are more likely to respond to a therapy. The CDx paradigm typically applies to drugs that target a specific gene product or biologic pathway involving a gene product of interest. It is possible, especially for popular therapeutic targets, for multiple drugs and multiple associated CDx to be developed for a single gene product or biologic pathway involving the gene product. Currently, each of these similar CDx must be applied to identify the best therapy. The present invention can determine the outcome of one CDx using an image of a tissue section used for another CDx. Using a single tissue section and a single CDx, it becomes possible to obtain the outcome of multiple, related CDx.

Abstract: One type of tissue-based assay, the companion diagnostic (“CDx”) allows for the identification of individuals within a larger patient population who are more likely to respond to a therapy. The CDx paradigm typically applies to drugs that target a specific gene product or biologic pathway involving a gene product of interest. It is possible, especially for popular therapeutic targets, for multiple drugs and multiple associated CDx to be developed for a single gene product or biologic pathway involving the gene product. Currently, each of these similar CDx must be applied to identify the best therapy. The present invention can determine the outcome of one CDx using an image of a tissue section used for another CDx. Using a single tissue section and a single CDx, it becomes possible to obtain the outcome of multiple, related CDx.

Abstract: Systems and methods related to communication with and control of network-enabled water devices and sensors of various water systems are disclosed. Such water systems may include water filtration systems, water reclamation systems, sump pump systems, pool or spa systems, water softening systems, and plumbing systems. Such water devices may include chemical controllers, smart valves, pool pumps, sump pumps, water softeners, residential appliances, and manifolds. Such sensor devices may include flow meters, splash detectors, motion sensors, moisture sensors, humidity sensors, chemical sensors, water level sensors, pressure sensors, and cameras. Data received from network-enabled water devices and sensors may be processed at a remote server or a local controller, which may cause corresponding alerts or maintenance requests to be sent to one or more user devices or service providers or may automatically control one or more of the water devices and sensors based on analysis of the data.

Abstract: Systems and methods are described, which relate to internet-of-things (IoT) feedback systems, in which data received from a given IoT device may be analyzed by a controller and used as a basis for controlling another IoT device and/or sending an alert to a user. The controller may activate a dehumidifier upon detecting activation of a sump pump. The controller may monitor water level data and alert a user and/or request maintenance of the sump pump upon detecting the water level exceeds a threshold. The controller may monitor dishwasher data to detect suboptimal performance of a dishwasher, in response to which the controller may instruct valves of a manifold and water heater to close to isolate the dishwasher, and instruct a water softener to elevate salt levels of softened water during a self-cleaning cycle of the dishwasher.

Abstract: Digital image analysis can simultaneously measure many multidimensional features of each data point within an image. Each data point can then be grouped into categories, or ‘clusters’, of data points by assessing all features of each data point and measuring similarity among all data points. Clustering multidimensional data allows one to visualize the structure of their data and visually represent groups of data points in a lower dimensional space, such as a 2D or 3D graph. If the data points are not tagged with a description before clustering, it is difficult to assess which data points belong to which cluster. In this method, we describe the clustering of tissue objects (data points) into clusters based on their image analysis features, then creating a cluster map in order to describe tissue objects based on cluster association.

Abstract: In accordance with the embodiments herein, a method for displaying differences and similarities between tissue samples utilizing a reference database consisting of tissue images, image analysis features, and derived score from patient tissue samples assayed with a tissue-based test for the purpose of scoring said patient sample and guiding treatment based on said score. The method described herein utilizes digital image analysis of a tissue image of one or more tissue sections to extract features which generates a dataset mathematically representing the image. This dataset is then compared to the reference database to determine reference images with similar and different feature values. Those images are then displayed with the similar and different features highlighted.

Abstract: In accordance with the embodiments herein, a method for displaying differences and similarities between tissue samples utilizing a reference database consisting of tissue images, image analysis features, and derived score from patient tissue samples assayed with a tissue-based test for the purpose of scoring said patient sample and guiding treatment based on said score. The method described herein utilizes digital image analysis of a tissue image of one or more tissue sections to extract features which generates a dataset mathematically representing the image. This dataset is then compared to the reference database to determine reference images with similar and different feature values. Those images are then displayed with the similar and different features highlighted.

Abstract: The human vision is limited in terms of perception of color hues and intensities. Moreover, the individual's ability in this context varies greatly from person to person. Herein are described methods to quantify staining of tissue samples via digital tissue image analysis, when staining of a certain biomarker is below or at the threshold of human visual perception, in terms of saturation. This enables for consistent detection of these staining characteristics, which allows for more reproducible data generation through minimizing inter- and intra-observer variability.

Abstract: Staining of tissue is a common approach utilized to visualize a gene product in tissue context. In certain applications, it is necessary to report a sum of events within the tissue as a specific function of the target tissue area, which is a sub-area of the total tissue, as a normalization factor for reporting the quantification. Here, we describe methods of determining target tissue area and reporting a quantification which is ratiometric to the target tissue area, utilizing computer algorithms. It is important to assign a value for the “target tissue area” in scenarios where a tissue area normalization factor is needed in the most pathologically relevant fashion during the application of tissue image analysis. We have created methods for determining and reporting “target tissue area” as normalization factor which are useful in diagnostic applications utilizing image analysis.

Abstract: The disclosure concerns a method for extracting spatial distribution statistics from patient tissue samples assayed with a tissue-based test for the purpose of scoring the patient tissue samples and guiding treatment based on the score(s). The method described herein utilizes digital image analysis of an image of one or more tissue sections to extract object-based (e.g., cells) features. The object-based features within the image of the tissue section is further processed using one or more algorithm processes to extract sophisticated spatial distribution features of one or more object type or sub-type in the tissue. Statistics describing the spatial distribution features are summarized to generate a patient-specific diagnostic score, and this score can be evaluated to guide patient treatment decisions.

Abstract: The disclosure concerns a method for assessing muscular dystrophy-linked protein expression in muscle fibers using digital image analysis of tissue. The method relates to assessing disease severity in individuals with muscular dystrophy. Muscle tissue samples are obtained from patients submitted for evaluation and processed to produce tissue sections mounted on glass slides which have been stained for a muscular dystrophy-linked protein. Digital images of the stained tissue sections are generated and analyzed by applying an algorithm process implemented by a computer to the images. The algorithm process extracts the morphometric and staining features of the muscular dystrophy-linked protein staining in the tissue, and parameters relating to these features are used to score the disease status for each patient submitted for evaluation.

Abstract: The disclosure concerns a method for extracting geographic distribution statistics from patient tissue samples assayed with a tissue-based test for the purpose of scoring the patient tissue samples and guiding treatment based on the score(s). The method described herein utilizes digital image analysis of an image of one or more tissue sections to extract object-based (e.g., cells) features to generate a data array representing said tissue numerically in image analysis feature space. The numerical representation of the image of the tissue section in image analysis feature space is further processed using one or more algorithm processes to extract sophisticated geographical distribution features of one or more object type or sub-type in the tissue. Statistics describing the geographic distribution features are summarized to generate a patient-specific diagnostic score, and this score can be evaluated to guide patient treatment decisions.

Abstract: The disclosure concerns a method for patient stratification and selection of patients who are candidates for a specific therapy is described which is based on quantifying one or more digital image analysis feature distributions from stained tissue. The method extends beyond the abilities of a manual observer and a microscope, and generally comprises: acquiring digital images of stained tissue sections from patients submitted for evaluation, applying an algorithm process to said images with a computer to extract the morphometric and staining features of image pixels and tissue objects, deriving one or more distribution function for one or more image analysis features, calculating a summary statistic of the one or more distribution functions, and using said summary statistic along with an associated predefined patient stratification paradigm to separate a patient cohort into distinct strata which correspond to a decision to include or exclude a patient for a specific therapy.

Abstract: The disclosure concerns a method for assessing muscular dystrophy-linked protein expression in muscle fibers using digital image analysis of tissue. The method relates to assessing disease severity in individuals with muscular dystrophy. Muscle tissue samples are obtained from patients submitted for evaluation and processed to produce tissue sections mounted on glass slides which have been stained for a muscular dystrophy-linked protein. Digital images of the stained tissue sections are generated and analyzed by applying an algorithm process implemented by a computer to the images. The algorithm process extracts the morphometric and staining features of the muscular dystrophy-linked protein staining in the tissue, and parameters relating to these features are used to score the disease status for each patient submitted for evaluation.

Abstract: The disclosure concerns a method for patient stratification and selection of patients who are candidates for a specific therapy is described which is based on quantifying one or more digital image analysis feature distributions from stained tissue. The method extends beyond the abilities of a manual observer and a microscope, and generally comprises: acquiring digital images of stained tissue sections from patients submitted for evaluation, applying an algorithm process to said images with a computer to extract the morphometric and staining features of image pixels and tissue objects, deriving one or more distribution function for one or more image analysis features, calculating a summary statistic of the one or more distribution functions, and using said summary statistic along with an associated predefined patient stratification paradigm to separate a patient cohort into distinct strata which correspond to a decision to include or exclude a patient for a specific therapy.

Abstract: Staining of tissue by is a common approach utilized to visualize a gene product in tissue context. In certain applications, it is necessary to report a sum of events within the tissue as a specific function of the target tissue area, which is a sub-area of the total tissue, as a normalization factor for reporting the quantification. Here, we describe methods of determining target tissue area and reporting a quantification which is ratiometric to the target tissue area, utilizing computer algorithms. It is important to assign a value for the “target tissue area” in scenarios where a tissue area normalization factor needed in the most pathologically relevant fashion during the application of tissue image analysis. We have created methods for determining and reporting “target tissue area” as normalization factor which are useful in diagnostic applications utilizing image analysis.

Abstract: A driven turret nozzle assembly includes a main body, a turret rotatably coupled to and in fluid communication with the main body, nozzle bodies circumferentially spaced about the turret, and a driver operationally coupled to the turret to effect automated selective rotation of the turret. Actuation of the driver rotates the turret to move an active nozzle body into fluid communication with a fluid path defined through the main body and the turret.

Abstract: Methods for cell-based tissue analysis utilize modern trends in digital microscopy to obtain, process, calibrate, and analyze digital images of tissue sections to quantify cell-based data for improved histological analysis. Using data from multiple images of a common tissue section, or data from images of multiple tissue sections, additional degrees of freedom are realized and the resulting analysis provides added depth to histological analysis of tissue samples. With computerized analytical methods, speed and accuracy of histological analysis is greatly improved.

Abstract: Systems and methods relating to a cell-based pattern recognition tool for microscopy images from tissue sections are described, wherein cell features are extracted and a classifier is built in accordance with a particular application using an interactive training tied to a computerized platform, the result is an application-specific classifier that further processes images in accordance with the specific application, thereby tuning an automated process for cell based pattern recognition.

Abstract: This disclosure concerns methods for evaluating inflammatory cells and modulators of the inflammatory response in tumor tissue and other relevant tissue types. The methods entail: obtaining a tissue sample and processing said tissue sample to produce histologic slides of tissue sections; staining of the tissue sections to identify inflammatory cells and modulators of the inflammatory response; digitizing slides to produce an image of the stained tissue sections; digitally stratifying the tissue sample into tumor and other relevant tissue compartments; and using digital image analysis to quantify cell-based and cell population-based features. The quantification of cell-based and cell population-based features within a tissue compartment of interest is used to develop a summary score of the immune system-tissue compartment of interest interaction. Patient stratification and selection as candidates for a therapeutic approach is ultimately based on the summary score value.