Abstract: An encapsulation method is disclosed for the capture of natural flavors within a clean-label derived from a malt comprising at least partially germinated whole grain seed, pulses, legumes, false grains or flour derived therefrom. Moreover, a spray dryable malted rice composition is disclosed that has rice present at an amount that is at least about 30% by weight.

Abstract: The present disclosure provides a process for producing “cookable”, fibrous meat analogs employing directional freezing. The process includes subjecting an ingestible hydrocolloid to directional freezing for inducing formation of elongated ice crystals in which the elongated ice crystals are aligned in a given direction in the directionally frozen hydrocolloid. Following this, elongated ice crystals are removed and are replaced by proteins and any other additives such as supplements which are located in the aligned channels originally containing the aligned ice crystals. Once the desired amount of protein loading is achieved, the protein-loaded hydrocolloid is subjected to conditions suitable to induce gelling of some of proteins to form protein gels in the aligned elongated channels.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: An oleogel composition and a method to create the oleogel composition are described. The method includes: combining a gelator with an oil, co-melting the gelator and the vegetable oil at a temperature to form a melt, dispersing at least one immiscible inclusion in the melt to form a mixture, cooling the mixture to create a solidified oleogel, and incorporating the solidified oleogel into a meat analogue mixture.

Abstract: The present disclosure provides a process for producing “cookable”, fibrous meat analogues employing directional freezing. The process includes subjecting an ingestible polysaccharide containing hydrogel of selected size and shape to directional freezing for inducing formation of elongated ice crystals with said elongated ice crystals aligned in a given direction in the ingestible hydrogel to form a textured hydrogel containing ice crystals. Following this the textured hydrogel is immersed in a solution containing an ingestible soluble protein at a preselected temperature so that as the ice crystals melt, the ingestible heat gelling protein diffuses into the texture hydrogel replacing the ice crystals. The textured hydrogel is immersed in the solution containing the ingestible heat gelling protein for a selected period of time required to give a desired amount of protein loading.

Abstract: An encapsulation method is disclosed for the capture of natural flavors within a clean-label derived from a malt comprising at least partially germinated whole grain seed, pulses, legumes, false grains or flour derived therefrom.

Abstract: Systems and methods are provided for generating clusters of anomalous images. Histopathological samples are images at an associated imaging system to produce a plurality of images. A plurality of anomalous images are identified from the plurality of images at an anomaly detection system, having an associated latent space. The plurality of anomalous images are clustered to generate a plurality of clusters within a feature space defined by a set of classification features. The set of classification features include a feature derived from the latent space associated with the anomaly detection system.

Abstract: A solid vinegar powder and a method to generate the solid vinegar powder is described. The powder comprises an amount of acetic acid in a range of approximately 5% to approximately 50%, an amount of malted wholegrain seed or flour in a range of approximately 50% to approximately 95%, and at least 1% of nutritional components from the malted wholegrain seed or flour that includes fats, proteins, minerals and fibers. The solid vinegar powder is free from an addition of maltodextrin, dextrin, sodium diacetate, and/or modified starch.

Abstract: Systems and methods are provided for screening histopathology tissue samples. An anomaly detection system is trained on a plurality of training images. Each of the plurality of training images represents a tissue sample that is substantially free of abnormalities. A test image, representing a tissue sample, is provided to the anomaly detection system. A deviation from normal score is generated for at least a portion of the test image. The deviation from normal score represents a degree of abnormality in the tissue sample represented by the test image.

Abstract: A low sugar, low viscosity syrup from whole grain seed or flour is disclosed. A readily soluble flour is also disclosed. The syrup comprises 90% to 100% of its solid components as water soluble solids and the wholegrain flour comprises 50% to 95% of its solid components as water soluble solids. By controlling the mashing and hydrolysis of a cooked whole grain seed or flour, a syrup can be obtained comprising a defined oligosaccharide content with a narrow molecular weight distribution (i.e. low in sugar and low DP 11+).

Abstract: Systems and methods are provided for screening a set of histopathology tissue samples representing a region of interest for abnormalities. A pattern recognition classifier is trained on a first set of images, each representing a tissue sample that is substantially free of abnormalities, and a second set of images, each representing one of the set of histopathology tissue samples representing the region of interest. At least one performance metric from the pattern recognition classifier is generated. A given performance metric represents one of an accuracy of the classifier in discriminating between images representing tissue that is substantially free of abnormalities and images of histopathology tissue samples representing the region of interest and a training rate of the pattern recognition classifier. A likelihood of abnormalities in the region of interest is determined from the at least one performance metric from the pattern recognition classifier.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: An encapsulation method is disclosed for the capture of natural flavors within a clean-label derived from a malt comprising at least partially germinated whole grain seed, pulses, legumes, false grains or flour derived therefrom.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: A convolutional neural network (CNN) is applied to identifying tumors in a histological image. The CNN has one channel assigned to each of a plurality of tissue classes that are to be identified, there being at least one class for each of non-tumorous and tumorous tissue types. Multi-stage convolution is performed on image patches extracted from the histological image followed by multi-stage transpose convolution to recover a layer matched in size to the input image patch. The output image patch thus has a one-to-one pixel-to-pixel correspondence with the input image patch such that each pixel in the output image patch has assigned to it one of the multiple available classes. The output image patches are then assembled into a probability map that can be co-rendered with the histological image either alongside it or over it as an overlay. The probability map can then be stored linked to the histological image.

Abstract: Systems and methods are provided for screening histopathology tissue samples. An anomaly detection system is trained on a plurality of training images. Each of the plurality of training images represents a tissue sample that is substantially free of abnormalities. A test image, representing a tissue sample, is provided to the anomaly detection system. A deviation from normal score is generated for at least a portion of the test image. The deviation from normal score represents a degree of abnormality in the tissue sample represented by the test image.

Abstract: The present invention provides methods and systems for automatic detection of the location of cell colonies on a specimen slide, in particular under the coverslip of a specimen slide. Slide scanning can be performed using an automated microscope with motorized axes. The location of the colonies can be determined by image analysis, which is followed by automatically finding metaphase cells and associating them with each colony. The invention also provides an automated, Hough-transform-based method for identifying the location of the slide coverslip and, if desired, analyzing only the image area contained within the coverslip.

Abstract: The present invention provides methods and systems for automatic detection of the location of cell colonies on a specimen slide, in particular under the coverslip of a specimen slide. Slide scanning can be performed using an automated microscope with motorized axes. The location of the colonies can be determined by image analysis, which is followed by automatically finding metaphase cells and associating them with each colony. The invention also provides an automated, Hough-transform-based method for identifying the location of the slide coverslip and, if desired, analyzing only the image area contained within the coverslip.

Abstract: Scanning and analysis of cytology and histology samples uses a flatbed scanner to capture images of the structures of interest such as tumor cells in a manner that results in sufficient image resolution to allow for the analysis of such common pathology staining techniques as ICC (immunocytochemistry), IHC (immunohistochemistry) or in situ hybridization. Very large volumes of such material are scanned in order to identify cells or clusters of cells which are positive or warrant more detailed examination, and if analysis at higher resolution is necessary, information regarding these positive events is transferred to a secondary microscope, such as a conventional scanning microscope, to allow further analysis and review of the selected regions of the slide containing the sample.

Abstract: Scanning and analysis of cytology and histology samples uses a flatbed scanner to capture images of the structures of interest such as tumor cells in a manner that results in sufficient image resolution to allow for the analysis of such common pathology staining techniques as ICC (immunocytochemistry), IHC (immunohistochemistry) or in situ hybridization. Very large volumes of such material are scanned in order to identify cells or clusters of cells which are positive or warrant more detailed examination, and if analysis at higher resolution is necessary, information regarding these positive events is transferred to a secondary microscope, such as a conventional scanning microscope, to allow further analysis and review of the selected regions of the slide containing the sample.