Abstract: The disclosure relates to methods and systems for isolating individual sperm from a semen sample using microfluidics. Examples include a microfluidics system for isolating individual sperm from a semen sample, the system including: a first laminar flow channel extending between a first inlet and a first outlet; a second laminar flow channel extending between a second inlet and a second outlet; and a third laminar flow channel extending between the first and second laminar flow channels and having a restriction configured to prevent passage of a single sperm through the third laminar flow channel, wherein flow of a semen sample through the first laminar flow channel at a pressure higher than flow of a liquid medium through the second laminar flow channel results in a single sperm being trapped by the restriction in the third laminar flow channel.

Abstract: A radiology workstation (24) includes at least one display component (30, 32); at least one user input device (28); and at least one microprocessor (26, 34) programmed to generate a contextual ranking of clinical codes for a context received via the at least one user input device (28) and to display information pertaining to the contextual ranking on the display component (30, 32) of the radiology workstation (24). The contextual ranking is computed by the microprocessor from (i) statistics of occurrences of the clinical codes in radiology reports contained in a radiology reports database (10) and satisfying the context and (ii) statistics of the clinical codes in problem lists contained in a problem lists database and satisfying the context.

Abstract: The invention relates to an aluminum secondary battery comprising at least one positive electrode, at least one negative electrode, at least one electrolyte, and at least one additive, the electrolyte comprising at least one room temperature ionic liquid (RTIL), made of at least one organic salt and at least one first aluminum salt, and/or at least one deep eutectic solvent (DES) made of at least one organic solvent and at least one first aluminum salt, the additive containing bivalent metal cations and/or being suitable for forming bivalent metal cations.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: Methods are provided for the analysis of microarray images. The methods described automate the image-based quality control of a microarray and the subsequent quantitative assessment of the analysis that the microarray image represents. The methods comprise generating a reference array using raw image data based on which the microarray can then be located. Once the array is located, the quality of the individual print spots can be analyzed.

Abstract: Image co-registration entails: emitting energy, and responsively and dynamically acquiring images, the acquired images having a given dimensionality; selecting, repeatedly, and dynamically with the acquiring, from among the acquired images and, as a result of the selecting, changing, repeatedly, and dynamically, membership in a set of images of the given dimensionality; and, synchronously with the change, dynamically and iteratively registering the set to an image having a dimensionality higher than the given dimensionality. The co-registration is realizable with an imaging probe for the acquiring, and a tracking system for tracking a location, and orientation, of the probe, the registering being specialized for the acquiring with the probe being dynamically, during the acquiring, any one or more of angulated, rotated and translated.

Abstract: A system for planning and performing a repeat interventional procedure is provided which includes a registration device and an image generation device which map current targets in a reference image for a first interventional procedure to at least one guidance image acquired from a different imaging modality. Biopsy locations are recorded to the guidance images during the first interventional procedure and the biopsy locations are mapped to the reference image to provide a planning image for use in a subsequent interventional procedure on the patient. In a subsequent interventional procedure, the prior planning image (124) may be registered to a current reference image and the prior biopsy locations and prior and current targets are mapped to a guidance image acquired from a different imaging modality. Biopsy locations are then mapped to the guidance image and mapped back to the current reference image.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: A method (100) for generating a domain-specific training set, comprising: generating (130) a generic corpus comprising a plurality of tokenized documents, comprising: (i) parsing (132) a document retrieved from the generic corpus; (ii) preprocessing (134) the parsed document; (iii) tokenizing (136) the preprocessed document; and (iv) storing (138) the tokenized document in the generic corpus; generating (140) an ontology database of tokenized entries, comprising: (i) parsing (142) an ontology entry retrieved from an ontology; (ii) preprocessing (144) the parsed entry; (iii) tokenizing (146) the preprocessed entry; and (iv) storing (148) the tokenized entry in the ontology database; querying (150), using domain-specific tokenized entries from the ontology database, the tokenized documents in the generic corpus; identifying (160), based on the query, a plurality of tokenized documents specific to the domain; and storing (170), in a training set database, the identified tokenized documents as a training set spec

Abstract: Processes and methods for refining ferronickel alloy, and producing nickel sulfate or other nickel product, are provided, where the ferronickel alloy is treated with an oxidizing leach. The oxidizing leach may be, for example, a pressure oxidation (POX) leach or a leach with peroxide or copper (II) ions. The treatment may be in the presence of added copper, such as by providing a copper sulfate solution. Producing nickel sulfate may comprise removing copper and iron after the leach, removing impurities, and either crystallizing the nickel sulfate or precipitating/winning another nickel product.

Abstract: The disclosure relates to methods and systems for detecting the orientation of an oocyte and its polar body. Examples include an automated method for detection of an orientation of an oocyte, the method including: i) acquiring an image of the oocyte; ii) defining first and second elliptical features in the image based on edges detected in the image; iii) calculating an orientation of the second elliptical feature relative to the first elliptical feature; and iv) outputting a relative orientation of a polar body of the oocyte based on the orientation of the second elliptical feature relative to the first elliptical feature.

Abstract: The present disclosure relates to a system, a method and a computer-readable tangible non-transitory medium for recommending live streaming data. The method includes identifying a set of live streaming programs according to an attribute of the user and a tag of each live streaming program in the set of live streaming programs, generating a first subset of live streaming programs from the set of live streaming programs according to a first criterion, and generating a second subset of live streaming programs from the set of live streaming programs according to a second criterion. The first criterion is different from the second criterion. The present disclosure can recommend contents in a more efficient way and increase user engagements.

Abstract: Processes and methods for refining ferronickel alloy, and producing nickel sulfate or other nickel product, are provided, where the ferronickel alloy is treated with an oxidizing leach. The oxidizing leach may be, for example, a pressure oxidation (POX) leach or a leach with peroxide or copper (II) ions. The treatment may be in the presence of added copper, such as by providing a copper sulfate solution. Producing nickel sulfate may comprise removing copper and iron after the leach, removing impurities, and either crystallizing the nickel sulfate or precipitating/winning another nickel product.

Abstract: A method for multi-modal tissue classification of an anatomical tissue involves a generation of a tissue classification volume (40) of the anatomical tissue derived from a spatial registration and an image extraction of one or more MRI features of the anatomical tissue and one or more ultrasound image features of the anatomical tissue. The method further involves a classification of each voxel of the tissue classification volume (40) as one of a plurality of tissue types including a healthy tissue voxel and an unhealthy tissue voxel.

Abstract: A system (100) includes an end point prediction engine (150) that predicts an end point (302) using a machine learning model (132) and one or more clinical report objects (152) for a patient, wherein the machine learning model inputs the one or more clinical report objects and outputs the predicted end point according to phrases or n-grams in the one or more clinical report objects. An end point visualization interface (160) visualizes the predicted end point (302) using a scorecard (162) or a timeline (164). An end point modeling engine (130) generates the machine learning model from training data that includes validated end points (122) and clinical report objects (116).

Abstract: A controller (122, 910/920) for interventional procedure optimization includes a processor (12210, 910) and a memory (12220, 920) that stores instructions. When executed by the processor, the instructions cause the controller (12210, 910) to implement a process that includes identifying (S210) anatomical characteristics from pre-interventional imagery of anatomy for each of multiple candidate types of an interventional procedure for the anatomy and comparing (S220) the anatomical characteristics with tool characteristics of candidate tools to use in each of the candidate types. The process also includes generating (S240) a feasibility report for each of the candidate types based on the identifying and the comparing. Each feasibility report includes a feasibility grade for each of the candidate types. The process also includes selecting (S260), based on the feasibility reports, an optimal interventional procedure type among the candidate types.

Abstract: The present disclosure is directed to methods and apparatus for validating and authenticating use of machine learning models. For example, various techniques are described herein to limit the vulnerability of machine learning models to attack and/or exploitation of the model for malicious use, and for detecting when such attack/exploitation has occurred. Additionally, various embodiments described herein promote the protection of sensitive and/or valuable data, for example by ensuring only licensed use is permissible. Moreover, techniques are described for version tracking, usage tracking, permission tracking, and evolution of machine learning models.

Abstract: Image co-registration entails: emitting energy, and responsively and dynamically acquiring images, the acquired images having a given dimensionality; selecting, repeatedly, and dynamically with the acquiring, from among the acquired images and, as a result of the selecting, changing, repeatedly, and dynamically, membership in a set of images of the given dimensionality; and, synchronously with the change, dynamically and iteratively registering the set to an image having a dimensionality higher than the given dimensionality. The co-registration is realizable with an imaging probe for the acquiring, and a tracking system for tracking a location, and orientation, of the probe, the registering being specialized for the acquiring with the probe being dynamically, during the acquiring, any one or more of angulated, rotated and translated.

Abstract: Image co-registration (S390) entails: emitting energy, and responsively and dynamically acquiring images (216), the acquired images having a given dimensionality; selecting, repeatedly, and dynamically with the acquiring, from among the acquired images and, as a result of the selecting, changing, repeatedly, and dynamically, membership in a set (226) of images of the given dimensionality; and, synchronously with the change (S376), dynamically and iteratively registering the set to an image having a dimensionality higher than the given dimensionality. The co-registration is realizable with an imaging probe (140) for the acquiring, and a tracking system for tracking a location, and orientation, of the probe, the registering being specialized for the acquiring with the probe being dynamically, during the acquiring, any one or more of angulated, rotated and translated.

Abstract: The present disclosure pertains to computer-assisted search of image slices. In some embodiments, an image slice having a detected finding (and representing a cross section of at least a portion of an individual) may be determined. A search space may be reduce to a subset of image slices respectively representing a cross section corresponding to the cross section represented by the determined image slice. The search space reduction may comprise filtering a set of image slices based on the cross section represented by the determined image slice. Image slice information related to the determined image slice and related one or more image slices (of the image slices subset) may be obtained based on the search space reduction. A determination of whether indications of the detected finding exist in the image slices (of the image slices subset) may be effectuated based on the image slice information.