Abstract: This invention describes ways of obtaining nano-particulated adjuvants formed by different synthetic variants of GM3 ganglioside. Depending on the fine structure of the fatty acid in the ceramide of the synthetic GM3, said adjuvants are able to stimulate specifically and in a specialized way the humoral or cellular immune response against accompanying antigens. Particularly, this invention provides immunogenic vaccine compositions that comprise peptides, polypeptides or proteins and the aforementioned nanoparticles, which are formed through the dispersion of hydrophobic proteins of the outer membrane complex (OMC) of Neisseria meningitidis in solutions containing fully synthetic variants of the GM3 ganglioside.

Abstract: Embodiments of the disclosure include methods and compositions for treating or preventing coronavirus infection using soluble vimentin of functional variant or derivative thereof. In specific embodiments, a vimentin derivative comprising the rod domain is utilized for treating or preventing any disease in which the blocking of viral particles from binding cell surface receptors is therapeutic. In specific embodiments, a fragment of vimentin that comprises part or all of the rod domain is employed.

Abstract: Embodiments of the disclosure encompass methods and compositions for maintaining a healthy fibrin network in an individual. The disclosure includes methods of targeting fibrin in an individual for the purpose of restoring fibrin that is subject to a level of fibrinolysis that is deleterious, such as excessive or reduced with respect to the general population. Such modifications of fibrin in an individual may include direct targeting of fibrin with the A2 domain of von Willebrand factor or a functional derivative or fragment thereof. In specific embodiments, the methods restore to a normal level any imbalance between coagulation and inflammation.

Abstract: A computer-implemented method and corresponding system for processing sensor data associated with a vehicle is provided. The sensor data may be compressed or encoded with a dictionary according to sparse approximation theory, resulting in a sparse representation of the sensor data. Processing may further comprise detecting an event associated with the vehicle, wherein an event may be an accident recorded by sensors of the vehicle providing the sensor data. The detection of the event may be based on processing of the sparse representation of the sensor data alone without decoding the sparse representation. The detection of the event may further employ machine learning methods trained to the detection of an event from the sparse representation of the sensor data, or a combination of sparse representations of sensor data originating from a plurality of vehicles or sensors.

Abstract: A computer-implemented method and corresponding system for processing sensor data associated with a vehicle is provided. The sensor data may be compressed or encoded with a dictionary according to sparse approximation theory, resulting in a sparse representation of the sensor data. Processing may further comprise detecting an event associated with the vehicle, wherein an event may be an accident recorded by sensors of the vehicle providing the sensor data. The detection of the event may be based on processing of the sparse representation of the sensor data alone without decoding the sparse representation. The detection of the event may further employ machine learning methods trained to the detection of an event from the sparse representation of the sensor data, or a combination of sparse representations of sensor data originating from a plurality of vehicles or sensors.

Abstract: One example apparatus associated with detecting mitosis in breast cancer pathology images by combining handcrafted (HC) and convolutional neural network (CNN) features in a cascaded architecture includes a set of logics that acquires an image of a region of tissue, partitions the image into candidate patches, generates a first probability that the patch is mitotic using an HC feature set and a second probability that the patch is mitotic using a CNN-learned feature set, and classifies the patch based on the first probability and the second probability. If the first and second probabilities do not agree, the apparatus trains a cascaded classifier on the CNN-learned feature set and the HC feature set, generates a third probability that the patch is mitotic, and classifies the patch based on a weighted average of the first probability, the second probability, and the third probability.

Abstract: The invention relates to a portable mixing platform for producing a heat-transfer fluid on site and on an industrial scale, said fluid consisting of a eutectic mixture of diphenyl (DP) and diphenyl oxide (DPO). The platform includes a device that stores DPO in liquid state, a device for supplying DP in solid state, a mixing device in which both components are mixed in liquid state and in the right amount for forming a eutectic mixture, and a device for inerting and treating gases which maintains the DPO and mixing devices under inert atmosphere, as well as drawing the gases vented from both devices to pass said gases through an activated-carbon filter prior to expelling same into the atmosphere. The present invention also relates to the method for producing the eutectic mixture of DP and DPO using the described portable mixing platform.

Abstract: Methods, apparatus, and other embodiments associated with detecting mitosis in breast cancer pathology images by combining handcrafted (HC) and convolutional neural network (CNN) features in a cascaded architecture are described. One example apparatus includes a set of logics that acquires an image of a region of tissue, partitions the image into candidate patches, generates a first probability that the patch is mitotic using an HC feature set and a second probability that the patch is mitotic using a CNN-learned feature set, and classifies the patch based on the first probability and the second probability. If the first and second probabilities do not agree, the apparatus trains a cascaded classifier on the CNN-learned feature set and the HC feature set, generates a third probability that the patch is mitotic, and classifies the patch based on a weighted average of the first probability, the second probability, and the third probability.