Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to image enhancement using a generative adversarial network (GAN). The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a training component that can train a discriminator of the GAN to score a texture of a CT image, wherein the texture can be derived from a difference of two conditionally independent estimates produced by a generator of the GAN by respectively processing two independent noisy samples of images.

Abstract: Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.

Abstract: Techniques are described that facilitate generating neural network (NNs) tailored to optimize specific properties of medical images using novel loss functions. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a training component that trains a NN to generate a modified version of computed tomography (CT) data comprising one or more optimized properties relative to the CT data using a loss function tailored to control learning adaptation of the NN based on error attributed to one or more defined components associated with the CT data, resulting in a trained NN, wherein the one or more defined components comprise at least one of a frequency component or a spatial feature component.

Abstract: A predictive context aware system, method and device tracks customer attributes comprising: an online customer behaviour of a particular customer of an entity when interacting with a computer application including a particular flow of navigational events when browsing the application indicative of the particular customer seeking assistance; provides the tracked customer attributes to a predictive machine learning model to determine a prediction of a primary intent comprising: at least one predicted problem encountered by the customer associated with the tracked customer attributes and a context of actions derived from the customer attributes, the model trained based on prior historical behaviour of other customers in the entity comprising browser navigational flows for others indicative of a known associated problem; dynamically determines a solution to the predicted problem based on accessing a database linking similar problems; and presents the solution and associated context of the solution to the computer

Abstract: Systems and methods for computed tomography imaging are provided. In one embodiment, a method includes acquiring an image, inputting the image to a machine learning model to generate a denoised image, the machine learning model trained with a loss function that weights variance differently from bias, and outputting the denoised image. In this way, structural details in denoised CT images may be improved while maintaining textural information in the denoised images.

Abstract: There is a need for delivery platforms with robust capacity that offer the possibility to deliver diverse protein-based therapeutics into specific cells. Described herein is a platform for delivering cargo polypeptides into cells, which is based on a recombinant molecule comprising: a cargo polypeptide, a diphtheria toxin enzymatic fragment (DTA), and a diphtheria toxin translocation fragment (DTB). The platform has been employed to deliver diverse cargo into cells, including those having low or high molecular weights. A hyper-stable cargo polypeptide has been delivered, as well as proteins of therapeutic significance (e.g, MecP2, SMN, FMRP, PNP, alpha-amylase, RRSP, GRA16, and GRA24). The platform is also useful for delivering genome-modifying proteins, such as the CRISPR protein, Cas9.

Abstract: There is a need for delivery platforms with robust capacity that offer the possibility to deliver diverse protein-based therapeutics into specific cells. Described herein is a platform for delivering cargo polypeptides into cells, which is based on a recombinant molecule comprising: a cargo polypeptide, a diphtheria toxin enzymatic fragment (DTA), and a diphtheria toxin translocation fragment (DTB). The platform has been employed to deliver diverse cargo into cells, including those having low or high molecular weights. A hyper-stable cargo polypeptide has been delivered, as well as proteins of therapeutic significance (e.g, MecP2, SMN, FMRP, PNP, alpha-amylase, RRSP, GRA16, and GRA24). The platform is also useful for delivering genome-modifying proteins, such as the CRISPR protein, Cas9.

Abstract: There is a need for delivery platforms with robust capacity that offer the possibility to deliver diverse protein-based therapeutics into specific cells. Described herein is a platform for delivering cargo polypeptides into cells, which is based on a recombinant molecule comprising: a cargo polypeptide, a diphtheria toxin enzymatic fragment (DTA), and a diphtheria toxin translocation fragment (DTB). The platform has been employed to deliver diverse cargo into cells, including those having low or high molecular weights. A hyper-stable cargo polypeptide has been delivered, as well as proteins of therapeutic significance (e.g, MecP2, SMN, FMRP, PNP, alpha-amylase, and RRSP). The platform is also useful for delivering genome-modifying proteins, such as the CRISPR protein, Cas9. Associated nucleic acids, pharmaceutical compositions, methods, uses, and kits are also described, including those of therapeutic significance aimed at treating diseases or disorders caused by enzyme or protein deficiency.

Abstract: An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.

Abstract: Clostridium difficile is a leading cause of antibiotic-associated infection in hospitals worldwide. There is a need for a vaccine to Clostridium difficile that can target toxic proteins, or that can elicit adequate immunity to prevent infection or reduce the severity of infection. Modified Clostridium difficile toxin A and B (TcdA and TcdB) proteins are described herein, which comprise mutations that reduce toxin A and B toxicity compared to the native toxin. The proteins described are highly similar to the native toxin of Clostridium difficile, but toxicity is reduced.

Abstract: There is a need for delivery platforms with robust capacity that offer the possibility to deliver diverse protein-based therapeutics into specific cells. Described herein is a platform for delivering cargo polypeptides into cells, which is based on a recombinant molecule comprising: a cargo polypeptide, a diphtheria toxin enzymatic fragment (DTA), and a diphtheria toxin translocation fragment (DTB). The platform has been employed to deliver diverse cargo into cells, including those having low or high molecular weights. A hyper-stable cargo polypeptide has been delivered, as well as proteins of therapeutic significance (e.g, MecP2, SMN, FMRP, PNP, alpha-amylase, and RRSP). The platform is also useful for delivering genome-modifying proteins, such as the CRISPR protein, Cas9. Associated nucleic acids, pharmaceutical compositions, methods, uses, and kits are also described, including those of therapeutic significance aimed at treating diseases or disorders caused by enzyme or protein deficiency.

Abstract: Clostridium difficile is a leading cause of antibiotic-associated infection in hospitals worldwide. There is a need for a vaccine to Clostridium difficile that can target toxic proteins, or that can elicit adequate immunity to prevent infection or reduce the severity of infection. Modified Clostridium difficile toxin A and B (TcdA and TcdB) proteins are described herein, which comprise mutations that reduce toxin A and B toxicity compared to the native toxin. The proteins described are highly similar to the native toxin of Clostridium difficile, but toxicity is reduced.

Abstract: An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.

Abstract: An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.

Abstract: An imaging system includes a computer programmed to estimate noise in computed tomography (CT) imaging data, correlate the noise estimation with neighboring CT imaging data to generate a weighting estimation based on the correlation, de-noise the CT imaging data based on the noise estimation and on the weighting, and reconstruct an image using the de-noised CT imaging data.

Abstract: A CT system includes an x-ray source configured to project an x-ray beam toward an object, a detector array, and a bowtie filter. The bowtie filter includes a first x-ray filtration region positioned to attenuate x-rays that pass through an isochannel of the detector array, a second x-ray filtration region positioned to attenuate x-rays that pass through channels of the detector array that are offcenter in a channel direction from the isochannel, and an x-ray attenuation material positionable to attenuate the x-rays that pass through the channels of the detector array that are offcenter in the channel direction from the isochannel. The CT system also includes a data acquisition system (DAS) connected to the detector array and configured to receive outputs from the detector array, and a computer programmed to acquire projections of imaging data of the object, and generate an image of the object using the imaging data.

Abstract: A CT system includes an x-ray source configured to project an x-ray beam toward an object, a detector array, and a bowtie filter. The bowtie filter includes a first x-ray filtration region positioned to attenuate x-rays that pass through an isochannel of the detector array, a second x-ray filtration region positioned to attenuate x-rays that pass through channels of the detector array that are offcenter in a channel direction from the isochannel, and an x-ray attenuation material positionable to attenuate the x-rays that pass through the channels of the detector array that are offcenter in the channel direction from the isochannel. The CT system also includes a data acquisition system (DAS) connected to the detector array and configured to receive outputs from the detector array, and a computer programmed to acquire projections of imaging data of the object, and generate an image of the object using the imaging data.