Abstract: The present disclosure relates to compositions and methods for producing a recombinant virus, e.g., a recombinant oncolytic adenovirus, using an A549 host cell.

Abstract: Disclosed herein are recombinant adenoviruses with one or more nucleotide sequences inserted between two viral transcription units, formulations comprising the recombinant adenoviruses, and methods of treatment using the recombinant adenoviruses. In some embodiments, the one or more nucleotide sequences are inserted in an IX-E2 insertion site and/or an L5-E4 insertion site.

Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., a TGF? trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion proteins, or expression vectors encoding for the fusion proteins, e.g., oncolytic adenoviral expression vectors, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.

Abstract: Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.

Abstract: The present disclosure relates to compositions and methods for producing a recombinant virus, e.g., a recombinant oncolytic adenovirus, using an A549 host cell.

Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., a TGF? trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion proteins, or expression vectors encoding for the fusion proteins, e.g., oncolytic adenoviral expression vectors, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.

Abstract: The invention relates to a recombinant adenovirus comprising two or more therapeutic transgenes, e.g., two components of a heterodimeric cytokine, separated by a cleavable linker.

Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.

Abstract: Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.

Abstract: Systems, apparatuses, and methods are described for inverting neural embeddings. One or more forward neural embeddings associated with meanings, features, and/or characteristics of data samples may be generated for one or more data samples. One or more inverse neural embeddings associated with the one or more forward neural embeddings may be determined. One or more inverse feature sets for the one or more inverse neural embeddings may be generated.

Abstract: The invention relates to a method for producing a recombinant virus, e.g., a recombinant oncolytic adenovirus, using an A549 host cell.

Abstract: A wearable health management system includes a flexible member configured to be worn on an affected area by a patient. At least one actuator is operably coupled to the flexible member. The at least one actuator is configured to be adjusted between a deployed state and a non-deployed state. At least one of a photoplethysmogram sensor and a bioimpedance sensor is coupled to the flexible member to obtain one or more health metrics from the patient. A controller is in communication with the at least one actuator. The controller is configured to adjust the at least one actuator to the deployed state to provide a selected pressure to the affected area.

Abstract: Aspects described herein may allow for the application of stochastic gradient boosting techniques to the training of deep neural networks by disallowing gradient back propagation from examples that are correctly classified by the neural network model while still keeping correctly classified examples in the gradient averaging. Removing the gradient contribution from correctly classified examples may regularize the deep neural network and prevent the model from overfitting. Further aspects described herein may provide for scheduled boosting during the training of the deep neural network model conditioned on a mini-batch accuracy and/or a number of training iterations. The model training process may start un-boosted, using maximum likelihood objectives or another first loss function.

Abstract: A wearable health management system includes a flexible member configured to be worn on an affected area by a patient. At least one actuator is operably coupled to the flexible member. The at least one actuator is configured to be adjusted between a deployed state and a non-deployed state. At least one of a photoplethysmogram sensor and a bioimpedance sensor is coupled to the flexible member to obtain one or more health metrics from the patient. A controller is in communication with the at least one actuator. The controller is configured to adjust the at least one actuator to the deployed state to provide a selected pressure to the affected area.

Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., an IL-10 trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion protein, or an expression vector encoding for the fusion proteins, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.

Abstract: Provided is a fusion protein, e.g., a cytokine receptor fusion protein, e.g., an IL-10 trap, with a novel linker sequence to permit the fusion protein to functionally optimally, e.g., to permit a cytokine receptor portion of a cytokine receptor fusion protein to bind optimally to its target cytokine. The fusion protein, or an expression vector encoding for the fusion proteins, can be used to treat cell proliferative diseases and disorders, including certain forms of cancer and inflammatory disorders.

Abstract: Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.

Abstract: Aspects discussed herein may relate to methods and techniques for embedding constrained and unconstrained optimization programs as layers in a neural network architecture. Systems are provided that implement a method of solving a particular optimization problem by a neural network architecture. Prior systems required use of external software to pre-solve optimization programs so that previously determined parameters could be used as fixed input in the neural network architecture. Aspects described herein may transform the structure of common optimization problems/programs into forms suitable for use in a neural network. This transformation may be invertible, allowing the system to learn the solution to the optimization program using gradient descent techniques via backpropagation of errors through the neural network architecture. Thus these optimization layers may be solved via operation of the neural network itself.

Abstract: Systems, apparatuses, and methods are described for inverting neural embeddings. One or more forward neural embeddings associated with meanings, features, and/or characteristics of data samples may be generated for one or more data samples. One or more inverse neural embeddings associated with the one or more forward neural embeddings may be determined. One or more inverse feature sets for the one or more inverse neural embeddings may be generated.

Abstract: A film creation and management system and platform enables efficient, cost-effective, collaborative, and secure creation of filmed or interactive content. A system and platform for creating high-resolution filmed content in an efficient, fast, secure, and collaborative manner are described. The system is a wide-ranging filming tool and film management system that enables cost-effective creation and manipulation of 8K or other high-resolution content. It has interfaces and tools for managing all stages of the film creation process from story development to post production, including marketing and administration. One objective of the system is to facilitate the transition of creating high-resolution content to a property that has a business outcome, thereby facilitating the transition from art to commerce.