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 provides for a recombinant adenoviral vector comprising a recombinant oncolytic adenovirus which has: (1) a modified transcription regulatory sequence wherein the adenoviral vector is transcriptionally active in cancer cells and/or hyperproliferative cells and transcriptionally attenuated in normal cells, and (2) a transgene encoding one or more cancer antigens that are specific to a subject. The invention further provides a method of producing said recombinant adenoviral vector. The recombinant oncolytic adenoviral vector described above is used in methods of stimulating a heightened immune response against a cancer antigen in a subject or a method of treating cancer in a subject.

Abstract: Aspects described herein may relate to the determination of data that is indicative of a greater range of speech properties than input text data. The determined data may be used as input to one or more speech processing tasks, such as model training, model validation, model testing, or classification. For example, after a model is trained based on the determined data, the model's performance may exhibit more resilience to a wider range of speech properties. The determined data may include one or more modified versions of the input text data. The one or more modified versions may be associated with the one or more speakers or accents and/or may be associated with one or more levels of semantic similarity in relation to the input text data. The one or more modified versions may be determined based on one or more machine learning algorithms.

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 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: 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: The invention relates to a recombinant adenovirus that expresses endostatin, angiostatin, or a combination of endostatin and angiostatin. The invention also relates to method of treating cancer in a subject in need thereof, the method comprising administering to the subject an effective amount of a combination of (i) a recombinant adenovirus and (ii) an anti-angiogenic agent to treat the cancer in the subject.

Abstract: Aspects described herein may relate to the determination of data that is indicative of a greater range of speech properties than input text data. The determined data may be used as input to one or more speech processing tasks, such as model training, model validation, model testing, or classification. For example, after a model is trained based on the determined data, the model's performance may exhibit more resilience to a wider range of speech properties. The determined data may include one or more modified versions of the input text data. The one or more modified versions may be associated with the one or more speakers or accents and/or may be associated with one or more levels of semantic similarity in relation to the input text data. The one or more modified versions may be determined based on one or more machine learning algorithms.

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

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 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: The invention provides, e.g., a recombinant virus comprising (i) a modified TATA box-based promoter, and/or (ii) a modified CAAT box-based promoter operably linked to a gene, wherein the modified TATA box-based promoter and/or modified CAAT box-based promoter lacks a functional TATA box and/or CAAT box and permit selective expression of the gene in a hyperproliferative cell. The recombinant viruses can be used to treat cell proliferative diseases and disorders, including certain forms of cancer.

Abstract: The invention provides a recombinant adenovirus comprising two (or more) therapeutic transgenes, e.g., CD80 and CD137L. The transgenes are preferably inserted into an E1b-19K insertion site and/or an E3 insertion site.

Abstract: Techniques for generating a forecast associated with an item may be described. For example, the forecast may be generated based on simulations of supply and demand variables associated with the item over a planning horizon. The forecast may include at least one of: a labor forecast or a capacity forecast associated with inventorying units of the item in an inventory over the planning horizon. Further, real-time data associated with realizations of the supply and demand variables during the planning horizon may be monitored. At least a portion of the real-time data may be available from a management system. The management system may be configured to manage orders for the units of the item from the inventory. The forecast may be updated during the planning horizon based on the real-time data. The update may include changing the at least one of: the labor forecast or the capacity forecast.

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: The invention relates to a method for optimally visualizing a morphologic region of interest of a bone in an X-ray image of a patient, comprising: —receiving a set of 3D medical images of the bone, —creating a 3D bone model of at least part of the bone comprising said region of interest from said set of 3D images, —determining a criterion representative of a visualization of the extent of said morphologic region of interest, —automatically determining from the 3D bone model optimal relative bone and X-ray orientation so as to optimize said criterion for said patient, —creating at least one virtual X-ray image of the bone from said set of 3D images according to said optimal relative bone and virtual X-ray orientation.

Abstract: The invention relates to a method for creating a surgical resection plan for treating a pathological deformity of a bone.

Abstract: The invention relates to a method for non-invasive reproducible determination of a corrected surface on a 3D bone surface model constructed from 3D medical image of a bone having a deformation consisting in a bump overgrowth at the head-neck junction; wherein said corrected surface comprises: i) a 3D spherical corrected surface patch on the head portion of said 3D bone surface model, and ii) a 3D smooth transition corrected surface patch on the neck portion of said 3D bone surface model, contiguous to said 3D spherical corrected surface patch; Said corrected surface patches are defined by a set of parameters comprising: iii) at least one first parameter (a*) representing a spherical extent value of said 3D spherical corrected surface patch, iv) and a set of at least one second parameter, said set determining the 3D correction boundary of said corrected surface patches, such that said corrected surface patches are continuous with said 3D bone surface model along said boundary.