Abstract: This disclosure relates generally to a method and system for online handwritten signature verification providing a simpler low cost system. The method comprises extracting signature data for the subject from a sensor array for the predefined time window at regular predefined time instants. Further, differentiating the matrix row wise and column wise to generate a row difference matrix and a column difference matrix. Further, determining an idle signature time fraction for the extracted signature data of the subject being monitored from the column difference matrix. Further, determining a plurality of signature parameters based on the row difference matrix and the column difference matrix.

Abstract: This disclosure relates generally to method and system for structure-based drug design using a multi-modal deep learning model. The method processes a target protein for designing at least one optimized molecule by using a multi-modal deep learning model. The GAT-VAE module obtains a latent vector of at least one active site graph comprising of key amino acid residues from the target protein. The SMILES-VAE module obtains at least one latent vector from the target protein. Further, the conditional molecular generator concatenates the active site graph with the latent vector to generate a set of molecules. The RL framework is iteratively performed on the concatenated latent vector to optimize at least one molecule by using the drug-target affinity (DTA) predictor module to predict an affinity value for the set of molecules towards the target protein. Further, at least one optimized molecule is designed with an affinity of the target protein.

Abstract: Conventionally, deep learning-based methods have shown some success in ligand-based drug design. However, these methods face data scarcity problems while designing drugs against novel targets. Embodiments of the present disclosure provide systems and methods that leverage the potential of deep learning and molecular modeling approaches to develop a drug design pipeline, which can be useful for cases where there is limited or no availability of target-specific ligand datasets. Inhibitors of other proteins, structurally similar to the target protein are screened at the active site of the target protein to create an initial target-specific dataset. Transfer learning is implemented to learn features of target-specific dataset and design new chemical entities/molecules using a deep generative model. A deep predictive model is used predict docking scores of newly designed/identified molecules.

Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.

Abstract: Pathogens invade and infect humans. Understanding the infection mechanism is essential for determining targets for new therapeutics. Existing methods provide too many false positive results. A method and system for predicting protein-protein interaction between a host and a pathogen has been provided. The disclosure provides a pipeline for predicting HPIs, which is a combination of biological knowledge-based filters, domain-based filter and sequence-based predictions. Biologically feasible interactions are only possible when both the proteins share common localization and overlapping expression profiles. This observation was used as the first filter to remove biologically irrelevant HPIs. Proteins interact with each other through domains. Both interacting and non-interacting protein pairs provide valuable information about the probability of protein-protein interactions and hence both were used to derive statistical inferences to remove improbable HPIs.

Abstract: Disclosed embodiments provide techniques for automatic software defect correction of a computer program. Computer program log files are scanned to identify runtime errors, corresponding to software defects. The software defects are analyzed to determine an error type, and identify the source file/code that caused the error. A solution template repository is searched for a solution template corresponding to the identified error type. If a solution template is found, the source code is checked out from the identified source repository. The template is applied to the “original” checked out source file to create a new source file with the fix, which is then uploaded back to the repository. A new software distribution is automatically built with the new source file, and the new software distribution is automatically deployed to the devices that experienced the error. Thus, defects can be automatically detected, repaired, and deployed without human intervention.

Abstract: Methods for prevention and treatment of asthma attacks involve the administration of one or more TRPV1 antagonists, one or more LPAr antagonists or preferably a combination of one or more TRPV1 antagonists and one or more LPAr antagonists. TRPV1 antagonists and/or LPAr antagonists or a combination of both inhibit or prevent carotid body activation during an acute asthma attack. TRPV1 antagonists, LPAr antagonists or a combination thereof are useful for preventing or ameliorating the symptoms of asthma attacks. Pharmaceutical compositions for use in treating asthma and more specifically for preventing or treating asthma attacks comprise a combination of a TRPV1 antagonist and an LPAr antagonist. Methods for making medicaments for such treatment are provided. Also provided are kits for treating asthma and for preventing or treating asthma attacks in which a TRPV1 antagonist and an LPAr antagonist are separately formulated for administration at the same time.