Abstract: A method and system in the User Equipment (UE) for maintaining rejected network slice selection assistance information (NSSAI) for Single Network Slice Selection Assistance Information (S-NSSAI) not available due to maximum number of UEs reached per access type. The method comprises receiving a Non-Access Stratum (NAS) message including at least one of a registration accept, registration reject or CONFIGURATION UPDATE COMMAND message along with at least one of rejected Single Network Slice Selection Assistance Information (S-NSSAI(s)) information and a cause for the S-NSSAI not available due to maximum number of UEs reached from a network. Thereafter, the method includes determining by the UE an access type on which the rejected S-NSSAI(s) is applicable based on the received S-NSSAI(s) information and then maintaining a storage of the rejected NSSAI for the S-NSSAI not available due to the maximum number of the UEs reached per access type based on the determination.

Abstract: In an example embodiment, a method of optimizing service delivery performed by a UE operating in EN-DC mode is disclosed. The method comprises detecting that a network service is ongoing at the UE using a first radio access technology (RAT), based on an application request sent by the UE to a network, wherein the first RAT is one of long-term evolution (LTE) and NR. The method further comprises detecting, during the ongoing network service being availed using the first RAT, a frequency of one or more NR network procedures being performed by the UE to be greater than a first threshold frequency. The method further comprises modifying a measurement interval associated with an NR cell measurement procedure, in response to detecting the frequency of the NR network procedures to be greater than the first threshold frequency. Furthermore, the method comprises continuing the Network Service using the first RAT.

Abstract: The embodiments of present disclosure herein address the inability of existing techniques to fragment both small molecules and substituents of a core scaffold. It addresses generation of lesser number of unique fragments which hinders application of graph propagation approaches to predict properties from molecular datasets. The method and system for extraction of small molecule fragments and their explanation for drug-like properties. A molecular graph representation is used to train graph convolution network (GCN) models for prediction of various absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. The models developed are compared with an existing atom-level graph model trained using a similar architecture. Further, the explanations obtained from the predictive models are validated based on their relevance to the existing knowledgebase of substructure contributions using matched molecular pairs (MMP) analysis.

Abstract: Embodiments herein provide a method for managing a priority of Visited Public Land Mobile Networks (VPLMNs) of a wireless communication network. The method includes receiving a registration accept message including steering of roaming (SoR) information from a VPLMN; in response to a security check of the SoR information being failed, adding the VPLMN to a SoR abort list including PLMNs where a registration was aborted due to SoR; and deleting the SoR abort list after a predetermined time period.

Abstract: According to various embodiments, a method performed by a user equipment (UE) in a wireless network may include receiving a steering of roaming connected mode control information (SOR-CMCI) configuration or SOR information from a network entity, initiating a timer in response to receiving the SOR-CMCI configuration or the SOR information from the network entity, identifying whether the UE is in an idle mode before expiry of the timer, in case that the UE is in the idle mode, identifying a condition under which the UE is in the idle mode, and performing, based on the condition, a first operation of allowing the timer to continue running, or a second operation of stopping the timer and selecting a public land mobile network (PLMN).

Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments herein disclose a wireless network and methods to maintain a MA PDU session at a NSACF entity. The method includes determining, by a SMF entity, that a PDU session is the MA PDU session based on an indication from a UE. Further, the method includes indicating, by the SMF entity, that the PDU session is the MA PDU session to the NSACF entity in response to determination. The wireless network may maintain the count accurately even though the PDU session is a MA PDU session.

Abstract: Drug induced gene expression provides information covering various aspects of drug discovery and development. Recent advances in accessibility of open-source drug-induced transcriptomic data along with ability of deep learning algorithms to understand hidden patterns have opened opportunity for designing drug molecules based on desired gene expression signatures. Embodiments herein provide method and system for cell specific model where gene expressions are processed via pretrained Simplified Molecular Input Line Entry System (SMILES) variational autoencoder (s-VAE) to produce new molecules. The model is trained with drug and drug induced gene expression data as input. Both pretrained s-VAE and profile variational autoencoder (p-VAE) are trained jointly. During joint training, difference between newly generated molecules and existing drug molecules is calculated as joint loss function composed of binary cross entropy loss and Kullback-Leibler divergence loss.

Abstract: The disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments herein provide a method for selecting route selection descriptor in network (1000) by a CE (100). The method includes receiving a URSP rule configuration from HPLMN apparatus (200). The URSP rule configuration includes a route selection criteria in a block. The block can be, for example, but not limited to a URSP information block of the URSP rule configuration, a traffic descriptor block of the URSP rule configuration, a route selection descriptor block of the URSP rule configuration, and a route validation criteria block of the URSP rule configuration. Further, the method includes detecting an initiation of an application in the UE. Further, the method includes determining that the URSP rule configuration is applicable to the application.

Abstract: Traditional drug discovery methods are target-based, time- and resource-intensive, and require a lot of resources for the initial hit molecule identification. Phenotype-based drug screening requires differential gene expression data of a large number of molecules for different combinations of cell-line, time point and dosage. Experimentally obtaining gene expression data for all these combinations is again a heavily resource-intensive process. The technical challenge in conventional methods that use prediction models is that they depend largely on the data processing and representation. The disclosure herein generally relates to drug-like molecule screening, and, more particularly, to a method and system for gene expression and machine learning-based drug screening. The embodiment, thus, provides a mechanism of a small molecule-induced gene expression prediction based on machine learning models.

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: 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.