Abstract: Disclosed are MGS peptides, MTS peptides, linkers and combinations combining any combination thereof including compositions.

Abstract: Disclosed are antigen delivery systems comprising a nanoparticle, wherein the nanoparticle is surface-modified with a cancer-specific cell targeting peptide and comprises an immunogenic HLA class I restricted peptide, wherein the HLA class I restricted peptide is a vaccine-dependent, immunogenic HLA class I restricted peptide; methods of treating a subject having cancer comprising administering said delivery systems; methods of killing cancer cells comprising contacting cancer cells delivery systems, wherein upon entry of the liposome into the cancer cells, the cancer cells present said peptide from said delivery system, wherein the cancer cells generate an immune response said peptide, and wherein the immune response to said peptide targets and kills the cancer cells presenting the vaccine-dependent, peptide; methods of generating a non-cancer secondary immune response that targets cancer cells comprising administering said delivery systems to a subject having cancer cells.

Abstract: Various examples are directed to systems and methods of maintaining a cloud-delivered analytics computing service. A version maintenance service may access version data for a subject instance of an analytics computing service and scan a plurality of remote data sources in communication with the subject instance of the analytics computing service to determine respective versions for the plurality of remote data sources. The version maintenance service may identify a first maintenance event describing a first maintenance instance of the analytics computing service that is different than the subject instance of the analytics computing service and has a version equivalent to the version of the subject instance of the analytics computing service. Responsive to identifying the first maintenance event, the version maintenance service may initiate a corrective action.

Abstract: Disclosed are MGS peptides, MTS peptides, linkers and combinations combining any combination thereof. Disclosed are MGS peptides comprising the amino acid sequence of any of the sequences set forth in SEQ ID NOs: 1-8 Disclosed are MTS peptides comprising the amino acid sequence of any of the sequences set forth in SEQ ID NOS: 9-14. Disclosed are compositions comprising at least one MGS peptide conjugated to at least one MTS peptide, wherein the MGS peptide comprises the amino acid sequence of GFHNVYPYTWGGFSDIDLMADEI (SEQ ID NO: 1); EQRWVQMLHILQTRYAGEWPG (SEQ ID NO: 2); FQHINPFPYTYSMEDTDVEIK (SEQ ID NO: 3); or YAAWPASGAWT (SEQ ID NO: 4). In some aspects, the disclosed compositions can further comprise one or more linkers.

Abstract: Various examples are directed to systems and a method for managing security at a public cloud platform deployment. A landscape service may determine a landscape score for an application instance executing at the public cloud platform deployment. An access frequency service may determine an access frequency score for the application instance. A last access service may determine a last access score for the application instance. The public cloud platform deployment may determine a total risk score for the application instance using the landscape score for the application instance, the access frequency score for the application instance, and the last access score for the application instance. Responsive to determining that the total risk score for the application instance is greater than a threshold value, an alert message may be sent to an administrative user account for the public cloud platform deployment.

Abstract: Disclosed are MGS peptides, MTS peptides, linkers and combinations combining any combination thereof. Disclosed are MGS peptides comprising the amino acid sequence of any of the sequences set forth in SEQ ID NOs: 1-8 Disclosed are MTS peptides comprising the amino acid sequence of any of the sequences set forth in SEQ ID NOS: 9-14. Disclosed are compositions comprising at least one MGS peptide conjugated to at least one MTS peptide, wherein the MGS peptide comprises the amino acid sequence of GFHNVYPYTWGGFSDIDLMADEI (SEQ ID NO: 1); EQRWVQMLHILQTRYAGEWPG (SEQ ID NO: 2); FQHINPFPYTYSMEDTDVEIK (SEQ ID NO: 3); or YAAWPASGAWT (SEQ ID NO: 4). In some aspects, the disclosed compositions can further comprise one or more linkers.

Abstract: Various examples are directed to systems and methods for migrating an analytics tool from a source deployment to a target deployment. A migration tool may access source metadata describing a plurality of source deployment queries to the at least one source deployment database. The migration tool may identify a plurality of source deployment database artifacts using the source metadata and migrate the plurality of source deployment database artifacts to a target database. The migration tool may apply a trained model to the plurality of source deployment database artifacts to generate a set of data relationships and generate a first analytics report from the migrated plurality of source deployment database artifacts at the target database, the first analytics report corresponding to at least one of the set of data arrangements.

Abstract: Various examples are directed to systems and methods of maintaining a cloud-delivered analytics computing service. A version maintenance service may access version data for a subject instance of an analytics computing service and scan a plurality of remote data sources in communication with the subject instance of the analytics computing service to determine respective versions for the plurality of remote data sources. The version maintenance service may identify a first maintenance event describing a first maintenance instance of the analytics computing service that is different than the subject instance of the analytics computing service and has a version equivalent to the version of the subject instance of the analytics computing service. Responsive to identifying the first maintenance event, the version maintenance service may initiate a corrective action.

Abstract: Disclosed are antigen delivery systems comprising a nanoparticle, wherein the nanoparticle is surface-modified with a cancer-specific cell targeting peptide and comprises an immunogenic HLA class I restricted peptide, wherein the HLA class I restricted peptide is a vaccine-dependent, immunogenic HLA class I restricted peptide; methods of treating a subject having cancer comprising administering said delivery systems; methods of killing cancer cells comprising contacting cancer cells delivery systems, wherein upon entry of the liposome into the cancer cells, the cancer cells present said peptide from said delivery system, wherein the cancer cells generate an immune response said peptide, and wherein the immune response to said peptide targets and kills the cancer cells presenting the vaccine-dependent, peptide; methods of generating a non-cancer secondary immune response that targets cancer cells comprising administering said delivery systems to a subject having cancer cells.

Abstract: Various examples are directed to systems and methods for migrating an analytics tool from a source deployment to a target deployment. A migration tool may access source metadata describing a plurality of source deployment queries to the at least one source deployment database. The migration tool may identify a plurality of source deployment database artifacts using the source metadata and migrate the plurality of source deployment database artifacts to a target database. The migration tool may apply a trained model to the plurality of source deployment database artifacts to generate a set of data relationships and generate a first analytics report from the migrated plurality of source deployment database artifacts at the target database, the first analytics report corresponding to at least one of the set of data arrangements.

Abstract: Disclosed herein are MTS peptides or targeting peptides. Disclosed are MTS peptides comprising the amino acid sequence of any one of SEQ ID NOs: 1- 12. Disclosed are compositions comprising a peptide, wherein the peptide comprises a first MTS peptide conjugated to a cargo. Also disclosed are methods of using the MTS peptides. For example, disclosed are methods of transporting cargo to the CNS comprising administering one or more of the disclosed compositions to a subject in need thereof, wherein the peptide conjugated to cargo enters the CNS.

Abstract: Various examples are directed to systems and a method for managing security at a public cloud platform deployment. A landscape service may determine a landscape score for an application instance executing at the public cloud platform deployment. An access frequency service may determine an access frequency score for the application instance. A last access service may determine a last access score for the application instance. The public cloud platform deployment may determine a total risk score for the application instance using the landscape score for the application instance, the access frequency score for the application instance, and the last access score for the application instance. Responsive to determining that the total risk score for the application instance is greater than a threshold value, an alert message may be sent to an administrative user account for the public cloud platform deployment.

Abstract: Disclosed are compositions comprising a nucleic acid sequence conjugated to one or more MGS peptides. Disclosed are methods of modifying gene expression of a gene of interest comprising administering to a cell a composition comprising a nucleic acid sequence conjugated to one or more MGS peptides, wherein the nucleic acid sequence binds to the RNA transcribed from the gene of interest, the gene of interest or a sequence upstream of the gene of interest. Disclosed are methods of targeting a gene of interest in an intracellular target comprising administering to a cell a composition comprising a nucleic acid sequence conjugated to one or more MGS peptides, wherein the MGS peptide targets the intracellular target, wherein the nucleic acid sequence binds to the RNA transcribed from the gene interest in an intracellular target.

Abstract: The present disclosure relates to computer-implemented methods, software, and systems for automated process execution. A neural network including machine learning rules encoded at a rules engine is trained to synthesize input and to generate output for automated execution of processes at a platform landscape of a platform environment. The neural network is configured to convert the received input requesting a process execution into a sequence of executable work units based on the machine learning rules. A first input is received that defines a request in a natural language format. The first input is synthesized based on the machine learning rules at the neural network to generate a first sequence of actions corresponding to the request. The first sequence of actions is generated based on an evaluation of the first input in relation to keywords, parameter names, and parameter values. The generated sequence of actions is provided for automated execution.

Abstract: The present disclosure involves analyzing data relevancy of particular fields within one or more databases in a big data system. In one example method, an interaction with at least one of a plurality of monitored data sources is identified, wherein the identified interactions is associated with a particular field of a database table of one of the monitored data sources. A set of data associated with the interaction is determined which includes an identification of each field associated with the identified interaction and a count of a number of interactions associated with each particular field. A relevancy index table is updated to include the determined set of data, wherein each identified field is associated with a row in the index table. At least one relevancy rule is identified for the relevancy index table and is executed to generate a relevancy score for at least one of the fields.

Abstract: The present disclosure involves analyzing data relevancy of particular fields within one or more databases in a big data system. In one example method, an interaction with at least one of a plurality of monitored data sources is identified, wherein the identified interactions is associated with a particular field of a database table of one of the monitored data sources. A set of data associated with the interaction is determined which includes an identification of each field associated with the identified interaction and a count of a number of interactions associated with each particular field. A relevancy index table is updated to include the determined set of data, wherein each identified field is associated with a row in the index table. At least one relevancy rule is identified for the relevancy index table and is executed to generate a relevancy score for at least one of the fields.