Abstract: Processes of making [1.1.1]propellane utilize reaction conditions that include reacting 1,1-dibromo-2,2-bis(chloromethyl)cyclopropane with an effective amount of solid magnesium.

Abstract: Described herein is a method for obtaining a selective estrogen receptor degrader, and compounds used in preparing the selective estrogen receptor degrader.

Abstract: The present application is related to an improved process for synthesising 1-(difluoromethyl)-3-iodobicyclo[1.1.1]pentane from difluoromethyl iodide and [1.1.1]propellane. Difluoromethyl iodide is made by reacting an iodide salt with chlorodifluoroacetic acid in the presence of a solvent such as sulfolane and an inorganic base, [1.1.1]propellane is synthesised by reacting 1,1-dibromo-2,2-cis(chloromethyl)cyclopropane with a reagent such as magnesium, methyllithium or phenyllithium.

Abstract: Disclosed herein are methods for preparing tertiary alcohols from an optionally substituted phenyl ketone or an optionally substituted pyridinyl ketone that includes the use of a chiral ligand and boron trifluoride diethyl etherate. Tertiary alcohols can be used to prepare synthetic versions of natural products and/or pharmaceuticals.

Abstract: Processes of making [1.1.1]propellane utilize reaction conditions that include reacting 1,1-dibromo-2,2-bis(chloromethyl)cyclopropane with an effective amount of solid magnesium.

Abstract: Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Various albumin nanoparticle Bcl-2 inhibitor formulations are described, along with methods of using them to treat conditions characterized by excessive cellular proliferation, such as cancer and tumors. In various embodiments, such Bcl-2 inhibitor formulations contain albumin and a compound of the following Formula (I), or a pharmaceutically acceptable salt thereof, where the variables in Formula (I) are defined herein.

Abstract: Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Abstract: Compounds of Formula (I) are provided herein. Such compounds, as well as pharmaceutically acceptable salts and compositions thereof, are useful for treating diseases or conditions, including conditions characterized by excessive cellular proliferation, such as breast cancer.

Abstract: Compounds of Formula (I) are provided herein. Such compounds, as well as pharmaceutically acceptable salts and compositions thereof, are useful for treating diseases or conditions, including conditions characterized by excessive cellular proliferation, such as breast cancer.

Abstract: Methods, apparatus, and processor-readable storage media for reactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user service requests, each comprising a description of problems and data pertaining to storage systems associated with the requests; calculating similarity measures for the user service requests by applying a machine learning algorithm to the user service requests; automatically grouping the user service requests into a set based on the similarity measures; automatically grouping, within the set, two or more of the user service requests into subsets based on a software version attributed to the storage systems associated with the two or more user service requests; generating an output pertaining to actions related to a software version update; and transmitting the output to at least one of the users corresponding to the user service requests in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for proactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining storage system data from multiple storage systems; determining performance issues among the storage systems by applying a machine learning algorithm to the storage system data; automatically grouping the storage system data into a set of groups based on issue type among the determined performance issues; automatically grouping, within the set, the storage system data into subsets based on a software version attributed to the corresponding storage system data; generating an output pertaining to actions to be performed with respect to at least one software version update; and transmitting the output to users of the storage systems which correspond to the storage system data in at least one of the subsets.

Abstract: Compounds of Formula (I) are provided herein. Such compounds, as well as pharmaceutically acceptable salts and compositions thereof, are useful for treating diseases or conditions, including conditions characterized by excessive cellular proliferation, such as breast cancer.

Abstract: Methods, apparatus, and processor-readable storage media for reactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user service requests, each comprising a description of problems and data pertaining to storage systems associated with the requests; calculating similarity measures for the user service requests by applying a machine learning algorithm to the user service requests; automatically grouping the user service requests into a set based on the similarity measures; automatically grouping, within the set, two or more of the user service requests into subsets based on a software version attributed to the storage systems associated with the two or more user service requests; generating an output pertaining to actions related to a software version update; and transmitting the output to at least one of the users corresponding to the user service requests in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for proactive storage system-based software version analysis using machine learning techniques are provided herein. An example computer-implemented method includes obtaining storage system data from multiple storage systems; determining performance issues among the storage systems by applying a machine learning algorithm to the storage system data; automatically grouping the storage system data into a set of groups based on issue type among the determined performance issues; automatically grouping, within the set, the storage system data into subsets based on a software version attributed to the corresponding storage system data; generating an output pertaining to actions to be performed with respect to at least one software version update; and transmitting the output to users of the storage systems which correspond to the storage system data in at least one of the subsets.

Abstract: Methods, apparatus, and processor-readable storage media for classification of storage systems and users thereof using machine learning techniques are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; classifying one or more of the storage systems by applying a first set of machine learning techniques to the processed input data; classifying one or more respective users of the classified storage systems by applying a second set of machine learning techniques to the processed input data associated with the classified storage systems; and outputting, via one or more user interfaces, at least a portion of the storage system classifications and at least a portion of the user classifications to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for classification of storage systems and users thereof using machine learning techniques are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; classifying one or more of the storage systems by applying a first set of machine learning techniques to the processed input data; classifying one or more respective users of the classified storage systems by applying a second set of machine learning techniques to the processed input data associated with the classified storage systems; and outputting, via one or more user interfaces, at least a portion of the storage system classifications and at least a portion of the user classifications to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for implementing a machine learning-based recommendation engine for storage system usage within an enterprise are provided herein. An example computer-implemented method includes processing input data pertaining to multiple storage systems within an enterprise; determining association rules applicable to the multiple storage systems by applying machine learning techniques to the processed input data; generating configuration-related recommendations applicable to one or more of the storage systems by applying content filtering techniques to the determined association rules; and outputting, via user interfaces, the configuration-related recommendations to a user for use in connection with storage system configuration actions and/or an entity within the enterprise for use in connection with user-support actions.

Abstract: Methods, apparatus, and processor-readable storage media for determining user retention values using machine learning and heuristic techniques are provided herein. An example computer-implemented method includes processing multiple forms of input data pertaining to interactions between a user and an enterprise; generating one or more user sentiment values from the processed input data by applying machine learning techniques to the processed input data; determining a user-specific estimate for the enterprise retaining the user, wherein determining the user-specific estimate comprises combining the one or more sentiment values with one or more storage system heuristics-based values derived from enterprise-related data; and outputting the user-specific estimate to at least one entity within the enterprise for use in connection with user-support actions.

Abstract: A network-based data discovery and consumption coordination service may provide data set listings for purchase offered by respective data vendors. The data discovery and consumption coordination service may provide listings in response to search requests based on various search criteria, as well as various filtering techniques based on user reviews, ratings, and other user purchase history for data set listings. The data discovery and consumption coordination service may receive a purchase request via a network-based interface for the service. Data consumption resources which may consume a purchased data set may also be identified via the network-based interface for the data discovery and consumption coordination service. The data discovery and consumption coordination service may direct the transfer of the data set for the purchased data set listing to the identified data consumption resource.