Abstract: A compound containing a bis(azanylylidene) sulfonyl structure of formula (I) as a PKR agonist and/or USP9X inhibitor, which can be used for treating related diseases mediated and regulated by PKR and USP9X, wherein the related diseases comprise, but are not limited to: sickle-cell anemia, ?-thalassemia, hereditary non-spherocytic hemolytic anemia, hemolytic anemia, hereditary spherocytosis, hereditary elliptocytosis, abetalipoproteinemia, paroxysmal nocturnal hemoglobinuria, acquired hemolytic anemia, congenital anemia, anemia of chronic diseases, colorectal cancer, kidney cancer, pancreatic cancer, breast cancer, lung cancer, esophageal cancer, melanoma, lymphoma, glioblastoma, or multiple myeloma.

Abstract: Disclosed is a method for preparing a C-nucleoside compound represented by Formula (III) or salt thereof. The present method has a high reaction yield, is simple to operate, uses a single metal reagent, has stable reaction temperature conditions, does not require frequent changes to the reaction system during the operation process, is suitable for scale-up synthesis, is suitable for large-scale production of Remdesivir, and has low costs.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for training machine learning models are presented. An automated task framework comprising a plurality of machine learning models for executing a task may be maintained. A natural language input may be processed by two or more of the machine learning models. An action corresponding to a task intent identified from the natural language input may be executed. User feedback related to the execution may be received. The feedback may be processed by a user sentiment engine. A determination may be made by the user sentiment engine that a machine learning model generated an incorrect output. The machine learning model that generated the incorrect output may be identified. The machine learning model that generated the incorrect output may be automatically penalized via training. Any machine learning models that a user expressed neutral or positive sentiment toward may be rewarded.