Abstract: The disclosure aims to provide a chemical synthesis method of a prodelphinidin B9 gallate, in which the prodelphinidin B9 gallate is synthesized by using a proanthocyanidin from Chinese bayberry leaves as a raw material, and using epigallocatechin gallate (EGCG) as a nucleophilic reagent to attack C4 sites of subunits EGCG and epigallocatechin (EGC) of the proanthocyanidin from Chinese bayberry leaves in the presence of hydrochloric acid as a catalyst. Compared with prodelphinidin B9 gallates extracted and separated from materials such as tea leaves, and Chinese bayberry leaves, the prodelphinidin B9 gallate prepared by the method provided in the present disclosure has higher purity and yield, and can be directly used as a nutrient enhancer and a natural antioxidant in the field of food.

Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.

Abstract: Described herein are compositions and methods for treating cancer and autoimmune diseases.

Abstract: A computer-implemented method is disclosed for training one or more machine-learned models. The method can include inputting a first image frame and a second image frame into a feature disentanglement model and receiving, as an output of the machine-learned feature disentanglement model, a state feature and a perspective feature. The method can include inputting the state feature and the perspective feature into a machine-learned decoder model and receiving, as an output of the machine-learned decoder model, the reconstructed image frame. The method can include comparing the reconstructed image frame with a third image frame corresponding with the location and the perspective orientation. The method can include adjusting one or more parameters of the machine-learned feature disentanglement model based on the comparison of the reconstructed image frame and the third image frame.