PYRUVATE DEHYDROGENASE KINASE (PDK)

Abstract: Several oximes of triterpenes with a 17-~ hydroxyl and abietane derivatives are inlfibitors of pyruvate dehydrogenase kinase (PDK) activity. The oxime 12 and dehydroabietyl amine 2 exhibit a blood glucose lowering effect in the diabetic ob/ob mouse after a single oral dose of 100 ~tmol/kg. However, the mechanism of the blood glucose lowering effect is likely unrelated to PDK inhibition

All-Electrical Skyrmionic Bits in a Chiral Magnetic Tunnel Junction

Topological spin textures such as magnetic skyrmions hold considerable promise as robust, nanometre-scale, mobile bits for sustainable computing. A longstanding roadblock to unleashing their potential is the absence of a device enabling deterministic electrical readout of individual spin textures. Here we present the wafer-scale realization of a nanoscale chiral magnetic tunnel junction (MTJ) hosting a single, ambient skyrmion. Using a suite of electrical and multi-modal imaging techniques, we show that the MTJ nucleates skyrmions of fixed polarity, whose large readout signal - 20-70% relative to uniform states - corresponds directly to skyrmion size. Further, the MTJ exploits complementary mechanisms to stabilize distinctly sized skyrmions at zero field, thereby realizing three nonvolatile electrical states. Crucially, it can write and delete skyrmions using current densities 1,000 times lower than state-of-the-art. These results provide a platform to incorporate readout and manipulation of skyrmionic bits across myriad device architectures, and a springboard to harness chiral spin textures for multi-bit memory and unconventional computing.Comment: 8 pages, 5 figure

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io