MyD88-dependent interplay between myeloid and endothelial cells in the initiation and progression of obesity-associated inflammatory diseases.

Low-grade systemic inflammation is often associated with metabolic syndrome, which plays a critical role in the development of the obesity-associated inflammatory diseases, including insulin resistance and atherosclerosis. Here, we investigate how Toll-like receptor-MyD88 signaling in myeloid and endothelial cells coordinately participates in the initiation and progression of high fat diet-induced systemic inflammation and metabolic inflammatory diseases. MyD88 deficiency in myeloid cells inhibits macrophage recruitment to adipose tissue and their switch to an M1-like phenotype. This is accompanied by substantially reduced diet-induced systemic inflammation, insulin resistance, and atherosclerosis. MyD88 deficiency in endothelial cells results in a moderate reduction in diet-induced adipose macrophage infiltration and M1 polarization, selective insulin sensitivity in adipose tissue, and amelioration of spontaneous atherosclerosis. Both in vivo and ex vivo studies suggest that MyD88-dependent GM-CSF production from the endothelial cells might play a critical role in the initiation of obesity-associated inflammation and development of atherosclerosis by priming the monocytes in the adipose and arterial tissues to differentiate into M1-like inflammatory macrophages. Collectively, these results implicate a critical MyD88-dependent interplay between myeloid and endothelial cells in the initiation and progression of obesity-associated inflammatory diseases

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Theoretical Mechanism on the Cellulose Regeneration from a Cellulose/EmimOAc Mixture in Anti-Solvents

The experiments on cellulose dissolution/regeneration have made some achievements to some extent, but the mechanism of cellulose regeneration in ionic liquids (ILs) and anti-solvent mixtures remains elusive. In this work, the cellulose regeneration mechanism in different anti-solvents, and at different temperatures and concentrations, has been studied with molecular dynamics (MD) simulations. The IL considered is 1-ethyl-3-methylimidazolium acetate (EmimOAc). In addition, to investigate the microcosmic effects of ILs and anti-solvents, EmimOAc-nH2O (n = 0–6) clusters have been optimized by Density Functional Theory (DFT) calculations. It can be found that water is beneficial to the regeneration of cellulose due to its strong polarity. The interactions between ILs and cellulose will become strong with the increase in temperature. The H-bonds of cellulose chains would increase with the rising concentrations of anti-solvents. The interaction energies between cellulose and the anions of ILs are stronger than that of cations. Furthermore, the anti-solvents possess a strong affinity for ILs, cation–anion pairs are dissociated to form H-bonds with anti-solvents, and the H-bonds between cellulose and ILs are destroyed to promote cellulose regeneration

A Survey of Multi-Tenant Deep Learning Inference on GPU

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x throughput and memory bandwidth for each generation. With such strong computing scaling of GPUs, multi-tenant deep learning inference by co-locating multiple DL models onto the same GPU becomes widely deployed to improve resource utilization, enhance serving throughput, reduce energy cost, etc. However, achieving efficient multi-tenant DL inference is challenging which requires thorough full-stack system optimization. This survey aims to summarize and categorize the emerging challenges and optimization opportunities for multi-tenant DL inference on GPU. By overviewing the entire optimization stack, summarizing the multi-tenant computing innovations, and elaborating the recent technological advances, we hope that this survey could shed light on new optimization perspectives and motivate novel works in future large-scale DL system optimization.Comment: Accepted in MLSys'22 Workshop on Cloud Intelligence / AIOp

Parthenolide Inhibits STAT3 Signaling by Covalently Targeting Janus Kinases

Aberrant activations of the STAT3 (signal transducer and activator of transcription 3) signaling pathway are associated with cancer and inflammatory diseases. Three of the four Janus kinases, JAK1, JAK2, and Tyk2, are the major upstream kinases of STAT3 in responses to cytokine stimulations. Among them, JAK2 is the key kinase in the IL-6-induced STAT3 phosphorylation. Here we report the mechanisms of a natural compound parthenolide from the medicinal herb Feverfew in regulating the JAK/STAT3 signaling. We found that parthenolide was a potent inhibitor of JAKs. It covalently modified the Cys178, Cys243, Cys335, and Cys480 of JAK2 and suppressed its kinase activity. It also interacted with other JAKs in a similar fashion. The binding of parthenolide to JAKs was selective. It preferentially bound to the JAKs, but not to the abundant proteins, such as tubulin and actin. Parthenolide also induced reactive oxygen species (ROS), but the increased ROS did not seem to contribute to the inhibition of JAK/STAT3 signaling. Furthermore, parthenolide inhibited the IL-6-induced cancer cell migration and preferentially inhibited the growth of cancer cells that had constitutively activated STAT3. Our study suggests a novel strategy to inactivate JAKs and provides a promising anti-inflammation and anticancer drug candidate

Magnetic Molecularly Imprinted Polymer Beads Obtained by Suspension Polymerization for the Adsorption of 2,4,6-Trichlorophenol from an Aqueous Solution in a Fixed-Bed Column

Highly effective magnetic molecularly imprinted polymer (MMIP) beads for adsorption of 2,4,6-trichlorophenol (2,4,6-TCP) were synthesized by suspension polymerization. The effect of various parameters such as bed depths (0.6, 1.1 and 1.7 cm), flow rate (1, 2 and 3 ml minute −1 ), influent 2,4,6-TCP concentrations (50, 100 and 200 mg l −1 ), influent solution pH (4, 6 and 8) and temperature (288, 298 and 308 K) was investigated. The breakthrough time increased with the increase of the bed depth, but decreased with an increase of the initial concentration, flow rate and temperature. The Adams–Bohart and Thomas models were applied to the adsorption process under experimental conditions to predict the breakthrough curves for process design. The Thomas model was in good agreement with the experimental data. Compared with magnetic non-imprinted polymers, MMIPs exhibited an excellent selectivity towards 2,4,6-TCP