TNF-alpha inhibition of adiponectin expression by targeting PPAR-gamma and C/EBP in adipocytes

Chronic inflammation is involved in the adipose tissue dysfunction through regulation of endocrine and storage function of adipocytes. As a representative proinflammatory cytokine, TNF-α was reported to inhibit expression of adiponectin. However, the mechanism of inhibition remains to be identified. Here, we provide experimental evidence that TNF-α inhibits adiponectin at both transcriptional and posttranscriptional levels. In three animal models (aP2-P65, ob/ob and high fat diets-fed mice), an increase in TNF-α expression was associated with a decrease in adiponectin expression. In 3T3-L1 adipocytes, TNF-α inhibition of adiponectin was observed at mRNA and protein levels. Luciferase reporter assay and mRNA stability tests suggest that the mRNA reduction is a consequence of inhibition of gene transcription and mRNA stability. TNF-α inhibited expression and function of PPAR-γ, an activator of adiponectin gene promoter. The inhibitory activity of TNF-α was blocked by chemical inhibitors of NF-κB or recombinant IκBα (ssIκBα), suggesting that the IκBα/NF-κB pathway mediates the TNF-α signal. The inhibition was attenuated by troglitazone, C/EBPs were required for PPAR-γ expression and their activities were reduced by HDAC3, a nuclear receptor corepressor. The study suggests a signaling pathway of TNF/NF-κB/HDAC3/CEBPs/PPAR-γ/-Adiponectin for inhibition of adiponectin transcription by TNF-α

Driving Characteristics of Teens With Attention Deficit Hyperactivity and Autism Spectrum Disorder

Vehicle crashes are a leading cause of death among teens. Teens with attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), or both (ADHD–ASD) may have a greater crash risk. We examined the between-groups demographic, clinical, and predriving performance differences of 22 teens with ADHD– ASD (mean age 5 15.05, standard deviation [SD] 5 0.95) and 22 healthy control (HC) teens (mean age 5 14.32, SD 5 0.72). Compared with HC teens, the teens with ADHD–ASD performed more poorly on righteye visual acuity, selective attention, visual–motor integration, cognition, and motor performance and made more errors on the driving simulator pertaining to visual scanning, speed regulation, lane maintenance, adjustment to stimuli, and total number of driving errors. Teens with ADHD–ASD, compared with HC teens, may have more predriving deficits and as such require the skills of a certified driving rehabilitation specialist to assess readiness to drive

A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation

Semi-supervised video anomaly detection (VAD) is a critical task in the intelligent surveillance system. However, an essential type of anomaly in VAD named scene-dependent anomaly has not received the attention of researchers. Moreover, there is no research investigating anomaly anticipation, a more significant task for preventing the occurrence of anomalous events. To this end, we propose a new comprehensive dataset, NWPU Campus, containing 43 scenes, 28 classes of abnormal events, and 16 hours of videos. At present, it is the largest semi-supervised VAD dataset with the largest number of scenes and classes of anomalies, the longest duration, and the only one considering the scene-dependent anomaly. Meanwhile, it is also the first dataset proposed for video anomaly anticipation. We further propose a novel model capable of detecting and anticipating anomalous events simultaneously. Compared with 7 outstanding VAD algorithms in recent years, our method can cope with scene-dependent anomaly detection and anomaly anticipation both well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, IITB Corridor and the newly proposed NWPU Campus datasets consistently. Our dataset and code is available at: https://campusvad.github.io.Comment: CVPR 202

Fueling growth and financing risk: The benefits and risks of China’s development finance in the global energy sector

This repository item contains a working paper from the Boston University Global Economic Governance Initiative. The Global Economic Governance Initiative (GEGI) is a research program of the Center for Finance, Law & Policy, the Frederick S. Pardee Center for the Study of the Longer-Range Future, and the Frederick S. Pardee School of Global Studies. It was founded in 2008 to advance policy-relevant knowledge about governance for financial stability, human development, and the environment.This paper is organized in four parts. Part one presents an overview and estimates of China’s emerging development finance architecture. Part two exhibits our estimates of the extent to which China’s development banks are financing energy projects in developing countries in comparative perspective. Part three identifies some of the risks associated with China’s overseas energy investments. Part four summarizes our findings and provides suggestions for further research and policy

Some Conclusion on Unique k

If a graph G admits a k-list assignment L such that G has a unique L-coloring, then G is called uniquely k-list colorable graph, or UkLC graph for short. In the process of characterizing UkLC graphs, the complete multipartite graphs K1*r,s(r,s∈N) are often researched. But it is usually not easy to construct the unique k-list assignment of K1*r,s. In this paper, we give some propositions about the property of the graph K1*r,s when it is UkLC, which provide a very significant guide for constructing such list assignment. Then a special example of UkLC graphs K1*r,s as a application of these propositions is introduced. The conclusion will pave the way to characterize UkLC complete multipartite graphs