Research on How Live Streaming Helps Revive the Chinese Economy under COVID-19

Since 2019, the epidemic has been the focus of people in all countries. Until now, it continues to frustrate economic development, human health, and social stability. How to help the economy recover under the epidemic has become a daunting task with far-reaching significance in this era. This paper focuses on China, where live streaming is prevalent, and thus explores how it has helped revitalize the Chinese economy. The research method is to cite the data brought by various live streaming platforms and government department reports to illustrate the contribution and importance of live streaming. Finally, this paper demonstrates that live streaming has the potential to make an outstanding contribution to the revitalization of the Chinese economy, including eradicating poverty in rural areas, stimulating consumption and taxation, and promoting the transformation of the economy

FedDD: Toward Communication-efficient Federated Learning with Differential Parameter Dropout

Federated Learning (FL) requires frequent exchange of model parameters, which leads to long communication delay, especially when the network environments of clients vary greatly. Moreover, the parameter server needs to wait for the slowest client (i.e., straggler, which may have the largest model size, lowest computing capability or worst network condition) to upload parameters, which may significantly degrade the communication efficiency. Commonly-used client selection methods such as partial client selection would lead to the waste of computing resources and weaken the generalization of the global model. To tackle this problem, along a different line, in this paper, we advocate the approach of model parameter dropout instead of client selection, and accordingly propose a novel framework of Federated learning scheme with Differential parameter Dropout (FedDD). FedDD consists of two key modules: dropout rate allocation and uploaded parameter selection, which will optimize the model parameter uploading ratios tailored to different clients' heterogeneous conditions and also select the proper set of important model parameters for uploading subject to clients' dropout rate constraints. Specifically, the dropout rate allocation is formulated as a convex optimization problem, taking system heterogeneity, data heterogeneity, and model heterogeneity among clients into consideration. The uploaded parameter selection strategy prioritizes on eliciting important parameters for uploading to speedup convergence. Furthermore, we theoretically analyze the convergence of the proposed FedDD scheme. Extensive performance evaluations demonstrate that the proposed FedDD scheme can achieve outstanding performances in both communication efficiency and model convergence, and also possesses a strong generalization capability to data of rare classes

Dexmedetomidine pretreatment alleviates ischemia reperfusion injury-induced inflammatory reaction via inhibition of TLR-4/NF-ΚB signaling pathway

Purpose: To study the effect of dexmedetomidine on ischemia-reperfusion injury (IRI)–induced inflammatory response, as well as its underlying mechanism of action. Methods: Three groups of healthy adult Sprague Dawley (SD) rats (mean weight, 275 ± 10 g): control, IRI and treatment groups were used. With the exception of control group, ligation was performed on left anterior descending coronary arteries for 30 min and blood perfusion was restored within 100 min to establish IRI. Control group rats were without left ligation. Rats in control and IRI groups received normal saline intraperitoneally 30 min prior to surgery, while the treatment group received 100 μg/kg dexmedetomidine via intraperitoneal injection 30 min before operation. Infarct volume was determined using triphenyl tetrazolium chloride (TTC) staining. IL-6) and TNF-α levels of myocardial tissues and serum were determined using enzyme-linked immunosorbent assay (ELISA). Western blotting was used to determine protein expressions of NF-ΚB and TLR-4 in myocardial tissues. Results: Pretreatment with dexmedetomidine markedly decreased infarct volume caused by IRI (p < 0.05). Serum and myocardial TNF-α and IL-6 were significantly upregulated in IRI group, relative to control group, but were downregulated by pretreatment with dexmedetomidine (p < 0.05). There was marked upregulation of NF-ΚB and TLR-4 proteins in IRI rats, relative to untreated rats (p < 0.05). Dexmedetomidine also down-regulated the expressions of these proteins (p < 0.05). Conclusion: Pretreatment with dexmedetomidine alleviates IRI-induced inflammatory reaction via suppression of TLR-4/NF-ΚB signaling pathway. This finding provides a basis for large-scale clinical trials with dexmedetomidine

Characterization of anti-leukemia components from Indigo naturalis using comprehensive two-dimensional K562/cell membrane chromatography and in silico target identification.

Traditional Chinese Medicine (TCM) has been developed for thousands of years and has formed an integrated theoretical system based on a large amount of clinical practice. However, essential ingredients in TCM herbs have not been fully identified, and their precise mechanisms and targets are not elucidated. In this study, a new strategy combining comprehensive two-dimensional K562/cell membrane chromatographic system and in silico target identification was established to characterize active components from Indigo naturalis, a famous TCM herb that has been widely used for the treatment of leukemia in China, and their targets. Three active components, indirubin, tryptanthrin and isorhamnetin, were successfully characterized and their anti-leukemia effects were validated by cell viability and cell apoptosis assays. Isorhamnetin, with undefined cancer related targets, was selected for in silico target identification. Proto-oncogene tyrosine-protein kinase (Src) was identified as its membrane target and the dissociation constant (Kd) between Src and isorhamnetin was 3.81 μM. Furthermore, anti-leukemia effects of isorhamnetin were mediated by Src through inducing G2/M cell cycle arrest. The results demonstrated that the integrated strategy could efficiently characterize active components in TCM and their targets, which may bring a new light for a better understanding of the complex mechanism of herbal medicines

The BR signaling pathway regulates primary root development and drought stress response by suppressing the expression of PLT1 and PLT2 in Arabidopsis thaliana

IntroductionWith the warming global climate, drought stress has become an important abiotic stress factor limiting plant growth and crop yield. As the most rapidly drought-sensing organs of plants, roots undergo a series of changes to enhance their ability to absorb water, but the molecular mechanism is unclear.Results and methodsIn this study, we found that PLT1 and PLT2, two important transcription factors of root development in Arabidopsis thaliana, are involved in the plant response to drought and are inhibited by BR signaling. PLT1- and PLT2-overexpressing plants showed greater drought tolerance than wild-type plants. Furthermore, we found that BZR1 could bind to the promoter of PLT1 and inhibit its transcriptional activity in vitro and in vivo. PLT1 and PLT2 were regulated by BR signaling in root development and PLT2 could partially rescue the drought sensitivity of bes1-D. In addition, RNA-seq data analysis showed that BR-regulated root genes and PLT1/2 target genes were also regulated by drought; for example, CIPK3, RCI2A, PCaP1, PIP1;5, ERF61 were downregulated by drought and PLT1/2 but upregulated by BR treatment; AAP4, WRKY60, and AT5G19970 were downregulated by PLT1/2 but upregulated by drought and BR treatment; and RGL2 was upregulated by drought and PLT1/2 but downregulated by BR treatment.DiscussionOur findings not only reveal the mechanism by which BR signaling coordinates root growth and drought tolerance by suppressing the expression of PLT1 and PLT2 but also elucidates the relationship between drought and root development. The current study thus provides an important theoretical basis for the improvement of crop yield under drought conditions

Kernel solver design of FPGA-based real-time simulator for active distribution networks

The field-programmable gate array (FPGA)-based real-time simulator takes advantage of many merits of FPGA, such as small time-step, high simulation precision, rich I/O interface resources, and low cost. The sparse linear equations formed by the node conductance matrix need to be solved repeatedly within each time-step, which introduces great challenges to the performance of the real-time simulator. In this paper, a fine-grained solver of the FPGA-based real-time simulator for active distribution networks is designed to meet the computational demand. The framework of the solver, offline process design on PC and online process design on FPGA are proposed in detail. The modified IEEE 33-node system with photovoltaics is simulated on a 4-FPGA-based real-time simulator. Simulation results are compared with PSCAD/EMTDC under the same conditions to validate the solver design

Synergetic Effect of Plasmonic Gold Nanorods and MgO for Perovskite Solar Cells

We report new structured perovskite solar cells (PSCs) using solution-processed TiO2/Au nanorods/MgO composite electron transport layers (ETLs). The proposed method is facile, convenient, and effective. Briefly, Au nanorods (NRs) were prepared and introduced into mesoporous TiO2 ETLs. Then, thin MgO overlayers were grown on the Au NRs modified ETLs by wet spinning and pyrolysis of the magnesium salt. By simultaneous use of Au NRs and MgO, the power conversion efficiency of the PSC device increases from 14.7% to 17.4%, displaying over 18.3% enhancement, compared with the reference device without modification. Due to longitudinal plasmon resonances (LPRs) of gold nanorods, the embedded Au NRs exhibit the ability to significantly enhance the near-field and far-field (plasmonic scattering), increase the optical path length of incident photons in the device, and as a consequence, notably improve external quantum efficiency (EQE) at wavelengths above 600 nm and power conversion efficiency (PCE) of PSC solar cells. Meanwhile, the thin MgO overlayer also contributes to enhanced performance by reducing charge recombination in the solar cell. Theoretical calculations were carried out to elucidate the PV performance enhancement mechanisms

MicroRNA miR-326 regulates TH-17 differentiation and is associated with the pathogenesis of multiple sclerosis

MicroRNA miR-326 regulates TH-17 differentiation and is associated with the pathogenesis of multiple sclerosis Changsheng Du1,5, Chang Liu1,5, Jiuhong Kang1,2, Guixian Zhao3, Zhiqiang Ye4, Shichao Huang1, Zhenxin Li3, Zhiying Wu3 & Gang Pei1,2 Interleukin 17 (IL-17)-producing T helper cells (TH-17 cells) are increasingly recognized as key participants in various autoimmune diseases, including multiple sclerosis. Although sets of transcription factors and cytokines are known to regulate TH-17 differentiation, the role of noncoding RNA is poorly understood. Here we identify a TH-17 cell–associated microRNA, miR-326, whose expression was highly correlated with disease severity in patients with multiple sclerosis and mice with experimental autoimmune encephalomyelitis (EAE). In vivo silencing of miR-326 resulted in fewer TH-17 cells and mild EAE, and its overexpression led to more TH-17 cells and severe EAE. We also found that miR-326 promoted TH-17 differentiation by targeting Ets-1, a negative regulator of TH-17 differentiation. Our data show a critical role for microRNA in TH-17 differentiation and the pathogenesis of multiple sclerosis