Pretreatment with scutellaria baicalensis stem-leaf total flavonoid prevents cerebral ischemia-reperfusion injury

Pretreatment with scutellaria baicalensis stem-leaf total flavonoid has protective effects against ischemia and attenuates myocardial ischemia-reperfusion injury. In this study, rats were given scutellaria baicalensis stem-leaf total flavonoid intragastrically at 50, 100, and 200 mg/kg per day for 7 days before focal cerebral ischemia-reperfusion injury models were established using the suture method. We then determined the protective effects of scutellaria baicalensis stem-leaf total flavon- oid pretreatment on focal cerebral ischemia-reperfusion injury. Results showed that neurological deficit scores increased, infarct volumes enlarged, apoptosis increased and Bcl-2 and Bax protein expression were upregulated at 24 hours after reperfusion. Pretreatment with scutellaria baicalensis stem-leaf total flavonoid at any dose lowered the neurological deficit scores, reduced the infarct volume, prevented apoptosis in hippocampal cells, attenuated neuronal and blood-brain barrier damage and upregulated Bcl-2 protein expression but inhibited Bax protein expression. Doses of 100 and 200 mg/kg were the most efficacious. Our findings indicate that pretreatment with scutel- laria baicalensis stem-leaf total flavonoid at 100 and 200 mg/kg can improve the neurological func- tions and have preventive and protective roles after focal cerebral ischemia-reperfusion injury.

Pretreatment with scutellaria baicalensis stem-leaf total flavonoid protects against cerebral ischemia/reperfusion injury in hippocampal neurons

Previous experimental studies have shown that cerebral infarction can be effectively reduced following treatment with scutellaria baicalensis stem-leaf total flavonoid （SSTF）. However, the mechanism of action of SSTF as a preventive drug to treat cerebral infarction remains unclear. In this study, Sprague-Dawley rats were pretreated with 50, 100, 200 mg/kg SSTF via intragastric ad- ministration for 1 week prior to the establishment of focal cerebral ischemia/reperfusion injury. The results showed that pretreatment with SSTF effectively improved neurological function, reduced brain water content and the permeability of blood vessels, ameliorated ischemia-induced morphology changes in hippocampal microvessels, down-regulated Fas and FasL protein expression, elevated the activity of superoxide dismutase and glutathione peroxidase, and decreased malondialdehyde content. In contrast to low-dose SSTF pretreatment, the above changes were most obvious after pretreatment with moderateand high-doses of SSTF. Experimental findings indicate that SSTF pretreatment can exert protective effects on the brain against cerebral ischemia/reperfusion injury. The underlying mechanisms may involve reducing brain water content, increasing microvascular recanalization, inhibiting the apoptosis of hippocampal neurons, and attenuating free radical damage.

Coupled Cross-correlation Neural Network Algorithm for Principal Singular Triplet Extraction of a Cross-covariance Matrix

This paper proposes a novel coupled neural network learning algorithm to extract the principal singular triplet(PST)of a cross-correlation matrix between two high-dimensional data streams. We firstly introduce a novel information criterion(NIC),in which the stationary points are singular triplet of the crosscorrelation matrix. Then, based on Newton's method, we obtain a coupled system of ordinary differential equations(ODEs) from the NIC. The ODEs have the same equilibria as the gradient of NIC, however, only the first PST of the system is stable(which is also the desired solution), and all others are(unstable)saddle points. Based on the system, we finally obtain a fast and stable algorithm for PST extraction. The proposed algorithm can solve the speed-stability problem that plagues most noncoupled learning rules. Moreover, the proposed algorithm can also be used to extract multiple PSTs effectively by using sequential method.

Kinematic calibration method with high measurement efficiency and robust identification for hybrid machine tools

Geometric error is the main factor affecting the machining accuracy of hybrid machine tools.Kinematic calibration is an effective way to improve the geometric accuracy of hybrid machine tools.The necessity to measure both position and orientation at each pose,as well as the instability of identification in case of incomplete measurements,severely affects the application of traditional calibration methods.In this study,a kinematic calibration method with high measurement efficiency and robust identification is proposed to improve the kinematic accuracy of a five-axis hybrid machine tool.First,the configuration is introduced,and an error model is derived.Further,by investigating the mechanism error characteristics,a measurement scheme that only requires tool centre point position error measurement and one alignment operation is proposed.Subsequently,by analysing the effects of unmeasured degrees of freedom(DOFs)on other DOFs,an improved nonlinear least squares method based on virtual measurement values is proposed to achieve stable parameter identification in case of incomplete measurement,without introducing additional parameters.Finally,the proposed calibration method is verified through simulations and experiments.The proposed method can efficiently accomplish the kinematic calibration of the hybrid machine tool.The accuracy of the hybrid machine tool is significantly improved after calibration,satisfying actual aerospace machining requirements.

Loss of GATA6-mediated up-regulation of UTX promotes pancreatic tumorigenesis and progression

Ubiquitously transcribed tetratricopeptide repeat on chromosome X(UTX),also known as lysine(K)-specific demethylase 6A(KDM6A),functions as a tumor suppressor gene or oncogene depending on the tumor type and context.However,its tumor-suppressive mechanisms remain largely unknown.Here,we investigated the clinical significance and biological effects of UTX expression in pancreatic ductal adenocarcinoma(PDA)and determined the potential mechanisms of its dysregulation.UTX expression and its association with clinicopathologic characteristics of PDA patients were analyzed using immunohistochemistry.UTX mRNA and protein expression and their regulation in PDA cell lines were measured using quantitative polymerase chain reaction and Western blot analyses.The biological functions of UTX in PDA cell growth,migration,and invasion were determined using gain-and loss-of-function assays with both in vitro and in vivo animal models.UTX expression was reduced in human PDA cell lines and specimens.Low UTX expression was associated with poor differentiation and prognosis in PDA.Forced UTX expression inhibited PDA proliferation,migration,and invasion in vitro and PDA growth and metastasis in vivo,whereas knockdown of UTX expression did the opposite.Mechanistically,UTX expression was trans-activated by GATA6 activation.GATA6-mediated PDA progression could be blocked,at least partially,by silencing UTX expression.In conclusion,loss of GATA6-mediated UTX expression was evident in human PDA and restored UTX expression suppressed PDA growth and metastasis.Thus,UTX is a tumor suppressor in PDA and may serve as a prognostic biomarker and therapeutic target.

Multi-agent Simulation for Strategic Bidding in Electricity Markets Using Reinforcement Learning

In this paper,a theoretical framework of Multiagent Simulation(MAS)is proposed for strategic bidding in electricity markets using reinforcement learning,which consists of two parts:one is a MAS system used to simulate the competitive bidding of the actual electricity market;the other is an adaptive learning strategy bidding system used to provide agents with more intelligent bidding strategies.An ExperienceWeighted Attraction(EWA)reinforcement learning algorithm(RLA)is applied to the MAS model and a new MAS method is presented for strategic bidding in electricity markets using a new Improved EWA(IEWA).From both qualitative and quantitative perspectives,it is compared with three other MAS methods using the Roth-Erev(RE),Q-learning and EWA.The results show that the performance of the MAS method using IEWA is proved to be better than the others.The four MAS models using four RLAs are built for strategic bidding in electricity markets.Through running the four MAS models,the rationality and correctness of the four MAS methods are verified for strategic bidding in electricity markets using reinforcement learning.

反向电渗析热机发生单元研究进展

反向电渗析热机技术通过"热能-化学势能"和"化学势能-电能"两个转化过程完成电力生产,是一种新型的低品位余热利用技术.热机由发生单元及反向电渗析电池单元构成,分别对应上述两个转化过程.其中,发生单元对系统性能有显著影响,故本文从提升发生单元效率和系统性能出发,系统地综述了发生单元工质和发生方法的研究进展.通过探究溶剂和溶质特性对发生单元及系统性能的影响机理,凝练出工质筛选原则:反向电渗析热机适合于低汽化潜热、低沸点、高活度系数和高电导率的工质.调研发现,现有发生方法主要有蒸馏法、膜蒸馏法和热分解法,其中蒸馏法为最常见的发生方法.3种发生方法的效率较低,不可逆损失大,未来研究可聚焦于系统最优浓度梯度的构建以及发生单元与发电单元的参数匹配.此外,反向电渗析热机技术除了设计用于回收低品位工业余热,亦可用于回收太阳能和地热能等中低品位可再生能源,促进新能源的高效利用.同时,热机工作时伴随有电极反应,表明采用反向电渗析热机进行发电的同时,亦可进行污水处理、有机物降解、制氢及废酸、废碱中和处理.

Day-ahead optimal scheduling method for grid-connected microgrid based on energy storage control strategy

A day-ahead optimal scheduling method for a grid-connected microgrid based on energy storage(ES)control strategy is proposed in this paper.The proposed method optimally schedules ES devices to minimize the total operating costs while satisfying the load requirements of cold,heat,and electricity in microgrids.By modeling the operating cost function of each stage,the proposed method is able to adapt to different types of electricity markets and pricing mechanisms.The technical characteristics of ES,such as self-discharge and round-trip efficiency,are considered in the control strategy with a multistage process model.An improved dynamic programing method is used to solve the optimization model.Finally,case studies are provided to illustrate the application process and verify the proposed method.

Molecular Epidemiological Characteristics of Gastroenteritis Outbreaks Caused by Norovirus GII.4 Sydney[P31]Strains—China,October 2016–December 2020

Introduction:Human noroviruses are the leading cause of acute viral gastroenteritis(AGE)worldwide in all age groups.GII.4 strains have been the predominant genotype circulating globally over the last 2 decades and since 2012.GII.4 Sydney viruses have emerged and caused the majority of AGE outbreaks worldwide.Methods:Data from norovirus outbreaks from the laboratory-based surveillance of norovirus outbreaks in China(CaliciNet China)between October 2016–December 2020 were analyzed.Results:During October 2016–December 2020,1,954 norovirus outbreaks were reported,and positive fecal samples from 1,352(69.19%)outbreaks were genotyped.GII.4 Sydney[P31]viruses accounted for 2.1%(October 2016–August 2017),5.5%(September 2017–August 2018),3.3%(September 2018–August 2018),26.6%(September 2019–August 2020),and and 1.1%(September 2020–December 2020)of GII outbreaks,respectively.Compared to reference strains of GII.4 Sydney[P31]from 2012 to 2013,7 amino acid mutations in epitopes[A(297,372 and 373),B(333),E(414),and H(309 and 310)]and 1 in human histo-blood group antigens binding site at site II 372 were found by analyzing 9 GII.4 Sydney[P31]complete genomic sequences.Conclusions:This report identified the genomic variation of GII.4 Sydney[P31]from CaliciNet China.Continued surveillance with prompt genotyping and genetic analysis is necessary to monitor the emergence of novel GII.4 variants.

一种柔性导电银胶的研发

导电银胶是柔性薄膜电子器件的重要基础材料,而高性能、低成本导电介质—低维银片及其制造技术是一项重要研究课题.本文发展了一种低成本、环保、且适合卷对卷制备的低维导电银片的制作方法.该方法制备的微纳银片,形貌尺寸可控(直径:20200μm;厚度20300 nm)、性能优异.进而,基于聚氨酯基底,制备了柔性、无助剂的导电银胶.该导电银胶渗流阈值远低于采用其他银颗粒填料所制备的银胶.当银片添加量仅为13.7 wt%时,导电银片能有效形成导电网络,其电导率高达5240 S/cm,显著优于同类导电银胶;而且,当拉伸比例为40%时,电导率未见明显变化.所制备超薄银片的大比表面积、光滑表面使其形成有效的错位堆积,构建有效电子通路,从而赋予导电银胶的高电导、高柔性.

One-step method for preparing the experimental pure state in nuclear magnetic resonance

Quantum computing is an emerging and promising research field in modern quantum physics and is committed to accelerating traditional calculation processes using the principle of quantum mechanics[1,2].Several computation models have been proposed,including the circuit model[3],topologic quantum computation[4],adiabatic quantum computing[5],and duality quantum computing[6-8].Universal quantum computation generally includes a system initialization,evolutionary control,and a final-state readout[9],where the preparation of the initial state is a key step in the entire process.

Adaptive LII-RMPLS based data-driven process monitoring scheme for quality-relevant fault detection

The partial least squares(PLS)method has been successfully applied for fault diagnosis in indus-trial production.Compared with the traditional PLS methods,the modified PLS(MPLS)approach is available for slow-time-varying data processing and quality-relevant fault detecting.How-ever,it encounters heavy computational load in model updating,and the static control limits often lead to the low fault detection rate(FDR)or high false alarm rate(FAR).In this article,we first introduce the recursive MPLS(RMPLS)method for quality-relevant fault detection and computational complexity reducing,and then combine the local information increment(LII)method to obtain the time-varying control limits.First,the proposed LII-RMPLS method is capa-ble of quality-relevant faults detection.Second,the adaptive threshold leads to higher FDRs and lower FARs compared with traditional methods.Third,the adaptive parameter-matrices-based model updating approach ensures that the proposed method has better robustness and lower computational complexity when dealing with time-varying factors.