CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy rain.Recent relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather events.However,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous states.To address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar echoes.Two practical radar echo datasets were used involving the FREM and CIKM 2017 datasets.Both quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,etc.In particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

Lumbar spine localisation method based on feature fusion

To eliminate unnecessary background information,such as soft tissues in original CT images and the adverse impact of the similarity of adjacent spines on lumbar image segmentation and surgical path planning,a two‐stage approach for localising lumbar segments is proposed.First,based on the multi‐scale feature fusion technology,a non‐linear regression method is used to achieve accurate localisation of the overall spatial region of the lumbar spine,effectively eliminating useless background information,such as soft tissues.In the second stage,we directly realised the precise positioning of each segment in the lumbar spine space region based on the non‐linear regression method,thus effectively eliminating the interference caused by the adjacent spine.The 3D Intersection over Union(3D_IOU)is used as the main evaluation indicator for the positioning accuracy.On an open dataset,3D_IOU values of 0.8339�0.0990 and 0.8559�0.0332 in the first and second stages,respectively is achieved.In addition,the average time required for the proposed method in the two stages is 0.3274 and 0.2105 s respectively.Therefore,the proposed method performs very well in terms of both pre-cision and speed and can effectively improve the accuracy of lumbar image segmentation and the effect of surgical path planning.

“线上线下-虚实结合”在有机化学实验教学中的应用探索--以正溴丁烷的制备实验为例

为应对新冠疫情对有机化学实验教学的影响,分析并讨论了传统有机化学实验教学中存在的局限性,并以正溴丁烷的制备实验为例,针对性地提出了一种"线上线下-虚实结合"的有机化学实验教学新模式:联合应用"腾讯会议→MLabs→实验室→微信群"等工具或场所建立了有机化学实验课程"教师课前线上导学→学生利用虚拟仿真平台预习和练习→师生实验室讨论与操作→课后综合考评与答疑"的教学新模式。经初步实践,发现对培养和提高学生的实践能力和创新能力有良好的效果,为"金课"背景下有机化学实验教学改革提供了参考。

抗氧化剂在猪精液冷冻保存中的作用

随着冷冻精液技术的发展,抗氧化剂作为精液稀释液中必不可少的一类成分,在降低精子的氧化损伤、提高精子活力质量上起到了关键的作用。文章对近些年来国内科研团队所使用的抗氧化剂进行综述,总结其对精液品质提升改善的能力,为猪精液冷冻技术的发展提供理论依据和参考。

Ssc-miR-133a-5p的靶基因预测及其相关信号通路的生物信息学分析

课题组前期通过高通量测序技术发现,microRNAssc-miR-133a-5p在肥胖猪脂肪组织中表达水平显著下调,推测其可能在脂肪沉积过程中发挥了重要作用。分析ssc-miR-133a-5p序列保守性,利用TargetScan,miRDB和miRWalk在线分析工具预测其候选靶基因,进一步对候选靶基因进行蛋白质互作分析以及KEGG分析,最后将预测的靶基因与课题组前期筛选出的与猪脂肪沉积能力相关的基因取交集。结果表明ssc-miR-133a-5p候选靶基因PPARGC1A与RORA等,参与AMPK,TNF,与胰岛素分泌等信号通路。结果提示ssc-miR-133a-5p的候选靶基因可能通过多种信号通路发挥重要作用。文章的结果可为microRNA参与猪的脂肪生成调节机制提供科学依据。

母猪批次化生产猪场的各类猪群存栏数及占栏数的计算

合理的猪群结构、猪群组成及其周转是猪场均衡生产和提高效率的关键因素。目前,母猪批次化生产技术在猪场实施的过程中,各种参数的制定、各类猪群存栏数及占栏数的计算依据不清晰,容易出现错误。文中的研究以600头基础母猪、7 d为一个生产周期猪场为例,详细地阐述了生产参数的制定、不同猪群的存栏数和占栏数的计算依据和过程,以期为猪场设计人员的猪场设计与建设和养猪生产者生产工艺的制定提供参考。

Admission clinical characteristics and early clinical outcomes among acute ischemic stroke patients

The purpose of the present study was to investigate the association between admission clinical characteristics and outcomes at discharge among acute ischemic stroke patients in the Chinese population. A total of 2,673 patients with acute ischemic stroke were included in the present study. The clinical characteristics at admission and other study variables were collected for all patients. The study outcome was defined as neurological deficiency （National Institute of Health Stroke Scale score ≥ 10） at discharge or in-hospital death. Compared with the subjects without neurological deficiency at discharge or in-hospital death, the subjects with neurological deficiency at discharge or in-hospital death had a significantly higher prevalence of hyperglycemia or history of atrial fibrillation at admission. Age ≥ 80 years, hyperglycemia, hypertension, and history of atrial fibrillation were significantly associated with neurological deficiency at discharge or in-hospital death after adjustment for other variables. It is concluded that old age （ ≥ 80 years）, hyperglycemia, hypertension and history of atrial fibrillation are significantly associated with neurological deficiency at discharge or in-hospital death among patients with acute ischemic stroke.

Fault Diagnosis of Motor in Frequency Domain Signal by Stacked De-noising Auto-encoder

With the rapid development of mechanical equipment,mechanical health monitoring field has entered the era of big data.Deep learning has made a great achievement in the processing of large data of image and speech due to the powerful modeling capabilities,this also brings influence to the mechanical fault diagnosis field.Therefore,according to the characteristics of motor vibration signals(nonstationary and difficult to deal with)and mechanical‘big data’,combined with deep learning,a motor fault diagnosis method based on stacked de-noising auto-encoder is proposed.The frequency domain signals obtained by the Fourier transform are used as input to the network.This method can extract features adaptively and unsupervised,and get rid of the dependence of traditional machine learning methods on human extraction features.A supervised fine tuning of the model is then carried out by backpropagation.The Asynchronous motor in Drivetrain Dynamics Simulator system was taken as the research object,the effectiveness of the proposed method was verified by a large number of data,and research on visualization of network output,the results shown that the SDAE method is more efficient and more intelligent.

Dynamics Modeling and Stability Analysis of Tilt Wing Unmanned Aerial Vehicle During Transition

In the transition mode of quad tilt wing-unmanned aerial vehicle(QTW-UAV),the system stability of UAV will change with the tilt angle changes,which will cause serious head drop down.Meanwhile,with the complex air flow and other disturbances,the system is prone to side bias,frying,stall and other kinetic stability problems,hence the system stability analysis has become an urgent problem to be solved.To solve the stability problem,we need the quantitative criteria of system stability and effective tool of stability analysis,and can improve the stability of the motion control by optimizing the structural parameters of the aircraft.Therefore,based on the design of the mechanical structure,the quantitative relationship between the structure parameters of the aerial vehicle and kinetic stability of the system transition mode is established by the Lyapunov exponent method.In this paper,the dynamic modeling of the position and attitude angle is carried out and the stability of the system is analyzed by Lyapunov exponent,the results show that changing the mechanical structure of the system can improve the flight stability for the system transition mode and lay a theoretical foundation for the system stability analysis.Compared with the Lyapunov direct method,this method can be construct easily,has a simple calculation process and so on.We improve the flight stability by optimizing the structure and the experiment confirms that expanding area can enhance flight stability within limits.

Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net

Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring.U-Net,an advanced machine learning(ML)method,is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A(FY-4A).First,in this algorithm,the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model(DEM)has been used as predictor variables for our model.Second,the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of U-Net to residual mapping.Then,in order to solve the problem of the network that would produce semantic differences when directly concentrated with low-level and high-level features,we use dense skip pathways to reuse feature maps of different layers as inputs for concatenate neural networks feature layers from different depths.Finally,according to the characteristics of precipitation clouds,the pooling layer of U-Net was replaced by a convolution operation to realize the detection of small precipitation clouds.It was experimentally concluded that the Pixel Accuracy(PA)and Mean Intersection over Union(MIoU)of the improved U-Net on the test set could reach 0.916 and 0.928,the detection of precipitation clouds over Tibet were well actualized.

MICROSTRUCTURE OF DIFFERENTLY TREATED Al-Mg-Sc-Zr ALLOYS

The microstructures of different treated Al-5Mg-0.5Sc-0. 1Zr alloys have been studied with opticalmicroscopy and transmission electron microscope (TEM). Coherent particles with the matrix were found inas-cast sample. composite Al3Sc/Al3Zr particles were found after 13 h homogenized treatment under 470℃.Coherent precipitates pinned dislocations and low-angle grain boundaries. and then restrained recrystallization. All kinds of particles of different treatments were not larger than 40nm. After high-temperaturestabilization treatments, most particles remain coherent with the matrix. The reasons of microstructurechange at different treated conditions have been analyzed.

Threshold-Based Adaptive Gaussian Mixture Model Integration(TA-GMMI)Algorithm for Mapping Snow Cover in Mountainous Terrain

Snow cover is an important parameter in the fields of computer modeling,engineering technology and energy development.With the extensive growth of novel hardware and software compositions creating smart,cyber physical systems’(CPS)efficient end-to-end workflows.In order to provide accurate snow detection results for the CPS’s terminal,this paper proposed a snow cover detection algorithm based on the unsupervised Gaussian mixture model(GMM)for the FY-4A satellite data.At present,most snow cover detection algorithms mainly utilize the characteristics of the optical spectrum,which is based on the normalized difference snow index(NDSI)with thresholds in different wavebands.These algorithms require a large amount of manually labeled data for statistical analysis to obtain the appropriate thresholds for the study area.Consideration must be given to both the high and low elevations in the study area.It is difficult to extract all snow by a fixed threshold in mountainous and rugged terrains.In this research,we avoid relying on a manual analysis for different elevations.Therefore,an algorithm based on the GMM is proposed,integrating the threshold-based algorithm and the GMM.First,the threshold-based algorithm with transferred thresholds from other satellites’analysis results are used to coarsely classify the surface objects.These results are then used to initialize the parameters of the GMM.Finally,the parameters of that model are updated by an expectation-maximum(EM)iteration algorithm,and the final results are outputted when the iterative conditions end.The results show that this algorithm can adjust itself to mountainous terrain with different elevations,and exhibits a better performance than the threshold-based algorithm.Compared with orbit satellites’snow products,the accuracy of the algorithm used for FY-4A is improved by nearly 2%,and the snow detection rate is increased by nearly 6%.Moreover,compared with microwave sensors’snow products,the accuracy is increased by nearly 3%.The validation results show that the proposed algorithm can be adapted to a complex terrain environment in mountainous areas and exhibits good performance under a transferred threshold without manually assigned labels.

Karyotypes of nineteen species of Asteraceae in the Hengduan Mountains and adjacent regions

The Hengduan Mountains region is a biodiversity hotspot. In this study, we report the karyotypes of 19 species(21 populations) of Asteraceae from this region, 14 of which are reported for the first time. We also examined polyploidy in Asteraceae plants and summarized karyotype data in the literature for 69 congeneric taxa. In these genera, there were five different ploidy levels in the region, though the most dominant was diploid(73.08%). There is no direct evidence that ploidy level and karyotype asymmetry are associated with the distribution of recorded Asteraceae species from the Hengduan Mountains. This suggests that polyploidy(26.92%) may not play an important role in the evolutionary history of these plants, even though, among these genera, the ratio of paleopolyploidy was high(46.15%).

Snow Cover Mapping for Mountainous Areas by Fusion of MODIS L1B and Geographic Data Based on Stacked Denoising Auto-Encoders

Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex topography.Aiming at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau.In this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free land.Moreover,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions.The proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and MYD10A1.The comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.

Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance

Stochastic resonance can use noise to enhance weak signals,effectively reducing the effect of noise signals on feature extraction.In order to improve the early fault recognition rate of rolling bearings,and to overcome the shortcomings of lack of interaction in the selection of SR(Stochastic Resonance)method parameters and the lack of validation of the extracted features,an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed.compared with the existing methods,the AGSR(Adaptive Genetic Stochastic Resonance)method uses genetic algorithms to optimize the system parameters,and further optimizes the parameters while considering the interaction between the parameters.This method can effectively extract the weak fault features of the bearing.In order to verify the effect of feature extraction,the feature signal extracted by AGSR method was input into the Fully connected neural network for fault diagnosis.the practicality of the algorithm is verified by simulation data and rolling bearing experimental data.the results show that the proposed method can effectively detect the early weak features of rolling bearings,and the fault diagnosis effect is better than the existing methods.

中药槐耳可能通过Cyclin-CDKs-CKIs途径引起MOLT4细胞G_1期阻滞和诱导凋亡(英文)

Objective: The purpose of the study was to study the effect of Huaier, a traditional Chinese medicine, on the cell cycle adjustment in MOLT4 cells in vitro. Methods: We used MTT assay to test cell viability, flow cytometry to detect cell cycle and apoptosis and western blot to examine the expression of cell-cycle and apoptotic proteins in MOLT4 cells induced by Huaier. Results: Huaier could reduce the viability of MOLT4 cell by inducing G1 arrest and apoptosis. The induction of apoptosis after treatment with Huaier for 24 h was demonstrated in a dose- and time-dependent manner by flow cytometry analysis. G1 arrest induced by Huaier was modulated through the increased expression of Cdki proteins(p21cip/waf1 and p27kip1) with a simultaneous decrease in Cdk2, Cdk4, Cdk6, cyclin D1 and cyclin E expression. Huaier also induced Bax and Bcl-2 expression and activation of Caspase-3. Conclusion: It is firstly demonstrated that Huaier can inhibit proliferation of MOLT4 cells via G1 arrest and apoptosis. These results suggest that Huaier is a cell-cycle anti-cancer drug.

Genome-wide enrichment of m6A-associated single nucleotide polymorphisms in the lipid loci

Background and Objective N6-methyladenosine(m6A)plays critical roles in many fundamental biological processes and a variety of diseases.The aim of this study was to investigate the effect of the m6ASNPs on lipid levels.Methods We examined the association of m6A-SNPs with lipid levels in a GWAS of 188,578 individuals.Furthermore,we performed eQTL and differential expression analyses to add additional information for the identified m6A-SNPs.

Detection of Enterocin Gene from Enterococcus

[ Objective] This study aimed to clone and identify enterocin gene from Enterococcus. [ Method] The genomic DNA of 12 enterocecci was extracted, and separately amplified with specific primers. The amplified fragments were ligated into PGEM-T Easy vector, which was then transformed into DH5α competent cells. The positive clones were sequenced. [Result] Enterocin A gene was 274 bp long. It was obtained from six enterococci, and the amino acids encoded by the enterocin genes cloned from five object enterocecci were the same as that of type IIa reference strains except only one amino acid. The homology among them reached 99.76 - 100%, suggesting that the bacteriocin isolated from the enterococcis belonged to type II. Structure prediction by DNAstar indicated that 22nd - 30th amino acids of enterocin A formed ot region, which had a hydrophilic region at its N-terminal and a hydrophobic region at its C-terminal, a transmembrane helix structure. [ Conclusion] This study will provide basis for the heterologous expression and applications of enterocins. Key words Enterocin gene; Enterocecci; PCR; Sequence analysis

A Case Report of Fever of Unknown Origin (FUO)

A 10-year-old boy had a long time of fever, and was diagnosed as JRA at first, but the patient’s condition got worse and worse after the treatment, then we did the cervical lymph node biopsy, which showed ALCL (Anaplastic Large Cell Lymphoma). After receiving the correct treatment, the patient’s condition got better.

Analysis of Cognitive Investigation and Intervention of COVID-19 in Different Populations

Objective:To understand the rate of cognition and protection of COVID-19 in a community in Hubei province.Methods:The related knowledge,epidemic situation and self-protective measures of COVID-19 were investigated by mobile phone questionnaire among 836 people in a community in Hubei province.Results:The rate of the awareness of COVID-19’s relevant knowledge and the ways to obtain the epidemic situation varied among different age groups.After multi-channel intervention in the community,the rate of the awareness of COVID-19’s relevant knowledge increased from 75.0%to 96.2%,and the implementation rate of personal protection increased from 59.6%to 92.6%.Conclusion:It is of great significance to improve residents’awareness and protection consciousness and block the spread of COVID-19 in the community through multi-channel intervention.