Research on the Current Situation and Guiding Path of College Students’ Participation in Charitable Activities

As the traditional virtue of the Chinese nation, the excellent quality of helping others is an important support for building a harmonious socialist society. Charity activities are an important form to carry forward its excellent quality. College students as the future hope of the motherland, by encouraging and guiding college students to participate in charitable activities to cultivate relevant excellent quality is the important content of shaping contemporary higher education talents and realizing their personal life value, but at present, there are still problems in college students’ participation in charitable activities that need to be solved urgently. Taking YuLin University as an example, this paper points out the relevant theoretical concepts, analyzes the significance of college students’ participation in public welfare and charitable activities. Through data investigation, it is concluded that there are some problems in YuLin University students’ participation in public welfare and charitable activities, such as less participation, impure participation motivation, single participation information acquisition channel and distrust of relevant groups. This paper puts forward the improvement path from the aspects of individual, family, school and society in order to play an optimization role

Mechanism of pulse magneto-oscillation grain refinement on pure Al

Pulse magneto-oscillation (PMO) was developed as a novel technique to refine the solidification structure of pure aluminium. Its grain refining mechanism was proposed. The PMO refinement mechanism is that the nucleus falls off from the mould wall and drifts into the melt under the action of PMO. The solidification structure of Al melt depends on the linear electric current density, and also the discharge and oscillation frequencies. The radial pressure of PMO sound wave is the major factor that contributes to the migration of nucleus into the melt

Genome-Wide Identification and Salt Stress Response Analysis of the bZIP Transcription Factor Family in Sugar Beet

As one of the largest transcription factor families in plants, bZIP transcription factors play important regulatory roles in different biological processes, especially in the process of stress response. Salt stress inhibits the growth and yield of sugar beet. However, bZIP-related studies in sugar beet (Beta vulgaris L.) have not been reported. This study aimed to identify the bZIP transcription factors in sugar beet and analyze their biological functions and response patterns to salt stress. Using bioinformatics, 48 BvbZIP genes were identified in the genome of sugar beet, encoding 77 proteins with large structural differences. Collinearity analysis showed that three pairs of BvbZIP genes were fragment replication genes. The BvbZIP genes were grouped according to the phylogenetic tree topology and conserved structures, and the results are consistent with those reported in Arabidopsis. Under salt stress, the expression levels of most BvbZIP genes were decreased, and only eight genes were up-regulated. GO analysis showed that the BvbZIP genes were mainly negatively regulated in stress response. Protein interaction prediction showed that the BvbZIP genes were mainly involved in light signaling and ABA signal transduction, and also played a certain role in stress responses. In this study, the structures and biological functions of the BvbZIP genes were analyzed to provide foundational data for further mechanistic studies and for facilitating the efforts toward the molecular breeding of stress-resilient sugar beet

Statistical and Biological Evaluation of Different Gene Set Analysis Methods

AbstractGene-set analysis (GSA) methods have been widely used in microarray data analysis. Owing to the unusual characteristics of microarray data, such as multi-dimension, small sample size and complicated relationship between genes, no generally accepted methods have been used to detect differentially expressed gene sets (DEGs) up to now. Our group assessed the statistical performance of some commonly used methods through Monte Carlo simulation combined with the analysis of real-world microarray data sets. Not only did we discover a few novel features of GSA methods during experiences, but also we find that some GSA methods are effective only if genes were assumed to be independent. And we also detected that model-based methods (GlobalTest and PCOT2) performed well when analyzing our simulated data sets in which the inter-gene correlation structure was incorporated into each gene set separately for more reasonable. Through analysis of real-world microarray data, we found GlobalTest is more effective. Then we concluded that GlobalTest is a more effective gene set analysis method, and recommended using it with microarray data analysis

Master-Slave Synchronization of Stochastic Neural Networks with Mixed Time-Varying Delays

This paper investigates the problem on master-salve synchronization for stochastic neural networks with both time-varying and distributed time-varying delays. Together with the drive-response concept, LMI approach, and generalized convex combination, one novel synchronization criterion is obtained in terms of LMIs and the condition heavily depends on the upper and lower bounds of state delay and distributed one. Moreover, the addressed systems can include some famous network models as its special cases, which means that our methods extend those present ones. Finally, two numerical examples are given to demonstrate the effectiveness of the presented scheme

GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy

Graph neural networks (GNNs) have been demonstrated as a powerful tool for analysing non-Euclidean graph data. However, the lack of efficient distributed graph learning (GL) systems severely hinders applications of GNNs, especially when graphs are big and GNNs are relatively deep. Herein, we present GraphTheta, a novel distributed and scalable GL system implemented in vertex-centric graph programming model. GraphTheta is the first GL system built upon distributed graph processing with neural network operators implemented as user-defined functions. This system supports multiple training strategies, and enables efficient and scalable big graph learning on distributed (virtual) machines with low memory each. To facilitate graph convolution implementations, GraphTheta puts forward a new GL abstraction named NN-TGAR to bridge the gap between graph processing and graph deep learning. A distributed graph engine is proposed to conduct the stochastic gradient descent optimization with a hybrid-parallel execution. Moreover, we add support for a new cluster-batched training strategy besides global-batch and mini-batch. We evaluate GraphTheta using a number of datasets with network size ranging from small-, modest- to large-scale. Experimental results show that GraphTheta can scale well to 1,024 workers for training an in-house developed GNN on an industry-scale Alipay dataset of 1.4 billion nodes and 4.1 billion attributed edges, with a cluster of CPU virtual machines (dockers) of small memory each (5$\sim$12GB). Moreover, GraphTheta obtains comparable or better prediction results than the state-of-the-art GNN implementations, demonstrating its capability of learning GNNs as well as existing frameworks, and can outperform DistDGL by up to $2.02\times$ with better scalability. To the best of our knowledge, this work presents the largest edge-attributed GNN learning task conducted in the literature.Comment: 18 pages, 14 figures, 5 table

A novel ZRS variant causes preaxial polydactyly type I by increased sonic hedgehog expression in the developing limb bud.

PurposePreaxial polydactyly (PPD) is a common congenital hand malformation classified into four subtypes (PPD I-IV). Variants in the zone of polarizing activity regulatory sequence (ZRS) within intron 5 of the LMBR1 gene are linked to most PPD types. However, the genes responsible for PPD I and the underlying mechanisms are unknown.MethodsA rare large four-generation family with isolated PPD I was subjected to genome-wide genotyping and sequence analysis. In vitro and in vivo functional studies were performed in Caco-2 cells, 293T cells, and a knockin transgenic mouse model.ResultsA novel g.101779T>A (reference sequence: NG_009240.2; position 446 of the ZRS) variant segregates with all PPD I-affected individuals. The knockin mouse with this ZRS variant exhibited PPD I phenotype accompanying ectopic and excess expression of Shh. We confirmed that HnRNP K can bind the ZRS and SHH promoters. The ZRS mutant enhanced the binding affinity for HnRNP K and upregulated SHH expression.ConclusionOur results identify the first PPD I disease-causing variant. The variant leading to PPD I may be associated with enhancing SHH expression mediated by HnRNP K. This study adds to the ZRS-associated syndromes classification system for PPD and clarifies the underlying molecular mechanisms

A Common Variant in CLDN14 is Associated with Primary Biliary Cirrhosis and Bone Mineral Density.

Primary biliary cirrhosis (PBC), a chronic autoimmune liver disease, has been associated with increased incidence of osteoporosis. Intriguingly, two PBC susceptibility loci identified through genome-wide association studies are also involved in bone mineral density (BMD). These observations led us to investigate the genetic variants shared between PBC and BMD. We evaluated 72 genome-wide significant BMD SNPs for association with PBC using two European GWAS data sets (n = 8392), with replication of significant findings in a Chinese cohort (685 cases, 1152 controls). Our analysis identified a novel variant in the intron of the CLDN14 gene (rs170183, Pfdr = 0.015) after multiple testing correction. The three associated variants were followed-up in the Chinese cohort; one SNP rs170183 demonstrated consistent evidence of association in diverse ethnic populations (Pcombined = 2.43 × 10(-5)). Notably, expression quantitative trait loci (eQTL) data revealed that rs170183 was correlated with a decline in CLDN14 expression in both lymphoblastoid cell lines and T cells (Padj = 0.003 and 0.016, respectively). In conclusion, our study identified a novel PBC susceptibility variant that has been shown to be strongly associated with BMD, highlighting the potential of pleiotropy to improve gene discovery

A machine learning method for locating subsynchronous oscillation source of VSCs in wind farm induced by open-loop modal resonance based on measurement

In recent years, sub-synchronous oscillation incidents have been reported to happen globally, which seriously threatens the safe and stable operation of the power system. It is difficult to locate the oscillation source in practice using the parameterized model of open-loop modal resonance. Therefore, this paper aims at the problem of oscillation instability caused by the interaction between the multiple voltage source converters in the wind farm grid-connected system, proposes a method for locating the oscillation source of a wind farm using measurement data based on the transfer learning algorithm of transfer component analysis. At the same time, in order to solve the problem of the lack of oscillation data and the inability to label in the real system, a simplified simulation system was proposed to generate large batches of labeled training samples. Then, the common features of the samples from simulation system and the real system were learned through the transfer component analysis algorithm. Afterward, a classifier was trained to classify samples with common features. Finally, two grid-connected wind farms with VSC access are used to verify that the proposed method has good locating performance. This has important reference value for the practical application of power grid dispatching and operation using measurement to identify oscillation sources