Early Warning and Physical Education Intervention of Psychological Crisis among College Students Based on Artificial Neural Network / Alerta Temprana e Intervención de Educación Física de Crisis Psicológica entre Estudiantes Universitarios Basada en Red Neuronal Artificial

The development of modern society has complicated the learning and living environment of college students, which induces significant psychological problems. However, most scholars focus on quantitative evaluation based on objective survey and statistical analysis, or contrastive experiments based on predesigned intervention methods. Few of them consider the interaction between influencing factors, and judge the degree of psychological crisis reliably. To solve the problem, this paper introduces artificial neural network (ANN) to the early warning and physical education (PE) intervention of psychological crisis among college students. Based on a reasonable index system, the psychological crisis of college students was studied with the structural equation, considering the interaction effect between influencing factors. Next, a bidirectional long short-term memory (BiLSTM) network was constructed for the analysis on the psychological crisis of college students, and the degree of psychological crisis of college students was evaluated, with the aid of emotional labeling attention mechanism. Finally, the effect of PE intervention on psychological stress easement of college students was discussed in details. The proposed method was proved valid through experiments.(AU)

Monitoring and Evaluating College Students' Mental Health Based on Big Data Analysis.

Objectives: Our aim was to overcome the low evaluation accuracy of traditional random sampling methods for college students' mental health, and to use the values of big data of college students' social network behaviors in the prediction and evaluation of their mental health. Methods: We monitored and evaluated college students' mental health through big data analysis. After generating the samples of college students' social network behaviors, a mental health monitoring and evaluation model was established based on a support vector machine (SVM) and decision tree (DT). Then, the DT model was pruned, and input data of the model were optimized by genetic algorithm (GA). Results: The optimal parameter combination was derived for our model. The maximum number of iterations was 60; the smallest number of samples needed for reclassifying internal nodes was 6; the number of samples with the fewest leaf nodes was 30. The mental health scores of most students fell in the interval [0, 6] for unobvious symptoms of mental crisis. The binary classification results of several models were as follows. On anxiety, all models surpassed the accuracy of 60%, except the traditional SVM. The optimal model, ie, Model 5, achieved an accuracy of 86.7%. On depression, all models exceeded the accuracy of 60%, and the GA-optimized DT 5 realized an accuracy as high as 83.1%. On drooping spirit, the optimal model, ie, GA-optimized DT 5, reached an accuracy of 89.5%, which is comparable to that of the GA-optimized SVM 4. Conclusions: The characteristic dimensions extracted by GA are representative. The primary mental states of college students can be estimated quickly and accurately by our model with a low cost of data storage, through the feature analysis of social network behaviors.

Differential Analysis on Mental Health Level of Physical Education Majors / Análisis Diferencial del Nivel de Salud Mental en Educación Física

College students can promote both physical fitness and mental health by taking a suitable amount of physical exercise at the right time. However, few scholars have conducted differential analysis on the mental health level of college students under the intervention of physical exercise, not to mention that of physical education (PE) majors. To fill up the gap, this paper carries out a differential analysis on the mental health level of PE majors. Firstly, the mental health level of PE majors was evaluated in 11 aspects, and a graded response model was established based on the 11 indices. Next, the mental health level of PE majors was subjected to fuzzy comprehensive evaluation (FCE). Finally, the mental health differences between PE majors and college students in other majors were analyzed accurately. The results show that the mental health level of physical education majors varies with genders, ages, grades, school types, durations per physical exercise, weekly frequencies of physical exercise, sports venues, and skill levels. The research findings provide a reference for further research of PE majors and their mental health level.(AU)

A major quantitative trait locus conferring subgynoecy in cucumber.

KEY MESSAGE: A major QTL conditioning high degree of femaleness in cucumber was identified by marker analysis and next generation sequencing. Cucumber (Cucumis sativus L.) is a model species for sex determination studies, and its yield is associated with the degree of femaleness. Subgynoecy represents a sex form with a high degree of femaleness for which the genetic basis remains elusive. In this study, genetic analysis in the F2 and BC1 populations developed from a cross between subgynoecious S-2-98 and monoecious M95 suggested a quantitative nature of subgynoecy. Application of simple sequence repeat markers between subgynoecious and monoecious bulks constructed from BC1 plants identified three QTLs: sg3.1, sg6.1, and sg6.2. The major QTL sg3.1 contributed to 54.6% of the phenotypic variation, and its presence was confirmed by genome-wide comparison of SNP profiles between parental lines and a subgynoecious bulk constructed from BC6 plants. Using PCR-based markers developed from the SNP profile, sg3.1 was further delimited to a genomic region of 799 kb. The genetic basis of subgynoecy revealed here shall shed light on the development of elite cultivars with high yield potential.

Genetic diversity and population structure of cucumber (Cucumis sativus L.).

Knowing the extent and structure of genetic variation in germplasm collections is essential for the conservation and utilization of biodiversity in cultivated plants. Cucumber is the fourth most important vegetable crop worldwide and is a model system for other Cucurbitaceae, a family that also includes melon, watermelon, pumpkin and squash. Previous isozyme studies revealed a low genetic diversity in cucumber, but detailed insights into the crop's genetic structure and diversity are largely missing. We have fingerprinted 3,342 accessions from the Chinese, Dutch and U.S. cucumber collections with 23 highly polymorphic Simple Sequence Repeat (SSR) markers evenly distributed in the genome. The data reveal three distinct populations, largely corresponding to three geographic regions. Population 1 corresponds to germplasm from China, except for the unique semi-wild landraces found in Xishuangbanna in Southwest China and East Asia; population 2 to Europe, America, and Central and West Asia; and population 3 to India and Xishuangbanna. Admixtures were also detected, reflecting hybridization and migration events between the populations. The genetic background of the Indian germplasm is heterogeneous, indicating that the Indian cucumbers maintain a large proportion of the genetic diversity and that only a small fraction was introduced to other parts of the world. Subsequently, we defined a core collection consisting of 115 accessions and capturing over 77% of the SSR alleles. Insight into the genetic structure of cucumber will help developing appropriate conservation strategies and provides a basis for population-level genome sequencing in cucumber.

Genome sequence of foxtail millet (Setaria italica) provides insights into grass evolution and biofuel potential.

Foxtail millet (Setaria italica), a member of the Poaceae grass family, is an important food and fodder crop in arid regions and has potential for use as a C(4) biofuel. It is a model system for other biofuel grasses, including switchgrass and pearl millet. We produced a draft genome (∼423 Mb) anchored onto nine chromosomes and annotated 38,801 genes. Key chromosome reshuffling events were detected through collinearity identification between foxtail millet, rice and sorghum including two reshuffling events fusing rice chromosomes 7 and 9, 3 and 10 to foxtail millet chromosomes 2 and 9, respectively, that occurred after the divergence of foxtail millet and rice, and a single reshuffling event fusing rice chromosome 5 and 12 to foxtail millet chromosome 3 that occurred after the divergence of millet and sorghum. Rearrangements in the C(4) photosynthesis pathway were also identified.

The genome of the cucumber, Cucumis sativus L.

Cucumber is an economically important crop as well as a model system for sex determination studies and plant vascular biology. Here we report the draft genome sequence of Cucumis sativus var. sativus L., assembled using a novel combination of traditional Sanger and next-generation Illumina GA sequencing technologies to obtain 72.2-fold genome coverage. The absence of recent whole-genome duplication, along with the presence of few tandem duplications, explains the small number of genes in the cucumber. Our study establishes that five of the cucumber's seven chromosomes arose from fusions of ten ancestral chromosomes after divergence from Cucumis melo. The sequenced cucumber genome affords insight into traits such as its sex expression, disease resistance, biosynthesis of cucurbitacin and 'fresh green' odor. We also identify 686 gene clusters related to phloem function. The cucumber genome provides a valuable resource for developing elite cultivars and for studying the evolution and function of the plant vascular system.

Molecular isolation of the M gene suggests that a conserved-residue conversion induces the formation of bisexual flowers in cucumber plants.

Sex determination in plants involves a variety of mechanisms. Here, we report the map-based cloning and characterization of the unisexual-flower-controlling gene M. M was identified as a previously characterized putative 1-aminocyclopropane-1-carboxylic acid synthase gene, while the m allele that mutated at a conserved site (Gly33Cys) lost activity in the original enzymatically active allele.