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ObjectiveThis study aimed to evaluate the nutritional knowledge-attitude-practice （KAP）， dietary status， dietary pattern， and physical fitness levels of freshmen in a medical university in Guangxi Zhuang Autonomous Region， and explore the relationship between nutritional knowledge， dietary pattern， and physical fitness level， to provide a reference for the healthy dietary education of university students.MethodsFrom May in， 2021， 2 125 freshmen from a medical university in Guangxi Zhuang Autonomous Region were surveyed using a nutritional KAP questionnaire， food frequency questionnaire， and physical fitness tests. Factor analysis was used to establish the dietary pattern of freshmen， and Pearson correlation analysis and Logistic regression analysis were conducted to analyze the correlation between dietary pattern and the total score of physical fitness test and each item. Simultaneously， descriptive analysis was conducted to analyze the nutritional KAP score， total energy， and average intake of each nutrient. A chi-square test was used to compare the differences in the distribution of nutritional behaviors among different physical test scores.ResultsThe intake of carbohydrate， vitamin A， calcium， dietary fiber， and other nutrients of freshmen was insufficient. Pearson correlation analysis showed that the “healthy diet” pattern （r = 0.064，P < 0.01） and the “aquatic product， livestock， and poultry meat” pattern （r = 0.062，P < 0.01）were significantly positively correlated with the physical test scores. A significant negative correlation was observed between the “fried and sweet food” pattern （r = -0.057， P = 0.01）. The scores of nutrition knowledge， attitude， and practice of freshmen were 14.54 ± 2.63， 24.60 ± 3.73， and 15.61 ± 2.51， respectively. A significant difference was observed in the distribution of the breakfast eating behavior between the students who passed the physical test and those who failed it （χ2 = 9.05，P < 0.05）. Binary logistic regression analysis showed that the “aquatic product， livestock， and poultry meat” pattern was significantly negatively correlated with the risk of failing the physical test in boys （OR = 0.35，95% CI = 0.14-0.91） and girls （OR = 0.41，95% CI = 0.19-0.87）， and the “preserved food” pattern was significantly positively correlated with the risk of failing the physical test in girls （OR = 2.21，95% CI = 1.15-4.27）.ConclusionThe dietary structure of the freshmen in a medical university in Guangxi Zhuang Autonomous Region is not reasonable， and their nutritional knowledge level does not match their nutritional behavior. Therefore， targeted nutritional education intervention should be performed according to the characteristics of the freshmen， so as to gradually develop healthy eating habits and improve their physical level.

Paradoxical leadership has emerged as an increasingly important research topic in the context of Chinese state-owned enterprises, which are currently facing contradictions between maintaining stability and implementing changes, short-term profits and long-term sustainable development, and public nature and marketization. Based on social cognitive theory and social exchange theory, this study employed a questionnaire survey to explore the influence of paradoxical leadership on employees’ proactive work behavior and the mediating role of superior-subordinate guanxi and self-efficacy. The study involved 540 employees working in Chinese state-owned enterprises. We conducted confirmatory factor analyses to test the validity of the measurement model and regression to evaluate the direct effects. Subsequently, we used bootstrapping to confirm mediation and serial mediation effects. The study found that (1) Paradoxical leadership can effectively enhance employees’ proactive work behavior; (2) The superior-subordinate guanxi plays a mediating role between paradoxical leadership and employees’ proactive work behavior, that is, paradoxical leadership enhances employees’ proactive work behavior by improving the superior-subordinate guanxi; (3) Self-efficacy plays a mediating role between paradoxical leadership and employees’ proactive work behaviors, that is, paradoxical leadership promotes employees’ proactive work behavior by enhancing their self-efficacy; (4) The superior-subordinate guanxi and self-efficacy play a chain mediating effect between paradoxical leadership and employees’ proactive work behavior, forming a chain of “Paradoxical leadership—Superior-subordinate Guanxi—Self-efficacy—Employees’ proactive work behaviors.” This study enriches the theoretical research on paradoxical leadership and provides suggestions for state-owned enterprises to enhance employees’ proactive work behavior.

Abstract Background Exosomes are nanosized membranous vesicles secreted by various types of cells, which facilitate intercellular communication by transporting bioactive compounds. Exosomes are abundant in biological fluids including semen, and their protein composition and the potential of seminal plasma exosomes (SPEs) as fertility biomarkers were elucidated in humans, however, little information is available regarding buffalo (Bubalus bubalis). Here, we examined protein correlation between spermatozoa, seminal plasma (SP), and SPEs, and we compared and analyzed protein differences between high-motility (H-motility) and low-motility (L-motility) SPEs in buffalo. Results SPEs were concentrated and purified by ultracentrifugation combined with sucrose density gradient centrifugation, followed by verification using western blotting, nanoparticle tracking analysis, and transmission electron microscopy. Protein composition in spermatozoa, SP and SPEs, and protein difference in H- and L-motility SPEs were identified by LC-MS/MS proteomic analysis and were functionally analyzed through comprehensive bioinformatics. Many SPEs proteins originated from spermatozoa and SP, and nearly one third were also present in spermatozoa and SP. A series of proteins associated with reproductive processes including sperm capacitation, spermatid differentiation, fertilization, sperm-egg recognition, membrane fusion, and acrosome reaction were integrated in a functional network. Comparative proteomic analyses showed 119 down-regulated and 41 up-regulated proteins in L-motility SPEs, compared with H-motility SPEs. Gene Ontology (GO) enrichment of differentially expressed proteins (DEPs) showed that most differential proteins were located in sperm and vesicles, with activities of hydrolase and metalloproteinase, and were involved in sperm-egg recognition, fertilization, single fertilization, and sperm-zona pellucida binding processes, etc. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that differential proteins were mainly involved in the PPRP signaling pathway, calcium signaling pathway, and cAMP signaling pathway, among others. Furthermore, 6 proteins associated with reproduction were validated by parallel reaction monitoring analysis. Conclusion This study provides a comprehensive description of the seminal plasma exosome proteome and may be of use for further screening of biomarkers associated with male infertility.

BackgroundRadioactive waste oil mainly comes from the maintenance of equipment such as main pumps and steam turbines of nuclear power plants (NPP), and is a typical radioactive organic liquid waste (ROLW). Because there is no suitable treatment technology for ROLW, it can only be stored for a long time. Long-term storage has potential safety hazards such as leakage and fire.PurposeThis study aims to solve the problem of radioactive waste oil treatment by carrying out an experimental study on the supercritical water oxidation (SCWO) treatment of lubricating oil from NPP.MethodsA continuous SCWO reaction system was used as experimental platform with maximum working temperature of the 600 ℃ and pressure of 28.4 MPa. The effects of different reaction temperature, reaction time and excess oxygen coefficient on the chemical oxygen demand (COD) removal rate of VG 32 (Mobil DTE light turbine oil) lubricants were investigated. In addition, the reaction kinetics of SCWO treatment of lubricating oil from NPP at 420~580 ℃ was studied.ResultsThe experimental results show that the best operating conditions are reaction temperature of 550 ℃, reaction time of 80 s and oxygen enrichment coefficient of 250%. Under these conditions, the COD removal rate of organic matter is up to 96%. The activation energy Ea of the reaction is 43.10 kJ·mol-1, and the pre exponential factor A is 8.861 s-1. The deviation between the calculated value of the kinetic model and the experimental value is less than 3%.ConclusionsThis work proves the credibity of the model, and lays an experimental basis for supercritical water oxidation treatment of radioactive waste oil.

Simple Summary: As an endemic and vulnerable fish from the upper Yangtze River in China, Schizothorax kozlovi holds significant scientific and ecological importance, yet it has received little attention so far. In this paper, we reported the characterization of the mitochondrial genome of S. kozlovi, and further investigated the phylogenetic relationships of Schizothorax. The results showed that the mitochondrial genome of S. kozlovi had a total size of 16,585 bp, a circular arrangement, and contained 13 PCGs, 22 tRNAs, two rRNAs, and two non-coding regions. Moreover, the phylogenetic analyses demonstrated that Schizothorax could be classified into four clades, and S. kozlovi was closely related to Schizothorax chongi. The present study enriched the basic biological data for S. kozlovi and provided fundamental references for the conservation of S. kozlovi and Schizothorax. Schizothorax kozlovi is an endemic and vulnerable fish species found in the upper Yangtze River in China. Over the past few years, the population resources of S. kozlovi have been nearly completely depleted owing to multiple contributing threats. While the complete mitochondrial genomes serve as important molecular markers for phylogenetic and genetic studies, the mitochondrial genome of S. kozlovi has still received little attention. In this study, we analyzed the characterization of the mitochondrial genome of S. kozlovi and investigated the phylogenetic relationships of Schizothorax. The complete mitochondrial genome of S. kozlovi was 16,585 bp in length, which contained thirty-seven genes (thirteen protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), twenty-two transfer RNA genes (tRNAs)) and two non-coding regions for the origin of light strand (OL) and the control region (CR). There were nine overlapping regions and seventeen intergenic spacers regions in the mitochondrial genome. The genome also showed a bias towards A + T content (55.01%) and had a positive AT-skew (0.08) and a negative GC-skew (−0.20). All the PCGs employed the ATG or GTG as the start codon and TAA, TAG, or single T as the stop codon. Additionally, all of the tRNAs displayed a typical cloverleaf secondary structure, except trnS1 which lacked the D arm. The phylogenetic analysis, based on the maximum likelihood (ML) and Bayesian inference (BI) methods, revealed that the topologies of the phylogenetic tree divided the Schizothorax into four clades and did not support the classification of Schizothorax based on morphology. The phylogenetic status of S. kozlovi was closely related to that of S. chongi. The present study provides valuable genomic information for S. kozlovi and new insights in phylogenetic relationships of Schizothorax. These data could also offer fundamental references and guidelines for the management and conservation of S. kozlovi and other species of Schizothorax. [ABSTRACT FROM AUTHOR]

Accurate delineation of debris-flow-endangered areas (e.g., the maximum runout distance) is a necessary prerequisite for the debris-flow risk assessment and countermeasures design. Recently, machine-learning models have been proved to be an effective tool in predicting debris-flow parameters. However, existing machine-learning models are generally developed based on a very limited number of observation data, which may result in the predictive model overfitting or underfitting. How to develop a robust model for accurate forecasting of debris-flow-endangered areas still remains a difficult task. This paper proposes a hybrid method for predicting debris-flow hazard zone by integrating machine-learning algorithms and an empirical regression model. The proposed method takes the calculated maximum runout distance obtained from the empirical model as supplementary inputs to increase the amount of training data to construct hybrid machine-learning models. Three commonly used machine-learning models (i.e., multivariate adaptive regression splines (MARS), random forest (RF), and support vector machine (SVM)) are developed based on the training datasets of a specific debris basin. Then, these three machine-learning models are combined with an empirical relationship developed using the same training datasets to generate corresponding hybrid models. Finally, the performance metrics (i.e., coefficient of determination (R2), root-mean-square error (RMSE), and mean absolute error (MAE)) of the proposed hybrid models are comprehensively investigated and compared with the single predictive model (i.e., MARS, RF, SVM, and the empirical model) under fivefold cross-validation. The proposed method is illustrated using 134 channelized debris-flow events in Sichuan province, China. Results show that compared with the three individual machine-learning models, hybridization of machine-learning algorithms and the empirical model results in R2, RMSE, and MAE improved by 70.5%, 32.9%, and 41.1%, respectively. In contrast to the empirical model, the R2, RMSE, and MAE value of the proposed hybrid models are improved by 29.6%, 22.3%, and 32.5%, respectively. The proposed hybrid models generally perform better than the single machine-learning and the empirical model, providing a promising tool for accurate forecasting of a debris-flow-endangered area. [ABSTRACT FROM AUTHOR]