Interactive effect of leader ethicality and competency on Chinese customs officers’ organizational citizenship behaviors

The dual qualities of an effective leader—ethicality and competency—have long been identified but seldom empirically examined. Using survey data from 329 Chinese customs officers collected in December 2022, this study investigates whether ethical leadership influences customs officers’ organizational citizenship behaviors indirectly through work engagement and trust in leader. Following the interactive approach, we further postulate that leader competency can accentuate these indirect relationships. Mplus 8.3, SPSS 26.0 and Hayes’ PROCESS macro for SPSS were used to conduct statistical analyses including descriptive statistical analysis, correlation analysis, common method deviation analysis, confirmatory factor analysis, and regression analysis. The results reveal that work engagement and trust in leader act as mediators in the ethical leadership and organizational citizenship behaviors relationship. Moreover, these indirect relationships are stronger when customs officers perceive their leaders are more competent. Theoretical and practical implications are discussed

Research Progress in Active Components of Plant Extracts Resistant to Pseudorabies Virus

Pseudorabies is a hot infectious disease, which can cause fever, encephalomyelitis, multiple organ failure, paralysis and other symptoms in infected animals. The pathogen of the disease is enveloped DNA virus, and pig is the only natural repository, which has strong resistance to the host and can attack the nervous system of the host. The prevalence of pseudorabies seriously harms the pig industry in China, therefore, it is listed among the diseases that need priority control and purification in China. At present, one of the main prevention and control measures against pseudorabies in pigs is vaccination. However, the virus is prone to mutate to produce new strains, which leads to the decline of the immune effect of the vaccine, and difficult to provide complete protection. Therefore, it is very important to find and develop new drugs as substitutes for the prevention and control of pseudorabies. There are many natural active ingredients in plant extracts, such as polyphenols, polysaccharides, alkaloids, and etc. These ingredients not only have antibacterial activity, antioxidant properties and antitoxin properties, but also have certain therapeutic effects on animal diseases. These active ingredients possess the advantages of multifunction and less side effects, which can effectively guarantee the safety of pig breeding and production. In the study, the active components of plant extracts are briefly summarized, the antiviral effects of different active components of plant extracts on pseudorabies virus are emphatically summarized, and the application prospects are discussed in order to provide references for the research and development of novel plant drugs

Deconstructing iterative optimization

Iterative optimization is a popular compiler optimization approach that has been studied extensively over the past decade. In this article, we deconstruct iterative optimization by evaluating whether it works across datasets and by analyzing why it works. Up to now, most iterative optimization studies are based on a premise which was never truly evaluated: that it is possible to learn the best compiler optimizations across datasets. In this article, we evaluate this question for the first time with a very large number of datasets. We therefore compose KDataSets, a dataset suite with 1000 datasets for 32 programs, which we release to the public. We characterize the diversity of KDataSets, and subsequently use it to evaluate iterative optimization. For all 32 programs, we find that there exists at least one combination of compiler optimizations that achieves at least 83% or more of the best possible speedup across all datasets on two widely used compilers (Intel's ICC and GNU's GCC). This optimal combination is program-specific and yields speedups up to 3.75x (averaged across datasets of a program) over the highest optimization level of the compilers (-O3 for GCC and -fast for ICC). This finding suggests that optimizing programs across datasets might be much easier than previously anticipated. In addition, we evaluate the idea of introducing compiler choice as part of iterative optimization. We find that it can further improve the performance of iterative optimization because different programs favor different compilers. We also investigate why iterative optimization works by analyzing the optimal combinations. We find that only a handful optimizations yield most of the speedup. Finally, we show that optimizations interact in a complex and sometimes counterintuitive way through two case studies, which confirms that iterative optimization is an irreplaceable and important compiler strategy

Alterations in gene expressions of Caco-2 cell responses to LPS and ploy(I:C) stimulation

The intestinal epithelium barrier serves as a highly dynamic immunologic frontier in the defense against invading pathogenic bacteria and viruses. Hence, understanding of the complicated underlying relationship between enteric pathogens and the intestinal epithelium barrier is vital for developing strategies to improve the intestinal health of farm animals. To this end, Caco-2 cells were stimulated by 1 µg/ml lipopolysaccharide (LPS) for 24 h and 5 µg/ml polyinosinic-polycytidylic acid (ploy(I:C)) for 4 h to imitate bacterial and viral infection processes, respectively. The specific alterations in gene expression of Caco-2 cells after stimulation were characterized by transcriptome sequencing. Seventy differentially expressed genes (DEGs) were identified under LPS exposure, and 17 DEGs were observed under ploy(I:C) exposure. We found that most DEGs were specific, and only one common DEG SPAG7 was observed. Gene Ontology (GO) annotation analysis indicated that all DEGs identified in the different treatments were mainly derived from GO terms related to the maintenance of cellular homeostasis. Moreover, specific DEGs such as SLC39A10, MT2A, and MT1E regulated by LPS treatment, while IFIT2 and RUNX2 mediated by ploy(I:C) treatment, which are derived from immune function modulation related GO terms, were confirmed by both transcriptome sequencing and qRT-PCR. In addition, both transcriptome sequencing and qRT-PCR results verified that LPS specifically down-regulated the DEGs INHBE and ARF6, which are involved in inflammation responses related to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway including the TGF-beta signaling pathways and the Ras signaling pathway. Ploy(I:C) uniquely suppressed the DEGs GABARAP and LAMTOR3, which participated in viral replication-associated pathways including autophagy and mTOR signaling pathway

Applying early divergent characters in higher rank taxonomy of Melampsorineae (Basidiomycota, Pucciniales)

ABSTRACTRust fungi in the order Pucciniales represent one of the largest groups of phytopathogens, which occur on mosses, ferns to advanced monocots and dicots. Seven suborders and 18 families have been reported so far, however recent phylogenetic studies have revealed para- or polyphyly of several morphologically defined suborders and families, particularly in Melampsorineae. In this study, a comprehensive phylogenetic framework was constructed based on a molecular phylogeny inferred from rDNA sequences of 160 species belonging to 16 genera in Melampsorineae (i.e. Chrysomyxa, Cerospora, Coleopuccinia, Coleosporium, Cronartium, Hylospora, Melampsora, Melampsorella, Melampsoridium, Milesina, Naohidemyces, Pucciniastrum, Quasipucciniastrum, Rossmanomyces, Thekopsora, Uredinopsis). Our phylogenetic inference indicated that 13 genera are monophyletic with strong supports, while Pucciniastrum is apparently polyphyletic. A new genus, Nothopucciniastrum was therefore established and segregated from Pucciniastrum, with ten new combinations proposed. At the family level, this study further demonstrates the importance of applying morphologies of spore-producing structures (basidia, spermogonia, aecia, uredinia and telia) in higher rank taxonomy, while those traditionally applied spore morphologies (basidiospores, spermatia, aeciospores, urediniospores and teliospores) represent later diverged characters that are more suitable for the taxonomy at generic and species levels. Three new families, Hyalopsoraceae, Nothopucciniastraceae and Thekopsoraceae were proposed based on phylogenetic and morphological distinctions, towards a further revision of Pucciniales in line with the phylogenetic relationships

Performance portability across heterogeneous SoCs using a generalized library-based approach

Because of tight power and energy constraints, industry is progressively shifting toward heterogeneous system-on-chip (SoC) architectures composed of a mix of general-purpose cores along with a number of accelerators. However, such SoC architectures can be very challenging to efficiently program for the vast majority of programmers, due to numerous programming approaches and languages. Libraries, on the other hand, provide a simple way to let programmers take advantage of complex architectures, which does not require programmers to acquire new accelerator-specific or domain-specific languages. Increasingly, library-based, also called algorithm-centric, programming approaches propose to generalize the usage of libraries and to compose programs around these libraries, instead of using libraries as mere complements. In this article, we present a software framework for achieving performance portability by leveraging a generalized library-based approach. Inspired by the notion of a component, as employed in software engineering and HW/SW codesign, we advocate nonexpert programmers to write simple wrapper code around existing libraries to provide simple but necessary semantic information to the runtime. To achieve performance portability, the runtime employs machine learning (simulated annealing) to select the most appropriate accelerator and its parameters for a given algorithm. This selection factors in the possibly complex composition of algorithms used in the application, the communication among the various accelerators, and the tradeoff between different objectives (i.e., accuracy, performance, and energy). Using a set of benchmarks run on a real heterogeneous SoC composed of a multicore processor and a GPU, we show that the runtime overhead is fairly small at 5.1% for the GPU and 6.4% for the multi-core. We then apply our accelerator selection approach to a simulated SoC platform containing multiple inexact accelerators. We show that accelerator selection together with hardware parameter tuning achieves an average 46.2% energy reduction and a speedup of 2.1× while meeting the desired application error target

Application of Flow Cytometry in Agricultural Research

Flow cytometry is a biological technique for counting and sorting out tiny particles suspended in fluid based on flow cytometer through the detection of scattering or coupled fluorescence signals to obtain a series of important biophysical and biochemical related characteristics of suspended particles (usually cells, bacteria and other tiny particles) in a quick, accurate, objective and high-throughput way, and to automatically analyze and sort out specific populations according to the pre-selected parameter range of cells, bacteria and other tiny particles. Flow cytometry is widely used in many scientific fields, especially biotechnology and medicine. Although the application of flow cytometry in agricultural research started relatively late, great progress has been made. With the continuous improvement of the performance of flow cytometer, the continuous development and improvement of labeling methods and detection technologies, the application prospect of flow cytometry will become wider, and it will also play an increasingly important role in the agricultural field. In this review, it introduces the working principle and functional classification of flow cytometer, discusses the application and research progress of flow cytometry in agricultural fields such as crop genome and protoplast analysis, stress resistance research, animal immunity and trace element analysis, sperm quality and sex control, toxin toxicity analysis, pathogenic bacteria and virus analysis, and then looks forward to the development prospect of this research field, providing a new direction for the potential application of flow cytometry in agricultural fields