Impact of Job Demands on Employee Learning: The Moderating Role of Human-Machine Cooperation Relationship.

New artificial intelligence (AI) technologies are applied to work scenarios, which may change job demands and affect employees' learning. Based on the resource conservation theory, the impact of job demands on employee learning was evaluated in the context of AI. The study further explores the moderating effect of the human-machine cooperation relationship between them. By collecting 500 valid questionnaires, a hierarchical regression for the test was performed. Results indicate that, in the AI application scenario, a U-shaped relationship exists between job demands and employee learning. Second, the human-machine cooperation relationship moderates the U-shaped curvilinear relationship between job demands and employees' learning. In this study, AI is introduced into the field of employee psychology and behavior, enriching the research into the relationship between job demands and employee learning.

Effects of human-machine interaction on employee's learning: A contingent perspective.

The popularization of intelligent machines such as service robot and industrial robot will make human-machine interaction, an essential work mode. This requires employees to adapt to the new work content through learning. However, the research involved human-machine interaction that how influences the employee's learning is still rarely. This paper was to reveal the relationship between human-machine interaction and employee's learning from the perspective of job characteristics and competence perception of employees. We sent questionnaire to 500 employees from 100 artificial intelligence companies in China and received 319 valid and complete responses. Then, we adopted a hierarchical regression for the test. Empirical results show that human-machine interaction has a U-shaped curvilinear relationship with employee learning, and employee's vitality mediates the curvilinear relationship. In addition, job characteristics (skill variety and job autonomy) moderate the U-shaped curvilinear relationship between human-machine interaction and employee's vitality, especially the results of moderating effects varying with employee's competence perception. Exploring the mechanism of the effect of human-machine interaction on employee's learning enriches the socially embedded model. Moreover, it provides managerial implications how to enhance individual adaptability with the introduction of AI into firms. However, our research focuses more on the impact of human-machine interaction on employees at the initial stage of AI development, and the level of machine intelligence in various industries will reach a high degree of autonomy in the future. The future research can explore the impact of human-machine interaction on individual's behavior at different stages, and the results may vary depending on the technologies mastered by different individuals. The study has theoretical and practical significance to human-machine interaction literature by underscoring the important of individual's behavior among individuals with different skills.

Characterization of the variable merozoite surface antigen (VMSA) gene family of Babesia orientalis.

Due to its wide presence in apicomplexan parasites as well as high polymorphism and antigenic diversity, the variable merozoite surface antigen (VMSA) family in Babesia sp. has attracted increasing attention of researchers. Here, all the reported VMSA genes of Babesia spp. were obtained from GenBank, and multiple alignments were performed by using conserved regions to blast the Babesia orientalis genome database (unpublished data). Five MSA genes (named MSA-2a1, MSA-2a2, MSA-2c1, MSA-1, and MSA-2c2, respectively) were identified, sequenced, and cloned from B. orientalis, which were shown to encode proteins with open reading frames ranging in size from 266 (MSA-2c1) to 317 (MSA-1) amino acids. All the five proteins contain an MSA-2c superfamily conserved domain, with an identical signal peptide and glycosyl phosphatidyl inositol (GPI)-anchor for each of them. The five proteins were also predicted to contain B cell epitopes, with only three for BoMSA-2c1, the smallest protein in the BoVMSA family, while at least six for each of the others. Notably, BoMSA-2a1 has 2 identical copies, a specific phenomenon only present in B. orientalis. This research has determined the MSA genes of B. orientalis and provides a genetic basis for further research of functional genes in B. orientalis.

ARHGEF38 as a novel biomarker to predict aggressive prostate cancer.

Prostate cancer (PCa) metastasis is considered the leading cause of cancer death in males. Therapeutic strategies and diagnosis for stage-specific PCa have not been well understood. Rho guanine nucleotide exchange factor 38 (ARHGEF38) is related to tumor cell polarization and is frequently expressed in PCa. Microarray data of PCa were downloaded from GEO and TCGA databases. A total of 243 DEGs were screened, of which, 32 genes were upregulated. The results of enrichment analysis showed the participation of these DEGs in the tumor cell metastasis pathway. ARHGEF38 was significantly up-regulated in the four most prevalent cancers worldwide (p < 0.05), and its expression was higher in the tumor samples with higher Gleason score (GS). IHC, qRT-PCR, and western-blot analyses showed the higher expression of ARHGEF38 in PCa than benign prostatic hyperplasia (BPH). In addition, IHC results demonstrated a higher expression of ARHGEF38 in high-grade PCa than the low-grade PCa.

An extract of Perilla stem inhibits Src homology phosphatase-1 (SHP)-1 and influences insulin signaling.

Protein tyrosine phosphatases (PTPs) are enzymes that catalyze protein tyrosine dephosphorylation of which Src homology phosphatase-1 (SHP-1) is one of the best-validated, a widely distributed intracellular tyrosine phosphatase that contains two SH2 domains. Down regulation of SHP-1 tyrosine phosphatases was significantly increased sensitivity to insulin in insulin signaling pathway. Through in vitro enzymatic reaction kinetics experiment, we found that the extract of Perilla stem was a potential inhibitor to δSHP-1, the catalytic domain of SHP-1 protein tyrosine phosphatase, and its IC(50) was 4ug/ml, and was more sensitive towards SHP-1than other PTPs, which indicated that SHP-1 might be a target of the extract of Perilla stem. It can strengthened the level of tyrosine phosphorylation of insulin receptor (IR) and extracellular signal-regulated protein kinase (ERK) in HepG2 cells, and then activated the insulin signaling pathway through inhibiting the protein phosphorylation of SHP-1. These results demonstrated that the extract of Perilla stem could play an important role for diabetes treatment through inhibiting the level of SHP-1 in insulin signaling pathway.