Search

Women are more likely than men to develop the chronic, progressive autoimmune disease known as rheumatoid arthritis (RA). Although there may be a complex interplay between sex-based differences and autoimmune dysfunction. Their function in RA is largely unknown, though. The purpose of this study was to pinpoint the crucial genes and metabolic pathways that control biological variations in RA between men and women.First, the Gene Expression Omnibus database's gene expression information for GSE39340 and GSE55457 was downloaded (GEO). R software was used to find each of the individually identified differentially expressed genes (DEGs) between the sexes. DEGs that overlapped were found. The interactions between the overlapping DEGs were then further examined using a protein-protein interaction (PPI) network. The Kyoto Encyclopedia of Genes and Genomes and Gene Ontology tools, respectively, were used to perform enrichment analyses.According to our findings, there were 1169 DEGs that overlapped between RA males and females, comprising 845 up-regulated genes and 324 down-regulated genes. Ten hub genes, including PIK3R1, RAC1, HRAS, PTPN11, UQCRB, NDUFV1, EGF, UBA1, UBE2G1, and UBE2E1, were discovered in the PPI network. According to a functional enrichment analysis, these genes were primarily enriched in neurodegenerative illnesses, including various disease pathways, MAPK signaling, insulin signaling, and autophagy.The current data point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. Key Points • Our research point to the possibility that the MAPK pathway and autophagy may be significant contributors to sex differences in RA. • PTPN11, EGF, and UBA1 may be important genes linked to the gender development of RA and are anticipated to be therapeutic targets for the disease. • These findings may aid in the development of novel diagnostic and treatment techniques for RA in men and women.

The rise of a new generation of artificial intelligence technology, represented by deep learning, has promoted the vigorous development of natural language processing technology. As a typical application of natural language processing technology, human-machine intelligent dialogue system, coupled with its commercial value in the fields of voice assistants and chat robots, has become a hot topic in the current academic and industrial circles. In the past, the responses generated by intelligent dialogue robots is single and universal, and even the content is inappropriate. Therefore, this study proposes to use the knowledge graph as the background knowledge when the dialogue model generates the responses[1]. In order to meet the needs of users more closely, it also proposes to introduce the user information participating in the dialogue and the dialogue scene into the model. The model is trained and evaluated on the DuRecDial public dataset, and the optimized model is compared with the original model. The experimental results show that the model with these two modules has better effect than the original model, especially in the generation index, the F1, BLEU2 and DIST-2 indexes have been improved by 0.91%, 0.5% and 0.9% respectively.

This paper explores the use of passive guides to assist a human in manipulating heavy loads. A guide acts as a frictionless rail which confines the load to a one-dimensional curve in its configuration space. In this paper we formulate the problem of designing guides to effectively assist the human, and we apply the formulation to two types of materials handling tasks: pushing a heavy cart and single-arm reaching motions. Guides may be implemented by fixed rails or programmable constraint machines. Initial experiments with humans suggest the potential benefits of guided manipulation.

Repetitive manual materials handling of heavy loads is common in assembly and is a common cause of low back disorders. The manual manipulation of a heavy load may be made more comfortable by constraining the load to move along a guide. The frictionless guide directs the motion of the load to the goal configuration as the human operator provides forces in directions that are comfortable. We present our first experimental results in guided manipulation with the purpose of understanding motions and forces that are comfortable for human operators.