According to current assembly situation of infant incubator in company A, basic industrial engineering means such as time study was used to analyze the actual products assembly production and an assembly line was designed. The assembly line was modeled and simulated with software Flexsim. The problem of the assembly line was found by comparing simulation result and actual data, then through optimization to obtain high efficiency assembly line.
Equipment Design ; Humans ; Incubators, Infant ; Infant ; Software

According to current assembly situation of infant incubator in company A, basic industrial engineering means such as time study was used to analyze the actual products assembly production and an assembly line was designed. The assembly line was modeled and simulated with software Flexsim. The problem of the assembly line was found by comparing simulation result and actual data, then through optimization to obtain high efficiency assembly line.

Objective To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients. Methods High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor. Results Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5-and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells. Conclusion TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.

Objective To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients. Methods High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor. Results Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5-and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells. Conclusion TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.