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Purpose: This study was aimed to identify the expression pattern of vascular endothelial growth factor (VEGF) in non-small cell lung cancer (NSCLC) and to explore its potential correlation with the progression of NSCLC. Methods: Gene expression profile GSE39345 was downloaded from the Gene Expression Omnibus database. Twenty healthy controls and 32 NSCLC samples before chemotherapy were analyzed to identify the differentially expressed genes (DEGs). Then pathway enrichment analysis of the DEGs was performed and protein-protein interaction networks were constructed. Particularly, VEGF genes and the VEGF signaling pathway were analyzed. The sub-network was constructed followed by functional enrichment analysis. Results: Total 1666 up-regulated and 1542 down-regulated DEGs were identified. The down-regulated DEGs were mainly enriched in the pathways associated with cancer. VEGFA and VEGFB were found to be the initiating factor of VEGF signaling pathway. In addition, in the epidermal growth factor receptor (EGFR), VEGFA and VEGFB associated sub-network, kinase insert domain receptor (KDR), fibronectin 1 (FN1), transforming growth factor beta induced (TGFBI) and proliferating cell nuclear antigen (PCNA) were found to interact with at least two of the three hub genes. The DEGs in this sub-network were mainly enriched in Gene Ontology terms related to cell proliferation. Conclusion: EGFR, KDR, FN1, TGFBI and PCNA may interact with VEGFA to play important roles in NSCLC tumorigenesis. These genes and corresponding proteins may have the potential to be used as the targets for either diagnosis or treatment of patients with NSCLC. [ABSTRACT FROM AUTHOR]

The flexible job shop scheduling problem (FJSP) is a difficult discrete combinatorial optimization problem, which has been widely studied due to its theoretical and practical significance. However, previous researchers mostly emphasized on the production efficiency criteria such as completion time, workload, flow time, etc. Recently, with considerations of sustainable development, low-carbon scheduling problems have received more and more attention. In this paper, a low-carbon FJSP model is proposed to minimize the sum of completion time cost and energy consumption cost in the workshop. A new bio-inspired metaheuristic algorithm called discrete whale optimization algorithm (DWOA) is developed to solve the problem efficiently. In the proposed DWOA, an innovative encoding mechanism is employed to represent two sub-problems: Machine assignment and job sequencing. Then, a hybrid variable neighborhood search method is adapted to generate a high quality and diverse population. According to the discrete characteristics of the problem, the modified updating approaches based on the crossover operator are applied to replace the original updating method in the exploration and exploitation phase. Simultaneously, in order to balance the ability of exploration and exploitation in the process of evolution, six adjustment curves of a are used to adjust the transition between exploration and exploitation of the algorithm. Finally, some well-known benchmark instances are tested to verify the effectiveness of the proposed algorithms for the low-carbon FJSP. [ABSTRACT FROM AUTHOR]

In this paper, a novel improved whale optimization algorithm (IWOA), based on the integrated approach, is presented for solving the flexible job shop scheduling problem (FJSP) with the objective of minimizing makespan. First of all, to make the whale optimization algorithm (WOA) adaptive to the FJSP, the conversion method between the whale individual position vector and the scheduling solution is firstly proposed. Secondly, a resultful initialization scheme with certain quality is obtained using chaotic reverse learning (CRL) strategies. Thirdly, a nonlinear convergence factor (NFC) and an adaptive weight (AW) are introduced to balance the abilities of exploitation and exploration of the algorithm. Furthermore, a variable neighborhood search (VNS) operation is performed on the current optimal individual to enhance the accuracy and effectiveness of the local exploration. Experimental results on various benchmark instances show that the proposed IWOA can obtain competitive results compared to the existing algorithms in a short time. [ABSTRACT FROM AUTHOR]

MicroRNA-940 (miR-940) has been extensively studied in the pathogenesis of numerous types of human cancer; however, the expression pattern, roles and molecular mechanisms underlying the regulatory actions of miR-940 in glioma remain unknown. The present study aimed to further investigate miR-940 by studying its expression, roles and mechanisms of action in glioma. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect miR-940 expression in glioma tissues and cell lines. The regulatory effects of miR-940 in glioma cell proliferation and invasion were determined using MTT and cell invasion assays. Bioinformatics analyses was performed to identify the potential target of miR-940, which was further confirmed by luciferase reporter assay, RT-qPCR and western blot analysis. In the present study, significantly increased miR-940 expression levels were observed in glioma tissues and cell lines compared with normal brain tissues and normal human astrocytes, respectively. Decreased miR-940 expression levels attenuated glioma cell proliferation and invasion in vitro. Kruppel-like factor 9 (KLF9) was predicted as a potential target of miR-940. Further assays demonstrated that miR-940 negatively regulated KLF9 expression in glioma cells by directly targeting the 3′-untranslated regions of KLF9. Additionally, KLF9 expression was downregulated in glioma tissues and was inversely correlated with miR-940. Furthermore, KLF9 knockdown was able to rescue the effects of miR-940 on glioma cell proliferation and invasion. The results of the present study suggest that miR-940 may function as an oncogene in glioma by targeting KLF9 and may be a considered a therapeutic target for the treatment of gliomas. [ABSTRACT FROM AUTHOR]