Glioblastoma (GBM) is a WHO grade 4 tumor and is the most malignant form of glioma. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), a mitochondrial enzyme involved in folate metabolism, has been reported to be highly expressed in several human tumors. However, little is known about the role of MTHFD2 in GBM. In this study, we aimed to explore the biological functions of MTHFD2 in GBM and identify the associated mechanisms. We performed experiments such as immunohistochemistry (IHC), western blot and transwell assays and found that MTHFD2 expression was lower in high-grade glioma than in low-grade glioma. Furthermore, a high expression of MTHFD2 was associated with a favorable prognosis, and MTHFD2 levels showed good prognostic accuracy for glioma patients. The overexpression of MTHFD2 could inhibit the migration, invasion, and proliferation of GBM cells, whereas its knockdown induced the opposite effect. Mechanistically, our findings revealed that MTHFD2 suppressed GBM progression independent of its enzymatic activity, likely by inducing cytoskeletal remodeling through the regulation of extracellular signal-regulated kinase 1/2 (ERK1/2) phosphorylation, thereby influencing GBM malignance. Collectively, these findings uncover a potential tumor-suppressor role of MTHFD2 in GBM cells. MTHFD2 may act as a promising diagnostic and therapeutic target for GBM treatment.

Frontline power grid workers are always facing plenty of stressors such as aerial work and high job demands, which may lead them to be less satisfied with their job. Therefore, this study aims to investigate frontline power grid workers’ job satisfaction (JS) and explore how it can be improved by its relationship with personality traits and work–family support (WFS). Data from 535 frontline power grid workers were collected from two power supply bureaus in Guangdong Province, China. Structural equation modeling (SEM) was adopted to examine the structural relationship between personality traits taken as independent variables, JS as dependent variable, and WFS as mediator. The bootstrap method was used to test the significance of indirect effects. Results suggested the overall job satisfaction of our sample is 3.34 ± 0.55 on a scale ranging from 1 to 5, and significantly correlated with personality traits and WFS. Moreover, the results of SEM and bootstrap indicated that WFS partially mediates the effect of neuroticism on JS and fully mediates the effect of conscientiousness and extraversion on JS. These findings shed light on how personality traits and environmental factors jointly impact JS and highlight the important role of WFS among frontline power grid workers.

The minimum weight dominating set problem (MWDSP) has been a popular research topic in recent years. The weights of vertexes may be considered as cost, time, or opponent’s payoff, which are uncertain in most cases. This paper discusses MWDSP under hybrid uncertain environments where the weights of vertexes are random fuzzy variables. First, random fuzzy theory is introduced to describe these hybrid uncertain variables. Then we propose three decision models based on three different decision criteria to solve MWDSP under hybrid uncertain environments. To solve the proposed models, we present a hybrid intelligent algorithm where random fuzzy simulation and genetic algorithm are embedded. Numerical experiments are performed in the last to show the robustness and effectiveness of the presented hybrid intelligent algorithm.

Edge covering problem, dominating set problem, and independent set problem are classic problems in graph theory except for vertex covering problem. In this paper, we study the maximum independent set problem under fuzzy uncertainty environments, which aims to search for the independent set with maximum value in a graph. First, credibility theory is introduced to describe the fuzzy variable. Three decision models are performed based on the credibility theory. A hybrid intelligence algorithm which integrates genetic algorithm and fuzzy simulation is proposed due to the unavailability of traditional algorithm. Finally, numerical experiments are performed to prove the efficiency of the fuzzy decision modes and the hybrid intelligence algorithm.

No abstract