Trust and trustworthiness are essential to an efficient economy and play crucial roles in social life. Previous evidence from behavioral experiments has revealed that the trustworthiness of individuals is closely related with their altruistic preference. It has been demonstrated that the ventromedial prefrontal cortex (vmPFC) is associated with decisions involving trustworthiness. Moreover, vmPFC lesion patients showed less trustworthiness and altruism than control subjects, indicating the indispensable role of this specific brain area in human social interactions. However, the causal relationship between this neural area and trustworthiness, as well as altruism, has not been fully revealed. The potential neural basis behind the behavior of trustees’ repayment has also seldom been discussed. In the present study, we aimed to provide evidence of a direct link between the neural and behavioral results through the application of transcranial direct current stimulation (tDCS) over the vmPFC of our participants. We found that activating the vmPFC could promote both the trustworthiness and altruism of our participants. We also show that enhancing the excitability of the vmPFC using tDCS increased the trustworthiness of the participants, and this promoting effect might be attributable to the enhancement of individuals’ altruistic preference. In addition, we revealed that the enhancing effect in trustworthiness and altruism might be specific to the activation of the vmPFC by applying tDCS over another brain region within the prefrontal cortex as a control site. Crucially, our findings provide direct evidence supporting the critical role of the vmPFC in cooperative behaviors in economic interactions, especially the trustees’ repayment in the trust game and the dictators’ altruistic transfer in the dictator game.

Fog computing provides users with data storage, computing, and other services by using fog layer devices close to edge devices. Tasks and resource scheduling in fog computing has become a research hotspot. For the multi-objective task-scheduling problem in fog computing, an adaptive multi-objective optimization task scheduling method for fog computing (FOG-AMOSM) is proposed in this paper. In this method, the total execution time and the task resource cost in the fog network are taken as the optimization target of resource allocation, and a multi-objective task scheduling model is designed. Since the objective model is a Pareto optimal solution problem, the global optimal solution can be obtained by using multi-objective optimization theory and the improved multi-objective evolutionary heuristic algorithm. Moreover, to obtain a better distribution of the current task scheduling group, the neighborhood is adaptively changed according to the current situation of the task scheduling group in fog computing, which avoids the problem that the neighborhood value caused by the neighborhood policy in the multi-objective algorithm affects the distribution of the task scheduling population. This algorithm is used to solve the non-inferior solution set of the utility function index of fog computing task scheduling to try to solve the multi-objective cooperative optimization problem in fog computing task scheduling. The results show that the proposed method has better performance than other methods in terms of total task execution time, resource cost and load dimensions. INDEX TERMS Cloud computing, fog computing, task scheduling, multi-objective optimization algorithm, cyber-physical-social service.

Village is an important implementation unit of national poverty alleviation and development strategies of rural China, and identifying the poverty degree, poverty type and poverty contributing factors of each poverty-stricken village is the precondition and guarantee of taking targeted measures in poverty alleviation strategies of China. To respond it, we construct a village-level multidimensional poverty measuring model, and use indicator contribution degree indices and linear regression method to explore poverty factors, while adopting Least Square Error (LSE) model and spatial econometric analysis model to identify the villages' poverty types and poverty difference. The case study shows that: (1) Spatially, there is obvious territoriality in the distribution of poverty-stricken villages, and the poverty-stricken villages are concentrated in contiguous poverty-stricken areas. The areas with the highest VPI, in a descending order, are Gansu, Yunnan, Guizhou, Guangxi, Hunan, Qinghai, Sichuan, and Xinjiang. (2) The main factors contributing to the poverty of poverty-stricken villages in rural China include road construction, terrain type, frequency of natural disasters, per capita net income, labor force ratio, and cultural quality of labor force. The main causes of poverty include underdeveloped road construction conditions, frequent natural disasters, low level of income, and labor conditions. (3) Chinese poverty-stricken villages include six main subtypes, and most poverty-stricken villages are affected by multiple poverty-forming factors, reflected by a relatively high proportion of the three-factor dominant type, four-factor coordinative type, and five-factor combinative type. (4) There exist significant poverty differences in terms of geographical location and policy support, and the governments still need to carry out targeted poverty alleviation measures according to local conditions. The research can not only draw a macro overall poverty-reduction outline of impoverished villages in China, but also depict the specific poverty characteristics of each village, helping the government departments of pov

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The results revealed endobronchial ultrasound elastography is a new technique with high sensitivity and specificity. It has a fine performance in diagnosing intrathoracic lymph nodes.

Angiogenesis contributes fundamentally to embryonic development, tissue homeostasis, and wound healing. Basic fibroblast growth factor (FGF2) is recognized as the first proangiogenic molecule discovered, and it facilitates angiogenesis by activating FGF receptor 1 (FGFR1) signaling in endothelial cells. However, the precise roles of FGFR and the FGF/FGFR signaling axis in angiogenesis remain unclear, especially because of the contradictory phenotypes of in vivo FGF and FGFR gene deficiency models. Our previous study results suggested a potential role of posttranslational small ubiquitin-like modifier modification (SUMOylation), with highly dynamic regulatory features, in vascular development and disorder. Here, we identified SENP1-regulated endothelial FGFR1 SUMOylation at conserved lysines responding to proangiogenic stimuli, while SENP1 functioned as the deSUMOylase. Hypoxia-enhanced FGFR1 SUMOylation restricted the tyrosine kinase activation of FGFR1 by modulating the dimerization of FGFR1 and FGFR1 binding with its phosphatase PTPRG. Consequently, it facilitated the recruitment of FRS2α to VEGFR2 but limited additional recruitment of FRS2α to FGFR1, supporting the activation of VEGFA/VEGFR2 signaling in endothelial cells. Furthermore, SUMOylation-defective mutation of FGFR1 resulted in exaggerated FGF2/FGFR1 signaling but suppressed VEGFA/VEGFR2 signaling and the angiogenic capabilities of endothelial cells, which were rescued by FRS2α overexpression. Reduced angiogenesis and endothelial sprouting in mice bearing an endothelial-specific, FGFR1 SUMOylation-defective mutant confirmed the functional significance of endothelial FGFR1 SUMOylation in vivo. Our findings identify the reversible SUMOylation of FGFR1 as an intrinsic fine-tuned mechanism in coordinating endothelial angiogenic signaling during neovascularization; SENP1-regulated FGFR1 SUMOylation and deSUMOylation controls the competitive recruitment of FRS2α by FGFR1 and VEGFR2 to switch receptor-complex formation responding to hypoxia and normoxia angiogenic environments.

Quinone methide (QM) as a latent trapping unit has been widely explored in activity‐based self‐immobilizing reagents. However, further application of this strategy has been largely hampered by the limited labeling efficiency to proteins. In this study, a thorough investigation on the labeling efficiency and the structure of QM‐based trapping unit is presented, from which a QM with multiple leaving groups was identified as an optimal trapping unit. An alkaline phosphatase (ALP) immobilizing reagent featured with this multiple‐labeling trapping unit exhibited lower nonspecific binding and, remarkably, a significantly higher labeling efficiency over other immobilizing reagents upon enzymatic activation. The utility of this imaging reagent was further demonstrated with the in vitro and in vivo visualization of the ALP activities. Furthermore, the multiple functional trapping unit may find greater value in the other activity‐based immobilizing probes.