Abstract Major depressive disorders are emerging health problems that affect millions of people worldwide. However, treatment options and targets for drug development are limited. Impaired adult hippocampal neurogenesis is emerging as a key contributor to the pathology of major depressive disorders. We previously demonstrated that increasing the expression of the multifunctional scaffold protein Axis inhibition protein (Axin) by administration of the small molecule XAV939 enhances embryonic neurogenesis and affects social interaction behaviors. This prompted us to examine whether increasing Axin protein level can enhance adult hippocampal neurogenesis and thus contribute to mood regulation. Here, we report that stabilizing Axin increases adult hippocampal neurogenesis and exerts an antidepressant effect. Specifically, treating adult mice with XAV939 increased the amplification of adult neural progenitor cells and neuron production in the hippocampus under both normal and chronic stress conditions. Furthermore, XAV939 injection in mice ameliorated depression-like behaviors induced by chronic restraint stress. Thus, our study demonstrates that Axin/XAV939 plays an important role in adult hippocampal neurogenesis and provides a potential therapeutic approach for mood-related disorders.
In recent years, deep learning has become prevalent in Remaining Useful-Life (RUL) prediction of bearings. The current deep-learning-based RUL methods tend to extract high dimensional features from the original vibration data to construct the Health Indicators (HIs), and then use the HIs to predict the remaining life of the bearings. These approaches ignore the sequential relationship of the original vibration data and seriously affect the prediction accuracy. In order to tackle this problem, we propose a hard negative sample contrastive learning prediction model (HNCPM) with encoder module, GRU regression module and decoder module, used for feature embedding, regression RUL prediction and vibration data reconstruction, respectively. We introduce self-supervised contrast learning by constructing positive and negative samples of vibration data rather than constructing any health indicators. Furthermore, to avoid the subtle variability of vibration data in the health stage to aggravate the degradation features learning of the model, we propose the hard negative samples by cosine similarity, which are most similar to the positive sample. Meanwhile, a novel infoNCE and MSE-based loss function is derived and applied to the HNCPM to simultaneously optimize a lower bound on mutual information of the positive and negative sample over life cycle, as well as the discrepancy between true and predicted values of the vibration data, such that the model can learn the fine-grained degradation representations by predicting the future without any HIs as labels. The HNCPM is validated on the IEEE PHM Challenge 2012 dataset. The results demonstrate that the prediction performance of our model is superior to the state-of-the-art methods.
The purpose of this study was to examine the emotional intelligence of undergraduates by using an Emotional Intelligence Scale. It was also to compare the gender and grade differences with respect to undergraduates' emotional intelligence. The results indicated that, (1)the undergraduates demonstrated better emotional intelligence in all aspects, but slightly less emotional management. (2)Also, male students exhibited higher emotional management than female students. (3)In addition, the senior students profoundly influenced by the university education had a better show in emotional intelligence, whereas the freshmen expressed less emotional intelligence. Based on these findings, the suggestions for the universities are to attach importance to the education of emotional management, the guidance of the students with emotional problems, and the freshmen emotional intelligence education.
The behavior of Schottky contacts in AlGaN/GaN high electron mobility transistors (HEMTs) is investigated by temperature-dependent current—voltage (T—I—V) measurements from 300 K to 473 K. The ideality factor and barrier height determined based on the thermionic emission (TE) theory are found to be strong functions of temperature, while present a great deviation from the theoretical value, which can be expounded by the barrier height inhomogeneities. In order to determine the forward current transport mechanisms, the experimental data are analyzed using numerical fitting method, considering the temperature-dependent series resistance. It is observed that the current flow at room temperature can be attributed to the tunneling mechanism, while thermionic emission current gains a growing proportion with an increase in temperature. Finally, the effective barrier height is derived based on the extracted thermionic emission component, and an evaluation of the density of dislocations is made from the I—V characteristics, giving a value of 1.49 × 107 cm−2.
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