The term “resilience” refers to the ability to adapt successfully to stress, trauma and adversity, enabling individuals to avoid stress-induced mental disorders such as depression, posttraumatic stress disorder (PTSD) and anxiety. Here, we review evidence from both animal models and humans that is increasingly revealing the neurophysiological and neuropsychological mechanisms that underlie stress susceptibility, as well as active mechanisms underlying the resilience phenotype. Ultimately, this growing understanding of the neurobiological mechanisms of resilience should result in the development of novel interventions that specifically target neural circuitry and brain areas that enhance resilience and lead to more effective treatments for stress-induced disorders. Stress resilience can be improved, but the outcomes and effects depend on the type of intervention and the species treated.

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Three new phenyl ether derivatives, 3‐hydroxy‐5‐(3‐hydroxy‐5‐methylphenoxy)benzoic acid (1), 3,4‐dihydroxy‐5‐(3‐hydroxy‐5‐methylphenoxy)benzoic acid (2), 3‐[3‐hydroxy‐5‐(hydroxymethyl)phenoxy]‐5‐methylphenol (3), and three known compounds 4–6 were obtained from the fermentation broth of Aspergillus carneus HQ889708, which was isolated from sea water from South China Sea. The structures of compounds 1–3 were established on the basis of spectroscopic methods including ESI‐MS and NMR. Compounds 4–6 were reported before as synthesized products, herein, they are reported from nature for the first time.

Due to the diversity of text expressions, the text sentiment classification algorithm based on semantic understanding is difficult to establish a perfect sentiment dictionary and sentence matching template, which leads to strong limitations of the algorithm. In particular, it has certain difficulties in the classification of student sentiments. Based on this, this paper analyzes the student sentiment classification model by neural network algorithm and uses the student group as an example to explore the application of neural network model in sentiment classification. Moreover, the regularization method is added to the loss function of LSTM so that the output at any time is related to the output at the previous time. In addition, the sentimental drift distribution of sentimental words on each sentimental label is added to the regularizer, and the sentimental information is merged with the two-way LSTM to allow the model to choose forward or reverse. Finally, in order to verify the research model, the performance of the model proposed in this paper is studied through experimental research. The research shows that the model proposed in this paper has better comprehensive performance than the traditional model and can meet the actual needs of students’ sentiment classification.