Nuclear receptors are transcriptional regulators involved in almost all biological processes such as cell growth, differentiation, apoptosis, substance metabolism and tumor formation, and they can be regulated by small molecules that bind to them. Autophagy is a special way of programmed cell death and it is a highly conserved metabolic process. Once autophagy defects or excessive autophagy occur, the disease will develop. In recent years, numerous studies have shown that nuclear receptors are related to autophagy. Therefore, this paper mainly reviews the research progress on nuclear receptors involved in the regulation of autophagy, and focuses on the mechanism of several nuclear receptors involved in the regulation of autophagy, aiming at understanding the molecular basis of how nuclear receptors participate in regulating autophagy, as well as providing possible ideas and strategies for the treatment of corresponding diseases.
Objective To understand the sexual behavior statu of married migrant workers in Chongqing, so as to provide the government with foundation for formulating related social strategies. Methods A multi-stage stratified cluster random sampling method was applied to select respondents, and the face-to-face interview was performed to investigate by trained investigators. The date was analyzed by using SAS9.0 software. Results A total of 377 married migrant workers were investigated. Among them, 226 (59.95%) lived with their spouses, of whom 13.72% had extramarital sexual behaviors, with 48.39% commercial sexual behaviors; the other 151 (40.05%) didn’t live with their spouses, of whom 49.25% had no normal sexual life for at least three months and 28.48% had extramarital sexual behaviors, with 60.47% commercial sexual behaviors. 66.41% of male migrant workers and 46.28% of female migrant workers had sexual depression, and different genders between the migrant workers were statistically significant (Plt;0.05). Conclusion There is sexual depression among married migrant workers. The related measures should be taken according to the status of their sexual behaviors, so as to improve their status of sexual depression.
Aiming at the problems of individual differences in the asynchrony process of human lower limbs and random changes in stride during walking, this paper proposes a method for gait recognition and prediction using motion posture signals. The research adopts an optimized gated recurrent unit (GRU) network algorithm based on immune particle swarm optimization (IPSO) to establish a network model that takes human body posture change data as the input, and the posture change data and accuracy of the next stage as the output, to realize the prediction of human body posture changes. This paper first clearly outlines the process of IPSO's optimization of the GRU algorithm. It collects human body posture change data of multiple subjects performing flat-land walking, squatting, and sitting leg flexion and extension movements. Then, through comparative analysis of IPSO optimized recurrent neural network (RNN), long short-term memory (LSTM) network, GRU network classification and prediction, the effectiveness of the built model is verified. The test results show that the optimized algorithm can better predict the changes in human posture. Among them, the root mean square error (RMSE) of flat-land walking and squatting can reach the accuracy of 10−3, and the RMSE of sitting leg flexion and extension can reach the accuracy of 10−2. The R2 value of various actions can reach above 0.966. The above research results show that the optimized algorithm can be applied to realize human gait movement evaluation and gait trend prediction in rehabilitation treatment, as well as in the design of artificial limbs and lower limb rehabilitation equipment, which provide a reference for future research to improve patients' limb function, activity level, and life independence ability.