Association between premenstrual syndrome and eating disturbance in college students: a cross-sectional study.

BACKGROUND: Premenstrual syndrome (PMS) is a severe problem in women, and a well-balanced diet helps improve PMS symptoms. Eating disturbances are a major health problem in young women. Limited research has explored the correlation between eating behaviors and PMS symptoms in Japan. This study aimed to compare eating disturbances and the severity of PMS symptoms in college students. METHODS: This study was conducted among female college students using an online questionnaire. The questionnaire included basic information (age, height, and weight), PMS symptoms, and eating behaviors assessed using the Eating Attitudes Test 26. RESULTS: The proportion of those with PMS symptoms who were disturbed by PMS symptoms was significantly higher in the group with eating disturbance. Those who were affected by the physical symptoms of PMS had significantly higher scores on the subscales related to diet, bulimia and food preoccupation. CONCLUSION: The results showed an association between PMS symptom severity and eating disturbance. The findings of this study indicate that individuals with eating disturbances may experience adverse effects on PMS symptoms, even in cases where weight is not at the extremes of excessive underweight or obesity.

An Unmanned Aerial Vehicle Indoor Low-Computation Navigation Method Based on Vision and Deep Learning.

Recently, unmanned aerial vehicles (UAVs) have found extensive indoor applications. In numerous indoor UAV scenarios, navigation paths remain consistent. While many indoor positioning methods offer excellent precision, they often demand significant costs and computational resources. Furthermore, such high functionality can be superfluous for these applications. To address this issue, we present a cost-effective, computationally efficient solution for path following and obstacle avoidance. The UAV employs a down-looking camera for path following and a front-looking camera for obstacle avoidance. This paper refines the carrot casing algorithm for line tracking and introduces our novel line-fitting path-following algorithm (LFPF). Both algorithms competently manage indoor path-following tasks within a constrained field of view. However, the LFPF is superior at adapting to light variations and maintaining a consistent flight speed, maintaining its error margin within ±40 cm in real flight scenarios. For obstacle avoidance, we utilize depth images and YOLOv4-tiny to detect obstacles, subsequently implementing suitable avoidance strategies based on the type and proximity of these obstacles. Real-world tests indicated minimal computational demands, enabling the Nvidia Jetson Nano, an entry-level computing platform, to operate at 23 FPS.

Lysine acetyltransferase 6A maintains CD4+ T cell response via epigenetic reprogramming of glucose metabolism in autoimmunity.

Augmented CD4+ T cell response in autoimmunity is characterized by extensive metabolic reprogramming. However, the epigenetic molecule that drives the metabolic adaptation of CD4+ T cells remains largely unknown. Here, we show that lysine acetyltransferase 6A (KAT6A), an epigenetic modulator that is clinically associated with autoimmunity, orchestrates the metabolic reprogramming of glucose in CD4+ T cells. KAT6A is required for the proliferation and differentiation of proinflammatory CD4+ T cell subsets in vitro, and mice with KAT6A-deficient CD4+ T cells are less susceptible to experimental autoimmune encephalomyelitis and colitis. Mechanistically, KAT6A orchestrates the abundance of histone acetylation at the chromatin where several glycolytic genes are located, thus affecting glucose metabolic reprogramming and subsequent CD4+ T cell responses. Treatment with KAT6A small-molecule inhibitors in mouse models shows high therapeutic value for targeting KAT6A in autoimmunity. Our study provides novel insights into the epigenetic programming of immunometabolism and suggests potential therapeutic targets for patients with autoimmunity.