Association between waist circumference and female infertility in the United States.

BACKGROUND: Obesity has significant implications for fertility and reproductive health. However, evidences linking abdominal obesity to female infertility were limited and inconclusive. Our objective was to figure out the potential relationship between waist circumference (WC) and infertility among women of childbearing age in the United States using data from the National Health and Nutrition Examination Survey (NHANES). METHODS: Our cross-sectional study included 3239 female participants aged 18-45 years. To explore the independent relationship between WC and female infertility, the weighted multivariable logistic regression and smoothed curve fitting were performed. Interaction and subgroup analyzes were then conducted for secondary analysis. RESULTS: WC was positively associated with female infertility independent of BMI after adjusting for BMI and other potential confounders. In fully adjusted model, for every 1cm increase in waist circumference, the risk of infertility increased by 3% (OR = 1.03, 95% CI: 1.01-1.06). When WC was divided into five equal groups, women in the highest quintile had 2.64 times risk of infertility than that in the lowest quintile (OR = 2.64, 95% CI: 1.31-5.30). Smooth curve fitting revealed a non-linear but positively dose-dependent relationship between WC and female infertility. Furthermore, we found an inverted U-shaped relationship (turning point: 113.5 cm) between WC and female infertility in participants who had moderate recreational activities and a J-shaped relationship (turning point: 103 cm) between WC and female infertility in participants who had deficient recreational activities. CONCLUSIONS: Waist circumference is a positive predictor of female infertility, independent of BMI. Moderate recreational activities can lower the risk of female infertility associated with abdominal obesity.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.