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Objective: To investigate the association between folate levels and the risk of gestational diabetes mellitus (GDM) risk during the whole pregnancy. Design: In this retrospective cohort study of pregnant women, serum folate levels were measured before 24 gestational weeks (GW). GDM was diagnosed between 24th and 28th GW based on the criteria of the International Association of Diabetes and Pregnancy Study Groups. General linear models were performed to examine the association of serum folate with plasma glucose (i.e. linear regressions) and risk of GDM (i.e. log-binomial regressions) after controlling for confounders. Restricted cubic spline regression was conducted to test the dosage–response relationship between serum folate and the risk of GDM. Setting: A sigle, urban hospital in Shanghai, China. Participants: A total of 42 478 women who received antenatal care from April 2013 to March 2017 were included. Results: Consistent positive associations were observed between serum folate and plasma glucose levels (fasting, 1-h, 2-h). The adjusted relative risks (RR) and 95 % CI of GDM across serum folate quartiles were 1·00 (reference), 1·15 (95 % CI (1·04, 1·26)), 1·40 (95 % CI (1·27, 1·54)) and 1·54 (95 % CI (1·40, 1·69)), respectively (P -for-trend < 0·001). The positive association between serum folate and GDM remained when stratified by vitamin B12 (adequate v. deficient groups) and the GW of serum folate measurement (≤13 GW v. >13 GWs) Conclusions: The findings of this study may provide important evidence for the public health and clinical guidelines of pregnancy folate supplementation in terms of GDM prevention. [ABSTRACT FROM AUTHOR]

Optimal sensor placement aims to use a limited number of sensors to obtain as much information about a structure as possible. This study investigates the optimal placement of triaxial accelerometers for automated monitoring of high-rise buildings using a newly developed hypotrochoid spiral optimization algorithm. The 632-meter-tall Shanghai Tower is used as an example structure to demonstrate and compare the hypotrochoid spiral optimization algorithm with seven existing optimization algorithms, including the artificial bee colony algorithm, flower pollination algorithm, spiral optimization algorithm, Jaya algorithm, lion pride optimization algorithm, particle swarm optimization algorithm, and teaching-learning based optimization algorithm. Three objective functions based on the modal assurance criterion are applied to measure the utility of sensor configurations for modal identification. Two different structural models with different types of elements are investigated to identify the effect of structural models on the optimal sensor placement. The results reveal that the hypotrochoid spiral optimization algorithm provides the best solution using a detailed structural model and multi-objective function. • The hypotrochoid spiral algorithm is applied to optimize sensor placement for the first time. • The optimal placement of triaxial accelerometers is investigated for automated monitoring. • A bi-objective function is presented and shows better results than single objective functions. • The proposed optimization method shows the highest accuracy and efficiency. • A detailed structural model is essential for accurate modal analysis and sensor placement. [ABSTRACT FROM AUTHOR]