This cross-sectional study assesses trends in the prevalence of obesity in children and adolescents in the US between 2011 and 2020.

Women with polycystic ovary syndrome (PCOS) have reduced GnRH sensitivity to suppression by ovarian steroids, which can be ameliorated by androgen blockade. We studied nine PCOS women and nine controls to determine whether metformin could change feedback inhibition by estradiol (E(2)) and progesterone (P). LH was measured every 10 min, and FSH, E(2), P, and testosterone (T) were measured every 2 h. Frequently sampled iv glucose tolerance test was performed at the end of each admission. After the first admission, metformin (500 mg, three times a day) was started. The second admission occurred on d 8-11 of the next menstrual cycle in controls and on d 28 in PCOS patients. Patients subsequently took E(2) and P for 1 wk until the third admission. At baseline, PCOS women had higher T, free T, androstenedione, and estrone. After 4 wk of metformin, controls had a slight reduction in total T, but free T was unchanged. However, PCOS patients had reduced insulin, T, and E(2), and increased LH mean/amplitude and FSH. After ovarian steroids, controls had a greater reduction in LH pulse frequency than PCOS (61 vs. 25%). These results suggest that the beneficial effects of metformin on ovulatory function in obese PCOS women are probably not mediated by enhanced hypothalamic sensitivity.

The item details page (IDP) is a web page on an e-commerce website that provides information on a specific product or item listing. Just below the details of the item on this page, the buyer can usually find recommendations for other relevant items. These are typically in the form of a series of modules or carousels, with each module containing a set of recommended items. The selection and ordering of these item recommendation modules are intended to increase discover-ability of relevant items and encourage greater user engagement, while simultaneously showcasing diversity of inventory and satisfying other business objectives. Item recommendation modules on the IDP are often curated and statically configured for all customers, ignoring opportunities for personalization. In this paper, we present a scalable end-to-end production system to optimize the personalized selection and ordering of item recommendation modules on the IDP in real-time by utilizing deep neural networks. Through extensive offline experimentation and online A/B testing, we show that our proposed system achieves significantly higher click-through and conversion rates compared to other existing methods. In our online A/B test, our framework improved click-through rate by 2.48% and purchase-through rate by 7.34% over a static configuration. CCS Concepts: • Information systems → Learning to rank; Novelty in information retrieval; • Computing methodologies → Ranking; • Applied computing → E-commerce infrastructure.

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