Confidentiality Discussions and Private Time With a Health-Care Provider for Youth,United States, 2016

Purpose

The objective of the study was to define factors associated with adolescent and young adult (AYA) experiences with private time and having discussed confidentiality and the impact of these experiences on improving delivery of clinical preventive services.

Quantification of 5-HT1A and 5-HT2A receptor Binding in Depressed Suicide Attempters and Non-Attempters

Objective: To determine serotonin system abnormalities related to major depression or previous suicidal behavior.

Methods: [¹¹C]WAY100635, [¹⁸F]altanserin and positron emission tomography were used to compare 5-HT_1A and 5-HT_2A binding in MDD patients divided into eight past suicide attempters (>4yrs prior to scanning) and eight lifetime non-attempters, and both groups were compared to eight healthy volunteers.

Results: The two receptor types differed in binding pattern across brain regions from each other, but there were no differences in binding between healthy volunteers and the two depressed groups or between depressed suicide attempters and non-attempters. No effects of depression severity or lifetime aggression were observed for either receptor.

Conclusion: Limitations of this study include small sample size and absence of high lethality suicide attempts in the depressed attempter group. No trait-like binding correlations with past suicide attempt or current depression were observed. Given the heterogeneity of nonfatal suicidal behavior, a larger sample study emphasizing higher lethality suicide attempts may find the serotonin biological phenotype seen in suicide decedents.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.