Research for all: building a diverse researcher community for the All of Us Research Program.

Objectives: The NIH All of Us Research Program (All of Us) is engaging a diverse community of more than 10 000 registered researchers using a robust engagement ecosystem model. We describe strategies used to build an ecosystem that attracts and supports a diverse and inclusive researcher community to use the All of Us dataset and provide metrics on All of Us researcher usage growth.

Materials And Methods: Researcher audiences and diversity categories were defined to guide a strategy.

Exploring Communication Needs and Challenges in the Intensive Care Unit: A Survey Study From Providers' and Patients' Perspectives.

Purpose: Effective communication between nonspeaking patients and providers is critical for the quality of care in intensive care units (ICUs). This study aims to evaluate perspectives of health care providers and nonspeaking patients on effective communication and communication barriers in the ICU.

Method: Qualitative and quantitative survey methodologies were employed to evaluate providers' and patients' perspectives on effective communication.

Gender Communication Differences on a Pediatric Provider Listserv.

Background And Objectives: Gender-based communication differences are described in educational online communities, but have not been rigorously evaluated in medical online communities. Understanding gender differences in communication may provide insight into gender disparities in the medical profession. Our objective was to describe gender differences in post frequency, content, and language styles on the American Academy of Pediatrics Section on Hospital Medicine (SOHM) listserv.

Blood Glucose Testing Lancet and Paper Clip as a Milia Extractor.

In low-resource settings, dermatologists may not have the preferred tools needed to evaluate a patient or perform a procedure. Commonplace affordable supplies can be substituted when needed. We describe the use of a blood glucose testing lancet and a paper clip for milia extraction.

A Hierarchical Learning Approach for Human Action Recognition.

In the domain of human action recognition, existing works mainly focus on using RGB, depth, skeleton and infrared data for analysis. While these methods have the benefit of being non-invasive, they can only be used within limited setups, are prone to issues such as occlusion and often need substantial computational resources. In this work, we address human action recognition through inertial sensor signals, which have a vast quantity of practical applications in fields such as sports analysis and human-machine interfaces.