Individuality and ethnicity eclipse a short-term dietary intervention in shaping microbiomes and viromes.

Many diseases linked with ethnic health disparities associate with changes in microbial communities in the United States, but the causes and persistence of ethnicity-associated microbiome variation are not understood. For instance, microbiome studies that strictly control for diet across ethnically diverse populations are lacking. Here, we performed multiomic profiling over a 9-day period that included a 4-day controlled vegetarian diet intervention in a defined geographic location across 36 healthy Black and White females of similar age, weight, habitual diets, and health status. We demonstrate that individuality and ethnicity account for roughly 70% to 88% and 2% to 10% of taxonomic variation, respectively, eclipsing the effects a short-term diet intervention in shaping gut and oral microbiomes and gut viromes. Persistent variation between ethnicities occurs for microbial and viral taxa and various metagenomic functions, including several gut KEGG orthologs, oral carbohydrate active enzyme categories, cluster of orthologous groups of proteins, and antibiotic-resistant gene categories. In contrast to the gut and oral microbiome data, the urine and plasma metabolites tend to decouple from ethnicity and more strongly associate with diet. These longitudinal, multiomic profiles paired with a dietary intervention illuminate previously unrecognized associations of ethnicity with metagenomic and viromic features across body sites and cohorts within a single geographic location, highlighting the importance of accounting for human microbiome variation in research, health determinants, and eventual therapies. Trial Registration: ClinicalTrials.gov ClinicalTrials.gov Identifier: NCT03314194.

Utilizing Smartphones for Approachable IoT Education in K-12.

Distributed computing, computer networking, and the Internet of Things (IoT) are all around us, yet only computer science and engineering majors learn the technologies that enable our modern lives. This paper introduces PhoneIoT, a mobile app that makes it possible to teach some of the basic concepts of distributed computation and networked sensing to novices. PhoneIoT turns mobile phones and tablets into IoT devices and makes it possible to create highly engaging projects through NetsBlox, an open-source block-based programming environment focused on teaching distributed computing at the high school level. PhoneIoT lets NetsBlox programs-running in the browser on the student's computer-access available sensors. Since phones have touchscreens, PhoneIoT also allows building a Graphical User Interface (GUI) remotely from NetsBlox, which can be set to trigger custom code written by the student via NetsBlox's message system. This approach enables students to create quite advanced distributed projects, such as turning their phone into a game controller or tracking their exercise on top of an interactive Google Maps background with just a few blocks of code.

Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support.

In a two-wave, 4-month longitudinal study of 308 adults, two hypotheses were tested regarding the relation of Twitter-based measures of online social media use and in-person social support with depressive thoughts and symptoms. For four of five measures, Twitter use by in-person social support interactions predicted residualized change in depression-related outcomes over time; these results supported a corollary of the social compensation hypothesis that social media use is associated with greater benefits for people with lower in-person social support. In particular, having a larger Twitter social network (i.e., following and being followed by more people) and being more active in that network (i.e., sending and receiving more tweets) are especially helpful to people who have lower levels of in-person social support. For the fifth measure (the sentiment of Tweets), no interaction emerged; however, a beneficial main effect offset the adverse main effect of low in-person social support.

Wireless sensor node localization.

For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

A modeling environment for patient portals.

Clinical Information Systems (CIS) are complex environments that integrate information technologies, humans, and patient data. Given the sensitivity of patient data, federal regulations require health care providers to define privacy and security policies and to deploy enforcement technologies. The introduction of model-based design techniques, combined with the development of high-level modeling abstractions and analysis methods, provide a mechanism to investigate these concerns by conceptually simplifying CIS without sacrificing expressive power. This work introduces the Model-based Design Environment for Clinical Information Systems (MODECIS), which is a graphical design environment that assists CIS architects in formalizing systems and services. MODECIS leverages Service-Oriented Architectures to create realistic system models as abstractions. MODECIS enables the analysis of legacy architectures and the design and simulation of future CIS. We present the feasibility of MODECIS by modeling operations, such as user authentication, of MyHealth@Vanderbilt, a real world patient portal in use at Vanderbilt University Medical Center.