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Sound speeds of pure fluid water are reported between 0.1 and 700 MPa, from 353 K down to the melting curves of the ice phases. The 2 sigma precision and accuracy of the new sound speed measurements are close to 0.02%, with an estimated pressure accuracy of 0.04% up to 700 MPa. Using additional published measurements, a new equation of state is derived extending from 240 to 500 K and from 0.1 to 2300 MPa, covering much of the sub-critical domain of water up to the ice VI - ice VII transition. Analysis of measurements and construction of the equation of state are accomplished with a flexible computational thermodynamic framework based on local basis functions in the form of tensor b splines. Relative to IAPWS-95 (the most comprehensive representation available), improvements in the accuracies of density, sound speed and specific heat are observed above 100 MPa, particularly near the solid-fluid phase boundaries. Accurate thermodynamic properties for aqueous solutions under an increasing range of pressures, temperatures and compositions are needed to address a variety of technical and scientific challenges: the present study provides measurements essential for improving the high-pressure and low temperature representation of water., Comment: Submitted to Journal of Chemical Physics

Social media analytics allows us to extract, analyze, and establish semantic from user-generated contents in social media platforms. This study utilized a mixed method including a three-step process of data collection, topic modeling, and data annotation for recognizing exercise related patterns. Based on the findings, 86% of the detected topics were identified as meaningful topics after conducting the data annotation process. The most discussed exercise-related topics were physical activity (18.7%), lifestyle behaviors (6.6%), and dieting (4%). The results from our experiment indicate that the exploratory data analysis is a practical approach to summarizing the various characteristics of text data for different health and medical applications.

Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a large scale conversational public health data set is a challenging research task. The goal of this research is to analyze the characteristics of the general public's opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter. A multi-component semantic and linguistic framework was developed to collect Twitter data, discover topics of interest about DDEO, and analyze the topics. From the extracted 4.5 million tweets, 8% of tweets discussed diabetes, 23.7% diet, 16.6% exercise, and 51.7% obesity. The strongest correlation among the topics was determined between exercise and obesity. Other notable correlations were: diabetes and obesity, and diet and obesity DDEO terms were also identified as subtopics of each of the DDEO topics. The frequent subtopics discussed along with Diabetes, excluding the DDEO terms themselves, were blood pressure, heart attack, yoga, and Alzheimer. The non-DDEO subtopics for Diet included vegetarian, pregnancy, celebrities, weight loss, religious, and mental health, while subtopics for Exercise included computer games, brain, fitness, and daily plan. Non-DDEO subtopics for Obesity included Alzheimer, cancer, and children. With 2.67 billion social media users in 2016, publicly available data such as Twitter posts can be utilized to support clinical providers, public health experts, and social scientists in better understanding common public opinions in regard to diabetes, diet, exercise, and obesity., Comment: International Journal of Information Management (2017)

Within-host genetic sequencing from samples collected over time provides a dynamic view of how viruses evade host immunity. Immune-driven mutations might stimulate neutralization breadth by selecting antibodies adapted to cycles of immune escape that generate within-subject epitope diversity. Comprehensive identification of immune-escape mutations is experimentally and computationally challenging. With current technology, many more viral sequences can readily be obtained than can be tested for binding and neutralization, making down-selection necessary. Typically, this is done manually, by picking variants that represent different time-points and branches on a phylogenetic tree. Such strategies are likely to miss many relevant mutations and combinations of mutations, and to be redundant for other mutations. Longitudinal Antigenic Sequences and Sites from Intrahost Evolution (LASSIE) uses transmitted-founder loss to identify virus "hot-spots" under putative immune selection and chooses sequences that represent recurrent mutations in selected sites. LASSIE favors earliest sequences in which mutations arise. With well-characterized longitudinal Env sequences, we confirmed selected sites were concentrated in antibody contacts and selected sequences represented diverse antigenic phenotypes. Practical applications include rapidly identifying immune targets under selective pressure within a subject, selecting minimal sets of reagents for immunological assays that characterize evolving antibody responses, and for immunogens in polyvalent "cocktail" vaccines., Comment: 73 pages, 10 figures, 8 supplemental figures; 18 full-resolution figures appear at the end of the manuscript. Presented at 22nd International Workshop on HIV Dynamics and Evolution, Budapest, May 2015