Respiratory Exacerbations Increase with Chronic PM Exposure in Current and Former Smokers.

Rationale: Short-term exposure to fine particulates (PM) transiently increases the risk of respiratory exacerbations, but the contribution of chronic, long-term particulate exposure to respiratory exacerbations is poorly defined.

Objectives: To assess long-term effects of PM exposure on risk of severe respiratory exacerbations.

Methods: A longitudinal cohort of current and former smokers with and without COPD were surveyed every six months for severe exacerbation events.

Assessing vulnerability for future Zika virus outbreaks using seroprevalence data and environmental suitability maps.

The 2015-17 Zika virus (ZIKV) epidemic in the Americas subsided faster than expected and evolving population immunity was postulated to be the main reason. Herd immunization is suggested to occur around 60-70% seroprevalence, depending on demographic density and climate suitability. However, herd immunity was only documented for a few cities in South America, meaning a substantial portion of the population might still be vulnerable to a future Zika virus outbreak.

A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA.

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental models, are based on the assumption of spatiotemporal homogeneity of the spread of the virus, which may cause forecasting to underperform, especially at high spatial resolutions. In this paper, we approach the forecasting task with an alternative technique-spatiotemporal machine learning.

Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions.

Measurements of human interaction through proxies such as social connectedness or movement patterns have proved useful for predictive modeling of COVID-19, which is a challenging task, especially at high spatial resolutions. In this study, we develop a Spatiotemporal autoregressive model to predict county-level new cases of COVID-19 in the coterminous US using spatiotemporal lags of infection rates, human interactions, human mobility, and socioeconomic composition of counties as predictive features. We capture human interactions through 1) Facebook- and 2) cell phone-derived measures of connectivity and human mobility, and use them in two separate models for predicting county-level new cases of COVID-19.

Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness.

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process.

MetricsVis: A Visual Analytics System for Evaluating Employee Performance in Public Safety Agencies.

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories.

VASSL: A Visual Analytics Toolkit for Social Spambot Labeling.

Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots.

The Validity, Generalizability and Feasibility of Summative Evaluation Methods in Visual Analytics.

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation methods specialized for VA.

A green route for the cross-coupling of azide anions with aryl halides under both base and ligand-free conditions: exceptional performance of a CuO-CuO-Cu-C nanocomposite.

A convenient, inexpensive and effective route for the preparation of a CuO-CuO-Cu-C nanocomposite is described here by applying Cu(ii) as a source of copper. Characterization of the nanocomposite was performed with X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), high-resolution TEM (HR-TEM), field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), and energy-dispersive X-ray spectroscopy (EDX). Analysis of the data showed that the particles of the nanocomposite are uniformly distributed and show high catalytic activity in the cross-coupling of sodium azide with various aryl iodides and bromides.

FT-IR study and solvent-implicit and explicit effect on stepwise tautomerism of Guanylurea: M06-2X as a case of study.

All 66 conformers of guanylurea were optimized and frequency calculations were performed at M06-2X/6-311++G(d,p) level of theory. Theses conformers were categorized into five tautomers, and the most stable conformer of each tautomer were found. Geometrical parameters indicated that these tautomers have almost planar structure.