Artemisinin combination therapies (ACTs) are the WHO-recommended first-line therapies for uncomplicated Plasmodium falciparum malaria. The emergence and spread of artemisinin-resistant genotypes is a major global public health concern due to the increased rate of treatment failures that result. This is particularly germane for WHO designated ‘high burden to high impact’ (HBHI) countries, such as Burkina Faso, where there is increased emphasis on improving guidance, strategy, and coordination of local malaria response in an effort to reduce the prevalence of P. falciparum malaria. To explore how the increased adoption of ACTs may affect the HBHI malaria setting of Burkina Faso, we added spatial structure to a validated individual-based stochastic model of P. falciparum transmission and evaluated the long-term effects of increased ACT use. We explored how de novo emergence of artemisinin-resistant genotypes, such as pfkelch13 580Y, may occur under scenarios in which private-market drugs are eliminated or multiple first-line therapies (MFT) are deployed. We found that elimination of private market drugs would result in lower treatment failures rates (between 11.98% and 12.90%) when compared to the status quo (13.11%). However, scenarios incorporating MFT with equal deployment of artemether-lumefantrine (AL) and dihydroartemisinin-piperaquine (DHA-PPQ) may accelerate near-term drug resistance (580Y frequency ranging between 0.62 to 0.84 in model year 2038) and treatment failure rates (26.69% to 34.00% in 2038), due to early failure and substantially reduced treatment efficacy resulting from piperaquine-resistant genotypes. A rebalanced MFT approach (90% AL, 10% DHA-PPQ) results in approximately equal long-term outcomes to using AL alone but may be difficult to implement in practice.

Interactive maps can serve as powerful environmental decision-support tools. However, designing an interactive map that meets the needs of diverse constituencies is a challenge. In this article, we evaluate and characterize user needs for an interactive map and spatial decision-support tool called Beescape. Beescape is designed to visualize resources and environmental risks to bees and other pollinators (such as availability of nutritional resources from flowering plants and exposure to pesticides) in order to help users make informed decisions about managing bee populations and associated landscapes. We conducted a needs assessment workshop with twenty stakeholders from four user groups including beekeepers, growers, conservationists, and pollinator scientists to elicit their knowledge to guide future Beescape development. The results of the workshop identify current analytical gaps with the existing Beescape prototype, including the need for predictive and historical tools, more actionable data layers, finer-grain spatial data, and better explanations on what data represent and how they were created. Our findings on user's analytical, informational, and interface needs can be utilized to guide the future development of spatial decision support tools like Beescape, and our methodological approach may apply to other environmental informatics tools where it is important to design for multiple constituent user groups.

We widely use emojis in social networking to heighten, mitigate or negate the sentiment of the text. Emoji suggestions already exist in many cross-platform applications but an emoji is predicted solely based a few prominent words instead of understanding the subject and substance of the text. Through this paper, we showcase the importance of using Twitter features to help the model understand the sentiment involved and hence to predict the most suitable emoji for the text. Hashtags and Application Sources like Android, etc. are two features which we found to be important yet underused in emoji prediction and Twitter sentiment analysis on the whole. To approach this shortcoming and to further understand emoji behavioral patterns, we propose a more balanced dataset by crawling additional Twitter data, including timestamp, hashtags, and application source acting as additional attributes to the tweet. Our data analysis and neural network model performance evaluations depict that using hashtags and application sources as features allows to encode different information and is effective in emoji prediction.

In ShortMeeting the digital education needs of faculty and students in the 21st century is a crucial aspect of the evolving landscape of higher education.Universities are currently preparing studen...

The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations.

Bovine tuberculosis (bTB) is a chronic disease of cattle that impacts productivity and represents a major public health threat. Despite the considerable economic costs and zoonotic risk consequences associated with the disease, accurate estimates of bTB prevalence are lacking in many countries, including India, where national control programmes are not yet implemented and the disease is considered endemic. To address this critical knowledge gap, we performed a systematic review of the literature and a meta-analysis to estimate bTB prevalence in cattle in India and provide a foundation for the future formulation of rational disease control strategies and the accurate assessment of economic and health impact risks. The literature search was performed in accordance with PRISMA guidelines and identified 285 cross-sectional studies on bTB in cattle in India across four electronic databases and handpicked publications. Of these, 44 articles were included, contributing a total of 82,419 cows and buffaloes across 18 states and one union territory in India. Based on a random-effects (RE) meta-regression model, the analysis revealed a pooled prevalence estimate of 7.3% (95% CI: 5.6, 9.5), indicating that there may be an estimated 21.8 million (95% CI: 16.6, 28.4) infected cattle in India-a population greater than the total number of dairy cows in the United States. The analyses further suggest that production system, species, breed, study location, diagnostic technique, sample size and study period are likely moderators of bTB prevalence in India and need to be considered when developing future disease surveillance and control programmes. Taken together with the projected increase in intensification of dairy production and the subsequent increase in the likelihood of zoonotic transmission, the results of our study suggest that attempts to eliminate tuberculosis from humans will require simultaneous consideration of bTB control in cattle population in countries such as India.

With the proliferation of Big Data, Social Science projects being developed, this work takes a step back to design research avenues that specifically look at smaller, realtime Social Science projects. Building on an already developed platform, called Dynamic Twitter Network Analysis (DTNA), we build out exploration into multiple world event types, which were captured in real-time and used smaller datasets to allow the user the ability to seek location and topic-specific data collections in parallel to events occurring. With these datasets, we first establish what could be learned during the event that mimics larger projects in the same domain. Secondly, we compare the events to help bring awareness to strategies that can evolve as specific events occur. The datasets examined are from a 24-hour period from specific locations of relevance with a focus on polarizing events. This includes: 1) Boston Marathon Bombing, 2) Sandy Hook Elementary Shooting, 3) Gezi Park Riots, 4) Hurricane Sandy, 5) Batkid, Make-a-Wish Foundation, 6) Brazil World Cup Protests, and 7) 2014 NBA Championship (Game 5). These networks will be analyzed both from social network analysis (SNA) and natural language processing (NLP) approaches (including sentiment analysis and part of speech tagging comparing personal pronoun use).

Adaptive visualization approaches attempt to tune the content and the topology of information visualization to various user characteristics. While adapting visualization to user cognitive traits, goals, or knowledge has been relatively well explored, some other user characteristics have received no attention. This paper presents a methodology to adapt a traditional cluster-based visualization of communities to user individual model of community organization. This class of user-adapted visualization is not only achievable, but expected due to real world situation where users cannot be segmented into heterogeneous communities since many users have affinity to more than one group. An interactive clustering and visualization approach presented in the paper allows the user communicate their personal mental models of overlapping communities to the clustering algorithm itself and obtain a community visualization image that more realistically fits their prospects.

Social network analysis and social network visualizations can provide a meaningful statistical and topological understanding of latent communities. However, the majority of current visualization approaches just represent sub-communities as clusters of closely related nodes in a node-link diagram and embed limitations to represent overlapping communities and multi-layer community structure frequently found from modern complex networks. We argue that visualizations based on points of interest can provide a better solution to represent overlapping latent sub-communities. We present two visualization systems, SuperVIBE and ContextForces, which implement this approach. These systems operate by creating two-dimensional latent spaces by means of grouping nodes using external variables not presented in the graph and by offering an interactive visualization to filter and map in these latent spaces. Understanding which latent groups are most central to a variety of topics and providing visual clues to the individuals critical to those groups provides a mechanism to explore and discover overlapping latent communities.

Developing a network based on Twitter data for social network analysis (SNA) is a common task in most academic domains. The need for real-time analysis is not as prevalent due to the fact that researchers are interested in the analysis of Twitter information after a major event or for an overall statistical or sociological study of general Twitter users. Dark network analysis is a specific field that focuses on criminal, terroristic, or people of interest networks in which evaluating information quickly and making decisions from this information is crucial. We propose a plaiform and visualization called Dynamic Twitter Network Analysis (DTNA) that incorporates real-time information from Twitter, its subsequent network topology, geographical placement of geotagged tweets on a Google Map, and storage for long-term analysis. The plaiform provides a SNA visualization that allows the user to interpret and change the search criteria quickly based on visual aesthetic properties built from key dark network utilities with a user interface that can be dynamic, up-to-date for time critical decisions and geographic specific.

When evaluating a network topology, occasionally data structures cannot be segmented into absolute, heterogeneous groups. There may be a spectrum to the dataset that does not allow for this hard clustering approach and may need to segment using fuzzy/overlapping communities or cliques. Even to this degree, when group members can belong to multiple cliques, there leaves an ever present layer of doubt, noise, and outliers caused by the overlapping clustering algorithms. These imperfections can either be corrected by an expert user to enhance the clustering algorithm or to preserve their own mental models of the communities. Presented is a visualization that models overlapping community membership and provides an interactive interface to facilitate a quick and efficient means of both sorting through large network topologies and preserving the user's mental model of the structure.

Soil liquefaction takes place during and/or after the occurrence of an earthquake and is a major contributor to urban seismic risk. Geologists use a technique called the cone penetration test (CPT) to determine the properties of soils, including liquefaction levels, which yields large amounts of soil data. The analysis of such massive amounts of data requires high-performance computing resources. In this paper, we present GQO (Grid Query Optimizer), a distributed algorithm that enables the analysis of large CPT data sets efficiently on a grid cyberinfrastructure.

Much research has recently been devoted to developing approaches, techniques, and technologies which assist people with navigation within buildings. Routing is an essential technique often requested by users prior to real-time navigation, providing them with appropriate routes within buildings. A survey of the literature reveals indoor routing approaches primarily based on shortest distance or fastest travel time as the main criteria. However, such routing criteria that are common in outdoor navigation may not be as applicable to indoor navigation. Presenting users with appropriate indoor routes could be beneficial to many users in various situations. For example, people with physical, cognitive, or sensory impairments may need routes that take into account their special needs as well as preferences rather than ones that are shortest or fastest. In this paper, we present a new ontology and an algorithm (ONALIN) that provides routing for individuals with various needs and preferences. To this end, ONALIN takes the ADA (American disability act) standards, among other requirements, into consideration. An indoor routing algorithm based on this ontology is presented and analyzed in detail where it first finds feasible routes with respect to the special needs of the users and then from those will select the comfortable routes.