Destination Area: Data and Decisions (D&D)
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The DA Data and Decisions advances the human condition and society with better decisions through data. D&D integrates all DAs and SGAs with data analytics and decision sciences. Work in this area embraces equity in the human condition by seeking the equitable distribution and availability of physical safety and well-being, psychological well-being, respect for human dignity, and access to crucial material and social resources throughout the world’s diverse communities. D&D also addresses policymaking and policy analysis, collaborating at the intersection of scientific evidence, governance, and analyses to translate scholarship into practice.
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- Aging and the Social Cognitive Determinants of Physical Activity Behavior and Behavior Change: Evidence from the Guide to Health TrialAnderson-Bill, Eileen Smith; Winett, Richard A.; Wojcik, Janet R.; Williams, David M. (Hindawi, 2011-04-28)Part one of this study investigated the effect of aging on social-cognitive characteristics related to physical activity (PA) among adults in the baseline phase of a health promotion intervention. Participants' questionnaire responses and activity logs indicated PA levels and self-efficacy declined with age, while social support and the use of self-regulatory behaviors (e.g., goal setting, planning, and keeping track) increased. With age participants were also less likely to expect PA to interfere with their daily routines and social obligations. Part two of the study was among overweight/obese, inactive participants completing the intervention; it examined whether improvements in psychosocial variables might counteract declining PA associated with age. After treatment, participants were more active and decreased body weight regardless of age, and improved self-efficacy, outcome expectations, and self-regulatory behaviors. In a causal model, increases in self-efficacy at 7-months lead to increased PA levels and, albeit marginally, weight loss at 16 months; increased PA was associated with greater weight loss. Aging adults who were more confident exercised more and as a result lost more weight. This longitudinal study suggests interventions that offset the effect of aging on self-efficacy may be more successful in helping older participants become more active and avoid weight gain.
- Aging, resistance training, and diabetes preventionFlack, Kyle D.; Davy, Kevin P.; Hulver, Matthew W.; Winett, Richard A.; Frisard, Madlyn I.; Davy, Brenda M. (2010-12-15)With the aging of the baby-boom generation and increases in life expectancy, the American population is growing older. Aging is associated with adverse changes in glucose tolerance and increased risk of diabetes; the increasing prevalence of diabetes among older adults suggests a clear need for effective diabetes prevention approaches for this population. The purpose of paper is to review what is known about changes in glucose tolerance with advancing age and the potential utility of resistance training (RT) as an intervention to prevent diabetes among middle-aged and older adults. Age-related factors contributing to glucose intolerance, which may be improved with RT, include improvements in insulin signaling defects, reductions in tumor necrosis factor-α, increases in adiponectin and insulin-like growth factor-1 concentrations, and reductions in total and abdominal visceral fat. Current RT recommendations and future areas for investigation are presented.
- AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadataLiang, Xiao; Akers, Kyle; Keenum, Ishi M.; Wind, Lauren L.; Gupta, Suraj; Chen, Chaoqi; Aldaihani, Reem; Pruden, Amy; Zhang, Liqing; Knowlton, Katharine F.; Xia, Kang; Heath, Lenwood S. (2021-03-10)Background Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance. Results Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis. Conclusion AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .
- Analysis of electricity consumption: a study in the wood products industryQuesada, Henry Jose; Wiedenbeck, Janice K.; Bond, Brian H. (2016-10)This paper evaluates the effect of industry segment, year, and US region on electricity consumption per employee, per dollar sales, and per square foot of plant area for wood products industries. Data was extracted from the Industrial Assessment Center (IAC) database and imported into MS Excel. The extracted dataset was examined for outliers and abnormalities with outliers outside the quantile range 0.5-99.5 dropped from the analysis. A logarithmic transformation was applied to eliminate the skewness of the original data distributions. Correlation measurements indicated a moderate association between the response variables; therefore, a multivariate analysis of variance test was performed to measure the impact of the three factors: industry type, year, and region, simultaneously on all response variables. The results indicated some effect associated with all three factors on the three measures of electricity consumption. Subsequently, univariate ANOVA tests were conducted to determine the levels of the factors that were different. Most levels of industry type were associated with significantly different energy consumption, an expected result since some of the industries are more energy intensive than others. The industries in Standard Industry Code (SIC) 2493 (reconstituted wood products) are the groups with the highest electricity consumption with means of 38,096.28 kWh/employee, 0.86 kWh/sales, and 154.14 kWh/plant area while industries grouped in SIC 2451 (mobile homes) have the smallest consumption with means of 6811.01 kWh/employee, 0.05 kWh/sales, and 9.45 kWh/plant area. Interestingly, differences in regional consumption were found to be linked to the proportion of industry types by region. Data analysis also indicated differences in electricity consumption per employee for the factor year, but for the other response variables, no differences were found. These main results indicate that industries in the wood products sector have different electricity consumption rates depending on the type of manufacturing processes they use. Therefore, industries in this sector can use these comparisons and metrics to benchmark their electricity consumption as well to understand better how electricity costs might vary depending on the region they are located.
- Application of alignment-free bioinformatics methods to identify an oomycete protein with structural and functional similarity to the bacterial AvrE effector proteinDeb, Devdutta; Mackey, David; Opiyo, Stephen O.; McDowell, John M. (PLOS, 2018-04-11)Diverse plant pathogens export effector proteins to reprogram host cells. One of the most challenging goals in the molecular plant-microbe field is to functionally characterize the complex repertoires of effectors secreted by these pathogens. For bacterial pathogens, the predominant class of effectors is delivered to host cells by Type III secretion. For oomycetes, the predominant class of effectors is defined by a signal peptide that mediates secretion from the oomycete and a conserved RxLR motif. Downy mildew pathogens and Phytophthora species maintain hundreds of candidate RxLR effector genes in their genomes. Although no primary sequence similarity is evident between bacterial Type III effectors (T3Es) and oomycete RXLR effectors, some bacterial and oomycete effectors have convergently evolved to target the same host proteins. Such effectors might have evolved domains that are functionally similar but sequence-unrelated. We reasoned that alignment-free bioinformatics approaches could be useful to identify structural similarities between bacterial and oomycete effectors. To test this approach, we used partial least squares regression, alignment-free bioinformatics methods to identify effector proteins from the genome of the oomycete Hyaloperonospora arabidopsidis that are similar to the well-studied AvrE1 effector from Pseudomonas syringae. This approach identified five RxLR proteins with putative structural similarity to AvrE1. We focused on one, HaRxL23, because it is an experimentally validated effector and it is conserved between distantly related oomycetes. Several experiments indicate that HaRxL23 is functionally similar to AvrE1, including the ability to partially rescue an AvrE1 loss-of-function mutant. This study provides an example of how an alignment-free bioinformatics approach can identify functionally similar effector proteins in the absence of primary sequence similarity. This approach could be useful to identify effectors that have convergently evolved regardless of whether the shared host target is known.
- Applying Best Supply Chain Practices to Humanitarian ReliefRussell, Roberta S.; Hiller, Janine S. (Penn State, 2015-05)With the growth in length and breadth of extended supply chains, more companies are employing risk management techniques and resilience planning to deal with burgeoning and costly supply chain disruptions. As companies can learn from humanitarian groups, so can humanitarian groups learn from industry how to respond, recover, and prepare for these disruptive events. This paper looks at industry leaders in supply chain risk management and explores how humanitarian supply chains can learn from industry best practices.
- Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in KuwaitG. Almatar, Muhammad; Alazmi, Huda S.; Li, Liuqing; Fox, Edward A. (MDPI, 2020-11-25)Researchers have developed various approaches for exploring the spatial information, temporal patterns, and Twitter content in topics of interest in order to generate a better understanding of human behavior; however, few investigations have integrated these three dimensions simultaneously. This study analyzes the content of tweets in order to conduct a spatiotemporal exploration of the main topics of interest in Kuwait in order to provide a deeper understanding of the topics people think about, when they think about them, and where they tweet about them. To this end, we collect, process, and analyze tweets from nearly 120 areas in Kuwait over a 10-month period. The study’s results indicate that religion, emotions, education, and public policy are the most popular topics of interest in Kuwait. Regarding the spatiotemporal analysis, people post more tweets regarding religion on Fridays, a holy day for Muslims in Kuwait. Moreover, people are more likely to tweet about policy and education on weekdays rather than weekends. In contrast, people tweet about emotional expressions more often on weekends. From the spatial perspectives, spatial clustering in topics occurs across the days of the week. The findings are applicable to further topic analysis and similar research in other countries.
- Are Ayurvedic herbs for diabetes effective?Shekelle, Paul G.; Hardy, Mary; Morton, Sally C.; Coulter, Ian; Venuturupalli, Swamy; Favreau, Joya; Hilton, Lara K. (Frontline Medical Communications Inc., 2005-10)Objective: To evaluate and synthesize the evidence on the effect of Ayurvedic therapies for diabetes mellitus. Design: Systematic review of trials. Measurements and main results: We found no study that assessed Ayurvedic as a system of care. Botanical therapy was by far the most commonly studied Ayurvedic treatment. Herbs were studied either singly or as formulas. In all, 993 titles in Western computerized databases and 318 titles identified by hand-searching journals in India were examined, yield ing 54 articles reporting the results of 62 studies. The most-studied herbs were G sylvestre, C indica, fenugreek, and Eugenia jambo/ana. A number of herbal formulas were tested, but Ayush- 82 and 0 -400 were most often studied. Thirty-five of the studies included came from the Western literature, 27 from the Indian. Seven were randomized controlled trials (RCTs) and 10 controlled clinical trials (CCTs) or natural experiments. Twenty-two studies went on to further analysis based on a set of criteria. Of these, 10 were RCTs, eCTs, or natural experiments, 12 were case series or cohort studies. There is evidence to suggest that the herbs C indica, holy basil, fenugreek, and G sylvestre, and the herbal formulas Ayush-82 and 0 -400 have a glucose-lowering effect and deserve further study. Evidence of effectiveness of several other herbs is less extensive (C tamala, E jambo/ana, and Momordica charantia). Conclusions: There is heterogeneity in the available literature on Ayurvedic treatment for diabetes. Most studies test herbal therapy. Heterogeneity exists in the herbs and formulas tested (more than 44 different interventions identified) and in the method of their preparation. Despite these limitations, there are sufficient data for several herbs or herbal formulas to warrant further studies.
- Arlington County Emergency Alerts: Enhancing Enrollment and EffectivenessGrant, Milicent; Tyner, Samantha; Ziemer, Kathryn (Virginia Tech, 2017)Emergency alerts are an efficient tool for keeping populations well informed during high-risk situations. In collaboration with Arlington County, our laboratory conducted a pilot study to identify messaging patterns that keep users engaged with their regional alert systems, focusing outreach efforts toward populations with the lowest levels of enrollment.
- Association of Blood Biomarkers With Acute Sport-Related Concussion in Collegiate Athletes: Findings From the NCAA and Department of Defense CARE ConsortiumMcCrea, Michael A.; Broglio, Steven P.; McAllister, Thomas W.; Gill, Jessica M.; Giza, Christopher C.; Huber, Daniel L.; Harezlak, Jaroslaw; Cameron, Kenneth L.; Houston, Megan N.; McGinty, Gerald T.; Jackson, Jonathan C.; Guskiewicz, Kevin M.; Mihalik, Jason P.; Brooks, M. Alison; Duma, Stefan M.; Rowson, Steven; Nelson, Lindsay D.; Pasquina, Paul; Meier, Timothy B.; Foroud, Tatiana; Katz, Barry P.; Saykin, Andrew J.; Campbell, Darren E.; Svoboda, Steven J.; Goldman, Joshua T.; DiFiori, John P. (2020-01-24)Question Is sport-related concussion associated with levels of traumatic brain injury biomarkers in collegiate athletes? Findings In this case-control study of 504 collegiate athletes with concussion, contact sport control athletes, and non-contact sport athletes, the athletes with concussion had significant elevations in multiple traumatic brain injury biomarkers compared with preseason baseline and with 2 groups of control athletes without concussion during the acute postinjury period. Meaning These results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in sport-related concussion. This case-control study examines the association between sport-related concussion and levels of traumatic brain injury biomarkers in collegiate athletes. Importance There is potential scientific and clinical value in validation of objective biomarkers for sport-related concussion (SRC). Objective To investigate the association of acute-phase blood biomarker levels with SRC in collegiate athletes. Design, Setting, and Participants This multicenter, prospective, case-control study was conducted by the National Collegiate Athletic Association (NCAA) and the US Department of Defense Concussion Assessment, Research, and Education (CARE) Consortium from February 20, 2015, to May 31, 2018, at 6 CARE Advanced Research Core sites. A total of 504 collegiate athletes with concussion, contact sport control athletes, and non-contact sport control athletes completed clinical testing and blood collection at preseason baseline, the acute postinjury period, 24 to 48 hours after injury, the point of reporting being asymptomatic, and 7 days after return to play. Data analysis was conducted from March 1 to November 30, 2019. Main Outcomes and Measures Glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase-L1 (UCH-L1), neurofilament light chain, and tau were quantified using the Quanterix Simoa multiplex assay. Clinical outcome measures included the Sport Concussion Assessment Tool-Third Edition (SCAT-3) symptom evaluation, Standardized Assessment of Concussion, Balance Error Scoring System, and Brief Symptom Inventory 18. Results A total of 264 athletes with concussion (mean [SD] age, 19.08 [1.24] years; 211 [79.9%] male), 138 contact sport controls (mean [SD] age, 19.03 [1.27] years; 107 [77.5%] male), and 102 non-contact sport controls (mean [SD] age, 19.39 [1.25] years; 82 [80.4%] male) were included in the study. Athletes with concussion had significant elevation in GFAP (mean difference, 0.430 pg/mL; 95% CI, 0.339-0.521 pg/mL; P < .001), UCH-L1 (mean difference, 0.449 pg/mL; 95% CI, 0.167-0.732 pg/mL; P < .001), and tau levels (mean difference, 0.221 pg/mL; 95% CI, 0.046-0.396 pg/mL; P = .004) at the acute postinjury time point compared with preseason baseline. Longitudinally, a significant interaction (group x visit) was found for GFAP (F-7,F-1507.36 = 16.18, P < .001), UCH-L1 (F-7,F-1153.09 = 5.71, P < .001), and tau (F-7,F-1480.55 = 6.81, P < .001); the interaction for neurofilament light chain was not significant (F-7,F-1506.90 = 1.33, P = .23). The area under the curve for the combination of GFAP and UCH-L1 in differentiating athletes with concussion from contact sport controls at the acute postinjury period was 0.71 (95% CI, 0.64-0.78; P < .001); the acute postinjury area under the curve for all 4 biomarkers combined was 0.72 (95% CI, 0.65-0.79; P < .001). Beyond SCAT-3 symptom score, GFAP at the acute postinjury time point was associated with the classification of athletes with concussion from contact controls (beta = 12.298; 95% CI, 2.776-54.481; P = .001) and non-contact sport controls (beta = 5.438; 95% CI, 1.676-17.645; P = .005). Athletes with concussion with loss of consciousness or posttraumatic amnesia had significantly higher levels of GFAP than athletes with concussion with neither loss of consciousness nor posttraumatic amnesia at the acute postinjury time point (mean difference, 0.583 pg/mL; 95% CI, 0.369-0.797 pg/mL; P < .001). Conclusions and Relevance The results suggest that blood biomarkers can be used as research tools to inform the underlying pathophysiological mechanism of concussion and provide additional support for future studies to optimize and validate biomarkers for potential clinical use in SRC.
- Association of Spinal Manipulative Therapy With Clinical Benefit and Harm for Acute Low Back Pain Systematic Review and Meta-analysisMorton, Sally C.; Paige, Neil M.; Miake-Lye, Isomi M.; Booth, Marika; Beroes, Jessica M.; Mardian, Aram S.; Dougherty, Paul; Branson, Richard; Tang, Baron (American Medical Association, 2017-04-11)IMPORTANCE Acute low back pain is common and spinal manipulative therapy (SMT) is a treatment option. Randomized clinical trials (RCTs) and meta-analyses have reported different conclusions about the effectiveness of SMT. OBJECTIVE To systematically review studies of the effectiveness and harms of SMT for acute ( 6 weeks) low back pain. DATA SOURCES Search of MEDLINE, Cochrane Database of Systematic Reviews, EMBASE, and Current Nursing and Allied Health Literature from January 1, 2011, through February 6, 2017, as well as identified systematic reviews and RCTs, for RCTs of adults with low back pain treated in ambulatory settings with SMT compared with sham or alternative treatments, and that measured pain or function outcomes for up to 6 weeks. Observational studies were included to assess harms. DATA EXTRACTION AND SYNTHESIS Data extractionwas done in duplicate. Study qualitywas assessed using the Cochrane Back and Neck (CBN) Risk of Bias tool. This tool has 11 items in the following domains: randomization, concealment, baseline differences, blinding (patient), blinding (care provider [care provider is a specific qualitymetric used by the CBN Risk of Bias tool]), blinding (outcome), co-interventions, compliance, dropouts, timing, and intention to treat. Prior research has shown the CBN Risk of Bias tool identifies studies at an increased risk of bias using a threshold of 5 or 6 as a summary score. The evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) criteria. MAIN OUTCOMES AND MEASURES Pain (measured by either the 100-mm visual analog scale, 11-point numeric rating scale, or other numeric pain scale), function (measured by the 24-point Roland Morris Disability Questionnaire or Oswestry Disability Index [range, 0-100]), or any harms measured within 6 weeks. FINDINGS Of 26 eligible RCTs identified, 15 RCTs (1711 patients) provided moderate-quality evidence that SMT has a statistically significant association with improvements in pain (pooled mean improvement in the 100-mm visual analog pain scale, −9.95 [95%CI, −15.6 to −4.3]). Twelve RCTs (1381 patients) produced moderate-quality evidence that SMT has a statistically significant association with improvements in function (pooled mean effect size, −0.39 [95%CI, −0.71 to −0.07]). Heterogeneity was not explained by type of clinician performing SMT, type of manipulation, study quality, or whether SMT was given alone or as part of a package of therapies. No RCT reported any serious adverse event. Minor transient adverse events such as increased pain, muscle stiffness, and headache were reported 50% to 67%of the time in large case series of patients treated with SMT. CONCLUSIONS AND RELEVANCE Among patients with acute low back pain, spinal manipulative therapy was associated with modest improvements in pain and function at up to 6 weeks, with transient minor musculoskeletal harms. However, heterogeneity in study results was large.
- Beginning A Patient-Centered Approach in the Design of A Diabetes Prevention ProgramSeidel, Richard W.; Pardo, Kimberlee A.; Estabrooks, Paul A.; You, Wen; Wall, Sarah S.; Davy, Brenda M.; Almeida, Fabio A. (MDPI, 2014-02-01)
- The Betrayal Aversion Elicitation Task: An Individual Level Betrayal Aversion MeasureAimone, Jason A.; Ball, Sheryl B.; Casas, Brooks (PLOS, 2015-09-02)Research on betrayal aversion shows that individuals’ response to risk depends not only on probabilities and payoffs, but also on whether the risk includes a betrayal of trust. While previous studies focus on measuring aggregate levels of betrayal aversion, the connection between an individual’s own betrayal aversion and other individually varying factors, including risk preferences, are currently unexplored. This paper develops a new task to elicit an individual’s level of betrayal aversion that can then be compared to individual characteristics. We demonstrate the feasibility of our new task and show that our aggregate individual results are consistent with previous studies. We then use this classification to ask whether betrayal aversion is correlated with risk aversion. While we find risk aversion and betrayal aversion have no significant relationship, we do observe that risk aversion is correlated with non-social risk preferences, but not the social, betrayal related, risk component of the new task.
- Bioinformatic Analysis of Coronary Disease Associated SNPs and Genes to Identify Proteins Potentially Involved in the Pathogenesis of AtherosclerosisMao, Chunhong; Howard, Timothy D.; Sullivan, Dan; Fu, Zongming; Yu, Guoqiang; Parker, Sarah J.; Will, Rebecca; Vander Heide, Richard S.; Wang, Yue; Hixson, James; Van Eyk, Jennifer; Herrington, David M. (Open Access Pub, 2017-03-04)Factors that contribute to the onset of atherosclerosis may be elucidated by bioinformatic techniques applied to multiple sources of genomic and proteomic data. The results of genome wide association studies, such as the CardioGramPlusC4D study, expression data, such as that available from expression quantitative trait loci (eQTL) databases, along with protein interaction and pathway data available in Ingenuity Pathway Analysis (IPA), constitute a substantial set of data amenable to bioinformatics analysis. This study used bioinformatic analyses of recent genome wide association data to identify a seed set of genes likely associated with atherosclerosis. The set was expanded to include protein interaction candidates to create a network of proteins possibly influencing the onset and progression of atherosclerosis. Local average connectivity (LAC), eigenvector centrality, and betweenness metrics were calculated for the interaction network to identify top gene and protein candidates for a better understanding of the atherosclerotic disease process. The top ranking genes included some known to be involved with cardiovascular disease (APOA1, APOA5, APOB, APOC1, APOC2, APOE, CDKN1A, CXCL12, SCARB1, SMARCA4 and TERT), and others that are less obvious and require further investigation (TP53, MYC, PPARG, YWHAQ, RB1, AR, ESR1, EGFR, UBC and YWHAZ). Collectively these data help define a more focused set of genes that likely play a pivotal role in the pathogenesis of atherosclerosis and are therefore natural targets for novel therapeutic interventions.
- Building Capacity for Data Driven Governance - Creating a New Foundation for DemocracyKeller, Sallie A.; Lancaster, V. A.; Shipp, S. S. (Francis & Taylor, 2017-03)Existing data flows at the local level, public and administrative records, geospatial data, social media, surveys, as well as other federal, state, and local databases, are ubiquitous in our everyday life. These data, when integrated, can tell the story of a community. The Community Learning Data Driven Discovery (CLD3) process liberates, integrates and makes these data available to government leaders and researchers to tell their community’s story and to use these stories to build an equitable and sustainable social transformation within and across communities to address their most pressing needs. The CLD3 process starts with asking local leaders what their questions are but cannot currently answer; identifying data sources that can provide insights; wrangling the data (profiling, cleaning, transforming, linking); using statistical and geospatial learning along with the communities’ collective knowledge to inform policy decisions; and developing, deploying, and evaluating intervention strategies based on scientifically based principles. CLD3 is a continuous, sustainable and controlled feedback loop. CLD3 is described conceptually and through examples as a process that builds capacity for data driven governance at the local level.
- Can Administrative Housing Data Replace Survey Data?Molfino, Emily; Korkmaz, Gizem; Keller, Sallie A.; Schroeder, Aaron; Shipp, Stephanie; Weinberg, Daniel H. (HUD, 2017)This article examines the feasibility of using local administrative data sources for enhancing and supplementing federally collected survey data to describe housing in Arlington County, Virginia. Using real estate assessment data and the American Community Survey (ACS) from 2009 to 2013, we compare housing estimates for six characteristics: number of housing units, type of housing unit, year built, number of bedrooms, housing value, and real estate taxes paid. The findings show that housing administrative data can be repurposed to enhance and supplement the ACS, but limitations exist. We then discuss the challenges of repurposing housing administrative data for research.
- Can long-term historical data from electronic medical records improve surveillance for epidemics of acute respiratory infections? A systematic evaluationZheng, Hongzhang; Woodall, William H.; Carlson, Abigail L.; DeLisle, Sylvain (PLOS, 2018-01-31)Background As the deployment of electronic medical records (EMR) expands, so is the availability of long-term datasets that could serve to enhance public health surveillance. We hypothesized that EMR-based surveillance systems that incorporate seasonality and other long-term trends would discover outbreaks of acute respiratory infections (ARI) sooner than systems that only consider the recent past. Methods We simulated surveillance systems aimed at discovering modeled influenza outbreaks injected into backgrounds of patients with ARI. Backgrounds of daily case counts were either synthesized or obtained by applying one of three previously validated ARI case-detection algorithms to authentic EMR entries. From the time of outbreak injection, detection statistics were applied daily on paired background+injection and background-only time series. The relationship between the detection delay (the time from injection to the first alarm uniquely found in the background+injection data) and the false-alarm rate (FAR) was determined by systematically varying the statistical alarm threshold. We compared this relationship for outbreak detection methods that utilized either 7 days (early aberrancy reporting system (EARS)) or 2 +/- 4 years of past data (seasonal autoregressive integrated moving average (SARIMA) time series modeling). Results In otherwise identical surveillance systems, SARIMA detected epidemics sooner than EARS at any FAR below 10%. The algorithms used to detect single ARI cases impacted both the feasibility and marginal benefits of SARIMA modeling. Under plausible real-world conditions, SARIMA could reduce detection delay by 5 +/- 16 days. It also was more sensitive at detecting the summer wave of the 2009 influenza pandemic. Conclusion Time series modeling of long-term historical EMR data can reduce the time it takes to discover epidemics of ARI. Realistic surveillance simulations may prove invaluable to optimize system design and tuning.
- Characterization and prediction of tropical cyclone forerunner surgeLiu, Yi; Irish, Jennifer L. (Elsevier, 2019)Forerunner surge, a water level rise ahead of tropical cyclone landfall, often strikes coastal communities unexpectedly, stranding people and increasing loss of life. Surge forecasting systems and emergency managers almost exclusively focus on peak surge, while much less attention is given to forerunner surge. To address the need for fast and accurate forecasting of forerunner surge, we analyze high-fidelity surge simulations in Virginia, New York/New Jersey and Texas and extract physical scaling laws between readily available storm track information and forerunner surge magnitude and timing. We demonstrate that a dimensionless relationship between central-pressure scaled surge and wind-duration scaled time may effectively be used for rapid forerunner surge forecasting, where uncertainty is considered. We use our method to predict forerunner surge for Hurricanes Ike (2008)—a significant forerunner surge event—and Harvey (2017). The predicted forerunner surge 24 to 6 hours before Hurricane Ike’s landfall ranged from 0.4 to 2.8 m, where the observed forerunner surge ranged from 0.4 to 2.6 m. This new method has the potential to be incorporated into real-time surge forecasting systems to aid emergency management and evacuation decisions.
- Characterizing the Genetic Basis for Nicotine Induced Cancer Development: A Transcriptome Sequencing StudyBavarva, Jasmin H.; Tae, Hongseok; Settlage, Robert E.; Garner, Harold R. (PLOS, 2013-06-18)Nicotine is a known risk factor for cancer development and has been shown to alter gene expression in cells and tissue upon exposure. We used Illumina® Next Generation Sequencing (NGS) technology to gain unbiased biological insight into the transcriptome of normal epithelial cells (MCF-10A) to nicotine exposure. We generated expression data from 54,699 transcripts using triplicates of control and nicotine stressed cells. As a result, we identified 138 differentially expressed transcripts, including 39 uncharacterized genes. Additionally, 173 transcripts that are primarily associated with DNA replication, recombination, and repair showed evidence for alternative splicing. We discovered the greatest nicotine stress response by HPCAL4 (up-regulated by 4.71 fold) and NPAS3 (down-regulated by -2.73 fold); both are genes that have not been previously implicated in nicotine exposure but are linked to cancer. We also discovered significant down-regulation (-2.3 fold) and alternative splicing of NEAT1 (lncRNA) that may have an important, yet undiscovered regulatory role. Gene ontology analysis revealed nicotine exposure influenced genes involved in cellular and metabolic processes. This study reveals previously unknown consequences of nicotine stress on the transcriptome of normal breast epithelial cells and provides insight into the underlying biological influence of nicotine on normal cells, marking the foundation for future studies.
- A Classifier to Detect Informational vs. Non-Informational Heart Attack TweetsKarajeh, Ola; Darweesh, Dirar; Darwish, Omar; Abu-El-Rub, Noor; Alsinglawi, Belal; Alsaedi, Nasser (MDPI, 2021-01-16)Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing “heart attack” and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., “Yesterday morning, my grandfather had a heart attack while he was walking around the garden.” On the other hand, there are non-informational tweets such as “Dropped my iPhone for the first time and almost had a heart attack.” The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naïve Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively.