Biotic interactions drive ecosystem responses to exotic plant invaders.

Ecosystem process rates typically increase after plant invasion, but the extent to which this is driven by (i) changes in productivity, (ii) exotic species' traits, or (iii) novel (non-coevolved) biotic interactions has never been quantified. We created communities varying in exotic plant dominance, plant traits, soil biota, and invertebrate herbivores and measured indicators of carbon cycling. Interactions with soil biota and herbivores were the strongest drivers of exotic plant effects, particularly on measures of soil carbon turnover. Moreover, plant traits related to growth and nutrient acquisition explained differences in the ways that exotic plants interacted with novel biota compared with natives. We conclude that novel biological interactions with exotic species are a more important driver of ecosystem transformation than was previously recognized.

Applying science to pressing conservation needs for penguins.

More than half of the world's 18 penguin species are declining. We, the Steering Committee of the International Union for Conservation of Nature Species Survival Commission Penguin Specialist Group, determined that the penguin species in most critical need of conservation action are African penguin (Spheniscus demersus), Galápagos penguin (Spheniscus mendiculus), and Yellow-eyed penguin (Megadyptes antipodes). Due to small or rapidly declining populations, these species require immediate scientific collaboration and policy intervention. We also used a pairwise-ranking approach to prioritize research and conservation needs for all penguins. Among the 12 cross-taxa research areas we identified, we ranked quantifying population trends, estimating demographic rates, forecasting environmental patterns of change, and improving the knowledge of fisheries interactions as the highest priorities. The highest ranked conservation needs were to enhance marine spatial planning, improve stakeholder engagement, and develop disaster-management and species-specific action plans. We concurred that, to improve the translation of science into effective conservation for penguins, the scientific community and funding bodies must recognize the importance of and support long-term research; research on and conservation of penguins must expand its focus to include the nonbreeding season and juvenile stage; marine reserves must be designed at ecologically appropriate spatial and temporal scales; and communication between scientists and decision makers must be improved with the help of individual scientists and interdisciplinary working groups.

Aplicación de Ciencia en las Necesidades de Conservación Urgentes para los Pingüinos. Resumen Más de la mitad de las 18 especies de pingüinos del mundo están disminuyendo. Nosotros, el Comité Directivo de la Unión Internacional para la Conservación de la Naturaleza, Grupo de Especialistas en Pingüinos, determinamos que las especies de pingüinos con necesidades críticas de conservación son el pingüino africano (Spheniscus demersus), el pingüino de las Galápagos (Spheniscus mendiculus) y el pingüino de ojos amarillos (Megadyptes antipodes). Debido a que sus poblaciones son pequeñas o están declinando rápidamente, estos pingüinos requieren colaboración científica e intervención política inmediatas. También utilizamos un método de clasificación por pares para priorizar las necesidades de investigación y conservación para todas las especies de pingüinos. Entre las 12 áreas de investigación que identificamos, las más prioritarias fueron: cuantificación de las tendencias poblacionales, estimación de las tasas demográficas, predicción de las patrones de cambio ambiental y mejora del conocimiento de las interacciones con pesquerías. Las mayores necesidades de conservación fueron: optimizar la planificación marina espacial, mejorar la colaboración de las partes interesadas y desarrollar planes de manejo de desastres y de acción para cada especie. Coincidimos en que, para mejorar la traducción de la ciencia en la conservación efectiva de los pingüinos, la comunidad científica y los organismos financiadores deben reconocer la importancia de la investigación a largo plazo y apoyarla; la investigación sobre pingüinos y su conservación debe expandir su enfoque para incluir la época no reproductiva y la etapa juvenil; las reservas marinas deben ser diseñadas a escalas espaciotemporales ecológicamente apropiadas; y la comunicación entre científicos y tomadores de decisiones debe mejorar con la ayuda de científicos individuales y grupos de trabajo interdisciplinario.

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures.

BACKGROUND: We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. NEW METHOD: GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications. RESULTS: In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. COMPARISON WITH EXISTING METHODS: Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. CONCLUSIONS: GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.

General, crystallized and fluid intelligence are not associated with functional global network efficiency: A replication study with the human connectome project 1200 data set.

One hallmark example of a link between global topological network properties of complex functional brain connectivity and cognitive performance is the finding that general intelligence may depend on the efficiency of the brain's intrinsic functional network architecture. However, although this association has been featured prominently over the course of the last decade, the empirical basis for this broad association of general intelligence and global functional network efficiency is quite limited. In the current study, we set out to replicate the previously reported association between general intelligence and global functional network efficiency using the large sample size and high quality data of the Human Connectome Project, and extended the original study by testing for separate association of crystallized and fluid intelligence with global efficiency, characteristic path length, and global clustering coefficient. We were unable to provide evidence for the proposed association between general intelligence and functional brain network efficiency, as was demonstrated by van den Heuvel et al. (2009), or for any other association with the global network measures employed. More specifically, across multiple network definition schemes, ranging from voxel-level networks to networks of only 100 nodes, no robust associations and only very weak non-significant effects with a maximal R2 of 0.01 could be observed. Notably, the strongest (non-significant) effects were observed in voxel-level networks. We discuss the possibility that the low power of previous studies and publication bias may have led to false positive results fostering the widely accepted notion of general intelligence being associated to functional global network efficiency.

Use of a Dose-Response Model to Study Temporal Trends in Spatial Exposure to Coxiella burnetii: Analysis of a Multiyear Outbreak of Q Fever.

The Netherlands underwent a large Q fever outbreak between 2007 and 2009. In this paper, we study spatial and temporal Coxiella burnetii exposure trends during this large outbreak as well as validate outcomes against other published studies and provide evidence to support hypotheses on the causes of the outbreak. To achieve this, we develop a framework using a dose-response model to translate acute Q fever case incidence into exposure estimates. More specifically, we incorporate a geostatistical model that accounts for spatial and temporal correlation of exposure estimates from a human Q fever dose-response model to quantify exposure trends during the outbreak. The 2051 cases, with the corresponding age, gender and residential addresses, reside in the region with the highest attack rates during the outbreak in the Netherlands between 2006 and 2009. We conclude that the multiyear outbreak in the Netherlands is caused by sustained release of infectious bacteria from the same sources, which suggests that earlier implementation of interventions may have prevented many of the cases. The model predicts the risk of infection and acute symptomatic Q fever from multiple exposure sources during a multiple-year outbreak providing a robust, evidence-based methodology to support decision-making and intervention design.

GraphVar: a user-friendly toolbox for comprehensive graph analyses of functional brain connectivity.

BACKGROUND: Graph theory provides a powerful and comprehensive formalism of global and local topological network properties of complex structural or functional brain connectivity. Software packages such as the Brain-Connectivity-Toolbox have contributed to graph theory's increasing popularity for characterization of brain networks. However, comparably comprehensive packages are command-line based and require programming experience; this precludes their use by users without a computational background, whose research would otherwise benefit from graph-theoretical methods. NEW METHOD: "GraphVar" is a user-friendly GUI-based toolbox for comprehensive graph-theoretical analyses of brain connectivity, including network construction and characterization, statistical analysis on network topological measures, network based statistics, and interactive exploration of results. RESULTS: GraphVar provides a comprehensive collection of graph analysis routines for analyses of functional brain connectivity in one single toolbox by combining features across multiple currently available toolboxes, such as the Brain Connectivity Toolbox, the Graph Analysis Toolbox, and the Network Based Statistic Toolbox (BCT, Rubinov and Sporns, 2010; GAT, Hosseini et al., 2012; NBS, Zalesky et al., 2010). GraphVar was developed under the GNU General Public License v3.0 and can be downloaded at www.rfmri.org/graphvar or www.nitrc.org/projects/graphvar. COMPARISON WITH EXISTING METHODS: By combining together features across multiple toolboxes, GraphVar will allow comprehensive graph-theoretical analyses in one single toolbox without resorting to code. CONCLUSIONS: GraphVar will make graph theoretical methods more accessible for a broader audience of neuroimaging researchers.

10-year spatial and temporal trends of PM2.5 concentrations in the southeastern US estimated using high-resolution satellite data.

Long-term PM2.5 exposure has been associated with various adverse health outcomes. However, most ground monitors are located in urban areas, leading to a potentially biased representation of true regional PM2.5 levels. To facilitate epidemiological studies, accurate estimates of the spatiotemporally continuous distribution of PM2.5 concentrations are important. Satellite-retrieved aerosol optical depth (AOD) has been increasingly used for PM2.5 concentration estimation due to its comprehensive spatial coverage. Nevertheless, previous studies indicated that an inherent disadvantage of many AOD products is their coarse spatial resolution. For instance, the available spatial resolutions of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging SpectroRadiometer (MISR) AOD products are 10 and 17.6 km, respectively. In this paper, a new AOD product with 1 km spatial resolution retrieved by the multi-angle implementation of atmospheric correction (MAIAC) algorithm based on MODIS measurements was used. A two-stage model was developed to account for both spatial and temporal variability in the PM2.5-AOD relationship by incorporating the MAIAC AOD, meteorological fields, and land use variables as predictors. Our study area is in the southeastern US centered at the Atlanta metro area, and data from 2001 to 2010 were collected from various sources. The model was fitted annually, and we obtained model fitting R2 ranging from 0.71 to 0.85, mean prediction error (MPE) from 1.73 to 2.50 µg m-3, and root mean squared prediction error (RMSPE) from 2.75 to 4.10 µg m-3. In addition, we found cross-validation R2 ranging from 0.62 to 0.78, MPE from 2.00 to 3.01 µgm-3, and RMSPE from 3.12 to 5.00 µgm-3, indicating a good agreement between the estimated and observed values. Spatial trends showed that high PM2.5 levels occurred in urban areas and along major highways, while low concentrations appeared in rural or mountainous areas. Our time-series analysis showed that, for the 10-year study period, the PM2.5 levels in the southeastern US have decreased by ∼20 %. The annual decrease has been relatively steady from 2001 to 2007 and from 2008 to 2010 while a significant drop occurred between 2007 and 2008. An observed increase in PM2.5 levels in year 2005 is attributed to elevated sulfate concentrations in the study area in warm months of 2005.

Estimating vaccine efficacy from outbreak size household data in the presence of heterogeneous transmission probabilities.

We develop a Bayesian approach for estimating vaccine efficacy for susceptibility (VEs) and infectiousness (VEI) using outbreak size household data. Our method allows for heterogeneity in transmission probabilities due to factors that are related to individuals' characteristics, such as age, in addition to vaccination status. It also allows for between-household heterogeneity in transmission probabilities due to random effects associated with households, such as genetic or environmental effects. Using age as a potential covariate causing heterogeneity in individuals' transmission probabilities in households consisting of adults and children, we present the results of a simulation study designed to evaluate the performance of the proposed estimators of VEs and VE(I). We found that estimates of VE(I) have larger bias and variance compared to those of VEs. We also use the approach to compare two vaccination designs: one vaccinating both adults and children, the other only children. Simulations reveal that the design that vaccinates both adults and children provides better estimates of VEs. There is no obvious difference between the two designs in the performance of the estimates of VE(I). In regard to random effects between households and the scenarios considered, models that do not account for between-households heterogeneity produce fairly robust estimates even when household-level random effects are present.

Assessing the role of long-distance translocation and spatial heterogeneity in the raccoon rabies epidemic in Connecticut.

Spatial heterogeneity and long-distance translocation (LDT) play important roles in the spatio-temporal dynamics and management of emerging infectious diseases and invasive species. We assessed the influence of LDT events on the invasive spread of raccoon rabies through Connecticut. We identified several putative LDT events, and developed a network-model to evaluate whether they became new foci for epidemic spread. LDT was fairly common, but many of the LDTs were isolated events that did not spread. Two putative LDT events did appear to become nascent foci that affected the epidemic in surrounding townships. In evaluating the role of LDT, we simultaneously revisited the problem of spatial heterogeneity. The spread of raccoon rabies is associated with forest cover--rabies moves up to three-times slower through the most heavily forested townships compared with those with less forestation. Forestation also modified the effect of rivers. In the best overall model, rabies did not cross the river separating townships that were heavily forested, and the spread slowed substantially between townships that were lightly forested. Our results suggest that spatial heterogeneity can be used to enhance the effects of rabies control by focusing vaccine bait distribution along rivers in lightly forested areas. LDT events are a concern, but this analysis suggests that at a local scale they can be isolated and managed.

Spatial dynamics and molecular ecology of North American rabies.

Rabies, caused by a single-stranded RNA virus, is arguably the most important viral zoonotic disease worldwide. Although endemic throughout many regions for millennia, rabies is also undergoing epidemic expansion, often quite rapid, among wildlife populations across regions of Europe and North America. A current rabies epizootic in North America is largely attributable to the accidental introduction of a particularly well-adapted virus variant into a naive raccoon population along the Virginia/West Virginia border in the mid-1970s. We have used the extant database on the spatial and temporal occurrence of rabid raccoons across the eastern United States to construct predictive models of disease spread and have tied patterns of emergence to local environmental variables, genetic heterogeneity, and host specificity. Rabies will continue to be a remarkable model system for exploring basic issues in the temporal and spatial dynamics of expanding infectious diseases and examining ties between disease population ecology and evolutionary genetics at both micro- and macro-evolutionary time scales.

Spatiotemporal analysis of epizootic raccoon rabies propagation in Connecticut, 1991-1995.

The quantitative analysis of pathogen transmission within its specific spatial context should improve our ability to predict and control the epizootic spread of that disease. We compared two methods for calibrating the effect of local, spatially distributed environmental heterogeneities on disease spread. Using the time-of-first-appearance of raccoon rabies across the 169 townships in Connecticut, we estimated local spatial variation in township-to-township transmission rate using Trend Surface Analysis (TSA) and then compared these estimates with those based on an earlier probabilistic simulation using the same data. Both the probabilistic simulation and the TSA reveal significant reduction in transmission when local spatial domains are separated by rivers. The probabilistic simulation suggested that township-to-township transmission was reduced sevenfold for townships separated by a river. The global effect of this sevenfold reduction is to increase the time-to-first-appearance in the eastern townships of Connecticut by approximately 29.7% (spread was from west to east). TSA revealed a similar effect of rivers with an overall reduction in rate of local propagation due to rivers of approximately 22%. The 7.7% difference in these two estimates reveals slightly different aspects of the spatial dynamics of this epizootic. Together, these two methods can be used to construct an overall picture of the combined effects of local spatial variation in township-to-township transmission on patterns of local rate of propagation at scales larger than the immediate nearest neighboring townships.

A civil action and statistical assessments of the spatial pattern of disease: do we have a cluster?

A reported "cluster" of excess childhood leukemia cases and possible environmental causes in Woburn, Massachusetts, formed a key motivation for the events described in the popular book and motion picture A Civil Action. Although statistical methods to assess spatial clustering existed prior to the events in Woburn, increasing interest in environmental risk factors and recent developments in geographical information systems and data availability prompt increased attention to such methods and their application to public health data. In this article, we review statistical and epidemiological concepts involved in the analysis of disease clusters. We discuss data issues, outline some methodological approaches, and illustrate ideas using data regarding leukemia incidence in upstate New York for the years 1978-1982.

A comparison of three tests to detect general clustering of a rare disease in Santa Clara County, California.

Statistical tests to detect clustering of a rare disease investigate whether an observed spatial pattern of cases appears to be due to chance alone. Heterogeneous population density and the geographic structure of the data under consideration complicate the ability to make comparisons of different tests. Further, interpretation of test results depends on the nature of the test used and what feature of the data it is designed to detect. With these issues in mind, we compare three recent tests for assessing general clustering among cases where the population is distributed heterogeneously across the study area, namely those of Besag and Newell, Turnbull et al. and Tango. We compare these methods using 1981 incidence data for severe cardiac birth defects from Santa Clara County, California.

Smoking cessation and lung function in mild-to-moderate chronic obstructive pulmonary disease. The Lung Health Study.

Previous studies of lung function in relation to smoking cessation have not adequately quantified the long-term benefit of smoking cessation, nor established the predictive value of characteristics such as airway hyperresponsiveness. In a prospective randomized clinical trial at 10 North American medical centers, we studied 3, 926 smokers with mild-to-moderate airway obstruction (3,818 with analyzable results; mean age at entry, 48.5 yr; 36% women) randomized to one of two smoking cessation groups or to a nonintervention group. We measured lung function annually for 5 yr. Participants who stopped smoking experienced an improvement in FEV(1) in the year after quitting (an average of 47 ml or 2%). The subsequent rate of decline in FEV(1) among sustained quitters was half the rate among continuing smokers, 31 +/- 48 versus 62 +/- 55 ml (mean +/- SD), comparable to that of never-smokers. Predictors of change in lung function included responsiveness to beta-agonist, baseline FEV(1), methacholine reactivity, age, sex, race, and baseline smoking rate. Respiratory symptoms were not predictive of changes in lung function. Smokers with airflow obstruction benefit from quitting despite previous heavy smoking, advanced age, poor baseline lung function, or airway hyperresponsiveness.

Out-of-hospital violence injury surveillance: quality of data collection.

STUDY OBJECTIVE: The recognized need to improve data collection for violence prevention may be met, in part, by using out-of-hospital data for injury surveillance. The purpose of the Prehospital Violence Injury Surveillance project was to examine the extent to which paramedics can adequately collect information about injuries, particularly intentional injuries, at emergency scenes. METHODS: Paramedics in a large Midwestern metropolitan area were trained to assess violence-related events and collect relevant data using a modified ambulance run report form. Data collected from 8 violence-related training scenarios and from 13 ride-along observations were analyzed to estimate paramedic interrater reliability using the kappa statistic. Data from 7,363 run report forms, filed during a 3-month study period, were abstracted and analyzed for completeness and quality. RESULTS: Paramedics demonstrated fair to good, and sometimes excellent, interrater agreement when documenting the training scenarios. Paramedics revealed barriers to collecting violence-related out-of-hospital data. The paramedics and the observer disagreed in documenting 77% of the ride-along observations. Overall, 73% of abstracted run report forms showed documentation errors, with more than 99% of these reports containing errors of omission and 29% showing internal documentation inconsistencies. Despite the emphasis on violence-related data, documentation of domestic abuse screening was missing from more than 99% of run reports from female patients. CONCLUSION: Significant barriers to quality out-of-hospital data collection were identified during study implementation and in abstracted run reports. These barriers included the following: lack of organizational support; characteristics of the violence-related data elements; design of the ambulance run report form; and paramedic knowledge, attitudes, and behaviors regarding data collection.

Work-related assault injuries among nurses.

Work-related violence is a major public health problem; however, there is a serious deficiency in the knowledge of risk factors for this problem. The purpose of this case-control study was to identify risk factors for work-related assault injuries among nurses. We used unconditional logistic regression to model the dependence of work-related assault injuries on each exposure of interest and the respective confounders. We found a decreased rate for the presence of security personnel (RR = 0.40; 95% CI = 0.19-0.82). We found increased rates for the following factors: the perception that administrators considered assault to be part of the job (RR = 8.14; 95% CI = 3.76-17.60); having received assault prevention training in the current workplace (RR = 4.64; 95% CI = 2.33-9.23); a high (>5) vs. low (<2) patient/personnel ratio (RR = 2.54; 95% CI = 1.13-5.70); working predominantly with patients with mental illness (RR = 3.5; 95% CI = 1.41-8.85); and working with patients who had more than 1- to 4-week and more than 4-week lengths of stay in the institution vs. <1 day (RR = 8.85; 95% CI = 1.58-49.52 and 4.25; 95% CI = 1.17-15.39, respectively).

Examining associations between childhood asthma and traffic flow using a geographic information system.

Using geographic information systems (GIS) and routinely collected data, we explored whether childhood residence near busy roads was associated with asthma in a low-income population in San Diego County, California. We examined the locations of residences of 5,996 children [less than/equal to] 14 years of age who were diagnosed with asthma in 1993 and compared them to a random control series of nonrespiratory diagnoses (n = 2,284). Locations of the children's residences were linked to traffic count data at streets within 550 ft. We also examined the number of medical care visits in 1993 for children with asthma to determine if the number of visits was related to traffic flow. Analysis of the distribution of cases and controls by quintiles and by the 90th, 95th, and 99th percentiles of traffic flow at the highest traffic street, nearest street, and total of all streets within a 550-ft buffer region did not show any significantly elevated odds ratios. However, among cases, those residing near high traffic flows (measured at the nearest street) were more likely than those residing near lower traffic flows to have two or more medical care visits for asthma than to have only one visit for asthma during the year. The results of this exploratory study suggest that higher traffic flows may be related to an increase in repeated medical visits for asthmatic children. Repeated exposure to particulate matter and other air pollutants from traffic exhaust may aggravate asthmatic symptoms in individuals already diagnosed with asthma.

The role of depression in the association between self-rated physical health and clinically defined illness.

We enrolled 543 elderly participants of a managed care organization in a cross-sectional study to test whether the association between self-rated physical health and clinically defined illness differs for persons who are not depressed compared with persons with minor or serious depression. Depression was measured with the Diagnostic Interview Schedule (DIS). Clinically defined illness was measured with the Chronic Disease Score (CDS), a pharmacy-based measure. Additional variables included age, sex, and self-reported pain and physical function. Self-rated physical health was associated with both minor and serious depression, independent of clinically defined illness; minor depression was no longer significant when self-reported pain and physical function were added to the model. A significant negative correlation between self-rated physical health and clinically defined illness was observed for minor and no depression, but no correlation was seen for serious depression. These results confirm the association between depression and self-rated physical health and emphasize that, for persons with serious depression, self-rated health provides a less accurate picture of clinically defined illness at both ends of the spectrum. Also, a diagnosis of minor depression should not forestall investigation of inconsistencies between patient report and clinical evidence.