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.

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.

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.

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.

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.

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.

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.

Cigarette smoking is associated with abnormal involuntary movements in the general male population--a study of men born in 1933.

Abnormal involuntary movements, i.e., spontaneous and tardive dyskinesias, were studied in a random population sample of 559 men, aged 59 years, who were participating in an epidemiological study focused on cardiovascular health. The research diagnostic criteria for tardive dyskinesia were fulfilled by 8.2% (n = 46), discounting the fact that all had not been exposed to neuroleptics. The mean Abnormal Involuntary Movements Scale score in the dyskinetic group was 5.0 (SD = 1.8), indicating mild to moderate symptoms. Dyskinetic men had higher daily cigarette consumption and higher scores for parkinsonian symptoms. They also had higher frequencies of psychiatric morbidity, exposure to neuroleptics, and alcohol dependence than the men without dyskinesia. In a logistic regression model, exposure to neuroleptics (p < .05) and daily cigarette consumption (p < .0005) were independently associated with dyskinesia. The probability of dyskinesia in a person smoking 20 or more cigarettes per day was 19%, whereas the probability associated with exposure to neuroleptics was 30%.

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 analysis of disease clusters, Part I: State of the art.

Public health professionals often are asked to investigate apparent clusters of human health events, or "disease clusters." A cluster is an excess of cases in space (a geographic cluster), in time (a temporal cluster), or in both space and time. This is part I of an introductory-level review of the analysis of disease clusters for physicians and health professionals concerned with infection surveillance in hospitals. It reviews the status of the field with the hope of expanding the use of cluster analysis methods for the routine surveillance of infectious disease in the hospital environment.

The analysis of disease clusters, Part II: Introduction to techniques.

Public health professionals often are asked to investigate apparent clusters of human health events or "disease clusters." A cluster is an excess of cases in space (a geographic cluster), in time (a temporal cluster), or in both space and time. This is the second part of an introductory-level review of the analysis of disease clusters for physicians and health professionals concerned with infection surveillance in hospitals. It reviews the status of the field with the hope of expanding the use of cluster analysis methods for the routine surveillance of infectious diseases in the hospital environment.

Clinical detection of depression among community-based elderly people with self-reported symptoms of depression.

BACKGROUND: Depression is under-diagnosed and under-treated in the primary care sector. The purpose of this study was to determine the association between self-reported indications of depression by community-dwelling elderly enrollees in a managed care organization and clinical detection of depression by primary care clinicians. METHODS: This was a 2-year cohort study of elderly people (n = 3410) who responded to the Geriatric Depression Scale (GDS) at the midpoint of the study period. A broad measure of clinical detection was used consisting of one or more of three indicators: diagnosis of depression, visit to a mental health specialist, or antidepressant medication treatment. RESULTS: Approximately half of the community-based elderly people with self-reported indications of depression (GDS > or = 11) did not have documentation of clinical detection of depression by health providers. Physician recognition of depression tended to increase with the severity of enrollees' self-reported feelings of depression. Men 65-74 years old and those > or = 85 years old were at highest risk for under-detection of depression by primary care providers. CONCLUSIONS: Clinical detection of depression of elderly people living in the community continues to be a problem. The implications of failure to recognize the possibility of depression among elderly White men suggest a serious public health problem.

Essential fatty acids and abnormal involuntary movements in the general male population: a study of men born in 1933.

Spontaneous and tardive dyskinesias were studied in a random population sample of 446 men, aged 59 years. Dyskinesia, defined as an AIMS score of at least 2 in any body part, was seen in 15.1% (n = 74). Dyskinetic men had a higher cigarette consumption, and they had higher frequencies of psychiatric morbidity and exposure to neuroleptics. Dyskinesia was also associated with several abnormalities in EFA concentrations in plasma, but the most consistent finding was the low arachidonic acid levels in phospholipids, triglycerides and cholesterol esters. In a logistic regression model, cigarette consumption (P < 0.02), exposure to neuroleptics (P < 0.01), and low arachidonic acid levels in the phospholipid fraction (P < 0.0001) were independently associated with dyskinesia.

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.

Pharmacists as HIV/AIDS information resources: survey of Alabama pharmacists.

Alabama pharmacists were surveyed by researchers from the University of Alabama at Birmingham's School of Public Health in collaboration with investigators at the Schools of Pharmacy at Auburn University and Stamford University as part of a statewide pharmacists' demonstration project to mobilize pharmacists and pharmacy locations as information resources for HIV/AIDS education and prevention. The objectives of the survey were to: (1) establish a baseline of knowledge and attitudes among Alabama pharmacists about HIV/AIDS; (2) to assess Alabama pharmacists' willingness to assume the role of HIV/AIDS information resources in their communities; and (3) to identify perceived barriers to assuming the role of information resources. The results of the survey were used in the development of an educational intervention program and will be used subsequently to assess the impact of the implementation of the Alabama demonstration project. Findings from this project will serve as a basis for development of a nationwide project as part of a collaborative agreement between the Centers for Disease Control and Prevention's National Partnership Program and the Foundation of Pharmacists and Corporate America for AIDS Education.

Environmental justice and statistical summaries of differences in exposure distributions.

Recent regulatory action requires the assessment of environmental justice (equitable protection from the burdens of environmental hazards across sociodemographic subpopulations) in the siting of hazardous waste sites, and prioritization of environmental remediation efforts. Assessments of environmental justice require linking exposure, demographic, and health data. The geographic nature of the data makes the use of geographic information systems attractive for environmental justice assessments. Typical geographic assessments compare the composition of 'exposed' populations, while typical statistical assessments focus on differences in health outcomes between population subgroups, possibly adjusted for exposure. We outline an alternate approach based on summarized differences between exposure distributions within each population subgroup. We illustrate how such summaries provide a tool for site evaluation (e.g., defining exposure inequities resulting from locating a new potential hazard at any of a number of possible sites). In addition, we describe summaries, based on dose-response relationships, to describe risk differences imposed by the observed exposure differences. Reported toxic emissions from Allegheny County, Pennsylvania illustrate the approach.

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.

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.