An overview of the wcd EST clustering tool

Summary: The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d 2 distance function or edit distance, improving existing implementations of d 2. It supports merging, refinement and reclustering of clusters. It is ‘drop in’ compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. It is distributed with an EMBOSS wrapper allowing wcd to be installed as part of an EMBOSS installation (and so provided by a web server)

Inferring ecological and behavioral drivers of African elephant movement using a linear filtering approach

Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse–response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human–elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial scales.This research was supported by NSF GRFP (A. N. Boettiger) and NIH grant GM083863-01 and USDI FWS Grant 98210-8-G745 to W. M. Getz.http://www.esajournals.org/loi/ecol

Out-of-hours care in western countries: assessment of different organizational models

Contains fulltext : 81655.pdf (publisher's version ) (Open Access)BACKGROUND: Internationally, different organizational models are used for providing out-of-hours care. The aim of this study was to assess prevailing models in order to identify their potential strengths and weaknesses. METHODS: An international web-based survey was done in 2007 in a sample of purposefully selected key informants from 25 western countries. The questions concerned prevailing organizational models for out-of-hours care, the most dominant model in each country, perceived weaknesses, and national plans for changes in out-of-hours care. RESULTS: A total of 71 key informants from 25 countries provided answers. In most countries several different models existed alongside each other. The Accident and Emergency department was the organizational model most frequently used. Perceived weaknesses of this model concerned the coordination and continuity of care, its efficiency and accessibility. In about a third of the countries, the rota group was the most dominant organizational model for out-of-hours care. A perceived weakness of this model was lowered job satisfaction of physicians. The GP cooperative existed in a majority of the participating countries; no weaknesses were mentioned with respect to this model. Most of the countries had plans to change the out-of-hours care, mainly toward large scale organizations. CONCLUSION: GP cooperatives combine size of scale advantages with organizational features of strong primary care, such as high accessibility, continuity and coordination of care. While specific patients require other organizational models, the co-existence of different organizational models for out-of-hours care in a country may be less efficient for health systems

Contingent Kernel Density Estimation

Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points. Such error can be the result of imprecise measurements or observation bias. Often this error is negligible and may be disregarded in analysis. In cases where the error is non-negligible, estimation methods should be adjusted to reduce resulting bias. Several modifications of kernel density estimation have been developed to address specific forms of errors. One form of error that has not yet been addressed is the case where observations are nominally placed at the centers of areas from which the points are assumed to have been drawn, where these areas are of varying sizes. In this scenario, the bias arises because the size of the error can vary among points and some subset of points can be known to have smaller error than another subset or the form of the error may change among points. This paper proposes a “contingent kernel density estimation” technique to address this form of error. This new technique adjusts the standard kernel on a point-by-point basis in an adaptive response to changing structure and magnitude of error. In this paper, equations for our contingent kernel technique are derived, the technique is validated using numerical simulations, and an example using the geographic locations of social networking users is worked to demonstrate the utility of the method

Deriving simple predictions from complex models to support environmental decision-making

Recent decades have seen great advances in ecological modelling and computing power, enabling ecologists to build increasingly detailed models to more accurately represent ecological systems. To better inform environmental decision-making, it is important that the predictions of these models are expressed in simple ways that are straightforward for stakeholders to comprehend and use. One way to achieve this is to predict threshold values for environmental perturbations (e.g. climate change, habitat modification, food loss, sea level rise) associated with negative impacts on individuals, populations, communities or ecosystems. These thresholds can be used by stakeholders to inform management and policy. In this paper we demonstrate how this approach can use individual-based models of birds, their prey and habitats, to provide the evidence-base for coastal bird conservation and shellfishery management. In particular, we show how such models can be used to identify threshold values for perturbations of food abundance that can impact negatively on bird populations. We highlight how environmental thresholds could be used more widely to inform management of species and habitats under environmental change

Primary care and health inequality : Difference-in-difference study comparing England and Ontario

BACKGROUND: It is not known whether equity-oriented primary care investment that seeks to scale up the delivery of effective care in disadvantaged communities can reduce health inequality within high-income settings that have pre-existing universal primary care systems. We provide some non-randomised controlled evidence by comparing health inequality trends between two similar jurisdictions-one of which implemented equity-oriented primary care investment in the mid-to-late 2000s as part of a cross-government strategy for reducing health inequality (England), and one which invested in primary care without any explicit equity objective (Ontario, Canada). METHODS: We analysed whole-population data on 32,482 neighbourhoods (with mean population size of approximately 1,500 people) in England, and 18,961 neighbourhoods (with mean population size of approximately 700 people) in Ontario. We examined trends in mortality amenable to healthcare by decile groups of neighbourhood deprivation within each jurisdiction. We used linear models to estimate absolute and relative gaps in amenable mortality between most and least deprived groups, considering the gradient between these extremes, and evaluated difference-in-difference comparisons between the two jurisdictions. RESULTS: Inequality trends were comparable in both jurisdictions from 2004-6 but diverged from 2007-11. Compared with Ontario, the absolute gap in amenable mortality in England fell between 2004-6 and 2007-11 by 19.8 per 100,000 population (95% CI: 4.8 to 34.9); and the relative gap in amenable mortality fell by 10 percentage points (95% CI: 1 to 19). The biggest divergence occurred in the most deprived decile group of neighbourhoods. DISCUSSION: In comparison to Ontario, England succeeded in reducing absolute socioeconomic gaps in mortality amenable to healthcare from 2007 to 2011, and preventing them from growing in relative terms. Equity-oriented primary care reform in England in the mid-to-late 2000s may have helped to reduce socioeconomic inequality in health, though other explanations for this divergence are possible and further research is needed on the specific causal mechanisms

Development and pilot of an internationally standardized measure of cardiovascular risk management in European primary care

Contains fulltext : 97806.pdf (publisher's version ) (Open Access)BACKGROUND: Primary care can play an important role in providing cardiovascular risk management in patients with established Cardiovascular Diseases (CVD), patients with a known high risk of developing CVD, and potentially for individuals with a low risk of developing CVD, but who have unhealthy lifestyles. To describe and compare cardiovascular risk management, internationally valid quality indicators and standardized measures are needed. As part of a large project in 9 European countries (EPA-Cardio), we have developed and tested a set of standardized measures, linked to previously developed quality indicators. METHODS: A structured stepwise procedure was followed to develop measures. First, the research team allocated 106 validated quality indicators to one of the three target populations (established CVD, at high risk, at low risk) and to different data-collection methods (data abstraction from the medical records, a patient survey, an interview with lead practice GP/a practice survey). Secondly, we selected a number of other validated measures to enrich the assessment. A pilot study was performed to test the feasibility. Finally, we revised the measures based on the findings. RESULTS: The EPA-Cardio measures consisted of abstraction forms from the medical-records data of established Coronary Heart Disease (CHD)-patients--and high-risk groups, a patient questionnaire for each of the 3 groups, an interview questionnaire for the lead GP and a questionnaire for practice teams. The measures were feasible and accepted by general practices from different countries. CONCLUSIONS: An internationally standardized measure of cardiovascular risk management, linked to validated quality indicators and tested for feasibility in general practice, is now available. Careful development and pilot testing of the measures are crucial in international studies of quality of healthcare

A cluster randomized trial to improve adherence to evidence-based guidelines on diabetes and reduce clinical inertia in primary care physicians in Belgium: study protocol [NTR 1369]

Contains fulltext : 70617.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: Most quality improvement programs in diabetes care incorporate aspects of clinician education, performance feedback, patient education, care management, and diabetes care teams to support primary care physicians. Few studies have applied all of these dimensions to address clinical inertia. AIM: To evaluate interventions to improve adherence to evidence-based guidelines for diabetes and reduce clinical inertia in primary care physicians. DESIGN: Two-arm cluster randomized controlled trial. PARTICIPANTS: Primary care physicians in Belgium. INTERVENTIONS: Primary care physicians will be randomly allocated to 'Usual' (UQIP) or 'Advanced' (AQIP) Quality Improvement Programs. Physicians in the UQIP will receive interventions addressing the main physician, patient, and office system factors that contribute to clinical inertia. Physicians in the AQIP will receive additional interventions that focus on sustainable behavior changes in patients and providers. OUTCOMES: Primary endpoints are the proportions of patients within targets for three clinical outcomes: 1) glycosylated hemoglobin < 7%; 2) systolic blood pressure differences </=130 mmHg; and 3) low density lipoprotein/cholesterol < 100 mg/dl. Secondary endpoints are individual improvements in 12 validated parameters: glycosylated hemoglobin, low and high density lipoprotein/cholesterol, total cholesterol, systolic blood pressure, diastolic blood pressure, weight, physical exercise, healthy diet, smoking status, and statin and anti-platelet therapy. PRIMARY AND SECONDARY ANALYSIS: Statistical analyses will be performed using an intent-to-treat approach with a multilevel model. Linear and generalized linear mixed models will be used to account for the clustered nature of the data, i.e., patients clustered withinimary care physicians, and repeated assessments clustered within patients. To compare patient characteristics at baseline and between the intervention arms, the generalized estimating equations (GEE) approach will be used, taking the clustered nature of the data within physicians into account. We will also use the GEE approach to test for differences in evolution of the primary and secondary endpoints for all patients, and for patients in the two interventions arms, accounting for within-patient clustering. TRIAL REGISTRATION: number: NTR 1369