Analysis of the atmospheric distribution, sources, and sinks of oxygenated volatile organic chemicals based on measurements over the Pacific during TRACE-P

Airborne measurements of a large number of oxygenated volatile organic chemicals (OVOC) were carried out in the Pacific troposphere (0.1 - 12 km) in winter/spring of 2001 (24 February to 10 April). Specifically, these measurements included acetone (CH3COCHA3), methylethyl ketone (CH3COC2H5, MEK), methanol (CH3OH), ethanol (C2H5OH), acetaldehyde (CH3CHO), propionaldehyde C2H 5CHO), peroxyacylnitrates (PANs) (CnH 2n+1COO2NO2), and organic nitrates (CnH2n+1ONO2). Complementary measurements of formaldehyde (HCHO), methyl hydroperoxide (CH 3OOH), and selected tracers were also available. OVOC were abundant in the clean troposphere and were greatly enhanced in the outflow regions from Asia. Background mixing ratios were typically highest in the lower troposphere and declined toward the upper troposphere and the lowermost stratosphere. Their total abundance (ΣOVOC) was nearly twice that of nonmethane hydrocarbons (Σ C2-C8 NMHC. Throughout the troposphere, the OH reactivity of OVOC is comparable to that of methane and far exceeds that of NMHC. A comparison of these data with western Pacific observations collected some 7 years earlier (February-March 1994) did not reveal significant differences. Mixing ratios of OVOC were strongly correlated with each other as well as with tracers of fossil and biomass/biofuel combustion. Analysis of the relative enhancement of selected OVOC with respect to CH 3Cl and CO in 12 plumes originating from fires and sampled in the free troposphere (3-11 km) is used to assess their primary and secondary emissions from biomass combustion. The composition of these plumes also indicates a large shift of reactive nitrogen into the PAN reservoir thereby limiting ozone formation. A three-dimensional global model that uses state of the art chemistry and source information is used to compare measured and simulated mixing ratios of selected OVOC. While there is reasonable agreement in many cases, measured aldehyde concentrations are significantly larger than predicted. At their observed levels, acetaldehyde mixing ratios are shown to be an important source of HCHO (and HOx) and PAN in the troposphere. On the basis of presently known chemistry, measured mixing ratios of aldehydes and PANs are mutually incompatible. We provide rough estimates of the global sources of several OVOC and conclude that collectively these are extremely large (150-500 Tg C yr-1) but remain poorly quantified. Copyright 2004 by the American Geophysical Union

Galaxies appear simpler than expected

Galaxies are complex systems the evolution of which apparently results from the interplay of dynamics, star formation, chemical enrichment, and feedback from supernova explosions and supermassive black holes. The hierarchical theory of galaxy formation holds that galaxies are assembled from smaller pieces, through numerous mergers of cold dark matter. The properties of an individual galaxy should be controlled by six independent parameters including mass, angular-momentum, baryon-fraction, age and size, as well as by the accidents of its recent haphazard merger history. Here we report that a sample of galaxies that were first detected through their neutral hydrogen radio-frequency emission, and are thus free of optical selection effects, shows five independent correlations among six independent observables, despite having a wide range of properties. This implies that the structure of these galaxies must be controlled by a single parameter, although we cannot identify this parameter from our dataset. Such a degree of organisation appears to be at odds with hierarchical galaxy formation, a central tenet of the cold dark matter paradigm in cosmology.Comment: 26 pages, 14 figure

Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse

Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a single a priori hypothesis test. This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified. We show that the probability of finding at least one ‘significant’ effect is high, even if all null hypotheses are true (e.g. 40% when starting with four predictors and their two-way interactions). This probability is close to theoretical expectations when the sample size (N) is large relative to the number of predictors including interactions (k). In contrast, type I error rates strongly exceed even those expectations when model simplification is applied to models that are over-fitted before simplification (low N/k ratio). The increase in false-positive results arises primarily from an overestimation of effect sizes among significant predictors, leading to upward-biased effect sizes that often cannot be reproduced in follow-up studies (‘the winner's curse’). Despite having their own problems, full model tests and P value adjustments can be used as a guide to how frequently type I errors arise by sampling variation alone. We favour the presentation of full models, since they best reflect the range of predictors investigated and ensure a balanced representation also of non-significant results

Models for short term malaria prediction in Sri Lanka

<p>Abstract</p> <p>Background</p> <p>Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control.</p> <p>Methods</p> <p>Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models.</p> <p>Results</p> <p>The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons.</p> <p>Conclusion</p> <p>Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed.</p

Nothing a Hot Bath Won't Cure: Infection Rates of Amphibian Chytrid Fungus Correlate Negatively with Water Temperature under Natural Field Settings

Dramatic declines and extinctions of amphibian populations throughout the world have been associated with chytridiomycosis, an infectious disease caused by the pathogenic chytrid fungus Batrachochytrium dendrobatidis (Bd). Previous studies indicated that Bd prevalence correlates with cooler temperatures in the field, and laboratory experiments have demonstrated that Bd ceases growth at temperatures above 28°C. Here we investigate how small-scale variations in water temperature correlate with Bd prevalence in the wild. We sampled 221 amphibians, including 201 lowland leopard frogs (Rana [Lithobates] yavapaiensis), from 12 sites in Arizona, USA, and tested them for Bd. Amphibians were encountered in microhabitats that exhibited a wide range of water temperatures (10–50°C), including several geothermal water sources. There was a strong inverse correlation between the water temperature in which lowland leopard frogs were captured and Bd prevalence, even after taking into account the influence of year, season, and host size. In locations where Bd was known to be present, the prevalence of Bd infections dropped from 75–100% in water <15°C, to less than 10% in water >30°C. A strong inverse correlation between Bd infection status and water temperature was also observed within sites. Our findings suggest that microhabitats where water temperatures exceed 30°C provide lowland leopard frogs with significant protection from Bd, which could have important implications for disease dynamics, as well as management applications

Case-finding of dementia in general practice and effects of subsequent collaborative care; design of a cluster RCT

<p>Abstract</p> <p>Background</p> <p>In the primary care setting, dementia is often diagnosed relatively late in the disease process. Case finding and proactive collaborative care may have beneficial effects on both patient and informal caregiver by clarifying the cause of cognitive decline and changed behaviour and by enabling support, care planning and access to services.</p> <p>We aim to improve the recognition and diagnosis of individuals with dementia in general practice. In addition to this diagnostic aim, the effects of case finding and subsequent care on the mental health of individuals with dementia and the mental health of their informal carers are explored.</p> <p>Methods and design</p> <p>Design: cluster randomised controlled trial with process evaluation.</p> <p>Participants: 162 individuals ≥ 65 years, in 15 primary care practices, in whom GPs suspect cognitive impairment, but without a dementia diagnosis.</p> <p>Intervention; case finding and collaborative care: 2 trained practice nurses (PNs) invite all patients with suspected cognitive impairment for a brief functional and cognitive screening. If the cognitive tests are supportive of cognitive impairment, individuals are referred to their GP for further evaluation. If dementia is diagnosed, a comprehensive geriatric assessment takes place to identify other relevant geriatric problems that need to be addressed. Furthermore, the team of GP and PN provide information and support.</p> <p>Control: GPs provide care and diagnosis as usual.</p> <p>Main study parameters: after 12 months both groups are compared on: 1) incident dementia (and MCI) diagnoses and 2) patient and caregiver quality of life (QoL-AD; EQ5D) and mental health (MH5; GHQ 12) and caregiver competence to care (SSCQ). The process evaluation concerns facilitating and impeding factors to the implementation of this intervention. These factors are assessed on the care provider level, the care recipient level and on the organisational level.</p> <p>Discussion</p> <p>This study will provide insight into the diagnostic yield and the clinical effects of case finding and collaborative care for individuals with suspected cognitive impairment, compared to usual care. A process evaluation will give insight into the feasibility of this intervention.</p> <p>The first results are expected in the course of 2013.</p> <p>Trial registration</p> <p>NTR3389</p

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Seasonal Pattern of Batrachochytrium dendrobatidis Infection and Mortality in Lithobates areolatus: Affirmation of Vredenburg's “10,000 Zoospore Rule”

To fully comprehend chytridiomycosis, the amphibian disease caused by the chytrid fungus Batrachochytrium dendrobatidis (Bd), it is essential to understand how Bd affects amphibians throughout their remarkable range of life histories. Crawfish Frogs (Lithobates areolatus) are a typical North American pond-breeding species that forms explosive spring breeding aggregations in seasonal and semipermanent wetlands. But unlike most species, when not breeding Crawfish Frogs usually live singly—in nearly total isolation from conspecifics—and obligately in burrows dug by crayfish. Crayfish burrows penetrate the water table, and therefore offer Crawfish Frogs a second, permanent aquatic habitat when not breeding. Over the course of two years we sampled for the presence of Bd in Crawfish Frog adults. Sampling was conducted seasonally, as animals moved from post-winter emergence through breeding migrations, then back into upland burrow habitats. During our study, 53% of Crawfish Frog breeding adults tested positive for Bd in at least one sample; 27% entered breeding wetlands Bd positive; 46% exited wetlands Bd positive. Five emigrating Crawfish Frogs (12%) developed chytridiomycosis and died. In contrast, all 25 adult frogs sampled while occupying upland crayfish burrows during the summer tested Bd negative. One percent of postmetamorphic juveniles sampled were Bd positive. Zoospore equivalents/swab ranged from 0.8 to 24,436; five out of eight frogs with zoospore equivalents near or >10,000 are known to have died. In summary, Bd infection rates in Crawfish Frog populations ratchet up from near zero during the summer to over 25% following overwintering; rates then nearly double again during and just after breeding—when mortality occurs—before the infection wanes during the summer. Bd-negative postmetamorphic juveniles may not be exposed again to this pathogen until they take up residence in crayfish burrows, or until their first breeding, some years later

CAGO: A Software Tool for Dynamic Visual Comparison and Correlation Measurement of Genome Organization

CAGO (Comparative Analysis of Genome Organization) is developed to address two critical shortcomings of conventional genome atlas plotters: lack of dynamic exploratory functions and absence of signal analysis for genomic properties. With dynamic exploratory functions, users can directly manipulate chromosome tracks of a genome atlas and intuitively identify distinct genomic signals by visual comparison. Signal analysis of genomic properties can further detect inconspicuous patterns from noisy genomic properties and calculate correlations between genomic properties across various genomes. To implement dynamic exploratory functions, CAGO presents each genome atlas in Scalable Vector Graphics (SVG) format and allows users to interact with it using a SVG viewer through JavaScript. Signal analysis functions are implemented using R statistical software and a discrete wavelet transformation package waveslim. CAGO is not only a plotter for generating complex genome atlases, but also a platform for exploring genome atlases with dynamic exploratory functions for visual comparison and with signal analysis for comparing genomic properties across multiple organisms. The web-based application of CAGO, its source code, user guides, video demos, and live examples are publicly available and can be accessed at http://cbs.ym.edu.tw/cago