Comparing meta-analysis and ecological-longitudinal analysis in time-series studies: a case study of the effects of air pollution on mortality in three Spanish cities

The objective of this paper is to introduce a diVerent approach, called the ecological-longitudinal, to carrying out pooled analysis in time series ecological studies. Because it gives a larger number of data points and, hence, increases the statistical power of the analysis, this approach, unlike conventional ones, allows the complementation of aspects such as accommodation of random effect models, of lags, of interaction between pollutants and between pollutants and meteorological variables, that are hardly implemented in conventional approaches. Design—The approach is illustrated by providing quantitative estimates of the short-termeVects of air pollution on mortality in three Spanish cities, Barcelona,Valencia and Vigo, for the period 1992–1994. Because the dependent variable was a count, a Poisson generalised linear model was first specified. Several modelling issues are worth mentioning. Firstly, because the relations between mortality and explanatory variables were nonlinear, cubic splines were used for covariate control, leading to a generalised additive model, GAM. Secondly, the effects of the predictors on the response were allowed to occur with some lag. Thirdly, the residual autocorrelation, because of imperfect control, was controlled for by means of an autoregressive Poisson GAM. Finally, the longitudinal design demanded the consideration of the existence of individual heterogeneity, requiring the consideration of mixed models. Main results—The estimates of the relative risks obtained from the individual analyses varied across cities, particularly those associated with sulphur dioxide. The highest relative risks corresponded to black smoke in Valencia. These estimates were higher than those obtained from the ecological-longitudinal analysis. Relative risks estimated from this latter analysis were practically identical across cities, 1.00638 (95% confidence intervals 1.0002, 1.0011) for a black smoke increase of 10 μg/m3 and 1.00415 (95% CI 1.0001, 1.0007) for a increase of 10 μg/m3 of sulphur dioxide. Because the statistical power is higher than in the individual analysis more interactions were statistically significant,especially those among air pollutants and meteorological variables. Conclusions—Air pollutant levels were related to mortality in the three cities of the study, Barcelona, Valencia and Vigo. These results were consistent with similar studies in other cities, with other multicentric studies and coherent with both, previous individual, for each city, and multicentric studies for all three cities

A Study on the Efficiency of Synbiotics for Prevention and Control of Diarrhoea in 21 Dogs Submitted to Radiotherapy

A synbiotic is a formulation containing both probiotics and prebiotics. This study aims to evaluate the effect of supplementation with a synbiotic containing Enterococcus faecium strain E1707 (NCIMB 10415) in preventing or controlling diarrhoea and other gastrointestinal signs in boarded canine radiotherapy patients. A double-blind, randomized, placebocontrolled clinical trial was carried out in 21 adult dogs undergoing radiotherapy and boarded for a duration period of 2 to 3 weeks to treat their cancers. Dogs were randomly divided between two groups: A and B, the synbiotic and placebo group, respectively. The content of the sachets was added to the food once daily. Faecal score was assessed daily, and dogs were also monitored for the development of diarrhoea and other gastrointestinal signs such as weight loss, reduced appetite and vomiting. The results from descriptive statistics seem to favour group B, however these findings were not validated with inferential statistics due to insufficient statistical sample power. Because of this, it is not possible to make conclusions about the benefits of synbiotic as supportive treatment for dogs undergoing radiotherapy. All results should be considered to be preliminary, until they are elucidated by further animal inclusion.

An observing system simulation experiment for climate monitoring with GNSS radio occulation data: setup and testbed study

The long-term stability, high accuracy, all-weather capability, high vertical resolution, and global coverage of Global Navigation Satellite System (GNSS) radio occultation (RO) suggests it as a promising tool for global monitoring of atmospheric temperature change. With the aim to investigate and quantify how well a GNSS RO observing system is able to detect climate trends, we are currently performing an (climate) observing system simulation experiment over the 25-year period 2001 to 2025, which involves quasi-realistic modeling of the neutral atmosphere and the ionosphere. We carried out two climate simulations with the general circulation model MAECHAM5 (Middle Atmosphere European Centre/Hamburg Model Version 5) of the MPI-M Hamburg, covering the period 2001–2025: One control run with natural variability only and one run also including anthropogenic forcings due to greenhouse gases, sulfate aerosols, and tropospheric ozone. On the basis of this, we perform quasi-realistic simulations of RO observables for a small GNSS receiver constellation (six satellites), state-of-the-art data processing for atmospheric profiles retrieval, and a statistical analysis of temperature trends in both the “observed” climatology and the “true” climatology. Here we describe the setup of the experiment and results from a test bed study conducted to obtain a basic set of realistic estimates of observational errors (instrument- and retrieval processing-related errors) and sampling errors (due to spatial-temporal undersampling). The test bed results, obtained for a typical summer season and compared to the climatic 2001–2025 trends from the MAECHAM5 simulation including anthropogenic forcing, were found encouraging for performing the full 25-year experiment. They indicated that observational and sampling errors (both contributing about 0.2 K) are consistent with recent estimates of these errors from real RO data and that they should be sufficiently small for monitoring expected temperature trends in the global atmosphere over the next 10 to 20 years in most regions of the upper troposphere and lower stratosphere (UTLS). Inspection of the MAECHAM5 trends in different RO-accessible atmospheric parameters (microwave refractivity and pressure/geopotential height in addition to temperature) indicates complementary climate change sensitivity in different regions of the UTLS so that optimized climate monitoring shall combine information from all climatic key variables retrievable from GNSS RO data.

New insights on growth mechanisms of protein clusters at surfaces: an AFM and simulation study

Despite its relevance to a wide range of technological and fundamental areas, a quantitative understanding of protein surface clustering dynamics is often lacking. In inorganic crystal growth, surface clustering of adatoms is well described by diffusion-aggregation models. In such models, the statistical properties of the aggregate arrays often reveal the molecular scale aggregation processes. We investigate the potential of these theories to reveal hitherto hidden facets of protein clustering by carrying out concomitant observations of lysozyme adsorption onto mica surfaces, using atomic force microscopy. and Monte Carlo simulations of cluster nucleation and growth. We find that lysozyme clusters diffuse across the substrate at a rate that varies inversely with size. This result suggests which molecular scale mechanisms are responsible for the mobility of the proteins on the substrate. In addition the surface diffusion coefficient of the monomer can also be extracted from the comparison between experiments and simulations. While concentrating on a model system of lysozyme-on-mica, this 'proof of concept' study successfully demonstrates the potential of our approach to understand and influence more biomedically applicable protein-substrate couples.

Spatial covariation of Azotobacter abundance and soil properties: A case study using the wavelet transform

The soil microflora is very heterogeneous in its spatial distribution. The origins of this heterogeneity and its significance for soil function are not well understood. A problem for understanding spatial variation better is the assumption of statistical stationarity that is made in most of the statistical methods used to assess it. These assumptions are made explicit in geostatistical methods that have been increasingly used by soil biologists in recent years. Geostatistical methods are powerful, particularly for local prediction, but they require the assumption that the variability of a property of interest is spatially uniform, which is not always plausible given what is known about the complexity of the soil microflora and the soil environment. We have used the wavelet transform, a relatively new innovation in mathematical analysis, to investigate the spatial variation of abundance of Azotobacter in the soil of a typical agricultural landscape. The wavelet transform entails no assumptions of stationarity and is well suited to the analysis of variables that show intermittent or transient features at different spatial scales. In this study, we computed cross-variograms of Azotobacter abundance with the pH, water content and loss on ignition of the soil. These revealed scale-dependent covariation in all cases. The wavelet transform also showed that the correlation of Azotobacter abundance with all three soil properties depended on spatial scale, the correlation generally increased with spatial scale and was only significantly different from zero at some scales. However, the wavelet analysis also allowed us to show how the correlation changed across the landscape. For example, at one scale Azotobacter abundance was strongly correlated with pH in part of the transect, and not with soil water content, but this was reversed elsewhere on the transect. The results show how scale-dependent variation of potentially limiting environmental factors can induce a complex spatial pattern of abundance in a soil organism. The geostatistical methods that we used here make assumptions that are not consistent with the spatial changes in the covariation of these properties that our wavelet analysis has shown. This suggests that the wavelet transform is a powerful tool for future investigation of the spatial structure and function of soil biota. (c) 2006 Elsevier Ltd. All rights reserved.

Treatment of written verb and written sentence production in an individual with aphasia: A clinical study

Background: Although the efficacy of treatments for spoken verb and sentence production deficits in aphasia has been documented widely, less is known about interventions for written verb and written sentence production deficits. Aims: This study documents a treatment aiming to improve production of (a) written subject-verb sentences (involving intransitive verbs) and (b) written subject-verb-object sentences (involving transitive verbs). Methods & Procedures: The participant, a 63-year-old female aphasic speaker, had a marked language comprehension deficit, apraxia of speech, relatively good spelling abilities, and no hemiplegia. The treatment involved intransitive verbs producing subject-verb active sentences and transitive verbs producing subject-verb-object active non-reversible sentences. The treatment was undertaken in the context of current UK clinical practice. Outcomes & Results: Statistical improvements were noted for the trained sets of verbs and sentences. Other improvements were also noted in LW's ability to retrieve some non-treated verbs and construct written sentences. Treatment did not generalise to sentence comprehension and letter spelling to dictation. Conclusions: Our participant's ability to write verbs and sentences improved as a result of the treatment.

Statistical downscaling of river flows

An extensive statistical ‘downscaling’ study is done to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in SW France for 51 gauging stations ranging from nival (snow-dominated) to pluvial (rainfall-dominated) river-systems. This study helps to select the appropriate statistical method at a given spatial and temporal scale to downscale hydrology for future climate change impact assessment of hydrological resources. The four proposed statistical downscaling models use large-scale predictors (derived from climate model outputs or reanalysis data) that characterize precipitation and evaporation processes in the hydrological cycle to estimate summary flow statistics. The four statistical models used are generalized linear (GLM) and additive (GAM) models, aggregated boosted trees (ABT) and multi-layer perceptron neural networks (ANN). These four models were each applied at two different spatial scales, namely at that of a single flow-gauging station (local downscaling) and that of a group of flow-gauging stations having the same hydrological behaviour (regional downscaling). For each statistical model and each spatial resolution, three temporal resolutions were considered, namely the daily mean flows, the summary statistics of fortnightly flows and a daily ‘integrated approach’. The results show that flow sensitivity to atmospheric factors is significantly different between nival and pluvial hydrological systems which are mainly influenced, respectively, by shortwave solar radiations and atmospheric temperature. The non-linear models (i.e. GAM, ABT and ANN) performed better than the linear GLM when simulating fortnightly flow percentiles. The aggregated boosted trees method showed higher and less variable R2 values to downscale the hydrological variability in both nival and pluvial regimes. Based on GCM cnrm-cm3 and scenarios A2 and A1B, future relative changes of fortnightly median flows were projected based on the regional downscaling approach. The results suggest a global decrease of flow in both pluvial and nival regimes, especially in spring, summer and autumn, whatever the considered scenario. The discussion considers the performance of each statistical method for downscaling flow at different spatial and temporal scales as well as the relationship between atmospheric processes and flow variability.

Food scares and trust: A European study

The complex interactions between the determinants of food purchase under risk are explored using the SPARTA model, based on the theory of planned behaviour, and estimated through a combination of multivariate statistical techniques. The application investigates chicken consumption choices in two scenarios: ( a) a 'standard' purchasing situation; and (b) following a hypothetical Salmonella scare. The data are from a nationally representative survey of 2,725 respondents from five European countries: France, Germany, Italy, the Netherlands and the United Kingdom. Results show that the effects and interactions of behavioural determinants vary significantly within Europe. Only in the case of a food scare do risk perceptions and trust come into play. The policy priority should be on building and maintaining trust in food and health authorities and research institutions, while food chain actors could mitigate the consequences of a food scare through public trust. No relationship is found between socio-demographic variables and consumer trust in food safety information.

Statistical evaluation of the fixed concentration procedure for acute inhalation toxicity assessment

The conventional method for the assessment of acute inhalation toxicity (OECD Test Guideline 403, 1981) uses death of animals as an endpoint to identify the median lethal concentration (LC50). A new OECD Testing Guideline called the Fixed Concentration Procedure (FCP) is being prepared to provide an alternative to Test Guideline 403. Unlike Test Guideline 403, the FCP does not provide a point estimate of the LC50, but aims to identify an airborne exposure level that causes clear signs of nonlethal toxicity. This is then used to assign classification according to the new Globally Harmonized System of Classification and Labelling scheme (GHS). The FCP has been validated using statistical simulation rather than byin vivo testing. The statistical simulation approach predicts the GHS classification outcome and the numbers of deaths and animals used in the test for imaginary substances with a range of LC50 values and dose response curve slopes. This paper describes the FCP and reports the results from the statistical simulation study assessing its properties. It is shown that the procedure will be completed with considerably less death and suffering than Test Guideline 403, and will classify substances either in the same or a more stringent GHS class than that assigned on the basis of the LC50 value.

An approximate Bayesian computation approach to overcome biases that arise when using amplified fragment length polymorphism markers to study population structure

There is great interest in using amplified fragment length polymorphism (AFLP) markers because they are inexpensive and easy to produce. It is, therefore, possible to generate a large number of markers that have a wide coverage of species genotnes. Several statistical methods have been proposed to study the genetic structure using AFLP's but they assume Hardy-Weinberg equilibrium and do not estimate the inbreeding coefficient, F-IS. A Bayesian method has been proposed by Holsinger and colleagues that relaxes these simplifying assumptions but we have identified two sources of bias that can influence estimates based on these markers: (i) the use of a uniform prior on ancestral allele frequencies and (ii) the ascertainment bias of AFLP markers. We present a new Bayesian method that avoids these biases by using an implementation based on the approximate Bayesian computation (ABC) algorithm. This new method estimates population-specific F-IS and F-ST values and offers users the possibility of taking into account the criteria for selecting the markers that are used in the analyses. The software is available at our web site (http://www-leca.uif-grenoble.fi-/logiciels.htm). Finally, we provide advice on how to avoid the effects of ascertainment bias.

The significance of "nonsignificance" in randomized controlled studies: a discussion inspired by a double-blinded study on St. John's Wort (Hypericum perforatum L.) for premenstrual symptoms

Objectives: This study aimed to investigate the efficacy of St. John's wort extract (SJW) as a treatment for premenstrual symptoms. Design: The study was a randomized, double-blinded, placebo-controlled trial, with two parallel treatment groups. After a no-treatment baseline cycle, volunteers were randomized to either SJW or placebo for a further two menstrual cycles. Settings/location: A postal trial conducted from The University of Reading, Berkshire, England. Subjects: One hundred and sixty-nine (169) normally menstruating women who experienced recurrent premenstrual symptoms were recruited onto the study. One hundred and twenty-five (125) completed the protocol and were included in the analysis. Interventions: Six hundred milligrams (600) mg of SJW (standardized to contain 1800 mug of hypericin) or placebo (containing lactose and cellulose). Outcome measure: A menstrual diary was used to assess changes in premenstrual symptoms. The anxiety-related subgroup of symptoms of this instrument was used as the primary outcome measure. Results: After averaging the effects of treatment over both treatment cycles it was found that there was a trend for SJW to be superior to placebo. However, this finding was not statistically significant. Conclusion: The possibility that this nonsignificant finding resulted from insufficient statistical power in the study, rather than a lack of efficacy of SJW, is discussed. Following this discussion the recommendation is made that, in future, similar studies should be powered to detect a minimum clinically relevant difference between treatments.

Time course of gag protein assembly in HIV-1-infected cells: a study by immunoelectron microscopy

Recent biochemical studies have identified high molecular complexes of the HIV Gag precursor in the cytosol of infected cells. Using immunoelectron microscopy we studied the time course of the synthesis and assembly of a HIV Gag precursor protein (pr55gag) in Sf9 cells infected with recombinant baculovirus expressing the HIV gag gene. We also immunolabeled for pr55gag human T4 cells acutely or chronically infected with HIV-1. In Sf9 cells, the time course study showed that the first Gag protein appeared in the cytoplasm at 28-30 h p.i. and that budding started 6-8 h later. Colloidal gold particles, used to visualize the Gag protein, were first scattered randomly throughout the cytoplasm, but soon clusters representing 100 to 1000 copies of pr55gag were also observed. By contrast, in cells with budding or released virus-like particles the cytoplasm was virtually free of gold particles while the released virus-like particles were heavily labeled. Statistical analysis showed that between 80 and 90% of the gold particles in the cytoplasm were seen as singles, as doublets, or in small groups of up to five particles probably representing small oligomers. Clusters of gold particles were also observed in acutely infected lymphocytes as well as in multinuclear cells of chronically infected cultures of T4 cells. In a few cases small aggregates of gold particles were found in the nuclei of T4 lymphocytes. These observations suggest that the Gag polyprotein forms small oligomers in the cytoplasm of expressing cells but that assembly into multimeric complexes takes place predominantly at the plasma membrane. Large accumulations of Gag protein in the cytoplasm may represent misfolded molecules destined for degradation.

A study of the effect of heat source location in a ventilated room using multiple regression analysis

Multiple regression analysis is a statistical technique which allows to predict a dependent variable from m ore than one independent variable and also to determine influential independent variables. Using experimental data, in this study the multiple regression analysis is applied to predict the room mean velocity and determine the most influencing parameters on the velocity. More than 120 experiments for four different heat source locations were carried out in a test chamber with a high level wall mounted air supply terminal at air change rates 3-6 ach. The influence of the environmental parameters such as supply air momentum, room heat load, Archimedes number and local temperature ratio, were examined by two methods: a simple regression analysis incorporated into scatter matrix plots and multiple stepwise regression analysis. It is concluded that, when a heat source is located along the jet centre line, the supply momentum mainly influences the room mean velocity regardless of the plume strength. However, when the heat source is located outside the jet region, the local temperature ratio (the inverse of the local heat removal effectiveness) is a major influencing parameter.

The significance of "nonsignificance" in randomized controlled studies: a discussion inspired by a double-blinded study on St. John's Wort (Hypericum perforatum L.) for premenstrual symptoms

Objectives: This study aimed to investigate the efficacy of St. John's wort extract (SJW) as a treatment for premenstrual symptoms. Design: The study was a randomized, double-blinded, placebo-controlled trial, with two parallel treatment groups. After a no-treatment baseline cycle, volunteers were randomized to either SJW or placebo for a further two menstrual cycles. Settings/location: A postal trial conducted from The University of Reading, Berkshire, England. Subjects: One hundred and sixty-nine (169) normally menstruating women who experienced recurrent premenstrual symptoms were recruited onto the study. One hundred and twenty-five (125) completed the protocol and were included in the analysis. Interventions: Six hundred milligrams (600) mg of SJW (standardized to contain 1800 mug of hypericin) or placebo (containing lactose and cellulose). Outcome measure: A menstrual diary was used to assess changes in premenstrual symptoms. The anxiety-related subgroup of symptoms of this instrument was used as the primary outcome measure. Results: After averaging the effects of treatment over both treatment cycles it was found that there was a trend for SJW to be superior to placebo. However, this finding was not statistically significant. Conclusion: The possibility that this nonsignificant finding resulted from insufficient statistical power in the study, rather than a lack of efficacy of SJW, is discussed. Following this discussion the recommendation is made that, in future, similar studies should be powered to detect a minimum clinically relevant difference between treatments.

Rapid quality assessment of wheat cultivars registered in Poland using the 2-g mixograph and multivariate statistical analysis

Baking and 2-g mixograph analyses were performed for 55 cultivars (19 spring and 36 winter wheat) from various quality classes from the 2002 harvest in Poland. An instrumented 2-g direct-drive mixograph was used to study the mixing characteristics of the wheat cultivars. A number of parameters were extracted automatically from each mixograph trace and correlated with baking volume and flour quality parameters (protein content and high molecular weight glutenin subunit [HMW-GS] composition by SDS-PAGE) using multiple linear regression statistical analysis. Principal component analysis of the mixograph data discriminated between four flour quality classes, and predictions of baking volume were obtained using several selected mixograph parameters, chosen using a best subsets regression routine, giving R-2 values of 0.862-0.866. In particular, three new spring wheat strains (CHD 502a-c) recently registered in Poland were highly discriminated and predicted to give high baking volume on the basis of two mixograph parameters: peak bandwidth and 10-min bandwidth.