Alteraciones en el material genético de pintores de carros expuestos a solventes orgánicos en una localidad de Bogotá

Los solventes orgánicos son sustancias químicas que por sus propiedades físico-químicas son fácilmente inhalados o absorbidos por la piel, pueden causar daños de diversa índole en la salud. En Colombia existen normas que contemplan las medidas de protección, sin embargo persiste la informalidad en el sector de pintores de autos, por lo cual los trabajadores expuestos, a largo plazo pueden ver afectada su salud. En este estudio se analizó la relación entre individuos expuestos laboralmente a los solventes orgánicos versus no expuestos con respecto a la longitud de sus telómeros y formación de fragilidades. Se emplearon muestras de sangre extraídas por venopunción, recolectada en dos tubos: uno con Heparina, destinado al cultivo de linfocitos, para obtener cromosomas metafásicos y evaluar en ellos la presencia de fragilidades; el otro tubo con EDTA, fue empleado para la extracción de ADN y se utilizó para obtener los valores de longitud telomérica mediante la técnica de PCR cuantitativa. Los análisis estadísticos se realizaron aplicando la prueba de rangos de Wilcoxon, en el caso de la presencia de fragilidades se analizó la razón No.Fragilidades/No.Metafases, aplicando el método de Wilcoxon se encontró que existe diferencia estadísticamente significativa entre expuestos y no expuestos (p = 0,036), en donde los expuestos presentan mayor frecuencia de fragilidades. Por otra parte el valor relativo de longitud telomérica del grupo de expuestos fue mayor que el observado en el grupo de no expuestos, esta diferencia fue estadísticamente significativa (Wilcoxon, p = 0.002).

gvSIG Mini y Phone Cache

gvSIG Mini es una aplicación open-source de usuario final cliente móvil de Infraestructura de Datos Espaciales IDEs con licencia GNU/ GPL, diseñada para teléfonos móviles Java y Android que permite la visualización y navegación sobre cartografía digital estructurada en tiles procedente de servicios web OGC como WMS(-C) y de servicios como OpenStreetMap (OSM), Yahoo Maps, Maps Bing, así como el almacenamiento en caché para reducir al mínimo el ancho de banda. gvSIG Mini puede acceder a servicios geoespaciales como NameFinder, para la búsqueda de puntos de interés y YOURS (Yet Another OpenStreetMap Routing Service) para el cálculo de rutas y la renderización de la información vectorial el lado del cliente. Por otra parte, gvSIG Mini también ofrece servicio de localización GPS. La versión de gvSIG Mini para Android, posee algunas características adicionales como son el soporte de localización Android o el uso del lacelerómetro para centrado. Esta versión también hace uso de servicios como son la predicción del tiempo o TweetMe que permite compartir una localización utilizando el popular servicio social Twitter. gvSIG Mini es una aplicación que puede ser descargada y usada libremente, convirtiéndose en una plataforma para el desarrollo de nuevas soluciones y aplicaciones en el campo de Location Based Services (LBS). gvSIG Mini ha sido desarrollado por Prodevelop, S.L. No es un proyecto oficial de gvSIG, pero se une a la familia a través del catálogo de extensiones no oficiales de gvSIG. Phone Cache es una extensión que funciona sobre gvSIG 1.1.2 que permite generar una caché, para poder utilizar gvSIG Mini para Java en modo desconectado

Crop yield reduction in the tropics under climate change: Processes and uncertainties

Many modelling studies examine the impacts of climate change on crop yield, but few explore either the underlying bio-physical processes, or the uncertainty inherent in the parameterisation of crop growth and development. We used a perturbed-parameter crop modelling method together with a regional climate model (PRECIS) driven by the 2071-2100 SRES A2 emissions scenario in order to examine processes and uncertainties in yield simulation. Crop simulations used the groundnut (i.e. peanut; Arachis hypogaea L.) version of the General Large-Area Model for annual crops (GLAM). Two sets of GLAM simulations were carried out: control simulations and fixed-duration simulations, where the impact of mean temperature on crop development rate was removed. Model results were compared to sensitivity tests using two other crop models of differing levels of complexity: CROPGRO, and the groundnut model of Hammer et al. [Hammer, G.L., Sinclair, T.R., Boote, K.J., Wright, G.C., Meinke, H., and Bell, M.J., 1995, A peanut simulation model: I. Model development and testing. Agron. J. 87, 1085-1093]. GLAM simulations were particularly sensitive to two processes. First, elevated vapour pressure deficit (VPD) consistently reduced yield. The same result was seen in some simulations using both other crop models. Second, GLAM crop duration was longer, and yield greater, when the optimal temperature for the rate of development was exceeded. Yield increases were also seen in one other crop model. Overall, the models differed in their response to super-optimal temperatures, and that difference increased with mean temperature; percentage changes in yield between current and future climates were as diverse as -50% and over +30% for the same input data. The first process has been observed in many crop experiments, whilst the second has not. Thus, we conclude that there is a need for: (i) more process-based modelling studies of the impact of VPD on assimilation, and (ii) more experimental studies at super-optimal temperatures. Using the GLAM results, central values and uncertainty ranges were projected for mean 2071-2100 crop yields in India. In the fixed-duration simulations, ensemble mean yields mostly rose by 10-30%. The full ensemble range was greater than this mean change (20-60% over most of India). In the control simulations, yield stimulation by elevated CO2 was more than offset by other processes-principally accelerated crop development rates at elevated, but sub-optimal, mean temperatures. Hence, the quantification of uncertainty can facilitate relatively robust indications of the likely sign of crop yield changes in future climates. (C) 2007 Elsevier B.V. All rights reserved.

Rock art and social life - Revisiting the Neolithic transition in Mediterranean Iberia

This article revisits the Neolithic transition in Mediterranean Iberia taking into account an aspect usually neglected in the archaeological discourse: the rock art styles that emerged in this context. These distinct styles have been generally attributed to different populations, according to a historicist point of view that equates stylistic variability and ethnic identity. However, the recent recognition that they were developed by the same social group requires the formulation of an alternative explanation. My proposal is based on the exploration of the social context of production and consumption of the rock art, through the analysis of the patterns of location of the sites within the landscape and the definition of their archaeological context.

For better or for worse, till the human development index do us part?

This paper describes the results of research intended to explore the volatility inherent in the United Nations Development Programme's (UNDP) Human Development Index (HDI). The HDI is intended to be a simple and transparent device for comparing progress in human development, and is an aggregate of life expectancy, education and GDP per capita. Values of the HDI for each country are presented in the Human Development Reports (HDRs), the first being published in 1990. However, while the methodology is consistent for all countries in each year there are notable differences between years that make temporal comparisons of progress difficult. The paper presents the results of recalculating the HDI for a simplified sample of 114 countries using various methodologies employed by the UNDP. The results are a set of deviations of recalculated HDI ranks compared to the original ranks given in the HDRs. The volatility that can result from such recalculation is shown to be substantial (+/-10-15 ranks), yet reports in the popular press are frequently sensitive to movements of only a few ranks. Such movement can easily be accounted for by changes in the HDI methodology rather than genuine progress in human development. While the HDRs often carry warnings about the inadvisability of such year-on-year comparisons, it is argued that the existence of such a high-profile index and the overt presentation within league tables do encourage such comparison. Assuming that the HDI will be retained as a focal point within the HDRs, then it is suggested that greater focus be upon more meaningful and robust categories of human development (e.g. low, medium and high) rather than league tables where shifts of a few places, perhaps as a result of nothing more than a methodological or data artefact, may be highlighted in the press and by policy makers. (C) 2003 Elsevier Science B.V. All rights reserved.

Modelling monsoons: understanding and predicting current and future behaviour

The global monsoon system is so varied and complex that understanding and predicting its diverse behaviour remains a challenge that will occupy modellers for many years to come. Despite the difficult task ahead, an improved monsoon modelling capability has been realized through the inclusion of more detailed physics of the climate system and higher resolution in our numerical models. Perhaps the most crucial improvement to date has been the development of coupled ocean-atmosphere models. From subseasonal to interdecadal time scales, only through the inclusion of air-sea interaction can the proper phasing and teleconnections of convection be attained with respect to sea surface temperature variations. Even then, the response to slow variations in remote forcings (e.g., El Niño—Southern Oscillation) does not result in a robust solution, as there are a host of competing modes of variability that must be represented, including those that appear to be chaotic. Understanding the links between monsoons and land surface processes is not as mature as that explored regarding air-sea interactions. A land surface forcing signal appears to dominate the onset of wet season rainfall over the North American monsoon region, though the relative role of ocean versus land forcing remains a topic of investigation in all the monsoon systems. Also, improved forecasts have been made during periods in which additional sounding observations are available for data assimilation. Thus, there is untapped predictability that can only be attained through the development of a more comprehensive observing system for all monsoon regions. Additionally, improved parameterizations - for example, of convection, cloud, radiation, and boundary layer schemes as well as land surface processes - are essential to realize the full potential of monsoon predictability. A more comprehensive assessment is needed of the impact of black carbon aerosols, which may modulate that of other anthropogenic greenhouse gases. Dynamical considerations require ever increased horizontal resolution (probably to 0.5 degree or higher) in order to resolve many monsoon features including, but not limited to, the Mei-Yu/Baiu sudden onset and withdrawal, low-level jet orientation and variability, and orographic forced rainfall. Under anthropogenic climate change many competing factors complicate making robust projections of monsoon changes. Absent aerosol effects, increased land-sea temperature contrast suggests strengthened monsoon circulation due to climate change. However, increased aerosol emissions will reflect more solar radiation back to space, which may temper or even reduce the strength of monsoon circulations compared to the present day. Precipitation may behave independently from the circulation under warming conditions in which an increased atmospheric moisture loading, based purely on thermodynamic considerations, could result in increased monsoon rainfall under climate change. The challenge to improve model parameterizations and include more complex processes and feedbacks pushes computing resources to their limit, thus requiring continuous upgrades of computational infrastructure to ensure progress in understanding and predicting current and future behaviour of monsoons.

Rapid word learning by 15-month-olds under tightly-controlled conditions

Infants (12 to 17 months) were taught 2 novel words for 2 images of novel objects, by pairing isolated auditory labels with to-be-associated images. Comprehension was tested using a preferential looking task in which the infant was presented with both images together with an isolated auditory label. The auditory label usually, but not always, matched one of the images. Infants looked preferentially at images that matched the auditory stimulus. The experiment controlled within-subjects for both side bias and preference for previously named items. Infants showed learning after 12 presentations of the new words. Evidence is presented that, in certain circumstances, the duration of longest look at a target may be a more robust measure of target preference than overall looking time. The experiment provides support for previous demonstrations of rapid word learning by pre-vocabulary spurt children, and offers some methodological improvements to the preferential looking task.

Assessing the uncertainty associated with intermittent rainfall fields

[1] In many practical situations where spatial rainfall estimates are needed, rainfall occurs as a spatially intermittent phenomenon. An efficient geostatistical method for rainfall estimation in the case of intermittency has previously been published and comprises the estimation of two independent components: a binary random function for modeling the intermittency and a continuous random function that models the rainfall inside the rainy areas. The final rainfall estimates are obtained as the product of the estimates of these two random functions. However the published approach does not contain a method for estimation of uncertainties. The contribution of this paper is the presentation of the indicator maximum likelihood estimator from which the local conditional distribution of the rainfall value at any location may be derived using an ensemble approach. From the conditional distribution, representations of uncertainty such as the estimation variance and confidence intervals can be obtained. An approximation to the variance can be calculated more simply by assuming rainfall intensity is independent of location within the rainy area. The methodology has been validated using simulated and real rainfall data sets. The results of these case studies show good agreement between predicted uncertainties and measured errors obtained from the validation data.

Religious and spiritual identity and citizenship education in the UK

If education is to be about ‘human flourishing’ (De Ruyter, 2004) as well as preparation for adulthood and work, then religious and citizenship education would seem to have a key contribution towards this goal, both offering opportunities for the exploration and development of a robust sense of identity. However, despite the opposition of most religious educators, religious education has been treated by successive UK governments simply as a form of inculcation into a homogenous notion of citizenship based on nominal church attendance. Moreover, the teaching of the relatively new subject of citizenship education, whilst recognising that the sense of identity and allegiance is complex, has not regularly included faith perspectives. I argue that the concept of ‘spiritual development’, which centres on an existential sense of identity, offers a justification for combining lessons in both religious and citizenship education. I conclude on a cautionary note, arguing that pupils need to be given a critical awareness of ways in which such identities can be provided for them by default, particularly since consumer culture increasingly makes use of ‘spiritual’ language and imagery.

Simulating current global river runoff with a global hydrological model: model revisions, validation, and sensitivity analysis

Global hydrological models (GHMs) model the land surface hydrologic dynamics of continental-scale river basins. Here we describe one such GHM, the Macro-scale - Probability-Distributed Moisture model.09 (Mac-PDM.09). The model has undergone a number of revisions since it was last applied in the hydrological literature. This paper serves to provide a detailed description of the latest version of the model. The main revisions include the following: (1) the ability for the model to be run for n repetitions, which provides more robust estimates of extreme hydrological behaviour, (2) the ability of the model to use a gridded field of coefficient of variation (CV) of daily rainfall for the stochastic disaggregation of monthly precipitation to daily precipitation, and (3) the model can now be forced with daily input climate data as well as monthly input climate data. We demonstrate the effects that each of these three revisions has on simulated runoff relative to before the revisions were applied. Importantly, we show that when Mac-PDM.09 is forced with monthly input data, it results in a negative runoff bias relative to when daily forcings are applied, for regions of the globe where the day-to-day variability in relative humidity is high. The runoff bias can be up to - 80% for a small selection of catchments but the absolute magnitude of the bias may be small. As such, we recommend future applications of Mac-PDM.09 that use monthly climate forcings acknowledge the bias as a limitation of the model. The performance of Mac-PDM.09 is evaluated by validating simulated runoff against observed runoff for 50 catchments. We also present a sensitivity analysis that demonstrates that simulated runoff is considerably more sensitive to method of PE calculation than to perturbations in soil moisture and field capacity parameters.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Translating climate forecasts into agricultural terms: advances and challenges

Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate'.

Livelihood diversification and moneylending in a Rajasthan village: what lessons for rural financial services?

The complexity of rural economies in developing countries is increasingly recognised, as is the need to tailor poverty reduction policies according to the diversity of rural households and their requirements. By reference to a village in Western India, the paper examines the results of a longitudinal micro-level research approach, employed for the study of livelihood diversification and use of informal finance. Over a 25-year period, livelihoods are shown to have become more complex, in terms of location, types of non-farm activities, and combinations of activities. Moreover, livelihood pathways taken continue to be critically affected by economic and social inequalities implicit in the caste system and tribal economy. A longitudinal micro-level research approach is shown to be one that can effectively identify the many complexities of rural livelihoods and the continued dependence on the informal financial sector, providing important insights into the requirements for rural financial products and services.

Variance estimation with highly stratified sampling designs with unequal probabilities

It is common practice to design a survey with a large number of strata. However, in this case the usual techniques for variance estimation can be inaccurate. This paper proposes a variance estimator for estimators of totals. The method proposed can be implemented with standard statistical packages without any specific programming, as it involves simple techniques of estimation, such as regression fitting.

CAMCR: Computer assisted mixture model analysis for capture-recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.