Moonligth, seas, and chocolate trees.

Esta antología ofrece una extensa selección de obras de muy diferentes géneros, que abarcan la ficción,la poesía, y no ficción. La selección de autores es igualmente amplia, de los poetas clásicos y autores modernos a los favorito de los niños, y de Shakespeare a los periodistas de hoy. Cada volumen de la antología corresponde a un solo año del ciclo superior de la escuela primaria. Para niños entre siete y once años.

Threshold detection: matching statistical methodology to ecological questions and conservation planning objectives

Two types of ecological thresholds are now being widely used to develop conservation targets: breakpoint-based thresholds represent tipping points where system properties change dramatically, whereas classification thresholds identify groups of data points with contrasting properties. Both breakpoint-based and classification thresholds are useful tools in evidence-based conservation. However, it is critical that the type of threshold to be estimated corresponds with the question of interest and that appropriate statistical procedures are used to determine its location. On the basis of their statistical properties, we recommend using piecewise regression methods to identify breakpoint-based thresholds and discriminant analysis or classification and regression trees to identify classification thresholds.

Trace element partitioning and soil particle characterisation around mining and smelting areas at Tharsis, Riotinto and Huelva, SW Spain

Trace elements may present an environmental hazard in the vicinity of mining and smelting activities. However, the factors controlling trace element distribution in soils around ancient and modem mining and smelting areas are not always clear. Tharsis, Riotinto and Huelva are located in the Iberian Pyrite Belt in SW Spain. Tharsis and Riotinto mines have been exploited since 2500 B.C., with intensive smelting taking place. Huelva, established in 1970 and using the Flash Furnace Outokumpu process, is currently one of the largest smelter in the world. Pyrite and chalcopyrite ore have been intensively smelted for Cu. However, unusually for smelters and mines of a similar size, the elevated trace element concentrations in soils were found to be restricted to the immediate vicinity of the mines and smelters, being found up to a maximum of 2 kin from the mines and smelters at Tharsis, Riotinto and Huelva. Trace element partitioning (over 2/3 of trace elements found in the residual immobile fraction of soils at Tharsis) and soil particles examination by SEM-EDX showed that trace elements were not adsorbed onto soil particles, but were included within the matrix of large trace element-rich Fe silicate slag particles (i.e. 1 min circle divide at least 1 wt.% As, Cu and Zn, and 2 wt.% Pb). Slag particle large size (I mm 0) was found to control the geographically restricted trace element distribution in soils at Tharsis, Riotinto and Huelva, since large heavy particles could not have been transported long distances. Distribution and partitioning indicated that impacts to the environment as a result of mining and smelting should remain minimal in the region. (c) 2006 Elsevier B.V. All rights reserved.

Improved evaporative flux partitioning and carbon flux in the land surface model JULES: impact on the simulation of land surface processes in temperate Europe

The primary role of land surface models embedded in climate models is to partition surface available energy into upwards, radiative, sensible and latent heat fluxes. Partitioning of evapotranspiration, ET, is of fundamental importance: as a major component of the total surface latent heat flux, ET affects the simulated surface water balance, and related energy balance, and consequently the feedbacks with the atmosphere. In this context it is also crucial to credibly represent the CO2 exchange between ecosystems and their environment. In this study, JULES, the land surface model used in UK weather and climate models, has been evaluated for temperate Europe. Compared to eddy covariance flux measurements, the CO2 uptake by the ecosystem is underestimated and the ET overestimated. In addition, the contribution to ET from soil and intercepted water evaporation far outweighs the contribution of plant transpiration. To alleviate these biases, adaptations have been implemented in JULES, based on key literature references. These adaptations have improved the simulation of the spatio-temporal variability of the fluxes and the accuracy of the simulated GPP and ET, including its partitioning. This resulted in a shift of the seasonal soil moisture cycle. These adaptations are expected to increase the fidelity of climate simulations over Europe. Finally, the extreme summer of 2003 was used as evaluation benchmark for the use of the model in climate change studies. The improved model captures the impact of the 2003 drought on the carbon assimilation and the water use efficiency of the plants. It, however, underestimates the 2003 GPP anomalies. The simulations showed that a reduction of evaporation from the interception and soil reservoirs, albeit not of transpiration, largely explained the good correlation between the carbon and the water fluxes anomalies that was observed during 2003. This demonstrates the importance of being able to discriminate the response of individual component of the ET flux to environmental forcing.

The stochastic component in choice and regression to the mean

In this article, we illustrate experimentally an important consequence of the stochastic component in choice behaviour which has not been acknowledged so far. Namely, its potential to produce ‘regression to the mean’ (RTM) effects. We employ a novel approach to individual choice under risk, based on repeated multiple-lottery choices (i.e. choices among many lotteries), to show how the high degree of stochastic variability present in individual decisions can distort crucially certain results through RTM effects. We demonstrate the point in the context of a social comparison experiment.

Differential uptake, partitioning and transfer of Cd and Zn in the soil-pea plant-aphid system

The biomagnification of trace metals during transfer from contaminated soil to higher trophic levels may potentially result in the exposure of predatory arthropods to toxic concentrations of these elements. This study examined the transfer of Cd and Zn in a soil−plant−arthropod system grown in series of field plots that had received two annual applications of municipal biosolids with elevated levels of Cd and Zn. Results showed that biosolids amendment significantly increased the concentration of Cd in the soil and the shoots of pea plants and the concentration of Zn in the soil, pea roots, shoots, and pods. In addition, the ratio of Cd to Zn concentration showed that Zn was preferentially transferred compared to Cd through all parts of the system. As a consequence, Zn was biomagnified by the system whereas Cd was biominimized. Cd and Zn are considered to exhibit similar behaviors in biological systems. However, the Cd/Zn ratios demonstrated that in this system, Cd is much less labile in the root−shoot−pod and shoot−aphid pathways than Zn.

A tillering inhibition gene influences root-shoot carbon partitioning and pattern of water use to improve wheat productivity in rainfed environments

Genetic modification of shoot and root morphology has potential to improve water and nutrient 19 uptake of wheat crops in rainfed environments. Near-isogenic lines (NILs) varying for a tillering 20 inhibition (tin) gene and representing multiple genetic backgrounds were investigated in contrasting 21 controlled environments for shoot and root growth. Leaf area, shoot and root biomass were similar 22 until tillering whereupon reduced tillering in tin-containing NILs produced reductions of up to 60% in 23 total leaf area and biomass, and increases in total root length of up to 120% and root biomass to 24 145%. Together, root-to-shoot ratio increased two-fold with the tin gene. The influence of tin on shoot 25 and root growth was greatest in the cv. Banks genetic background, particularly in the biculm-selected 26 NIL, and was typically strongest in cooler environments. A separate de-tillering study confirmed 27 greater root-to-shoot ratios with regular tiller removal in non-tin containing genotypes. In validating 28 these observations in a rainfed field study, the tin allele had a negligible effect on seedling growth but 29 was associated with significantly (P<0.05) reduced tiller number (-37%), leaf area index (-26%) and 30 spike number (-35%) to reduce plant biomass (-19%) at anthesis. Root biomass, root-to-shoot ratio at 31 early stem elongation and root depth at maturity were increased in tin-containing NILs. Soil water use 32 was slowed in tin-containing NILs resulting in greater water availability, greater stomatal 33 conductance, cooler canopy temperatures and maintenance of green leaf area during grain-filling. 34 Together these effects contributed to increases in harvest index and grain yield. In both the controlled 35 and field environments, the tin gene was commonly associated with increased root length and biomass 36 but the significant influence of genetic background and environment suggests careful assessment of 37 tin-containing progeny in selection for genotypic increases in root growth.

Problem and solution trees : a practical approach for identifying potential interventions to improve population nutrition

Population nutrition problems have a diversity of contributory factors and, ideally, multi-sectoral solutions should be developed by the relevant stakeholders, based on a common understanding of these factors. The problem and solution tree approach is a participatory process of working through the layers of determinants and then developing potential interventions for a specific issue, using the available data and expertise. We tailored this approach for non-communicable disease-related nutrition problems in Pacific Islands and applied it in several countries. The process led to the identification of a considerable range of determinants of unhealthy diets and potential interventions to improve the situation. This practical approach also offered the additional benefit of developing stakeholder awareness in the issues. Problem trees are a relatively simple tool to implement, easy to adapt to differing needs, can generate a wealth of information and can be more widely used.

Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability

Decision trees and self organising feature maps (SOFM) are frequently used to identify groups. This research aims to compare the similarities between any groupings found between supervised (Classification and Regression Trees - CART) and unsupervised classification (SOFM), and to identify insights into factors associated with doctor-patient stability. Although CART and SOFM uses different learning paradigms to produce groupings, both methods came up with many similar groupings. Both techniques showed that self perceived health and age are important indicators of stability. In addition, this study has indicated profiles of patients that are at risk which might be interesting to general practitioners.

Bayesian networks and decision trees in the diagnosis of female urinary incontinence

This study compares the effectiveness of Bayesian networks versus Decision Trees in modeling the Integral Theory of Female Urinary Incontinence diagnostic algorithm. Bayesian networks and Decision Trees were developed and trained using data from 58 adult women presenting with urinary incontinence symptoms. A Bayesian Network was developed in collaboration with an expert specialist who regularly utilizes a non-automated diagnostic algorithm in clinical practice. The original Bayesian network was later refined using a more connected approach. Diagnoses determined from all automated approaches were compared with the diagnoses of a single human expert. In most cases, Bayesian networks were found to be at least as accurate as the Decision Tree approach. The refined Connected Bayesian Network was found to be more accurate than the Original Bayesian Network accurately discriminated between diagnoses despite the small sample size. In contrast, the Connected and Decision Tree approaches were less able to discriminate between diagnoses. The Original Bayesian Network was found to provide an excellent basis for graphically communicating the correlation between symptoms and laxity defects in a given anatomical zone. Performance measures in both networks indicate that Bayesian networks could provide a potentially useful tool in the management of female pelvic floor dysfunction. Before the technique can be utilized in practice, well-established learning algorithms should be applied to improve network structure. A larger training data set should also improve network accuracy, sensitivity, and specificity.

An efficient algorithm for partitioning and authenticating problem-solutions of elearning contents

Content authenticity and correctness is one of the important challenges in eLearning as there can be many solutions to one specific problem in cyber space. Therefore, the authors feel it is necessary to map problems to solutions using graph partition and weighted bipartite matching. This article proposes an efficient algorithm to partition question-answer (QA) space and explores the best possible solution to a particular problem. The approach described can be efficiently applied to social eLearning space where there are one-to-many and many-to-many relationships with a level of bonding. The main advantage of this approach is that it uses QA ranking by adjusted edge weights provided by subject-matter experts or the authors' expert database.

Disease mapping and regression with count data in the presence of overdispersion and spatial autocorrelation: a Bayesian model averaging approach

This paper applies the generalised linear model for modelling geographical variation to esophageal cancer incidence data in the Caspian region of Iran. The data have a complex and hierarchical structure that makes them suitable for hierarchical analysis using Bayesian techniques, but with care required to deal with problems arising from counts of events observed in small geographical areas when overdispersion and residual spatial autocorrelation are present. These considerations lead to nine regression models derived from using three probability distributions for count data: Poisson, generalised Poisson and negative binomial, and three different autocorrelation structures. We employ the framework of Bayesian variable selection and a Gibbs sampling based technique to identify significant cancer risk factors. The framework deals with situations where the number of possible models based on different combinations of candidate explanatory variables is large enough such that calculation of posterior probabilities for all models is difficult or infeasible. The evidence from applying the modelling methodology suggests that modelling strategies based on the use of generalised Poisson and negative binomial with spatial autocorrelation work well and provide a robust basis for inference.