Magnetic resonance imaging determinants of intraindividual variability in the elderly: combined analysis of grey and white matter.

Elderly individuals display a rapid age-related increase in intraindividual variability (IIV) of their performances. This phenomenon could reflect subtle changes in frontal lobe integrity. However, structural studies in this field are still missing. To address this issue, we computed an IIV index for a simple reaction time (RT) task and performed magnetic resonance imaging (MRI) including voxel based morphometry (VBM) and the tract based spatial statistics (TBSS) analysis of diffusion tensor imaging (DTI) in 61 adults aged from 22 to 88 years. The age-related IIV increase was associated with decreased fractional anisotropy (FA) as well as increased radial (RD) and mean (MD) diffusion in the main white matter (WM) fiber tracts. In contrast, axial diffusion (AD) and grey matter (GM) densities did not show any significant correlation with IIV. In multivariate models, only FA has an age-independent effect on IIV. These results revealed that WM but not GM changes partly mediated the age-related increase of IIV. They also revealed that the association between WM and IIV could not be only attributed to the damage of frontal lobe circuits but concerned the majority of interhemispheric and intrahemispheric corticocortical connections.

Lutzomyia longipalpis abundance in the city of Posadas, northeastern Argentina: variations at different spatial scales

The distribution of Lutzomyia longipalpis is heterogeneous with a pattern of high abundance areas (HAAs) embedded in a matrix of low abundance areas (LAAs). The objective of this study was to describe the variability in the abundance of Lu. longipalpis at two different spatial levels and to analyse the relationship between the abundance and multiple environmental variables. Of the environmental variables analysed in each household, the condition that best explained the differences in vector abundance between HAA-LAA was the variable "land_grass", with greater average values in the peridomestic environments within the LAA, and the variables "#sp tree", "#pots" and "dist_water" that were higher in the HAA. Of the environmental variables analysed in the patches, the variable "unpaved_streets" was higher in the LAAs and the variable "prop_inf_dogs" was higher in the HAAs. An understanding of the main environmental variables that influence the vector distribution could contribute to the development of strategies for the prevention and control of visceral leishmaniasis (VL). This is the first work in which environmental variables are analysed at the micro-scale in urban areas at the southern edge of the current range of Lu. longipalpis. Our results represent a significant contribution to the understanding of the abundance of the vector in the peridomestic habitats of the region.

Biodiversity and global change in Mediterranean benthic communities: perspectives over large spatial and long temporal scales

Many terrestrial and marine systems are experiencing accelerating decline due to the effects of global change. This situation has raised concern about the consequences of biodiversity losses for ecosystem function, ecosystem service provision, and human well-being. Coastal marine habitats are a main focus of attention because they harbour a high biological diversity, are among the most productive systems of the world and present high anthropogenic interaction levels. The accelerating degradation of many terrestrial and marine systems highlights the urgent need to evaluate the consequence of biodiversity loss. Because marine biodiversity is a dynamic entity and this study was interested global change impacts, this study focused on benthic biodiversity trends over large spatial and long temporal scales. The main aim of this project was to investigate the current extent of biodiversity of the high diverse benthic coralligenous community in the Mediterranean Sea, detect its changes, and predict its future changes over broad spatial and long temporal scales. These marine communities are characterized by structural species with low growth rates and long life spans; therefore they are considered particularly sensitive to disturbances. For this purpose, this project analyzed permanent photographic plots over time at four locations in the NW Mediterranean Sea. The spatial scale of this study provided information on the level of species similarity between these locations, thus offering a solid background on the amount of large scale variability in coralligenous communities; whereas the temporal scale was fundamental to determine the natural variability in order to discriminate between changes observed due to natural factors and those related to the impact of disturbances (e.g. mass mortality events related to positive thermal temperatures, extreme catastrophic events). This study directly addressed the challenging task of analyzing quantitative biodiversity data of these high diverse marine benthic communities. Overall, the scientific knowledge gained with this research project will improve our understanding in the function of marine ecosystems and their trajectories related to global change.

Climatic extremes improve predictions of spatial patterns of tree species.

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D(2), +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

Waveform inversion of crosshole georadar data: influence of source wavelet variability and the suitability of a single wavelet assumption

Waveform-based tomographic imaging of crosshole georadar data is a powerful method to investigate the shallow subsurface because of its ability to provide images of electrical properties in near-surface environments with unprecedented spatial resolution. A critical issue with waveform inversion is the a priori unknown source signal. Indeed, the estimation of the source pulse is notoriously difficult but essential for the effective application of this method. Here, we explore the viability and robustness of a recently proposed deconvolution-based procedure to estimate the source pulse during waveform inversion of crosshole georadar data, where changes in wavelet shape with location as a result of varying near-field conditions and differences in antenna coupling may be significant. Specifically, we examine whether a single, average estimated source current function can adequately represent the pulses radiated at all transmitter locations during a crosshole georadar survey, or whether a separate source wavelet estimation should be performed for each transmitter gather. Tests with synthetic and field data indicate that remarkably good tomographic reconstructions can be obtained using a single estimated source pulse when moderate to strong variability exists in the true source signal with antenna location. Only in the case of very strong variability in the true source pulse are tomographic reconstructions clearly improved by estimating a different source wavelet for each transmitter location.

Variability of apparently homogeneous soilscapes in São Paulo state, Brazil: II. quality of soil maps

The quality of semi-detailed (scale 1:100.000) soil maps and the utility of a taxonomically based legend were assessed by studying 33 apparently homogeneous fields with strongly weathered soils in two regions in São Paulo State: Araras and Assis. An independent data set of 395 auger sites was used to determine purity of soil mapping units and analysis of variance within and between mapping units and soil classification units. Twenty three soil profiles were studied in detail. The studied soil maps have a high purity for some legend criteria, such as B horizon type (> 90%) and soil texture class (> 80%). The purity for the "trophic character" (eutrophic, dystrophic, allic) was only 55% in Assis. It was 88% in Araras, where many soil units had been mapped as associations. In both regions, the base status of clay-textured soils was generally better than suggested by the maps. Analysis of variance showed that mapping was successful for "durable" soil characteristics such as clay content (> 80% of variance explained) and cation exchange capacity (≥ 50% of variance explained) of 0-20 and 60-80 cm layers. For soil characteristics that are easily modified by management, such as base saturation of the 0-20 cm layer, the maps had explained very little (< 15%) of the total variance in the study areas. Intermediate results were obtained for base saturation of the 60-80 cm layer (56% in Assis; 42% in Araras). Variance explained by taxonomic groupings that formed the basis for the legend of the soil maps was similar to, often even smaller than, variance explained by mapping units. The conclusion is that map boundaries have been very carefully located, but descriptions of mapping units could be improved. In future mappings, this could possibly be done at low cost by (a) bulk sampling to remove short range variation and enhance visualization of spatial patterns at distances > 100 m; (b) taking advantage of correlations between easily measured soil characteristics and chemical soil properties and, (c) unbending the link between legend criteria and a taxonomic system. The maps are well suited to obtain an impression of land suitability for high-input farming. Additional field work and data on former land use/management are necessary for the evaluation of chemical properties of surface horizons.

Spatial and linear correlations between soil and corn

The Technologies setting at Agricultural production system have the main characteristics the vertical productivity, reduced costs, soil physical, chemical and biological improvement to promote production sustainable growth. Thus, the study aimed to determine the variability and the linear and special correlations between the plant and soil attributes in order to select and indicate good representation of soil physical quality for forage productivity. In the growing season of 2006, on the Fazenda Bonança in Pereira Barreto (SP), the productivity of autumn corn forage (FDM) in an irrigated no-tillage system and the soil physical properties were analyzed. The purpose was to study the variability and the linear and spatial correlations between the plant and soil properties, to select an indicator of soil physical quality related to corn forage yield. A geostatistical grid was installed to collect soil and plant data, with 125 sampling points in an area of 2,500 m². The results show that the studied properties did not vary randomly and that data variability was low to very high, with well-defined spatial patterns, ranging from 7.8 to 38.0 m. On the other hand, the linear correlation between the plant and the soil properties was low and highly significant. The pairs forage dry matter versus microporosity and stem diameter versus bulk density were best correlated in the 0-0.10 m layer, while the other pairs - forage dry matter versus macro - and total porosity - were inversely correlated in the same layer. However, from the spatial point of view, there was a high inverse correlation between forage dry matter with microporosity, so that microporosity in the 0-0.10 m layer can be considered a good indicator of soil physical quality, with a view to corn forage yield.

Variability of soil fertility properties in areas planted to sugarcane in the State of Goias, Brazil

Soil sampling should provide an accurate representation of a given area so that recommendations for amendments of soil acidity, fertilization and soil conservation may be drafted to increase yield and improve the use of inputs. The aim of this study was to evaluate the variability of soil fertility properties of Oxisols in areas planted to sugarcane in the State of Goias, Brazil. Two areas of approximately 8,100 m² each were selected, representing two fields of the Goiasa sugarcane mill in Goiatuba. The sugarcane crop had a row spacing of 1.5 m and subsamples were taken from 49 points in the row and 49 between the row with a Dutch auger at depths of 0.0-0.2 and 0.2-0.4 m, for a total of 196 subsamples for each area. The samples were individually subjected to chemical analyses of soil fertility (pH in CaCl2, potential acidity, organic matter, P, K, Ca and Mg) and particle size analysis. The number of subsamples required to compose a sample within the acceptable ranges of error of 5, 10, 20 and 40 % of each property were computed from the coefficients of variation and the Student t-value for 95 % confidence. The soil properties under analysis exhibited different variabilities: high (P and K), medium (potential acidity, Ca and Mg) and low (pH, organic matter and clay content). Most of the properties analyzed showed an error of less than 20 % for a group of 20 subsamples, except for P and K, which were capable of showing an error greater than 40 % around the mean. The extreme variability in phosphorus, particularly at the depth of 0.2-0.4 m, attributed to banded application of high rates of P fertilizers at planting, places limitations on assessment of its availability due to the high number of subsamples required for a composite sample.

SPATIAL DEPENDENCE OF ELECTRICAL CONDUCTIVITY AND CHEMICAL PROPERTIES OF THE SOIL BY ELECTROMAGNETIC INDUCTION

Brazilian soils have natural high chemical variability; thus, apparent electrical conductivity (ECa) can assist interpretation of crop yield variations. We aimed to select soil chemical properties with the best linear and spatial correlations to explain ECa variation in the soil using a Profiler sensor (EMP-400). The study was carried out in Sidrolândia, MS, Brazil. We analyzed the following variables: electrical conductivity - EC (2, 7, and 15 kHz), organic matter, available K, base saturation, and cation exchange capacity (CEC). Soil ECa was measured with the aid of an all-terrain vehicle, which crossed the entire area in strips spaced at 0.45 m. Soil samples were collected at the 0-20 cm depth with a total of 36 samples within about 70 ha. Classical descriptive analysis was applied to each property via SAS software, and GS+ for spatial dependence analysis. The equipment was able to simultaneously detect ECa at the different frequencies. It was also possible to establish site-specific management zones through analysis of correlation with chemical properties. We observed that CEC was the property that had the best correlation with ECa at 15 kHz.

A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties

Aim Conservation strategies are in need of predictions that capture spatial community composition and structure. Currently, the methods used to generate these predictions generally focus on deterministic processes and omit important stochastic processes and other unexplained variation in model outputs. Here we test a novel approach of community models that accounts for this variation and determine how well it reproduces observed properties of alpine butterfly communities. Location The western Swiss Alps. Methods We propose a new approach to process probabilistic predictions derived from stacked species distribution models (S-SDMs) in order to predict and assess the uncertainty in the predictions of community properties. We test the utility of our novel approach against a traditional threshold-based approach. We used mountain butterfly communities spanning a large elevation gradient as a case study and evaluated the ability of our approach to model species richness and phylogenetic diversity of communities. Results S-SDMs reproduced the observed decrease in phylogenetic diversity and species richness with elevation, syndromes of environmental filtering. The prediction accuracy of community properties vary along environmental gradient: variability in predictions of species richness was higher at low elevation, while it was lower for phylogenetic diversity. Our approach allowed mapping the variability in species richness and phylogenetic diversity projections. Main conclusion Using our probabilistic approach to process species distribution models outputs to reconstruct communities furnishes an improved picture of the range of possible assemblage realisations under similar environmental conditions given stochastic processes and help inform manager of the uncertainty in the modelling results

A pipeline approach with spatial information for segmenting multiple sclerosis lesions on brain magnetic resonance imaging

Background: Conventional magnetic resonance imaging (MRI) techniques are highly sensitive to detect multiple sclerosis (MS) plaques, enabling a quantitative assessment of inflammatory activity and lesion load. In quantitative analyses of focal lesions, manual or semi-automated segmentations have been widely used to compute the total number of lesions and the total lesion volume. These techniques, however, are both challenging and time-consuming, being also prone to intra-observer and inter-observer variability.Aim: To develop an automated approach to segment brain tissues and MS lesions from brain MRI images. The goal is to reduce the user interaction and to provide an objective tool that eliminates the inter- and intra-observer variability.Methods: Based on the recent methods developed by Souplet et al. and de Boer et al., we propose a novel pipeline which includes the following steps: bias correction, skull stripping, atlas registration, tissue classification, and lesion segmentation. After the initial pre-processing steps, a MRI scan is automatically segmented into 4 classes: white matter (WM), grey matter (GM), cerebrospinal fluid (CSF) and partial volume. An expectation maximisation method which fits a multivariate Gaussian mixture model to T1-w, T2-w and PD-w images is used for this purpose. Based on the obtained tissue masks and using the estimated GM mean and variance, we apply an intensity threshold to the FLAIR image, which provides the lesion segmentation. With the aim of improving this initial result, spatial information coming from the neighbouring tissue labels is used to refine the final lesion segmentation.Results:The experimental evaluation was performed using real data sets of 1.5T and the corresponding ground truth annotations provided by expert radiologists. The following values were obtained: 64% of true positive (TP) fraction, 80% of false positive (FP) fraction, and an average surface distance of 7.89 mm. The results of our approach were quantitatively compared to our implementations of the works of Souplet et al. and de Boer et al., obtaining higher TP and lower FP values.Conclusion: Promising MS lesion segmentation results have been obtained in terms of TP. However, the high number of FP which is still a well-known problem of all the automated MS lesion segmentation approaches has to be improved in order to use them for the standard clinical practice. Our future work will focus on tackling this issue.

Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

The objective of this work was to assess the spatial and temporal variability of sugarcane yield efficiency and yield gap in the state of São Paulo, Brazil, throughout 16 growing seasons, considering climate and soil as main effects, and socioeconomic factors as complementary. An empirical model was used to assess potential and attainable yields, using climate data series from 37 weather stations. Soil effects were analyzed using the concept of production environments associated with a soil aptitude map for sugarcane. Crop yield efficiency increased from 0.42 to 0.58 in the analyzed period (1990/1991 to 2005/2006 crop seasons), and yield gap consequently decreased from 58 to 42%. Climatic factors explained 43% of the variability of sugarcane yield efficiency, in the following order of importance: solar radiation, water deficit, maximum air temperature, precipitation, and minimum air temperature. Soil explained 15% of the variability, considering the average of all seasons. There was a change in the correlation pattern of climate and soil with yield efficiency after the 2001/2002 season, probably due to the crop expansion to the west of the state during the subsequent period. Socioeconomic, biotic and crop management factors together explain 42% of sugarcane yield efficiency in the state of São Paulo.

Monitoring spatial and temporal dynamics of bird communities in Mediterranean landscapes affected by largewildfires = Seguimiento de la dinámica espacial y temporal de comunidades de aves en paisajes mediterráneos afectados por grandes fuegos

We presented a bird-monitoring database inMediterranean landscapes (Catalonia, NE Spain) affected by wildfires and we evaluated: 1) the spatial and temporal variability in the bird community composition and 2) the influence of pre-fire habitat configuration in the composition of bird communities. The DINDIS database results fromthemonitoring of bird communities occupying all areas affected by large wildfires in Catalonia since 2000.We used bird surveys conducted from 2006 to 2009 and performed a principal components analysis to describe two main gradients of variation in the composition of bird communities, which were used as descriptors of bird communities in subsequent analyses. We then analysed the relationships of these community descriptors with bioclimatic regions within Catalonia, time since fire and pre-fire vegetation (forest or shrubland).We have conducted 1,918 bird surveys in 567 transects distributed in 56 burnt areas. Eight out of the twenty most common detected species have an unfavourable conservation status, most of them being associated to open-habitats. Both bird communities’ descriptors had a strong regional component and were related to pre-fire vegetation, and to a lesser extent to the time since fire.We came to the conclusion that the responses of bird communities to wildfires are heterogeneous, complex and context dependent. Large-scale monitoring datasets, such as DINDIS, might allow identifying factors acting at different spatial and temporal scales that affect the dynamics of species and communities, giving additional information on the causes under general trends observed using other monitoring systems

The Spatial Distribution of Human Capital: Can It Really Be Explained by Regional Differences in Market Access?

This paper tests the robustness of estimates of market access impact on regional variability in human capital, as previously derived in the NEG literature. Our hypothesis is that these estimates of the coefficient of market access, in fact, capture the effects of regional differences in the industrial mix and the spatial dependence in the distribution of human capital. Results for the Spanish provinces indicate that the estimated impact of market access vanishes and becomes non-significant once these two elements are included in the empirical analysis.

Spatial and Temporal Trends in Sunshine Duration over Western Europe

This work analyzes sunshine duration variability in the western part of Europe (WEU) over the 1938– 2004 period. A principal component analysis is applied to cluster the original series from 79 sites into 6 regions, and then annual and seasonal mean series are constructed on regional and also for the whole WEU scales. Over the entire period studied here, the linear trend of annual sunshine duration is found to be nonsignificant. However, annual sunshine duration shows an overall decrease since the 1950s until the early 1980s, followed by a subsequent recovery during the last two decades. This behavior is in good agreement with the dimming and brightening phenomena described in previous literature. From the seasonal analysis, the most remarkable result is the similarity between spring and annual series, although the spring series has a negative trend; and the clear significant increase found for the whole WEU winter series, being especially large since the 1970s. The behavior of the major synoptic patterns for two seasons is investigated, resulting in some indications that sunshine duration evolution may be partially explained by changes in the frequency of some of them