A spatial accommodation by neighboring cells is required for organ initiation in Arabidopsis.

Lateral root formation in plants can be studied as the process of interaction between chemical signals and physical forces during development. Lateral root primordia grow through overlying cell layers that must accommodate this incursion. Here, we analyze responses of the endodermis, the immediate neighbor to an initiating lateral root. Endodermal cells overlying lateral root primordia lose volume, change shape, and relinquish their tight junction-like diffusion barrier to make way for the emerging lateral root primordium. Endodermal feedback is absolutely required for initiation and growth of lateral roots, and we provide evidence that this is mediated by controlled volume loss in the endodermis. We propose that turgidity and rigid cell walls, typical of plants, impose constraints that are specifically modified for a given developmental process.

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

Effect of a lifestyle intervention on adiposity and fitness in socially disadvantaged subgroups of preschoolers: a cluster-randomized trial (Ballabeina).

OBJECTIVE: A multidimensional lifestyle intervention performed in 652 preschoolers (72% of migrant, 38% of low educational level (EL) parents) reduced body fat, but not BMI and improved fitness. The objective of this study is to examine whether the intervention was equally effective in children of migrant and/or low EL parents.¦METHODS: Cluster-randomized controlled single blinded trial, conducted in 2008/09 in 40 randomly selected preschools in Switzerland. The culturally tailored intervention consisted of a physical activity program and lessons on nutrition, media use and sleep. Primary outcomes included BMI and aerobic fitness. Secondary outcomes included %body fat, waist circumference and motor agility.¦RESULTS: Children of migrant parents benefitted similarly from the intervention compared to their counterparts (p for interaction≥ 0.09). However, children of low EL parents benefitted less, although these differences did not reach statistical significance (p for interaction≥ 0.06). Average intervention effect sizes for BMI were -0.10, -0.05, -0.11 and 0.04 kg/m(2) and for aerobic fitness were 0.55, 0.20, 0.37 and -0.05 stages for children of non-migrant, migrant, middle/high EL and low EL parents, respectively.¦CONCLUSIONS: This intervention was similarly effective among preschoolers of migrant parents compared to their counterparts, while children of low EL parents benefitted less.

Compendium of projects in the European nanosafety cluster

This is the second edition of the compendium. Since the first edition a number of important initiatives have been launched in the shape of large projects targeting integration of research infrastructure and new technology for toxicity studies and exposure monitoring.The demand for research in the area of human health and environmental safety management of nanotechnologies is present since a decade and identified by several landmark reports and studies. Several guidance documents have been published. It is not the intention of this compendium to report on these as they are widely available.It is also not the intention to publish scientific papers and research results as this task is covered by scientific conferences and the peer reviewed press.The intention of the compendium is to bring together researchers, create synergy in their work, and establish links and communication between them mainly during the actual research phase before publication of results. Towards this purpose we find useful to give emphasis to communication of projects strategic aims, extensive coverage of specific work objectives and of methods used in research, strengthening human capacities and laboratories infrastructure, supporting collaboration for common goals and joint elaboration of future plans, without compromising scientific publication potential or IP Rights.These targets are far from being achieved with the publication in its present shape. We shall continue working, though, and hope with the assistance of the research community to make significant progress. The publication will take the shape of a dynamic, frequently updated, web-based document available free of charge to all interested parties. Researchers in this domain are invited to join the effort, communicating the work being done. [Auteurs]

Influence of bedrock structures on the spatial pattern of erosional landforms in small alpine catchments

Structural settings and lithological characteristics are traditionally assumed to influence the development of erosional landforms, such as gully networks and rock couloirs, in steep mountain rock basins. The structural control of erosion of two small alpine catchments of distinctive rock types is evaluated by comparing the correspondences between the orientations of their gullies and rock couloirs with (1) the sliding orientations of potential slope failures mechanisms, and (2) the orientation of the maximum joint frequency, this latter being considered as the direction exploited primarily by erosion and mass wasting processes. These characteristic orientations can be interpreted as structural weaknesses contributing to the initiation and propagation of erosion. The morphostructural analysis was performed using digital elevation models and field observations. The catchment comprised of magmatic intrusive rocks shows a clear structural control, mostly expressed through potential wedges failure. Such joint configurations have a particular geometry that encourages the development of gullies in hard rock, e.g. through enhanced gravitational and hydrological erosional processes. In the catchment underlain by sedimentary rocks, penetrative joints that act as structural weaknesses seem to be exploited by gullies and rock couloirs. However, the lithological setting and bedding configuration prominently control the development of erosional landforms, and influence not only the local pattern of geomorphic features, but the general morphology of the catchment. The orientations of the maximum joint frequency are clearly associated with the gully network, suggesting that its development is governed by anisotropy in rock strength. These two catchments are typical of bedrock-dominated basins prone to intense processes of debris supply. This study suggests a quantitative approach for describing the relationship between bedrock jointing and geomorphic features geometry. Incorporation of bedrock structure can be relevant when studying processes governing the transfer of clastic material, for the assessment of sediment yields and in landforms evolution models.

New Developments and Applications of the MP2RAGE Sequence - Focusing the Contrast and High Spatial Resolution R1 Mapping.

MR structural T1-weighted imaging using high field systems (>3T) is severely hampered by the existing large transmit field inhomogeneities. New sequences have been developed to better cope with such nuisances. In this work we show the potential of a recently proposed sequence, the MP2RAGE, to obtain improved grey white matter contrast with respect to conventional T1-w protocols, allowing for a better visualization of thalamic nuclei and different white matter bundles in the brain stem. Furthermore, the possibility to obtain high spatial resolution (0.65 mm isotropic) R1 maps fully independent of the transmit field inhomogeneities in clinical acceptable time is demonstrated. In this high resolution R1 maps it was possible to clearly observe varying properties of cortical grey matter throughout the cortex and observe different hippocampus fields with variations of intensity that correlate with known myelin concentration variations.

What drives invasibility? A multi-model inference test and spatial modelling of alien plant species richness patterns in northern Portugal

Understanding and anticipating biological invasions can focus either on traits that favour species invasiveness or on features of the receiving communities, habitats or landscapes that promote their invasibility. Here, we address invasibility at the regional scale, testing whether some habitats and landscapes are more invasible than others by fitting models that relate alien plant species richness to various environmental predictors. We use a multi-model information-theoretic approach to assess invasibility by modelling spatial and ecological patterns of alien invasion in landscape mosaics and testing competing hypotheses of environmental factors that may control invasibility. Because invasibility may be mediated by particular characteristics of invasiveness, we classified alien species according to their C-S-R plant strategies. We illustrate this approach with a set of 86 alien species in Northern Portugal. We first focus on predictors influencing species richness and expressing invasibility and then evaluate whether distinct plant strategies respond to the same or different groups of environmental predictors. We confirmed climate as a primary determinant of alien invasions and as a primary environmental gradient determining landscape invasibility. The effects of secondary gradients were detected only when the area was sub-sampled according to predictions based on the primary gradient. Then, multiple predictor types influenced patterns of alien species richness, with some types (landscape composition, topography and fire regime) prevailing over others. Alien species richness responded most strongly to extreme land management regimes, suggesting that intermediate disturbance induces biotic resistance by favouring native species richness. Land-use intensification facilitated alien invasion, whereas conservation areas hosted few invaders, highlighting the importance of ecosystem stability in preventing invasions. Plants with different strategies exhibited different responses to environmental gradients, particularly when the variations of the primary gradient were narrowed by sub-sampling. Such differential responses of plant strategies suggest using distinct control and eradication approaches for different areas and alien plant groups.

Impact of electronic monitoring of drug adherence on blood pressure control in primary care: a cluster 12-month randomised controlled study.

BACKGROUND: Poor long-term adherence is an important cause of uncontrolled hypertension. We examined whether monitoring drug adherence with an electronic system improves long-term blood pressure (BP) control in hypertensive patients followed by general practitioners (GPs). METHODS: A pragmatic cluster randomised controlled study was conducted over one year in community pharmacists/GPs' networks randomly assigned either to usual care (UC) where drugs were dispensed as usual, or to intervention (INT) group where drug adherence could be monitored with an electronic system (Medication Event Monitoring System). No therapy change was allowed during the first 2 months in both groups. Thereafter, GPs could modify therapy and use electronic monitors freely in the INT group. The primary outcome was a target office BP<140/90 mmHg. RESULTS: Sixty-eight treated uncontrolled hypertensive patients (UC: 34; INT: 34) were enrolled. Over the 12-month period, the likelihood of reaching the target BP was higher in the INT group compared to the UC group (p<0.05). At 4 months, 38% in the INT group reached the target BP vs. 12% in the UC group (p<0.05), and 21% vs. 9% at 12 months (p: ns). Multivariate analyses, taking account of baseline characteristics, therapy modification during follow-up, and clustering effects by network, indicate that being allocated to the INT group was associated with a greater odds of reaching the target BP at 4 months (p<0.01) and at 12 months (p=0.051). CONCLUSION: GPs monitoring drug adherence in collaboration with pharmacists achieved a better BP control in hypertensive patients, although the impact of monitoring decreased with time.

Spatial relational learning and memory abilities do not differ between men and women in a real-world, open-field environment

This study assesses gender differences in spatial and non-spatial relational learning and memory in adult humans behaving freely in a real-world, open-field environment. In Experiment 1, we tested the use of proximal landmarks as conditional cues allowing subjects to predict the location of rewards hidden in one of two sets of three distinct locations. Subjects were tested in two different conditions: (1) when local visual cues marked the potentially-rewarded locations, and (2) when no local visual cues marked the potentially-rewarded locations. We found that only 17 of 20 adults (8 males, 9 females) used the proximal landmarks to predict the locations of the rewards. Although females exhibited higher exploratory behavior at the beginning of testing, males and females discriminated the potentially-rewarded locations similarly when local visual cues were present. Interestingly, when the spatial and local information conflicted in predicting the reward locations, males considered both spatial and local information, whereas females ignored the spatial information. However, in the absence of local visual cues females discriminated the potentially-rewarded locations as well as males. In Experiment 2, subjects (9 males, 9 females) were tested with three asymmetrically-arranged rewarded locations, which were marked by local cues on alternate trials. Again, females discriminated the rewarded locations as well as males in the presence or absence of local cues. In sum, although particular aspects of task performance might differ between genders, we found no evidence that women have poorer allocentric spatial relational learning and memory abilities than men in a real-world, open-field environment.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Pom1 regulates the assembly of Cdr2-Mid1 cortical nodes for robust spatial control of cytokinesis.

Proper division plane positioning is essential to achieve faithful DNA segregation and to control daughter cell size, positioning, or fate within tissues. In Schizosaccharomyces pombe, division plane positioning is controlled positively by export of the division plane positioning factor Mid1/anillin from the nucleus and negatively by the Pom1/DYRK (dual-specificity tyrosine-regulated kinase) gradients emanating from cell tips. Pom1 restricts to the cell middle cortical cytokinetic ring precursor nodes organized by the SAD-like kinase Cdr2 and Mid1/anillin through an unknown mechanism. In this study, we show that Pom1 modulates Cdr2 association with membranes by phosphorylation of a basic region cooperating with the lipid-binding KA-1 domain. Pom1 also inhibits Cdr2 interaction with Mid1, reducing its clustering ability, possibly by down-regulation of Cdr2 kinase activity. We propose that the dual regulation exerted by Pom1 on Cdr2 prevents Cdr2 assembly into stable nodes in the cell tip region where Pom1 concentration is high, which ensures proper positioning of cytokinetic ring precursors at the cell geometrical center and robust and accurate division plane positioning.