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.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

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.

Spatial pattern and process at the plant neighbourhood scale: insights from communities dominated by the clonal grass Elymus repens (L.) Gould.

Question: How do clonal traits of a locally dominant grass (Elymus repens (L.) Gould.) respond to soil heterogeneity and shape spatial patterns of its tillers? How do tiller spatial patterns constrain seedling recruitment within the community?Locations: Artificial banks of the River Rhone, France.Material and Methods: We examined 45 vegetation patches dominated by Elymus repens. During a first phase we tested relationships between soil variables and three clonal traits (spacer length, number of clumping tillers and branching rate), and between the same clonal traits and spatial patterns (i.e. density and degree of spatial aggregation) of tillers at a very fine scale. During a second phase, we performed a sowing experiment to investigate effects of density and spatial patterns of E. repens on recruitment of eight species selected from the regional species pool.Results: Clonal traits had clear effects - especially spacer length - on densification and aggregation of E. repens tillers and, at the same time, a clear response of these same clonal traits as soil granulometry changed. The density and degree of aggregation of E. repens tillers was positively correlated to total seedling cover and diversity at the finest spatial scales.Conclusions: Spatial patterning of a dominant perennial grass responds to soil heterogeneity through modifications of its clonal morphology as a trade-off between phalanx and guerrilla forms. In turn, spatial patterns have strong effects on abundance and diversity of seedlings. Spatial patterns of tillers most probably led to formation of endogenous gaps in which the recruitment of new plant individuals was enhanced. Interestingly, we also observed more idiosyncratic effects of tiller spatial patterns on seedling cover and diversity when focusing on different growth forms of the sown species.

Spatial organization of nucleotide excision repair proteins after UV-induced DNA damage in the human cell nucleus.

Nucleotide excision repair (NER) is an evolutionary conserved DNA repair system that is essential for the removal of UV-induced DNA damage. In this study we investigated how NER is compartmentalized in the interphase nucleus of human cells at the ultrastructural level by using electron microscopy in combination with immunogold labeling. We analyzed the role of two nuclear compartments: condensed chromatin domains and the perichromatin region. The latter contains transcriptionally active and partly decondensed chromatin at the surface of condensed chromatin domains. We studied the distribution of the damage-recognition protein XPC and of XPA, which is a central component of the chromatin-associated NER complex. Both XPC and XPA rapidly accumulate in the perichromatin region after UV irradiation, whereas only XPC is also moderately enriched in condensed chromatin domains. These observations suggest that DNA damage is detected by XPC throughout condensed chromatin domains, whereas DNA-repair complexes seem preferentially assembled in the perichromatin region. We propose that UV-damaged DNA inside condensed chromatin domains is relocated to the perichromatin region, similar to what has been shown for DNA replication. In support of this, we provide evidence that UV-damaged chromatin domains undergo expansion, which might facilitate the translocation process. Our results offer novel insight into the dynamic spatial organization of DNA repair in the human cell nucleus.

The influence of environmental spatial structure on the life history traits and diversity of species in a metacommunity

Several models have been proposed to understand how so many species can coexist in ecosystems. Despite evidence showing that natural habitats are often patchy and fragmented, these models rarely take into account environmental spatial structure. In this study we investigated the influence of spatial structure in habitat and disturbance regime upon species' traits and species' coexistence in a metacommunity. We used a population-based model to simulate competing species in spatially explicit landscapes. The species traits we focused on were dispersal ability, competitiveness, reproductive investment and survival rate. Communities were characterized by their species richness and by the four life-history traits averaged over all the surviving species. Our results show that spatial structure and disturbance have a strong influence on the equilibrium life-history traits within a metacommunity. In the absence of disturbance, spatially structured landscapes favour species investing more in reproduction, but less in dispersal and survival. However, this influence is strongly dependent on the disturbance rate, pointing to an important interaction between spatial structure and disturbance. This interaction also plays a role in species coexistence. While spatial structure tends to reduce diversity in the absence of disturbance, the tendency is reversed when disturbance occurs. In conclusion, the spatial structure of communities is an important determinant of their diversity and characteristic traits. These traits are likely to influence important ecological properties such as resistance to invasion or response to climate change, which in turn will determine the fate of ecosystems facing the current global ecological crisis.

La politique régionale : entre compétitivité et respect de l'environnement

Aujourd'hui plus que jamais, le développement économique ne peut se faire au détriment de notre environnement naturel. Dès lors se pose la question de savoir comment parvenir à maintenir la compétitivité d'une économie, tout en tenant compte de l'impact qu'elle a sur révolution du cadre naturel. La présente recherche propose d'investiguer sur la question en se penchant sur les politiques publiques de promotion économique, et plus spécifiquement sur la politique régionale. Faut-il maintenir la confiance dans les courants néoclassiques, comme le laisse supposer la situation actuelle, renforcer la position d'une économie s'inscrivant au sein d'un cadre socio-environnemental ou encore repenser notre mode de fonctionnement économique quant à son développement ? Dans le cas présent, la politique régionale suisse est évaluée à la lumière de trois stratégies de développement économique. D'une part, il y a l'économie fondée sur la connaissance. Cette dernière est à la base de la philosophie actuelle en matière de politique régionale. Ensuite, il y a l'écologie industrielle, qui pour sa part fait la promesse d'un développement économique éco-compatible. Enfin, la troisième stratégie est celle de l'économie de fonctionnalité, qui propose de maximiser l'efficience d'une unité de matière première en limitant notamment la notion de propriété. Au travers d'une grille d'analyse construite sur le modèle des géographies de la grandeur, les trois stratégies sont confrontées aux objectifs de la nouvelle politique régionale suisse (NPR) ainsi qu'à ses modalités de mise en oeuvre. Il en ressort qu'en l'état actuel, la stratégie misant sur l'économie de la connaissance est la plus à même de relever le défi d'un développement économique durable. Toutefois, moyennant adaptations, les autres stratégies pourraient également se révéler être pertinentes. On constate notamment que les éléments clés sont ici l'innovation, ainsi que les dimensions spatiale et temporelle des stratégies. Nous recommandons dès lors d'adopter une approche territorialisée du développement économique, selon une logique de projet au sens de Boltanski & Chiapello. A notre sens, seules les propositions à même de fédérer les acteurs et disposant d'une vision intégrée du développement ont une chance de permettre un développement économique en harmonie avec notre cadre environnemental. - Today more than ever, economic development can't go ahead without consideration for our natural environment. This lays us with the question of how to maintain the competitiveness of an economy, and at the same time to manage the impact of it on the natural frame. This research aims to investigate this question through public policies fostering economy, more specifically through the regional policy. Must one trust the neo-classical way, as the actual situation let's think about it, reinforce the position of an economy within a socio- environmental frame or moreover reinvent our economical modus regarding development? In this case, an assessment of the Swiss regional policy is lead through three strategies of economic development. First, there is the knowledge economy. It is the core concept of the actual philosophy regarding regional policy. Second, there is the industrial ecology which aims to promote an eco-compatible economic development. Last, there is the functional economy, which proposes to maximize the efficiency of every used unit of natural resources by limiting in particular the notion of propriety. Through an analytic frame built on the model of geographies of greatness (géographies de la grandeuή, the three strategies are confronted to the objectives of the Swiss new regional policy (NRP) as well as its implementation. It turns out that actually, the strategy laying on the knowledge economy happens to be the best solution in order to promote a sustainable economic development. Nevertheless, with few adaptations, the other strategies could be pregnant as well. What we can see is that key- elements are here the innovation, as well as the spatial and temporal dimensions of these strategies. We recommend therefore to adopt a territorialised approach of economic development, with a project-based logic as meant by Boltanski & Chiapello. We are convinced that only propositions which are able to unit actors, and with an integrated point of view, have a chance to promote economic development in harmony with our environmental frame.