GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Qualitative and Hierarchical Analysis of Protective Factors against Illicit Use of Doping Substances in Athletes Calling a National Anti-Doping Phone-Help Service

Evidence of a sport-specific hierarchy of protective factors against doping would thus be a powerful aid in adapting information and prevention campaigns to target the characteristics of specific athlete groups, and especially those athletes most vulnerable for doping control. The contents of phone calls to a free and anonymous national anti-doping service called 'ecoute dopage' were analysed (192 bodybuilders, 124 cyclists and 44 footballers). The results showed that the protective factors that emerged from analysis could be categorised into two groups. The first comprised 'Health concerns', 'Respect for the law' and 'Doping controls from the environment' and the second comprised 'Doubts about the effectiveness of illicit products, 'Thinking skills' and 'Doubts about doctors'. The ranking of the factors for the cyclists differed from that of the other athletes. The ordering of factors was 1) respect for the law, 2) doping controls from the environment, 3) health concerns 4) doubts about doctors, and 5) doubts about the effectiveness illicit products. The results are analysed in terms of the ranking in each athlete group and the consequences on the athletes' experience and relationship to doping. Specific prevention campaigns are proposed to limit doping behaviour in general and for each sport.

La nouvelle classification de Savary des oesophagites de reflux [Savary's new endoscopic classification of reflux oesophagitis]

Comme la classification de Savary-Miller dont elle adopte l'essentiel, la classification de l'oesophagite par refluxen 5 types de Savary-Monnier repose sur une analyse rigoureuse et précise des lésions endoscopiques. Ses principales qualités sont d'être simple, complète, logique et souple. Son impact sur la pratique est certain puisqu'elle a une excellente valeur pronostique et qu'elle permet de choisir la bonne stratégie thérapeutique. De plus, en isolant les cicatrices cylindriques (seules précancéroses à surveiller à long terme) elle permet de les utiliser pour préciser la topographie des oesophagites de reflux.

Effects of pro-cholinergic treatment in patients suffering from spatial neglect.

Spatial neglect is a neurological condition characterized by a breakdown of spatial cognition contralateral to hemispheric damage. Deficits in spatial attention toward the contralesional side are considered to be central to this syndrome. Brain lesions typically involve right fronto-parietal cortices mediating attentional functions and subcortical connections in underlying white matter. Convergent findings from neuroimaging and behavioral studies in both animals and humans suggest that the cholinergic system might also be critically implicated in selective attention by modulating cortical function via widespread projections from the basal forebrain. Here we asked whether deficits in spatial attention associated with neglect could partly result from a cholinergic deafferentation of cortical areas subserving attentional functions, and whether such disturbances could be alleviated by pro-cholinergic therapy. We examined the effect of a single-dose transdermal nicotine treatment on spatial neglect in 10 stroke patients in a double-blind placebo-controlled protocol, using a standardized battery of neglect tests. Nicotine-induced systematic improvement on cancellation tasks and facilitated orienting to single visual targets, but had no significant effect on other tests. These results support a global effect of nicotine on attention and arousal, but no effect on other spatial mechanisms impaired in neglect.

Morphostructural analysis of an alpine debris flows catchment: implication for debris supply

Rock slope instabilities are implicitly linked to the supply of sediment and debris recharging channels prone to debris flow. Hence, the incorporation of bedrock structure and terrain morphology can be relevant in the analysis of sediment budget and debris flow hazard assessment. Here, the mode of debris production of the Manival catchment (northern French Alps) is documented by the study of its morphostructural aspects extracted from high resolution DEM. Terrain implication in the process of debris supply is evaluated by: a) A systematic classification of the major morphological units based on the slope gradient that enables a spatial analysis of zones of debris production and deposition. b) A detailed structural analysis performed on DEM in order to identify potential unstable slopes. c) An analysis of the gullies orientation that informs in term of structural control of the sources zones. d) Localisation of high density joints sets that document about whether sources of continuous debris production are controlled by the structural setting of the catchment. These DEM-based indicators can be used as proxies for assessing the influences of the current topography and enable to quantify a degree of susceptibility to mass wasting and hillslope erosion activity. This present contribution suggests some directions for characterizing sediment flux dynamic in small alpine catchment.

The InterPro protein families database: the classification resource after 15 years.

The InterPro database (http://www.ebi.ac.uk/interpro/) is a freely available resource that can be used to classify sequences into protein families and to predict the presence of important domains and sites. Central to the InterPro database are predictive models, known as signatures, from a range of different protein family databases that have different biological focuses and use different methodological approaches to classify protein families and domains. InterPro integrates these signatures, capitalizing on the respective strengths of the individual databases, to produce a powerful protein classification resource. Here, we report on the status of InterPro as it enters its 15th year of operation, and give an overview of new developments with the database and its associated Web interfaces and software. In particular, the new domain architecture search tool is described and the process of mapping of Gene Ontology terms to InterPro is outlined. We also discuss the challenges faced by the resource given the explosive growth in sequence data in recent years. InterPro (version 48.0) contains 36 766 member database signatures integrated into 26 238 InterPro entries, an increase of over 3993 entries (5081 signatures), since 2012.

Dispersal strategies, few dominating or many coexisting: the effect of environmental spatial structure and multiple sources of mortality.

Interspecific competition, life history traits, environmental heterogeneity and spatial structure as well as disturbance are known to impact the successful dispersal strategies in metacommunities. However, studies on the direction of impact of those factors on dispersal have yielded contradictory results and often considered only few competing dispersal strategies at the same time. We used a unifying modeling approach to contrast the combined effects of species traits (adult survival, specialization), environmental heterogeneity and structure (spatial autocorrelation, habitat availability) and disturbance on the selected, maintained and coexisting dispersal strategies in heterogeneous metacommunities. Using a negative exponential dispersal kernel, we allowed for variation of both species dispersal distance and dispersal rate. We showed that strong disturbance promotes species with high dispersal abilities, while low local adult survival and habitat availability select against them. Spatial autocorrelation favors species with higher dispersal ability when adult survival and disturbance rate are low, and selects against them in the opposite situation. Interestingly, several dispersal strategies coexist when disturbance and adult survival act in opposition, as for example when strong disturbance regime favors species with high dispersal abilities while low adult survival selects species with low dispersal. Our results unify apparently contradictory previous results and demonstrate that spatial structure, disturbance and adult survival determine the success and diversity of coexisting dispersal strategies in competing metacommunities.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Spatial pattern of floral morphology: a possible insight into the effects of pollinators on plant distribution

Pollination syndromes involve convergent evolution towards phenotypes composed of specific scents, colours or floral morphologies that attract or restrict pollinator access to reward. How these traits might influence the distributions of plant species in interaction with pollinators has rarely been investigated. We sampled 870 vegetation plots in the western Swiss Alps and classified the plant species into seven blossom types according to their floral morphology (wind, disk, funnel, tube, bilabiate, head or brush). We investigated the environmental features of plots with functional diversity (FD) lower than expected by chance alone to detect potential pollination filtering and related the proportions of the seven blossom types to a combination of environmental descriptors. From these results, we inferred the potential effect of the pollinator on the spatial distribution of plant species. The vegetation plots with significantly lower FD of blossom types than expected by chance were found at higher altitudes, and the proportions of blossom types were strongly patterned along the same gradient. These results support a biotic filtering effect on plant species assemblages through pollination: disk blossoms became dominant at higher altitudes, resulting in a lower FD. In harsh conditions at high altitudes, pollinators usually decrease in activity, and the openness of the disk blossom grants access to any available pollinator. Inversely, bilabiate blossoms, which are mostly pollinated by bees, were more abundant at lower elevations, which are characterised by greater abundance and diversity of bees. Generalisation through openness of the blossom could be advantageous at high elevations, while specialisation could be a successful alternative strategy at lower elevations. The approach used in this study is purely correlative, and further investigations should be conducted to infer the nature of the causal relationship between plant and pollinator distributions.

Individual Classification of Mild Cognitive Impairment Subtypes by Support Vector Machine Analysis of White Matter DTI.

BACKGROUND AND PURPOSE: MCI was recently subdivided into sd-aMCI, sd-fMCI, and md-aMCI. The current investigation aimed to discriminate between MCI subtypes by using DTI. MATERIALS AND METHODS: Sixty-six prospective participants were included: 18 with sd-aMCI, 13 with sd-fMCI, and 35 with md-aMCI. Statistics included group comparisons using TBSS and individual classification using SVMs. RESULTS: The group-level analysis revealed a decrease in FA in md-aMCI versus sd-aMCI in an extensive bilateral, right-dominant network, and a more pronounced reduction of FA in md-aMCI compared with sd-fMCI in right inferior fronto-occipital fasciculus and inferior longitudinal fasciculus. The comparison between sd-fMCI and sd-aMCI, as well as the analysis of the other diffusion parameters, yielded no significant group differences. The individual-level SVM analysis provided discrimination between the MCI subtypes with accuracies around 97%. The major limitation is the relatively small number of cases of MCI. CONCLUSIONS: Our data show that, at the group level, the md-aMCI subgroup has the most pronounced damage in white matter integrity. Individually, SVM analysis of white matter FA provided highly accurate classification of MCI subtypes.

Testing Spatial Autocorrelation in Weighted Networks : the Modes Permutation Test

In a weighted spatial network, as specified by an exchange matrix, the variances of the spatial values are inversely proportional to the size of the regions. Spatial values are no more exchangeable under independence, thus weakening the rationale for ordinary permutation and bootstrap tests of spatial autocorrelation. We propose an alternative permutation test for spatial autocorrelation, based upon exchangeable spatial modes, constructed as linear orthogonal combinations of spatial values. The coefficients obtain as eigenvectors of the standardised exchange matrix appearing in spectral clustering, and generalise to the weighted case the concept of spatial filtering for connectivity matrices. Also, two proposals aimed at transforming an acessibility matrix into a exchange matrix with with a priori fixed margins are presented. Two examples (inter-regional migratory flows and binary adjacency networks) illustrate the formalism, rooted in the theory of spectral decomposition for reversible Markov chains.

Comparison of Energy Minimization Methods for 3-D Brain Tissue Classification

This paper presents 3-D brain tissue classificationschemes using three recent promising energy minimizationmethods for Markov random fields: graph cuts, loopybelief propagation and tree-reweighted message passing.The classification is performed using the well knownfinite Gaussian mixture Markov Random Field model.Results from the above methods are compared with widelyused iterative conditional modes algorithm. Theevaluation is performed on a dataset containing simulatedT1-weighted MR brain volumes with varying noise andintensity non-uniformities. The comparisons are performedin terms of energies as well as based on ground truthsegmentations, using various quantitative metrics.

Assessment of a new MRI classification of spinal stenosis based on cross-sectional morphology of the dural sac : FM2

Introduction: Quantitative measures of degree of lumbar spinal stenosis (LSS) such as antero-posterior diameter of the canal or dural sac cross sectional area vary widely and do not correlate with clinical symptoms or results of surgical decompression. In an effort to improve quantification of stenosis we have developed a grading system based on the morphology of the dural sac and its contents as seen on T2 axial images. The grading comprises seven categories ranging form normal to the most severe stenosis and takes into account the ratio of rootlet/CSF content. Material and methods: Fifty T2 axial MRI images taken at disc level from twenty seven symptomatic lumbar spinal stenosis patients who underwent decompressive surgery were classified into seven categories by five observers and reclassified 2 weeks later by the same investigators. Intra- and inter-observer reliability of the classification were assessed using Cohen's and Fleiss' kappa statistics, respectively. Results: Generally, the morphology grading system itself was well adopted by the observers. Its success in application is strongly influenced by the identification of the dural sac. The average intraobserver Cohen's kappa was 0.53 ± 0.2. The inter-observer Fleiss' kappa was 0.38 ± 0.02 in the first rating and 0.3 ± 0.03 in the second rating repeated after two weeks. Discussion: In this attempt, the teaching of the observers was limited to an introduction to the general idea of the morphology grading system and one example MRI image per category. The identification of the dimension of the dural sac may be a difficult issue in absence of complete T1 T2 MRI image series as it was the case here. The similarity of the CSF to possibly present fat on T2 images was the main reason of mismatch in the assignment of the cases to a category. The Fleiss correlation factors of the five observers are fair and the proposed morphology grading system is promising.