Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis.

INTRODUCTION: In patients with multiple sclerosis (MS), conventional magnetic resonance imaging (MRI) provides only limited insights into the nature of brain damage with modest clinic-radiological correlation. In this study, we applied recent advances in MRI techniques to study brain microstructural alterations in early relapsing-remitting MS (RRMS) patients with minor deficits. Further, we investigated the potential use of advanced MRI to predict functional performances in these patients. METHODS: Brain relaxometry (T1, T2, T2*) and magnetization transfer MRI were performed at 3T in 36 RRMS patients and 18 healthy controls (HC). Multicontrast analysis was used to assess for microstructural alterations in normal-appearing (NA) tissue and lesions. A generalized linear model was computed to predict clinical performance in patients using multicontrast MRI data, conventional MRI measures as well as demographic and behavioral data as covariates. RESULTS: Quantitative T2 and T2* relaxometry were significantly increased in temporal normal-appearing white matter (NAWM) of patients compared to HC, indicating subtle microedema (P = 0.03 and 0.004). Furthermore, significant T1 and magnetization transfer ratio (MTR) variations in lesions (mean T1 z-score: 4.42 and mean MTR z-score: -4.09) suggested substantial tissue loss. Combinations of multicontrast and conventional MRI data significantly predicted cognitive fatigue (P = 0.01, Adj-R (2) = 0.4), attention (P = 0.0005, Adj-R (2) = 0.6), and disability (P = 0.03, Adj-R (2) = 0.4). CONCLUSION: Advanced MRI techniques at 3T, unraveled the nature of brain tissue damage in early MS and substantially improved clinical-radiological correlations in patients with minor deficits, as compared to conventional measures of disease.

Nature conservation efforts by forest owners : intentions and practice in a Swedish case study

Abstract

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Chemical nature of soil humified fractions and their bioactivity

The aim of this work was to evaluate the humus composition from an Ultisol from Campos dos Goytacazes, RJ, Brazil. Soil samples of four depths (0-0.05, 0.05-0.10, 0.10-0.20 and 0.20-0.40 m) and its chemical nature were analysed by elemental composition, E4/E6 ratios and Fourier transformed infrared spectroscopy. The bioactivity of these humified substances was evaluated through their action on maize root growth and H+-ATPase activity of roots microsomes. In topsoil, the content of high condensed alkaline soluble humic substances is greater than that found in the subsuperficial layers. The chemical nature of humic and fulvic acids also varied with the soil depth. The humic acids isolated from the soil samples exhibited higher bioactivity compared with the fulvic acids. Moreover, the results suggest that more condensed humic substances can promote highest stimulation of the microsomal H+-ATPases from maize roots. These data reinforce the concept that the activity of the H+ pumps can be used as a biochemical marker for evaluation of humic substances bioactivity.

Learning form Nature to improve the heat generation of iron-oxide nanoparticles for magnetic hyperthermia applications.

The performance of magnetic nanoparticles is intimately entwined with their structure, mean size and magnetic anisotropy. Besides, ensembles offer a unique way of engineering the magnetic response by modifying the strength of the dipolar interactions between particles. Here we report on an experimental and theoretical analysis of magnetic hyperthermia, a rapidly developing technique in medical research and oncology. Experimentally, we demonstrate that single-domain cubic iron oxide particles resembling bacterial magnetosomes have superior magnetic heating efficiency compared to spherical particles of similar sizes. Monte Carlo simulations at the atomic level corroborate the larger anisotropy of the cubic particles in comparison with the spherical ones, thus evidencing the beneficial role of surface anisotropy in the improved heating power. Moreover we establish a quantitative link between the particle assembling, the interactions and the heating properties. This knowledge opens new perspectives for improved hyperthermia, an alternative to conventional cancer therapies.

Enhanced compressed sensing recovery with level set normals.

We propose a compressive sensing algorithm that exploits geometric properties of images to recover images of high quality from few measurements. The image reconstruction is done by iterating the two following steps: 1) estimation of normal vectors of the image level curves, and 2) reconstruction of an image fitting the normal vectors, the compressed sensing measurements, and the sparsity constraint. The proposed technique can naturally extend to nonlocal operators and graphs to exploit the repetitive nature of textured images to recover fine detail structures. In both cases, the problem is reduced to a series of convex minimization problems that can be efficiently solved with a combination of variable splitting and augmented Lagrangian methods, leading to fast and easy-to-code algorithms. Extended experiments show a clear improvement over related state-of-the-art algorithms in the quality of the reconstructed images and the robustness of the proposed method to noise, different kind of images, and reduced measurements.

Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems

This paper proposes a very fast method for blindly approximating a nonlinear mapping which transforms a sum of random variables. The estimation is surprisingly good even when the basic assumption is not satisfied.We use the method for providing a good initialization for inverting post-nonlinear mixtures and Wiener systems. Experiments show that the algorithm speed is strongly improved and the asymptotic performance is preserved with a very low extra computational cost.

Vers une gestion de la nature en termes d'action publique : analyse d'une dynamique d'appropriation des ressources végétales à Tobré, Nord Bénin

Cette étude se fonde sur des recherches ethnographiques menées autour de la mise en oeuvre d'une politique publique de gestion de la nature à Tobré, une région baatonu au Bénin. L'étude se place dans une dynamique d'appropriation des ressources naturelles en rapport avec l'entrée en jeu de multiples acteurs publics et privés dans l'arène locale de gestion de la nature. En effet, ces acteurs ont acquis ces dernières années de l'expérience et de l'efficacité à la faveur des changements institutionnels et politiques et s'incluent davantage dans le processus de décision au point de suppléer l'Etat dans les actions qui étaient ses prérogatives. L'étude met en lumière les rapports de conflits, de compétitions, de concurrence ou de compromis autour des enjeux sociaux, politiques et économiques de protection de la nature. Elle décrit et analyse les formes d'interactions entre les différents acteurs individuels, collectifs et institutionnels, afin de voir en quoi ces interactions participent du processus de mobilisation d'acteurs locaux et de confrontation de vision, qui finalement définit et oriente les politiques locales de protection de la nature. Pour atteindre cet objectif, l'étude mobilise des apports théoriques de plusieurs disciplines, notamment de la socio- anthropologie du développement, de l'histoire et de la sociologie politique. Elle repose également sur des données empiriques collectées à partir d'une combinaison d'outils techniques, des entretiens et des observations jusqu'au dépouillement de presse et des études de cas. Les principaux résultats montrent que les acteurs locaux, particulièrement les comités de gestion sont parvenus à inscrire la gestion des ressources naturelles dans l'espace public à travers les débats dans les médias, les forums, les marches de protestation, etc. De fait, ils ont été capables d'influer sur le processus de définition et de mise en oeuvre des politiques locales de protection de la nature et donc de reconfigurer l'arène locale de gestion des ressources naturelles. Ce qui amène l'Etat et ses services déconcentrés, en premier lieu les services des Eaux et Forêts et la mairie à pactiser avec ces différents comités, sans pour autant perdre de leur notoriété. Au contraire, ils participent de la gouvernance de l'ensemble des actions et permettent de mettre en place des formes de gestion négociée. - This is an ethnographic research on the implementation of a public policy for the management of natural resources in Tobre, a baatonu village in Benin. The study focuses on the dynamic of natural resources ownership in a context of multiple stakeholders in the public and private sectors. In recent years, these stakeholders have become more experienced and efficient as a result of several institutional and policy changes. The stakeholders are more involved in the decision making process to the point that they can complement the role of the State in natural resources management. This study highlights the conflicts, competition, or compromise associated with the social, political and economic constraints of nature conservation. The interactions between individual, collective and institutional stakeholders were analyzed, to understand their role in the process of mobilizing local stakeholders and confronting their visions. This process ultimately defines and guides local policies on the management of nature. To achieve this goal, this study combined theoretical approaches from developmental socio-anthropology, history and political sociology with empirical data collected using interviews, observations, newspapers analysis and case studies. The results show that local stakeholders, particularly the management committees, were successful at introducing the need for sustainable natural resource management in the mainstream public discourse through the media, forums, and demonstrations. They were able to influence the process of identifying and implementing local policies on nature conservancy. This has encouraged the State, through its forestry services, and the district mayors to collaborate with these committees in the shared governance of natural resources.