Sensitivity of temperate grassland species to elevated atmospheric CO2 and the interaction with temperature and water stress

Selostus: Kohonneen hiilidioksidipitoisuuden, lämpötilan ja kuivuuden vaikutus nurmikasveihin

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Manipulating the sensitivity of signal-induced repression: quantification and consequences of altered brinker gradients.

Traditionally, the analysis of gene regulatory regions suffered from the caveat that it was restricted to artificial contexts (e.g. reporter constructs of limited size). With the advent of the BAC recombineering technique, genomic constructs can now be generated to test regulatory elements in their endogenous environment. The expression of the transcriptional repressor brinker (brk) is negatively regulated by Dpp signaling. Repression is mediated by small sequence motifs, the silencer elements (SEs), that are present in multiple copies in the regulatory region of brk. In this work, we manipulated the SEs in the brk locus. We precisely quantified the effects of the individual SEs on the Brk gradient in the wing disc by employing a 1D data extraction method, followed by the quantification of the data with reference to an internal control. We found that mutating the SEs results in an expansion of the brk expression domain. However, even after mutating all predicted SEs, repression could still be observed in regions of maximal Dpp levels. Thus, our data point to the presence of additional, low affinity binding sites in the brk locus.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

A high-sensitivity differential scanning calorimeter with magnetic field for magnetostructural transitions

We have developed a differential scanning calorimeter capable of working under applied magnetic fields of up to 5 T. The calorimeter is highly sensitive and operates over the temperature range 10¿300 K. It is shown that, after a proper calibration, the system enables determination of the latent heat and entropy changes in first-order solid¿solid phase transitions. The system is particularly useful for investigating materials that exhibit the giant magnetocaloric effect arising from a magnetostructural phase transition. Data for Gd5(Si0.1Ge0.9)4 are presented.

Comparative sensitivity of tumor and non-tumor cell lines as reliable approach for in vitro cytotoxicity screening of lysine-based surfactants with potential pharmaceutical applications

Surfactants are used as additives in topical pharmaceuticals and drug delivery systems. The biocompatibility of amino acid-based surfactants makes them highly suitable for use in these fields, but tests are needed to evaluate their potential toxicity. Here we addressed the sensitivity of tumor (HeLa, MCF-7) and non-tumor (3T3, 3T6, HaCaT, NCTC 2544) cell lines to the toxic effects of lysine-based surfactants by means of two in vitro endpoints (MTT and NRU). This comparative assay may serve as a reliable approach for predictive toxicity screening of chemicals prior to pharmaceutical applications. After 24-h of cell exposure to surfactants, differing toxic responses were observed. NCTC 2544 and 3T6 cell lines were the most sensitive, while both tumor cells and 3T3 fibroblasts were more resistant to the cytotoxic effects of surfactants. IC50-values revealed that cytotoxicity was detected earlier by MTT assay than by NRU assay, regardless of the compound or cell line. The overall results showed that surfactants with organic counterions were less cytotoxic than those with inorganic counterions. Our findings highlight the relevance of the correct choice and combination of cell lines and bioassays in toxicity studies for a safe and reliable screen of chemicals with potential interest in pharmaceutical industry.

The Lorenz-maximal core allocations and the kernel in some classes of assignment games

En aquest treball demostrem que en la classe de jocs d'assignació amb diagonal dominant (Solymosi i Raghavan, 2001), el repartiment de Thompson (que coincideix amb el valor tau) és l'únic punt del core que és maximal respecte de la relació de dominància de Lorenz, i a més coincideix amb la solucié de Dutta i Ray (1989), també coneguda com solució igualitària. En segon lloc, mitjançant una condició més forta que la de diagonal dominant, introduïm una nova classe de jocs d'assignació on cada agent obté amb la seva parella òptima almenys el doble que amb qualsevol altra parella. Per aquests jocs d'assignació amb diagonal 2-dominant, el repartiment de Thompson és l'únic punt del kernel, i per tant el nucleolo.

Exercise dose and insulin sensitivity: relevance for diabetes prevention.

PURPOSE: Exercise improves insulin resistance and is a first line for the prevention and treatment of type 2 diabetes. The extent, however, to which these responses are dose dependent is not known. The purpose of this study was to examine whether exercise dose was associated with improvements in insulin sensitivity after 4 months of exercise training in previously sedentary adults. METHODS: Fifty-five healthy volunteers participated in a 16-wk supervised endurance exercise intervention with a pre/postintervention design. Insulin sensitivity was assessed by euglycemic hyperinsulinemic clamp, peak oxygen uptake by a graded exercise test, and body composition by dual-energy x-ray absorptiometry. The exercise intervention consisted of three to five sessions per week with a minimum of three sessions supervised. A ramped exercise prescription protocol was used to achieve 75% of peak HR for 45 min per session. Exercise dose, expressed as average kilocalories expended per week, was computed as the product of exercise intensity, duration and frequency. RESULTS: Improved insulin sensitivity was significantly related to exercise dose in a graded dose-response relationship. No evidence of threshold or maximal dose-response effect was observed. Age and gender did not influence this dose-response relationship. Exercise intensity was also significantly related to improvements in insulin sensitivity, whereas frequency was not. CONCLUSIONS: This study identifies a graded dose-response relationship between exercise dose and improvements in insulin sensitivity. The implication of this observation is of importance for the adaptation of exercise prescription in clinical situations.

Effects of fructose and glucose overfeeding on hepatic insulin sensitivity and intrahepatic lipids in healthy humans.

OBJECTIVE: To assess how intrahepatic fat and insulin resistance relate to daily fructose and energy intake during short-term overfeeding in healthy subjects. DESIGN AND METHODS: The analysis of the data collected in several studies in which fasting hepatic glucose production (HGP), hepatic insulin sensitivity index (HISI), and intrahepatocellular lipids (IHCL) had been measured after both 6-7 days on a weight-maintenance diet (control, C; n = 55) and 6-7 days of overfeeding with 1.5 (F1.5, n = 7), 3 (F3, n = 17), or 4 g fructose/kg/day (F4, n = 10), with 3 g glucose/kg/day (G3, n = 11), or with 30% excess energy as saturated fat (fat30%, n = 10). RESULTS: F3, F4, G3, and fat30% all significantly increased IHCL, respectively by 113 ± 86, 102 ± 115, 59 ± 92, and 90 ± 74% as compared to C (all P < 0.05). F4 and G3 increased HGP by 16 ± 10 and 8 ± 11% (both P < 0.05), and F3 and F4 significantly decreased HISI by 20 ± 22 and 19 ± 14% (both P < 0.01). In contrast, there was no significant effect of fat30% on HGP or HISI. CONCLUSIONS: Short-term overfeeding with fructose or glucose decreases hepatic insulin sensitivity and increases hepatic fat content. This indicates short-term regulation of hepatic glucose metabolism by simple carbohydrates.