Genome-wide meta-analysis for serum calcium identifies significantly associated SNPs near the calcium-sensing receptor (CASR) gene.

Calcium has a pivotal role in biological functions, and serum calcium levels have been associated with numerous disorders of bone and mineral metabolism, as well as with cardiovascular mortality. Here we report results from a genome-wide association study of serum calcium, integrating data from four independent cohorts including a total of 12,865 individuals of European and Indian Asian descent. Our meta-analysis shows that serum calcium is associated with SNPs in or near the calcium-sensing receptor (CASR) gene on 3q13. The top hit with a p-value of 6.3 x 10(-37) is rs1801725, a missense variant, explaining 1.26% of the variance in serum calcium. This SNP had the strongest association in individuals of European descent, while for individuals of Indian Asian descent the top hit was rs17251221 (p = 1.1 x 10(-21)), a SNP in strong linkage disequilibrium with rs1801725. The strongest locus in CASR was shown to replicate in an independent Icelandic cohort of 4,126 individuals (p = 1.02 x 10(-4)). This genome-wide meta-analysis shows that common CASR variants modulate serum calcium levels in the adult general population, which confirms previous results in some candidate gene studies of the CASR locus. This study highlights the key role of CASR in calcium regulation.

Dissolution-reprecipitation of zircon at low-temperature, high-pressure conditions (Lanzo Massif, Italy)

An eclogite facies meta-plagiogranite from the Lanzo massif (western Alps, Italy) contains crystals of zircon intimately associated with allanite. Zircon displays different microtextures ranging from pristine, euhedral, and magmatic to fractured, porous varieties with mosaic zoning, and pervasive recrystallization into euhedral microcrystals. Fractures and voids in the recrystallized zircon microcrystals are mainly filled by high-pressure Na-rich pyroxene. Electron backscattered diffraction analysis revealed a similar crystallographic orientation for primary magmatic zircon crystals and microcrystals, with less than 2 degrees misorientation among neighboring microdomains. The textural change is coupled with chemical and isotopic modifications: recrystallized zircon domains contain significantly less Th and light- to mid-REE, but are richer in Sr than magmatic zircon crystals. Magmatic zircon preserves the protolith U-Pb age of 163.5 +/- 1.7 Ma, whereas zircon microcrystals have a mean age of 55 +/- 1 Ma. The coexisting allanite also contains inclusions of Na-rich pyroxene and has chemical features (elevated Sr and Ni contents and lack of Eu anomaly) indicating formation at high pressure. Despite being associated texturally with zircon, allanite yields a younger Th-Pb age of 46.5 +/- 3.0 Ma, suggesting that the Lanzo unit remained at relatively high pressure conditions for similar to 8 m.y. Zircon recrystallization proceeded with volume reduction and loss of material to an alkaline metamorphic fluid that acted as the agent for a coupled dissolution-reprecipitation process. Recrystallization occurred with minimum transport, in a low-strain environment, and was not significantly enhanced by metamictization. The source of the fluid for zircon recrystallization is most probably related to prograde devolatilization reactions in the surrounding serpentinite.

Geographical and altitudinal population genetic structure of two dung fly species with contrasting mobility and temperature preference.

Local adaptation of populations requires some degree of spatio-temporal isolation. Previous studies of the two dung fly species Scathophaga stercoraria and Sepsis cynipsea have revealed low levels of geographic and altitudinal genetic differentiation in quantitative life history and morphological traits, but instead high degrees of phenotypic plasticity. These patterns suggest that gene flow is extensive despite considerable geographic barriers and large spatio-temporal variation in selection on body size and related traits. In this study we addressed this hypothesis by investigating genetic differentiation of dung fly populations throughout Switzerland based on the same 10 electrophoretic loci in each species. Overall, we found no significant geographic differentiation of populations for either species. This is inconsistent with the higher rates of gene flow expected due to better flying capacity of the larger S. stercoraria. However, heterozygote deficiencies within populations indicated structuring on a finer scale, seen for several loci in S. cynipsea, and for the locus PGM (Phosphoglucomutase) in S. stercoraria. Additionally, S. cynipsea showed a tendency towards a greater gene diversity at higher altitudes, mediated primarily by the locus MDH (malate dehydrogenase), at which a second allele was only present in populations above 1000 m. This may be caused by increased environmental stress at higher altitudes in this warm-adapted species. MDH might thus be a candidate locus subject to thermal selection in this species, but this remains to be corroborated by direct evidence. In S. stercoraria, no altitudinal variation was found.

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.

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

Caspase-3 and RasGAP: a stress-sensing survival/demise switch.

The final decision on cell fate, survival versus cell death, relies on complex and tightly regulated checkpoint mechanisms. The caspase-3 protease is a predominant player in the execution of apoptosis. However, recent progress has shown that this protease paradoxically can also protect cells from death. Here, we discuss the underappreciated, protective, and prosurvival role of caspase-3 and detail the evidence showing that caspase-3, through differential processing of p120 Ras GTPase-activating protein (RasGAP), can modulate a given set of proteins to generate, depending on the intensity of the input signals, opposite outcomes (survival vs death).

Juxtaposition of melt impregnation and high-temperature shear zones in the upper mantle; field and petrological constraints from the Lanzo peridotite (Northern Italy)

Results of a field and microstructural study between the northern and the central bodies of the Lanzo plagioclase peridotite massif (NW Italy) indicate that the spatial distribution of deformation is asymmetric across kilometre-scale mantle shear zones. The southwestern part of the shear zone (footwall) shows a gradually increasing degree of deformation from porphyroclastic peridotites to mylonite, whereas the northeastern part (hanging wall) quickly grades into weakly deformed peridotites. Discordant gabbroic and basaltic dykes are asymmetrically distributed and far more abundant in the footwall of the shear zone. The porphyroclastic peridotite displays porphyroclastic zones and domains of igneous crystallization whereas mylonites are characterized by elongated porphyroclasts, embedded between fine-grained, polycrystalline bands of olivine, plagioclase, clinopyroxene, orthopyroxene, spinel, rare titanian pargasite, and domains of recrystallized olivine. Two types of melt impregnation textures have been found: (1) clinopyroxene porphyroclasts incongruently reacted with migrating melt to form orthopyroxene plagioclase; (2) olivine porphyroclasts are partially replaced by interstitial orthopyroxene. The meltrock reaction textures tend to disappear in the mylonites, indicating that deformation in the mylonite continued under subsolidus conditions. The pyroxene chemistry is correlated with grain size. High-Al pyroxene cores indicate high temperatures (11001030C), whereas low-Al neoblasts display lower final equilibration temperatures (860C). The spinel Cr-number [molar Cr/(Cr Al)] and TiO2 concentrations show extreme variability covering almost the entire range known from abyssal peridotites. The spinel compositions of porphyroclastic peridotites from the central body are more variable than spinel from mylonite, mylonite with ultra-mylonite bands, and porphyroclastic rocks of the northern body. The spinel compositions probably indicate disequilibrium and would favour rapid cooling, and a faster exhumation of the central peridotite body, relative to the northern one. Our results indicate that melt migration and high-temperature deformation are juxtaposed both in time and space. Meltrock reaction may have caused grain-size reduction, which in turn led to localization of deformation. It is likely that melt-lubricated, actively deforming peridotites acted as melt focusing zones, with permeabilities higher than the surrounding, less deformed peridotites. Later, under subsolidus conditions, pinning in polycrystalline bands in the mylonites inhibited substantial grain growth and led to permanent weak zones in the upper mantle peridotite, with a permeability that is lower than in the weakly deformed peridotites. Such an inversion in permeability might explain why actively deforming, fine-grained peridotite mylonite acted as a permeability barrier and why ascending mafic melts might terminate and crystallize as gabbros along actively deforming shear zones. Melt-lubricated mantle shear zones provide a mechanism for explaining the discontinuous distribution of gabbros in oceancontinent transition zones, oceanic core complexes and ultraslow-spreading ridges.

Heated Optical Fiber for Distributed Soil-Moisture Measurements: A Lysimeter Experiment

An Actively Heated Fiber Optics (AHFO) method to estimate soil moisture is tested and the analysis technique improved on. The measurements were performed in a lysimeter uniformly packed with loam soil with variable water content profiles. In the first meter of the soil profi le, 30 m of fiber optic cable were installed in a 12 loops coil. The metal sheath armoring the fiber cable was used as an electrical resistance heater to generate a heat pulse, and the soil response was monitored with a Distributed Temperature Sensing (DTS) system. We study the cooling following three continuous heat pulses of 120 s at 36 W m(-1) by means of long-time approximation of radial heat conduction. The soil volumetric water contents were then inferred from the estimated thermal conductivities through a specifically calibrated model relating thermal conductivity and volumetric water content. To use the pre-asymptotic data we employed a time correction that allowed the volumetric water content to be estimated with a precision of 0.01-0.035 (m(3) m(-3)). A comparison of the AHFO measurements with soil-moisture measurements obtained with calibrated capacitance-based probes gave good agreement for wetter soils [discrepancy between the two methods was less than 0.04 (m(3) m(-3))]. In the shallow drier soils, the AHFO method underestimated the volumetric water content due to the longertime required for the temperature increment to become asymptotic in less thermally conductive media [discrepancy between the two methods was larger than 0.1 (m(3) m(-3))]. The present work suggests that future applications of the AHFO method should include longer heat pulses, that longer heating and cooling events are analyzed, and, temperature increments ideally be measured with higher frequency.

Brain glucose sensing, counterregulation, and energy homeostasis.

Neuronal circuits in the central nervous system play a critical role in orchestrating the control of glucose and energy homeostasis. Glucose, beside being a nutrient, is also a signal detected by several glucose-sensing units that are located at different anatomical sites and converge to the hypothalamus to cooperate with leptin and insulin in controlling the melanocortin pathway.

Temperature-dependent turnovers in sex-determination mechanisms: a quantitative model.

Sex determination is often seen as a dichotomous process: individual sex is assumed to be determined either by genetic (genotypic sex determination, GSD) or by environmental factors (environmental sex determination, ESD), most often temperature (temperature sex determination, TSD). We endorse an alternative view, which sees GSD and TSD as the ends of a continuum. Both effects interact a priori, because temperature can affect gene expression at any step along the sex-determination cascade. We propose to define sex-determination systems at the population- (rather than individual) level, via the proportion of variance in phenotypic sex stemming from genetic versus environmental factors, and we formalize this concept in a quantitative-genetics framework. Sex is seen as a threshold trait underlain by a liability factor, and reaction norms allow modeling interactions between genotypic and temperature effects (seen as the necessary consequences of thermodynamic constraints on the underlying physiological processes). As this formalization shows, temperature changes (due to e.g., climatic changes or range expansions) are expected to provoke turnovers in sex-determination mechanisms, by inducing large-scale sex reversal and thereby sex-ratio selection for alternative sex-determining genes. The frequency of turnovers and prevalence of homomorphic sex chromosomes in cold-blooded vertebrates might thus directly relate to the temperature dependence in sex-determination mechanisms.

New insight on bio-sensing by nano-fabricated memristors

We report here on a new insight for bio- sensing based on the memristive effect of functional- ized Schottky-barrier memristive silicon nanowire in dry environment. The device concept is discussed. Elec- trical measurements confirm the bio-detection by the narrowing of the memristive Ids − Vds hysteresis upon interaction of antigen with antibody-functionalized nanowire.

Sensing of mammalian IL-17A regulates fungal adaptation and virulence.

Infections by opportunistic fungi have traditionally been viewed as the gross result of a pathogenic automatism, which makes a weakened host more vulnerable to microbial insults. However, fungal sensing of a host's immune environment might render this process more elaborate than previously appreciated. Here we show that interleukin (IL)-17A binds fungal cells, thus tackling both sides of the host-pathogen interaction in experimental settings of host colonization and/or chronic infection. Global transcriptional profiling reveals that IL-17A induces artificial nutrient starvation conditions in Candida albicans, resulting in a downregulation of the target of rapamycin signalling pathway and in an increase in autophagic responses and intracellular cAMP. The augmented adhesion and filamentous growth, also observed with Aspergillus fumigatus, eventually translates into enhanced biofilm formation and resistance to local antifungal defenses. This might exemplify a mechanism whereby fungi have evolved a means of sensing host immunity to ensure their own persistence in an immunologically dynamic environment.

Chemical sensing in Drosophila.

Chemical sensing begins when peripheral receptor proteins recognise specific environmental stimuli and translate them into spatial and temporal patterns of sensory neuron activity. The chemosensory system of the fruit fly, Drosophila melanogaster, has become a dominant model to understand this process, through its accessibility to a powerful combination of molecular, genetic and electrophysiological analysis. Recent results have revealed many surprises in the biology of peripheral chemosensation in Drosophila, including novel structural and signalling properties of the insect odorant receptors (ORs), combinatorial mechanisms of chemical recognition by the gustatory receptors (GRs), and the implication of Transient Receptor Potential (TRP) ion channels as a novel class of chemosensory receptors.