846 resultados para Kernel Functions
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Interdependence is the main feature of dyadic relationships and, in recent years, various statistical procedures have been proposed for quantifying and testing this social attribute in different dyadic designs. The purpose of this paper is to develop several functions for this kind of statistical tests in an R package, known as nonindependence, for use by applied social researchers. A Graphical User Interface (GUI) is also developed to facilitate the use of the functions included in this package. Examples drawn from psychological research and simulated data are used to illustrate how the software works.
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This study investigated the development of all 3 components of episodic memory (EM), as defined by Tulving, namely, core factual content, spatial context, and temporal context. To this end, a novel, ecologically valid test was administered to 109 participants aged 4-16 years. Results showed that each EM component develops at a different rate. Ability to memorize factual content emerges early, whereas context retrieval abilities continue to improve until adolescence, due to persistent encoding difficulties (isolated by comparing results on free recall and recognition tasks). Exploration of links with other cognitive functions revealed that short-term feature-binding abilities contribute to all EM components, and executive functions to temporal and spatial context, although ability to memorize temporal context is predicted mainly by age.
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Proteases control many vital aspects of humoral and cellular immune responses, including the maturation of cytokines and the killing of target cells. Recently, it has become evident that triggering of the T-cell receptor controls T-cell proliferation through proteases such as mucosa-associated lymphoid tissue 1 (MALT1) and Caspase-8 that act both as adapters and enzymes. Here, we discuss the role of these and other proteases that are relevant to the control of the T-cell response and represent interesting targets of therapeutic immunomodulation.
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The specific heat, cp, of two amorphous silicon (a-Si) samples has been measured by differential scanning calorimetry in the 100–900K temperature range. When the hydrogen content is reduced by thermal annealing, cp approaches the value of crystalline Si (c-Si). Within experimental accuracy, we conclude that cp of relaxed pure a-Si coincides with that of c-Si. This result is used to determine the enthalpy, entropy, and Gibbs free energy of defect-free relaxed a-Si. Finally, the contribution of structural defects on these quantities is calculated and the melting point of several states of a-Si is predicted
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Adult mammalian central nervous system (CNS) axons have a limited regrowth capacity following injury. Myelin-associated inhibitors (MAIs) limit axonal outgrowth and their blockage improves the regeneration of damaged fiber tracts. Three of these proteins, Nogo-A, MAG and OMgp, share two common neuronal receptors: NgR1, together with its co-receptors (p75(NTR), TROY and Lingo-1), and the recently described paired immunoglobulin-like receptor B (PirB). These proteins impair neuronal regeneration by limiting axonal sprouting. Some of the elements involved in the myelin inhibitory pathways may still be unknown, but the discovery that blocking both PirB and NgR1 activities leads to near-complete release from myelin inhibition, sheds light on one of the most competitive and intense fields of neuroregeneration study during in recent decades. In parallel with the identification and characterization of the roles and functions of these inhibitory molecules in axonal regeneration, data gathered in the field strongly suggest that most of these proteins have roles other than axonal growth inhibition. The discovery of a new group of interacting partners for myelin-associated receptors and ligands, as well as functional studies within or outside the CNS environment, highlights the potential new physiological roles for these proteins in processes such as development, neuronal homeostasis, plasticity and neurodegeneration.
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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.
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Following a scheme of Levin we describe the values that functions in Fock spaces take on lattices of critical density in terms of both the size of the values and a cancelation condition that involves discrete versions of the Cauchy and Beurling-Ahlfors transforms.
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We study the possibility of splitting any bounded analytic function $f$ with singularities in a closed set $E\cup F$ as a sum of two bounded analytic functions with singularities in $E$ and $F$ respectively. We obtain some results under geometric restrictions on the sets $E$ and $F$ and we provide some examples showing the sharpness of the positive results.
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Diurnal oscillations of gene expression controlled by the circadian clock underlie rhythmic physiology across most living organisms. Although such rhythms have been extensively studied at the level of transcription and mRNA accumulation, little is known about the accumulation patterns of proteins. Here, we quantified temporal profiles in the murine hepatic proteome under physiological light-dark conditions using stable isotope labeling by amino acids quantitative MS. Our analysis identified over 5,000 proteins, of which several hundred showed robust diurnal oscillations with peak phases enriched in the morning and during the night and related to core hepatic physiological functions. Combined mathematical modeling of temporal protein and mRNA profiles indicated that proteins accumulate with reduced amplitudes and significant delays, consistent with protein half-life data. Moreover, a group comprising about one-half of the rhythmic proteins showed no corresponding rhythmic mRNAs, indicating significant translational or posttranslational diurnal control. Such rhythms were highly enriched in secreted proteins accumulating tightly during the night. Also, these rhythms persisted in clock-deficient animals subjected to rhythmic feeding, suggesting that food-related entrainment signals influence rhythms in circulating plasma factors.
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We characterize the weighted Hardy inequalities for monotone functions in Rn +. In dimension n = 1, this recovers the standard theory of Bp weights. For n > 1, the result was previously only known for the case p = 1. In fact, our main theorem is proved in the more general setting of partly ordered measure spaces.
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RAPPORT DE SYNTHÈSE : Les profils des granules cytotoxiques des cellules T CD8 mémoires sont corrélés à la fonction, à leur état de différentiation et à l'exposition à l'antigène. Les lymphocytes T-CD8 cytotoxiques exercent leur fonction antivirale et antitumorale surtout par la sécrétion des granules cytotoxiques. En général, ce sont l'activité de dégranulation et les granules cytotoxiques (contenant perforine et différentes granzymes) qui définissent les lymphocytes T-CD8 cytotoxiques. Dans cette étude, nous avons investigué l'expression de granzyme K par cytométrie en flux, en comparaison avec l'expression de granzyme A, granzyme B et de perforine. L'expression des granules cytotoxiques a été déterminée dans lymphocytes T-CD8 qui étaient spécifiques pour des différents virus, en particulier spécifique pour le virus d'influenza (flu), le virus Ebstein Barr (EBV), le virus de cytomégalie (CMV) et le virus de l'immunodéficience humaine (HIV). Nous avons observé une dichotomie entre l'expression du granzyme K et de la perforine dans les lymphocytes T-CD8 qui étaient spécifiques aux virus mentionnés. Les profils des lymphocytes T-CD8 spécifiques à flu étaient positifs soit pour granzyme A et granzyme K soit pour le granzyme K seul, mais dans l'ensemble négatifs pour perforine et granzyme B. Les cellules spécifiques à CMV étaient dans la plupart positives pour perforine, granzyme B et A, mais négatives pour le granzyme K. Les cellules spécifiques à EBV et HIV étaient dans la majorité positives pour granzyme A, B et K, et dans la moitié des cas négatives pour la perforine. Nous avons également analysé, selon les marqueurs de mémoire de CD45 et CD127, les profils de différentiation cellulaire: Les cellules avec les granules cytotoxiques contenant exclusivement le granzyme K, étaient associées à un état de différentiation précoce. Au contraire, les protéines cytolytiques perforine, granzyme A et B, correspondent à une différentiation avancée. En outre, les protéines perforine et granzyme B, mais pas les granzymes A et K, sont corrélées à une activité cytotoxique. Finalement, des changements dans l'exposition d'antigène in vitro et in vivo suivant une infection primaire d' HIV ou une vaccination modulent le profil de granules cytotoxiques. Ces résultats nous permettent d'étendre la compréhension de la relation entre les différents profils de granules cytotoxiques des lymphocytes T-CD8 et leur fonction, leur état de différentiation et l'exposition à l'antigène.
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PURPOSE: The aim of this study was to develop models based on kernel regression and probability estimation in order to predict and map IRC in Switzerland by taking into account all of the following: architectural factors, spatial relationships between the measurements, as well as geological information. METHODS: We looked at about 240,000 IRC measurements carried out in about 150,000 houses. As predictor variables we included: building type, foundation type, year of construction, detector type, geographical coordinates, altitude, temperature and lithology into the kernel estimation models. We developed predictive maps as well as a map of the local probability to exceed 300 Bq/m(3). Additionally, we developed a map of a confidence index in order to estimate the reliability of the probability map. RESULTS: Our models were able to explain 28% of the variations of IRC data. All variables added information to the model. The model estimation revealed a bandwidth for each variable, making it possible to characterize the influence of each variable on the IRC estimation. Furthermore, we assessed the mapping characteristics of kernel estimation overall as well as by municipality. Overall, our model reproduces spatial IRC patterns which were already obtained earlier. On the municipal level, we could show that our model accounts well for IRC trends within municipal boundaries. Finally, we found that different building characteristics result in different IRC maps. Maps corresponding to detached houses with concrete foundations indicate systematically smaller IRC than maps corresponding to farms with earth foundation. CONCLUSIONS: IRC mapping based on kernel estimation is a powerful tool to predict and analyze IRC on a large-scale as well as on a local level. This approach enables to develop tailor-made maps for different architectural elements and measurement conditions and to account at the same time for geological information and spatial relations between IRC measurements.
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The peroxisome proliferator-activated receptors (PPARs) are fatty acid and eicosanoid inducible nuclear receptors, which occur in three different isotypes. Upon activator binding, they modulate the expression of various target genes implicated in several important physiological pathways. During the past few years, the identification of both PPAR ligands, natural and synthetic, and PPAR targets and their associated functions has been one of the most important achievements in the field. It underscores the potential therapeutic application of PPAR-specific compounds on the one side, and the crucial biological roles of endogenous PPAR ligands on the other.