University of Grenoble

About: University of Grenoble is a education organization based out in Saint-Martin-d'Hères, France. It is known for research contribution in the topics: Population & Context (language use). The organization has 25658 authors who have published 45143 publications receiving 909760 citations.

Observation of New Properties of Secondary Cosmic Rays Lithium, Beryllium, and Boron by the Alpha Magnetic Spectrometer on the International Space Station

Abstract: We report on the observation of new properties of secondary cosmic rays Li, Be, and B measured in the rigidity (momentum per unit charge) range 1.9 GV to 3.3 TV with a total of 5.4×10^{6} nuclei collected by AMS during the first five years of operation aboard the International Space Station. The Li and B fluxes have an identical rigidity dependence above 7 GV and all three fluxes have an identical rigidity dependence above 30 GV with the Li/Be flux ratio of 2.0±0.1. The three fluxes deviate from a single power law above 200 GV in an identical way. This behavior of secondary cosmic rays has also been observed in the AMS measurement of primary cosmic rays He, C, and O but the rigidity dependences of primary cosmic rays and of secondary cosmic rays are distinctly different. In particular, above 200 GV, the secondary cosmic rays harden more than the primary cosmic rays.

Search for pair-produced third-generation squarks decaying via charm quarks or in compressed supersymmetric scenarios in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: Results of a search for supersymmetry via direct production of third-generation squarks are reported, using 20.3 fb(-1) of proton-proton collision data at root s = 8 TeV recorded by the ATLAS experiment at the LHC in 2012. Two different analysis strategies based on monojetlike and c-tagged event selections are carried out to optimize the sensitivity for direct top squark-pair production in the decay channel to a charm quark and the lightest neutralino ((t) over tilde (1) -> c + (chi) over tilde (0)(1)) across the top squark-neutralino mass parameter space. No excess above the Standard Model background expectation is observed. The results are interpreted in the context of direct pair production of top squarks and presented in terms of exclusion limits in the (m((t) over tilde1), m((chi) over tilde 10)) parameter space. A top squark of mass up to about 240 GeV is excluded at 95% confidence level for arbitrary neutralino masses, within the kinematic boundaries. Top squark masses up to 270 GeV are excluded for a neutralino mass of 200 GeV. In a scenario where the top squark and the lightest neutralino are nearly degenerate in mass, top squark masses up to 260 GeV are excluded. The results from the monojetlike analysis are also interpreted in terms of compressed scenarios for top squark-pair production in the decay channel (t) over tilde (1) -> b + ff' + (chi) over tilde (0)(1) and sbottom pair production with (b) over tilde -> b + (chi) over tilde (0)(1), leading to a similar exclusion for nearly mass-degenerate third-generation squarks and the lightest neutralino. The results in this paper significantly extend previous results at colliders.

Exosomes secreted by cortical neurons upon glutamatergic synapse activation specifically interact with neurons

Abstract: Exosomes are nano-sized vesicles of endocytic origin released into the extracellular space upon fusion of multivesicular bodies with the plasma membrane. Exosomes represent a novel mechanism of cell–cell communication allowing direct transfer of proteins, lipids and RNAs. In the nervous system, both glial and neuronal cells secrete exosomes in a way regulated by glutamate. It has been hypothesized that exosomes can be used for interneuronal communication implying that neuronal exosomes should bind to other neurons with some kind of specificity. Here, dissociated hippocampal cells were used to compare the specificity of binding of exosomes secreted by neuroblastoma cells to that of exosomes secreted by cortical neurons. We found that exosomes from neuroblastoma cells bind indiscriminately to neurons and glial cells and could be endocytosed preferentially by glial cells. In contrast, exosomes secreted from stimulated cortical neurons bound to and were endocytosed only by neurons. Thus, our results demonstrate for the first time that exosomes released upon synaptic activation do not bind to glial cells but selectively to other neurons suggesting that they can underlie a novel aspect of interneuronal communication. Keywords: extracellular vesicles; exosomes; neurons; multivesicular bodies; CD63 tetraspanin; tetanus toxin; central nervous system; intercellular communication (Published: 13 November 2014) Citation: Journal of Extracellular Vesicles 2014, 3 : 24722 - http://dx.doi.org/10.3402/jev.v3.24722

The cytotoxic Staphylococcus aureus PSMα3 reveals a cross-α amyloid-like fibril

Abstract: Amyloids are ordered protein aggregates, found in all kingdoms of life, and are involved in aggregation diseases as well as in physiological activities. In microbes, functional amyloids are often key virulence determinants, yet the structural basis for their activity remains elusive. We determined the fibril structure and function of the highly toxic, 22-residue phenol-soluble modulin α3 (PSMα3) peptide secreted by Staphylococcus aureus PSMα3 formed elongated fibrils that shared the morphological and tinctorial characteristics of canonical cross-β eukaryotic amyloids. However, the crystal structure of full-length PSMα3, solved de novo at 1.45 angstrom resolution, revealed a distinctive "cross-α" amyloid-like architecture, in which amphipathic α helices stacked perpendicular to the fibril axis into tight self-associating sheets. The cross-α fibrillation of PSMα3 facilitated cytotoxicity, suggesting that this assembly mode underlies function in S. aureus.

Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification

Abstract: In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community—can a limited amount of highly-discriminative (e.g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e.g., multispectral) data? Traditional semi-supervised manifold alignment methods do not perform sufficiently well for such problems, since the hyperspectral data is very expensive to be largely collected in a trade-off between time and efficiency, compared to the multispectral data. To this end, we propose a novel semi-supervised cross-modality learning framework, called learnable manifold alignment (LeMA). LeMA learns a joint graph structure directly from the data instead of using a given fixed graph defined by a Gaussian kernel function. With the learned graph, we can further capture the data distribution by graph-based label propagation, which enables finding a more accurate decision boundary. Additionally, an optimization strategy based on the alternating direction method of multipliers (ADMM) is designed to solve the proposed model. Extensive experiments on two hyperspectral-multispectral datasets demonstrate the superiority and effectiveness of the proposed method in comparison with several state-of-the-art methods.