Institution
University of Grenoble
Education•Saint-Martin-d'Hères, France•
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 & Large Hadron Collider. The organization has 25658 authors who have published 45143 publications receiving 909760 citations.
Topics: Population, Large Hadron Collider, Planet, Nanowire, Stars
Papers published on a yearly basis
Papers
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TL;DR: Current knowledge on pyrethroid resistance mechanisms in mosquitoes is updated and available data on the impact of the environment on mosquito response to pyrethroids, including key environmental factors, such as the presence of urban or agricultural pollutants and biotic interactions between mosquitoes and their microbiome are discussed, and research perspectives to fill in knowledge gaps are suggested.
282 citations
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TL;DR: A generalization of the short-time Fourier-based synchrosqueezing transform using a new local estimate of instantaneous frequency enables not only to achieve a highly concentrated time-frequency representation for a wide variety of amplitude- and frequency-modulated multicomponent signals but also to reconstruct their modes with a high accuracy.
Abstract: This paper puts forward a generalization of the short-time Fourier-based synchrosqueezing transform using a new local estimate of instantaneous frequency. Such a technique enables not only to achieve a highly concentrated time-frequency representation for a wide variety of amplitude- and frequency-modulated multicomponent signals but also to reconstruct their modes with a high accuracy. Numerical investigation on synthetic and gravitational-wave signals shows the efficiency of this new approach.
282 citations
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TL;DR: A statistical nonlinear machine learning classification algorithm is used, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns of language networks in patients and classify 55 participants as healthy subjects or patients with epilepsy.
Abstract: Our goal was to apply a statistical approach to allow the identification of atypical language patterns and to differentiate patients with epilepsy from healthy subjects, based on their cerebral activity, as assessed by functional MRI (fMRI). Patients with focal epilepsy show reorganization or plasticity of brain networks involved in cognitive functions, inducing ‘atypical’ (compared to ‘typical’ in healthy people) brain profiles. Moreover, some of these patients suffer from drug-resistant epilepsy, and they undergo surgery to stop seizures. The neurosurgeon should only remove the zone generating seizures and must preserve cognitive functions to avoid deficits. To preserve functions, one should know how they are represented in the patient’s brain, which is in general different from that of healthy subjects. For this purpose, in the pre-surgical stage, robust and efficient methods are required to identify atypical from typical representations. Given the frequent location of regions generating seizures in the vicinity of language networks, one important function to be considered is language. The risk of language impairment after surgery is determined pre-surgically by mapping language networks. In clinical settings, cognitive mapping is classically performed with fMRI. The fMRI analyses allowing the identification of atypical patterns of language networks in patients are not sufficiently robust and require additional statistic approaches. In this study, we report the use of a statistical nonlinear machine learning classification, the Extreme Gradient Boosting (XGBoost) algorithm, to identify atypical patterns and classify 55 participants as healthy subjects or patients with epilepsy. XGBoost analyses were based on neurophysiological features in five language regions (three frontal and two temporal) in both hemispheres and activated with fMRI for a phonological (PHONO) and a semantic (SEM) language task. These features were combined into 135 cognitively plausible subsets and further submitted to selection and binary classification. Classification performance was scored with the Area Under the receiver operating characteristic curve (AUC). Our results showed that the subset SEM_LH BA_47-21 (left fronto-temporal activation induced by the SEM task) provided the best discrimination between the two groups (AUC of 91 ± 5%). The results are discussed in the framework of the current debates of language reorganization in focal epilepsy.
281 citations
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TL;DR: An excess of events in the data is interpreted as evidence for the presence of a new particle consistent with the standard model Higgs boson, which is produced in association with a weak vector boson and decays to a bottom-antibottom quark pair.
Abstract: We combine searches by the CDF and D0 Collaborations for the associated production of a Higgs boson with a W or Z boson and subsequent decay of the Higgs boson to a bottom-antibottom quark pair. The data, originating from Fermilab Tevatron p (p) over bar collisions at root s = 1.96 TeV, correspond to integrated luminosities of up to 9.7 fb(-1). The searches are conducted for a Higgs boson with mass in the range 100-150 GeV/c(2). We observe an excess of events in the data compared with the background predictions, which is most significant in the mass range between 120 and 135 GeV/c(2). The largest local significance is 3.3 standard deviations, corresponding to a global significance of 3.1 standard deviations. We interpret this as evidence for the presence of a new particle consistent with the standard model Higgs boson, which is produced in association with a weak vector boson and decays to a bottom-antibottom quark pair.
281 citations
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University of Alaska Fairbanks1, National Center for Atmospheric Research2, Lawrence Berkeley National Laboratory3, Met Office4, Auburn University5, Oak Ridge National Laboratory6, Los Alamos National Laboratory7, St. Francis Xavier University8, University of Grenoble9, Beijing Normal University10, Alfred Wegener Institute for Polar and Marine Research11, University of Maine12, Chinese Academy of Sciences13, University of Lapland14, University of Tyumen15, Northern Arizona University16, University of Colorado Boulder17, Purdue University18
TL;DR: A model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 climate suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of thepermafrost carbon–climate feedback.
Abstract: We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon-climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km2 for the RCP4.5 climate and between 6 and 16 million km2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbon varied between 66-Pg C (1015-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. This assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon-climate feedback.
280 citations
Authors
Showing all 25961 results
Name | H-index | Papers | Citations |
---|---|---|---|
Dieter Lutz | 139 | 671 | 67414 |
Marcella Bona | 137 | 1391 | 92162 |
Nicolas Berger | 137 | 1581 | 96529 |
Cordelia Schmid | 135 | 464 | 103925 |
J. F. Macías-Pérez | 134 | 486 | 94715 |
Marina Cobal | 132 | 1078 | 85437 |
Lydia Roos | 132 | 1284 | 89435 |
Tetiana Hryn'ova | 131 | 1059 | 84260 |
Johann Collot | 131 | 1018 | 82865 |
Remi Lafaye | 131 | 1012 | 83281 |
Jan Stark | 131 | 1186 | 87025 |
Sabine Crépé-Renaudin | 129 | 1142 | 82741 |
Isabelle Wingerter-Seez | 129 | 930 | 79689 |
James Alexander | 129 | 886 | 75096 |
Jessica Levêque | 129 | 1006 | 70208 |