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The increase in habitat fragmentation impacts plant-pollinator interactions and threatens the sustainability of plant species. Astragalus tragacantha (Fabaceae), is a rare endangered plant species along the coastal habitats where the plant populations have undergone considerable fragmentation and decline of size. Controlled pollination treatments, the observation of pollinator activity, and pollinator captures, have been conducted to study: (1) the mating system of A. tragacantha and the potential for inbreeding depression and/or outbreeding depression based on controlled pollination treatments, (2) the pollinator composition among populations using a correspondence analysis and a hierarchical clustering, and (3) the link between pollinators and the plant reproductive success using a path-analysis model. In this study, we demonstrated that this plant was not autogamous self-pollinating and depended on pollinators for its reproduction. The absence of difference between manual and open pollinations regarding the reproductive success showed an absence of pollen limitation in our populations. We showed that populations differed in the composition of their pollinator guilds. Some pollinator species were predominant in certain populations. The pollination treatments revealed the existence of a mixed mating system in A. tragacantha populations. We showed an inbreeding depression potentially linked to a predominant pollinator-facilitated selfing, and the existence of outbreeding depression between some distant populations. These differences in pollinator guild and plant mating systems among populations must be considered during the restoration of populations along the Mediterranean coastal habitats in order to enhance the reproductive success and sustainability of A. tragacantha.

We analyze the impact of data from the full Run 1 of the LHC at 7 and 8 TeV on the CMSSM with μ > 0 and < 0 and the NUHM1 with μ > 0 , incorporating the constraints imposed by other experiments such as precision electroweak measurements, flavour measurements, the cosmological density of cold dark matter and the direct search for the scattering of dark matter particles in the LUX experiment. We use the following results from the LHC experiments: ATLAS searches for events with E / T accompanied by jets with the full 7 and 8 TeV data, the ATLAS and CMS measurements of the mass of the Higgs boson, the CMS searches for heavy neutral Higgs bosons and a combination of the LHCb and CMS measurements of BR ( B s → μ + μ - ) and BR ( B d → μ + μ - ) . Our results are based on samplings of the parameter spaces of the CMSSM for both μ > 0 and μ < 0 and of the NUHM1 for μ > 0 with 6.8 × 10 6 , 6.2 × 10 6 and 1.6 × 10 7 points, respectively, obtained using the MultiNest tool. The impact of the Higgs-mass constraint is assessed using FeynHiggs 2.10.0, which provides an improved prediction for the masses of the MSSM Higgs bosons in the region of heavy squark masses. It yields in general larger values of M h than previous versions of FeynHiggs, reducing the pressure on the CMSSM and NUHM1. We find that the global χ 2 functions for the supersymmetric models vary slowly over most of the parameter spaces allowed by the Higgs-mass and the E / T searches, with best-fit values that are comparable to the χ 2 / dof for the best Standard Model fit. We provide 95 % CL lower limits on the masses of various sparticles and assess the prospects for observing them during Run 2 of the LHC.

IL-17A is a T cell–specific cytokine 1 that is involved in chronic inflammations, such as Mycobacterium infection 2 , Crohn's disease 3 , rheumatoid arthritis 4 and multiple sclerosis 5 . Mouse models have explained the molecular basis of IL-17A production 6 , 7 and have shown that IL-17A has a positive effect not only on granuloma formation 8 and neurodegeneration 9 through unknown mechanisms, but also on bone resorption through Receptor activator of NF-κB ligand (RANKL) induction in osteoblasts 4 , 10 . Langerhans cell histiocytosis (LCH) is a rare disease of unknown etiology, lacking an animal model, that cumulates symptoms that are found separately in various IL-17A–related diseases, such as aggressive chronic granuloma formation, bone resorption and soft tissue lesions with occasional neurodegeneration 11 , 12 . We examined IL-17A in the context of LCH and found that there were high serum levels of IL-17A during active LCH and unexpected IL-17A synthesis by dendritic cells (DCs), the major cell type in LCH lesions. We also found an IL-17A–dependent pathway for DC fusion, which was highly potentiated by IFN-γ and led to giant cells expressing three major tissue-destructive enzymes: tartrate resistant acidic phosphatase and matrix metalloproteinases 9 and 12. IFN-γ expression has been previously documented in LCH 13 and observed in IL-17A–related diseases 14 , 15 , 16 , 17 . Notably, serum IL-17A–dependent fusion activity correlates with LCH activity. Thus, IL-17A and IL-17A–stimulated DCs represent targets that may have clinical value in the treatment of LCH and other IL-17A–related inflammatory disorders.

Global frequentist fits to the CMSSM and NUHM1 using the MasterCode framework predicted M h ≃119 GeV in fits incorporating the ( g −2) μ constraint and ≃126 GeV without it. Recent results by ATLAS and CMS could be compatible with a Standard Model-like Higgs boson around M h ≃125 GeV. We use the previous MasterCode analysis to calculate the likelihood for a measurement of any nominal Higgs mass within the range of 115 to 130 GeV. Assuming a Higgs mass measurement at M h ≃125 GeV, we display updated global likelihood contours in the ( m 0 , m 1/2 ) and other parameter planes of the CMSSM and NUHM1, and present updated likelihood functions for , BR( B s → μ + μ − ) and the spin-independent dark matter cross section . The implications of dropping ( g −2) μ from the fits are also discussed. We furthermore comment on a hypothetical measurement of M h ≃119 GeV.

We update previous frequentist analyses of the CMSSM and NUHM1 parameter spaces to include the public results of searches for supersymmetric signals using ∼1/fb of LHC data recorded by ATLAS and CMS and ∼0.3/fb of data recorded by LHCb in addition to electroweak precision and B-physics observables. We also include the constraints imposed by the cosmological dark matter density and the XENON100 search for spin-independent dark matter scattering. The LHC data set includes ATLAS and CMS searches for jets + events and for the heavier MSSM Higgs bosons, and the upper limits on BR( B s → μ + μ − ) from LHCb and CMS. The absences of jets + signals in the LHC data favour heavier mass spectra than in our previous analyses of the CMSSM and NUHM1, which may be reconciled with ( g −2) μ if tan β ∼40, a possibility that is, however, under pressure from heavy Higgs searches and the upper limits on BR( B s → μ + μ − ). As a result, the p -value for the CMSSM fit is reduced to ∼15(38)%, and that for the NUHM1 to ∼16(38)%, to be compared with ∼9(49)% for the Standard Model limit of the CMSSM for the same set of observables (dropping ( g −2) μ ), ignoring the dark matter relic density. We discuss the sensitivities of the fits to the ( g −2) μ and BR( b → sγ ) constraints, contrasting fits with and without the ( g −2) μ constraint, and combining the theoretical and experimental errors for BR( b → sγ ) linearly or in quadrature. We present predictions for , BR( B s → μ + μ − ), M h and M A , and update predictions for spin-independent dark matter scattering, incorporating the uncertainty in the π -nucleon σ term Σ πN . Finally, we present predictions based on our fits for the likely thresholds for sparticle pair production in e + e − collisions in the CMSSM and NUHM1.

Misdiagnosis in acute dyspneic patients (ADP) has consequences on their outcome. Lung ultrasound (LUS) is an accurate tool to improve diagnostic performance. The main goal of this study was to assess the determinants of increased diagnostic accuracy using LUS. Multicentre, prospective, randomized study including emergency physicians and critical care physicians treating ADP on a daily basis. Each participant received three difficult clinical cases of ADP: one with only clinical data (OCD), one with only LUS data (OLD), and one with both. Ultrasound video loops of A, B and C profiles were associated with the cases. Which physician received what data for which clinical case was randomized. Physicians assessed the diagnostic probability from 0 to 10 for each possible diagnosis. The number of uncertain diagnoses (NUD) was the number of diagnoses with a diagnostic probability between 3 and 7, inclusive. Seventy-six physicians responded to the study cases (228 clinical cases resolved). Among the respondents, 28 (37%) were female, 64 (84%) were EPs, and the mean age was 37±8 years. The mean NUDs, respectively, when physicians had OCD, OLD, and both were 2.9±1.8, 2.2±1.7, 2.2±1.8 (p = 0.02). Ultrasound data and ultrasound frequency of use were the only variables related to the NUD. Higher frequency of ultrasound use by physicians decreased the number of uncertain diagnoses in difficult clinical cases with ultrasound data (OLD or associated with clinical data). LUS decreases the NUD in ADP. The ultrasound frequency of use decreased the NUD in ADP clinical cases with LUS data.

We make frequentist analyses of the CMSSM, NUHM1, VCMSSM and mSUGRA parameter spaces taking into account all the public results of searches for supersymmetry using data from the 2010 LHC run and the XENON100 direct search for dark matter scattering. The LHC data set includes ATLAS and CMS searches for events (with or without leptons) and for the heavier MSSM Higgs bosons, and the upper limit on BR( B s → μ + μ − ) including data from LHCb as well as CDF and DØ. The absence of signals in the LHC data favours somewhat heavier mass spectra than in our previous analyses of the CMSSM, NUHM1 and VCMSSM, and somewhat smaller dark matter scattering cross sections, all close to or within the pre-LHC 68% CL ranges, but does not impact significantly the favoured regions of the mSUGRA parameter space. We also discuss the impact of the XENON100 constraint on spin-independent dark matter scattering, stressing the importance of taking into account the uncertainty in the π -nucleon σ term Σ πN , which affects the spin-independent scattering matrix element, and we make predictions for spin-dependent dark matter scattering. Finally, we discuss briefly the potential impact of the updated predictions for sparticle masses in the CMSSM, NUHM1, VCMSSM and mSUGRA on future e + e − colliders.

The CMS and ATLAS Collaborations have recently published the results of initial direct LHC searches for supersymmetry analyzing ∼35/pb of data taken at 7 TeV in the centre of mass. We incorporate these results into a frequentist analysis of the probable ranges of parameters of simple versions of the minimal supersymmetric extension of the Standard Model (MSSM), namely the constrained MSSM (CMSSM), a model with common non-universal Higgs masses (NUHM1), the very constrained MSSM (VCMSSM) and minimal supergravity (mSUGRA). We present updated predictions for the gluino mass, , the light-Higgs boson mass, M h , BR( B s → μ + μ − ) and the spin-independent dark matter scattering cross section,  . The CMS and ATLAS data make inroads into the CMSSM, NUHM1 and VCMSSM (but not mSUGRA) parameter spaces, thereby strengthening previous lower limits on sparticle masses and upper limits on in the CMSSM and VCMSSM. The favoured ranges of BR( B s → μ + μ − ) in the CMSSM, VCMSSM and mSUGRA are close to the Standard Model, but considerably larger values of BR( B s → μ + μ − ) are possible in the NUHM1. Applying the CMS and ATLAS constraints improves the consistency of the model predictions for M h with the LEP exclusion limits.

We make a frequentist analysis of the parameter space of minimal supergravity (mSUGRA), in which, as well as the gaugino and scalar soft supersymmetry-breaking parameters being universal, there is a specific relation between the trilinear, bilinear and scalar supersymmetry-breaking parameters, A 0 = B 0 + m 0 , and the gravitino mass is fixed by m 3/2 = m 0 . We also consider a more general model, in which the gravitino mass constraint is relaxed (the VCMSSM). We combine in the global likelihood function the experimental constraints from low-energy electroweak precision data, the anomalous magnetic moment of the muon, the lightest Higgs boson mass  M h , B  physics and the astrophysical cold dark matter density, assuming that the lightest supersymmetric particle (LSP) is a neutralino. In the VCMSSM, we find a preference for values of m 1/2 and m 0 similar to those found previously in frequentist analyses of the constrained MSSM (CMSSM) and a model with common non-universal Higgs masses (NUHM1). On the other hand, in mSUGRA we find two preferred regions: one with larger values of both m 1/2 and m 0 than in the VCMSSM, and one with large m 0 but small m 1/2 . We compare the probabilities of the frequentist fits in mSUGRA, the VCMSSM, the CMSSM and the NUHM1: the probability that mSUGRA is consistent with the present data is significantly less than in the other models. We also discuss the mSUGRA and VCMSSM predictions for sparticle masses and other observables, identifying potential signatures at the LHC and elsewhere.

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.