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The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the largest flux limited sample of X-ray clusters at $0.35 < z < 0.65$. It was selected from ROSAT data in the 10,000 square degrees of overlap with BOSS, mapping a total number of 2770 high-z galaxy clusters. We present here the full results of the CFHT CODEX program on cluster mass measurement, including a reanalysis of CFHTLS Wide data, with 25 individual lensing-constrained cluster masses. We employ $lensfit$ shape measurement and perform a conservative colour-space selection and weighting of background galaxies. Using the combination of shape noise and an analytic covariance for intrinsic variations of cluster profiles at fixed mass due to large scale structure, miscentring, and variations in concentration and ellipticity, we determine the likelihood of the observed shear signal as a function of true mass for each cluster. We combine 25 individual cluster mass likelihoods in a Bayesian hierarchical scheme with the inclusion of optical and X-ray selection functions to derive constraints on the slope $��$, normalization $��$, and scatter $��_{\ln ��| ��}$ of our richness-mass scaling relation model in log-space: $\left = ����+ ��$, with $��= \ln (M_{200c}/M_{\mathrm{piv}})$, and $M_{\mathrm{piv}} = 10^{14.81} M_{\odot}$. We find a slope $��= 0.49^{+0.20}_{-0.15}$, normalization $ \exp(��) = 84.0^{+9.2}_{-14.8}$ and $��_{\ln ��| ��} = 0.17^{+0.13}_{-0.09}$ using CFHT richness estimates. In comparison to other weak lensing richness-mass relations, we find the normalization of the richness statistically agreeing with the normalization of other scaling relations from a broad redshift range ($0.0 37 pages, 12 figures

This study presents a multiparameter analysis of aerosol trends over the last 2 decades at regional and global scales. Regional time series have been computed for a set of nine optical, chemical-composition and mass aerosol properties by using the observations from several ground-based networks. From these regional time series the aerosol trends have been derived for the different regions of the world. Most of the properties related to aerosol loading exhibit negative trends, both at the surface and in the total atmospheric column. Significant decreases in aerosol optical depth (AOD) are found in Europe, North America, South America, North Africa and Asia, ranging from −1.2 % yr−1 to −3.1 % yr−1. An error and representativity analysis of the spatially and temporally limited observational data has been performed using model data subsets in order to investigate how much the observed trends represent the actual trends happening in the regions over the full study period from 2000 to 2014. This analysis reveals that significant uncertainty is associated with some of the regional trends due to time and space sampling deficiencies. The set of observed regional trends has then been used for the evaluation of 10 models (6 AeroCom phase III models and 4 CMIP6 models) and the CAMS reanalysis dataset and of their skills in reproducing the aerosol trends. Model performance is found to vary depending on the parameters and the regions of the world. The models tend to capture trends in AOD, the column Ångström exponent, sulfate and particulate matter well (except in North Africa), but they show larger discrepancies for coarse-mode AOD. The rather good agreement of the trends, across different aerosol parameters between models and observations, when co-locating them in time and space, implies that global model trends, including those in poorly monitored regions, are likely correct. The models can help to provide a global picture of the aerosol trends by filling the gaps in regions not covered by observations. The calculation of aerosol trends at a global scale reveals a different picture from that depicted by solely relying on ground-based observations. Using a model with complete diagnostics (NorESM2), we find a global increase in AOD of about 0.2 % yr−1 between 2000 and 2014, primarily caused by an increase in the loads of organic aerosols, sulfate and black carbon.

International audience; We investigated gene expression patterns in whole blood and fecal microbiota profile as potential predictors of immune response to vaccination, using healthy M. hyopneumoniae infection free piglets (n = 120). Eighty piglets received a dose of prophylactic antibiotics during the first two days of life, whereas the remaining 40 did not. Blood samples for RNA-Seq analysis were collected on experimental Day 0 (D0; 28 days of age) just prior to vaccination, D2, and D6 post-vaccination. A booster vaccine was given at D24. Fecal samples for microbial 16SrRNA sequencing were collected at 7 days of age, and at D0 and D35 post-vaccination. Pigs were ranked based on the levels of M. hyopneumoniae-specific antibodies in serum samples collected at D35, and groups of 'high' (HR) and 'low' (LR) responder pigs (n = 15 each) were selected. Prophylactic antibiotics did not influence antibody titer levels and differential expression analysis did not reveal differences between HR and LR at any time-point (FDR > 0.05); however, based on functional annotation with Ingenuity Pathway Analysis, D2 post-vaccination, HR pigs were enriched for biological terms relating to increased activation of immune cells. In contrast, the immune activation decreased in HR, 6 days post-vaccination. No significant differences were observed prior to vaccination (D0). Two days post-vaccination, multivariate analysis revealed that ADAM8, PROSER3, B4GALNT1, MAP7D1, SPP1, HTRA4, and ENO3 genes were the most promising potential biomarkers. At D0, OTUs annotated to Prevotella, CF21, Bacteroidales and S24-7 were more abundant in HR, whereas Fibrobacter, Paraprevotella, Anaerovibrio, [Prevotella], YRC22, and Helicobacter positively correlated with the antibody titer as well as MYL1, SPP1, and ENO3 genes. Our study integrates gene differential expression and gut microbiota to predict vaccine response in pigs. The results indicate that post-vaccination gene-expression and early-life gut microbiota profile could potentially predict vaccine response in pigs, and inform a direction for future research.

Understanding how populations of microbial pathogens and arthropod pests develop over time is critical for timely and effective intervention to control disease epidemics and pest infestations in agricultural production systems. Various elements including the pathogen or pest, host plant, natural enemies or competitors, environment, and human activity interact in complex ways, and some of these elements can be factored into mathematical models for pest population increase and disease progress. Greenhouse production affords a level of control over climate and growth environment, as well as the opportunity to release biological control agents, and thus the potential to influence pathogen and arthropod pest populations and their development to a much greater degree than in field production. To this end, thresholds for intervention must be derived based on the relationship between losses and yields weighed against the cost of intervention. In the context of integrated pest management, monitoring of pathogen and pest populations, as well as of the environment and the development of resistance to chemical pesticides such as fungicides and insecticides, is necessary to estimate the risk to the crop posed by these diseases and pests and to select the optimal method for their control.

© 2019 International Society for Heart and Lung Transplantation BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA)or 4-archetype (4AA)unsupervised algorithms to estimate rejection. In the present study we examined the stability of machine-learning algorithms in new biopsies, compared 3AA vs 4AA algorithms, assessed supervised binary classifiers trained on histologic or molecular diagnoses, created a report combining many scores into an ensemble of estimates, and examined possible automated sign-outs. METHODS: We studied 889 EMBs from 454 transplant recipients at 8 centers: the initial cohort (N = 331)and a new cohort (N = 558). Published 3AA algorithms derived in Cohort 331 were tested in Cohort 558, the 3AA and 4AA models were compared, and supervised binary classifiers were created. RESULTS: A‘lgorithms derived in Cohort 331 performed similarly in new biopsies despite differences in case mix. In the combined cohort, the 4AA model, including a parenchymal injury score, retained correlations with histologic rejection and DSA similar to the 3AA model. Supervised molecular classifiers predicted molecular rejection (areas under the curve [AUCs]>0.87)better than histologic rejection (AUCs <0.78), even when trained on histology diagnoses. A report incorporating many AA and binary classifier scores interpreted by 1 expert showed highly significant agreement with histology (p < 0.001), but with many discrepancies, as expected from the known noise in histology. An automated random forest score closely predicted expert diagnoses, confirming potential for automated signouts. CONCLUSIONS: Molecular algorithms are stable in new populations and can be assembled into an ensemble that combines many supervised and unsupervised estimates of the molecular disease states.

Research summary We develop a new conceptual framework to uncover governance-related determinants of family firms’ internationalization, building upon internalization theory. We assess how family firm governance features determine internationalization patterns on two key dimensions: location choice and operating mode. We focus on family governance characteristics that might drive sub-optimal internationalization patterns, and on removing such sub-optimality. We conclude that bifurcation bias, defined as the de facto differential treatment of family or heritage assets versus non-family assets, represents a critical family-firm specific barrier to achieving efficiency in international operations. In the short run, the key difference in international governance is between bifurcation-biased family MNEs and all other types of MNEs. In the longer run, inefficient, bifurcation biased decision making will make place for comparatively more efficient governance. Managerial summary Family firms are susceptible to bifurcation bias – a default preferential treatment of family members and resource bundles that hold positive emotional meaning to the family, i.e., heritage assets. Such preferential treatment contrasts with that afforded to professional, non-family managers and other resources, with which the founding family does not entertain a positive emotional connection. If left unremedied, bifurcation bias will lead to poor decisions in family-owned multinationals that undertake international expansion, in terms of the choices of which markets to enter and how to enter these. These types of dysfunctional decisions will lead to a decline in competitiveness as compared to non-family multinationals. Family firms should therefore identify and actively prevent bifurcation bias, by implementing the specific safeguarding strategies suggested in this study.

Cancer is a complex disease and the role played by innate lymphoid cells (ILCs) in cancer development has begun to be uncovered over recent years. We aim to provide an exhaustive summary of the knowledge acquired on the role of ILCs in cancer. ILCs are classified into 3 different categories, ILC1s, ILC2s, and ILC3s, each encompassing specific and unique functions. ILC1s exhibit NK cells characteristics and can exert anti-tumor functions, but surprisingly their IFNγ production is not associated with a better immune response. In response to TGF-β or IL-12, ILC1s were shown to exert pro-tumor functions and to favor tumor growth. ILC2s role in cancer immune response is dependent on cytokine context. The production of IL-13 by ILC2s is associated with a negative outcome in cancer. ILC2s can also produce IL-5, leading to eosinophil activation and an increased anti-tumor immune response in lung cancer. ILC3s produce IL-22, which could promote tumor growth. In contrast, ILC3s recognize tumor cells and facilitate leukocyte tumor entry, increasing anti-tumor immunity. In some contexts, ILC3s were found at the edge of tertiary lymphoid structures, associated with a good prognostic. We are at the dawn of our understanding of ILCs role in cancer. This review aims to thoroughly analyze existing data and to provide a comprehensive overview of our present knowledge on the impact of ILCs in cancer.

Abstract Marinesco-Sjögren syndrome is a rare human disorder caused by biallelic mutations in SIL1 characterized by cataracts in infancy, myopathy and ataxia, symptoms which are also associated with a novel disorder caused by mutations in INPP5K. While these phenotypic similarities may suggest commonalties at a molecular level, an overlapping pathomechanism has not been established yet. In this study, we present six new INPP5K patients and expand the current mutational and phenotypical spectrum of the disease showing the clinical overlap between Marinesco-Sjögren syndrome and the INPP5K phenotype. We applied unbiased proteomic profiling on cells derived from Marinesco-Sjögren syndrome and INPP5K patients and identified alterations in d-3-PHGDH as a common molecular feature. d-3-PHGDH modulates the production of l-serine and mutations in this enzyme were previously associated with a neurological phenotype, which clinically overlaps with Marinesco-Sjögren syndrome and INPP5K disease. As l-serine administration represents a promising therapeutic strategy for d-3-PHGDH patients, we tested the effect of l-serine in generated sil1, phgdh and inpp5k a+b zebrafish models, which showed an improvement in their neuronal phenotype. Thus, our study defines a core phenotypical feature underpinning a key common molecular mechanism in three rare diseases and reveals a common and novel therapeutic target for these patients.