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Action Plan B3 of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA) focuses on the integrated care of chronic diseases. Area 5 (Care Pathways) was initiated using chronic respiratory diseases as a model. The chronic respiratory disease action plan includes (1) AIRWAYS integrated care pathways (ICPs), (2) the joint initiative between the Reference site MACVIA-LR (Contre les MAladies Chroniques pour un VIeillissement Actif) and ARIA (Allergic Rhinitis and its Impact on Asthma), (3) Commitments for Action to the European Innovation Partnership on Active and Healthy Ageing and the AIRWAYS ICPs network. It is deployed in collaboration with the World Health Organization Global Alliance against Chronic Respiratory Diseases (GARD). The European Innovation Partnership on Active and Healthy Ageing has proposed a 5-step framework for developing an individual scaling up strategy: (1) what to scale up: (1-a) databases of good practices, (1-b) assessment of viability of the scaling up of good practices, (1-c) classification of good practices for local replication and (2) how to scale up: (2-a) facilitating partnerships for scaling up, (2-b) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. This strategy has already been applied to the chronic respiratory disease action plan of the European Innovation Partnership on Active and Healthy Ageing.
Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.
The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.
High-energy jets recoiling against missing transverse energy (MET) are powerful probes of dark matter at the LHC. Searches based on large MET signatures require a precise control of the \({Z(ν\overline{ν})}+\) jet background in the signal region. This can be achieved by taking accurate data in control regions dominated by \(Z(ℓ^+ℓ^−)+\) jet, \(W(ℓν)+\) jet and \(γ+\) jet production, and extrapolating to the \({Z(ν\overline{ν})}+\) jet background by means of precise theoretical predictions. In this context, recent advances in perturbative calculations open the door to significant sensitivity improvements in dark matter searches. In this spirit, we present a combination of state-of-the-art calculations for all relevant \(V+\) jets processes, including throughout NNLO QCD corrections and NLO electroweak corrections supplemented by Sudakov logarithms at two loops. Predictions at parton level are provided together with detailed recommendations for their usage in experimental analyses based on the reweighting of Monte Carlo samples. Particular attention is devoted to the estimate of theoretical uncertainties in the framework of dark matter searches, where subtle aspects such as correlations across different \(V+\) jet processes play a key role. The anticipated theoretical uncertainty in the \({Z(ν\overline{ν})}+\) jet background is at the few percent level up to the TeV range.
The clustering of different types of B-cell malignancies in families raises the possibility of shared aetiology. To examine this, we performed cross-trait linkage disequilibrium (LD)-score regression of multiple myeloma (MM) and chronic lymphocytic leukaemia (CLL) genome-wide association study (GWAS) data sets, totalling 11,734 cases and 29,468 controls. A significant genetic correlation between these two B-cell malignancies was shown (Rg = 0.4, P = 0.0046). Furthermore, four of the 45 known CLL risk loci were shown to associate with MM risk and five of the 23 known MM risk loci associate with CLL risk. By integrating eQTL, Hi-C and ChIP-seq data, we show that these pleiotropic risk loci are enriched for B-cell regulatory elements and implicate B-cell developmental genes. These data identify shared biological pathways influencing the development of CLL and, MM and further our understanding of the aetiological basis of these B-cell malignancies.
Multiple myeloma (MM) is a plasma cell malignancy with a significant heritable basis. Genome-wide association studies have transformed our understanding of MM predisposition, but individual studies have had limited power to discover risk loci. Here we perform a meta-analysis of these GWAS, add a new GWAS and perform replication analyses resulting in 9,866 cases and 239,188 controls. We confirm all nine known risk loci and discover eight new loci at 6p22.3 (rs34229995, P=1.31 × 10−8), 6q21 (rs9372120, P=9.09 × 10−15), 7q36.1 (rs7781265, P=9.71 × 10−9), 8q24.21 (rs1948915, P=4.20 × 10−11), 9p21.3 (rs2811710, P=1.72 × 10−13), 10p12.1 (rs2790457, P=1.77 × 10−8), 16q23.1 (rs7193541, P=5.00 × 10−12) and 20q13.13 (rs6066835, P=1.36 × 10−13), which localize in or near to JARID2, ATG5, SMARCD3, CCAT1, CDKN2A, WAC, RFWD3 and PREX1. These findings provide additional support for a polygenic model of MM and insight into the biological basis of tumour development.
Monoclonal gammopathy of undetermined significance is one of the most common pre-malignant disorders. IgG and IgA monoclonal gammopathy of undetermined significance are precursor conditions of multiple myeloma; light-chain monoclonal gammopathy of undetermined significance of light-chain multiple myeloma; and IgM monoclonal gammopathy of undetermined significance of Waldenstrom's macroglobulinemia and other lymphoproliferative disorders. Clonal burden, as determined by bone marrow plasma cell percentage or M-protein level, as well as biological characteristics, including heavy chain isotype and light chain production, are helpful in predicting risk of progression of monoclonal gammopathy of undetermined significance to symptomatic disease. Furthermore, alterations in the bone marrow microenvironment of monoclonal gammopathy of undetermined significance patients result in an increased risk of venous and arterial thrombosis, infections, osteoporosis, and bone fractures. In addition, the small clone may occasionally be responsible for severe organ damage through the production of a monoclonal protein that has autoantibody activity or deposits in tissues. These disorders are rare and often require therapy directed at eradication of the underlying plasma cell or lymphoplasmacytic clone. In this review, we provide an overview of the clinical relevance of monoclonal gammopathy of undetermined significance. We also give general recommendations of how to diagnose and manage patients with monoclonal gammopathy of undetermined significance.
Background
There are not enough clinical data from rare critical events to calculate statistics to decide if the management of actual events might be below what could reasonably be expected (i.e. was an outlier).
Objectives
In this project we used simulation to describe the distribution of management times as an approach to decide if the management of a simulated obstetrical crisis scenario could be considered an outlier.
Design
Twelve obstetrical teams managed 4 scenarios that were previously developed. Relevant outcome variables were defined by expert consensus. The distribution of the response times from the teams who performed the respective intervention was graphically displayed and median and quartiles calculated using rank order statistics.
Results
Only 7 of the 12 teams performed chest compressions during the arrest following the 'cannot intubate/cannot ventilate' scenario. All other outcome measures were performed by at least 11 of the 12 teams. Calculation of medians and quartiles with 95% CI was possible for all outcomes. Confidence intervals, given the small sample size, were large.
Conclusion
We demonstrated the use of simulation to calculate quantiles for management times of critical event. This approach could assist in deciding if a given performance could be considered normal and also point to aspects of care that seem to pose particular challenges as evidenced by a large number of teams not performing the expected maneuver. However sufficiently large sample sizes (i.e. from a national data base) will be required to calculate acceptable confidence intervals and to establish actual tolerance limits.