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Headwater streams represent a substantial proportion of river systems and many of them have intermittent flows due to their upstream position in the network. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The objective of this paper is to quantify how discrete (in space and time) field observations of flow intermittence help to extrapolate over time the daily probability of drying (defined at the regional scale). Two empirical models based on linear or logistic regressions have been developed to predict the daily probability of intermittence at the regional scale across France. Explanatory variables were derived from available daily discharge and groundwater-level data of a dense gauging/piezometer network, and models were calibrated using discrete series of field observations of flow intermittence. The robustness of the models was tested using an independent, dense regional dataset of intermittence observations and observations of the year 2017 excluded from the calibration. The resulting models were used to extrapolate the daily regional probability of drying in France: (i) over the period 2011-2017 to identify the regions most affected by flow intermittence; (ii) over the period 1989-2017, using a reduced input dataset, to analyse temporal variability of flow intermittence at the national level. The two empirical regression models performed equally well between 2011 and 2017. The accuracy of predictions depended on the number of continuous gauging/piezometer stations and intermittence observations available to calibrate the regressions. Regions with the highest performance were located in sedimentary plains, --where the monitoring network was dense and --where the regional probability of drying was the highest. Conversely, the worst performances were obtained in mountainous regions. Finally, temporal projections (1989-2016) suggested the highest probabilities of intermittence (> 35 %) in 1989-1991, 2003 and 2005. A high density of intermittence observations improved the information provided by gauging stations and piezometers to extrapolate the temporal variability of intermittent rivers and ephemeral streams.

Understanding large-scale patterns in flow intermittence is important for effective river management. The duration and frequency of zero-flow periods are associated with the ecological characteristics of rivers and have important implications for water resources management. We used daily flow records from 628 gauging stations on rivers with minimally modified flows distributed throughout France to predict regional patterns of flow intermittence. For each station we calculated two annual times series describing flow intermittence; the frequency of zero-flow periods (consecutive days of zero flow) in each year of record (FREQ; yr−1), and the total number of zero-flow days in each year of record (DUR; days). These time series were used to calculate two indices for each station, the mean annual frequency of zero-flow periods (mFREQ; yr−1), and the mean duration of zero-flow periods (mDUR; days). Approximately 20% of stations had recorded at least one zero-flow period in their record. Dissimilarities between pairs of gauges calculated from the annual times series (FREQ and DUR) and geographic distances were weakly correlated, indicating that there was little spatial synchronization of zero flow. A flow-regime classification for the gauging stations discriminated intermittent and perennial stations, and an intermittence classification grouped intermittent stations into three classes based on the values of mFREQ and mDUR. We used random forest (RF) models to relate the flow-regime and intermittence classifications to several environmental characteristics of the gauging station catchments. The RF model of the flow-regime classification had a cross-validated Cohen's kappa of 0.47, indicating fair performance and the intermittence classification had poor performance (cross-validated Cohen's kappa of 0.35). Both classification models identified significant environment-intermittence associations, in particular with regional-scale climate patterns and also catchment area, shape and slope. However, we suggest that the fair-to-poor performance of the classification models is because intermittence is also controlled by processes operating at scales smaller than catchments, such as groundwater-table fluctuations and seepage through permeable channels. We suggest that high spatial heterogeneity in these small-scale processes partly explains the low spatial synchronization of zero flows. While 20% of gauges were classified as intermittent, the flow-regime model predicted 39% of all river segments to be intermittent, indicating that the gauging station network under-represents intermittent river segments in France. Predictions of regional patterns in flow intermittence provide useful information for applications including environmental flow setting, estimating assimilative capacity for contaminants, designing bio-monitoring programs and making preliminary predictions of the effects of climate change on flow intermittence.

Key questions dominating contemporary ecological research and management concern interactions between biodiversity, ecosystem processes, and ecosystem services provision in the face of global change. This is particularly salient for freshwater biodiversity and in the context of river drying and flow-regime change. Rivers that stop flowing and dry, herein intermittent rivers, are globally prevalent and dynamic ecosystems on which the body of research is expanding rapidly, consistent with the era of big data. However, the data encapsulated by this work remain largely fragmented, limiting our ability to answer the key questions beyond a case-by-case basis. To this end, the Intermittent River Biodiversity Analysis and Synthesis (IRBAS; http://irbas.cesab.org) project has collated, analyzed, and synthesized data from across the world on the biodiversity and environmental characteristics of intermittent rivers. The IRBAS database integrates and provides free access to these data, contributing to the growing, and global, knowledge base on these ubiquitous and important river systems, for both theoretical and applied advancement. The IRBAS database currently houses over 2000 data samples collected from six countries across three continents, primarily describing aquatic invertebrate taxa inhabiting intermittent rivers during flowing hydrological phases. As such, there is room to expand the biogeographic and taxonomic coverage, for example, through addition of data collected during nonflowing and dry hydrological phases. We encourage contributions and provide guidance on how to contribute and access data. Ultimately, the IRBAS database serves as a portal, storage, standardization, and discovery tool, enabling collation, synthesis, and analysis of data to elucidate patterns in river biodiversity and guide management. Contribution creates high visibility for datasets, facilitating collaboration. The IRBAS database will grow in content as the study of intermittent rivers continues and data retrieval will allow for networking, meta-analyses, and testing of generalizations across multiple systems, regions, and taxa.

Mapped environmental classifications are defined using various procedures, but there has been little evaluation of the differences in their ability to discriminate variation in independent ecological characteristics. We tested the performance of environmental classifications of the streams and rivers of France that had been defined from the same environmental data using geographic regionalization and numerical classification of individual river valley segments. Test data comprised invertebrate assemblages, water chemistry, and hydrological indexes obtained from sites throughout France. Classification performance was measured by analysis of similarity (ANOSIM). Geometric regions defined by a regular grid and without regard to environmental variables and a posteriori classifications based on clustering the test datasets defined lower and upper bounds of performance for a given number of classes. Differences in classification performances were generally small. The ANOSIM statistics for the a posteriori classifications were around twice that of all environmental classifications, including geometrically defined regions. The hydro-ecoregions performed slightly better for the invertebrate data and the network classification performed slightly better for the chemistry and hydrological data. Our results indicate that environmental classifications that are defined using different procedures can be comparable in terms of their ability to discriminate variation of ecological characteristics and that alleged differences in performance arising from different classification procedures can be small relative to unexplained variation. We conclude that definition procedures might have little effect on the performance of large-scale environmental classifications and decisions over which procedures to use should be based primarily on pragmatic considerations.

Top-down methods for defining stream classifications are based on a conceptual model or expert-defined rules, whereas bottom-up methods use biological training data and statistical modelling. We compared the performance of six classification methods for explaining the taxonomic composition of invertebrate and fish assemblages recorded at 327 and 511 sites, respectively, distributed throughout France. Classification 1 and 2 were top-down classifications; The European Water Framework System A (WFDa,) and the French Hydro-ecoregions (HER 2). Four bottom-up classification procedures of increasing complexity were defined based on 11 variables that included watershed characteristics describing climate, topography, and geology, and site characteristics including elevation, bed slope and temperature. Classification 3 was defined using matrix correlation (MC) to select a combination of variable categories that produced the best discrimination of the observed taxonomic composition. Classification 4 and 5 were defined by clustering the sites based on their taxonomic data and then using linear discriminant analysis (LDA) and Random forests (RF) to discriminate the clusters based on the environmental variables. Classification 6 was defined using generalized dissimilarity modelling (GDM). Our hypothesis was that the bottom-up classifications would perform better because they flexibly accommodate complex relationships between compositional and environmental variation. We tested the classifications using the classification strength statistic (CS). The RF-based classification fitted the taxonomic patterns better than GDM or LDA and these latter classifications generally fitted better than the MC, WFDa or HER classifications. Cross validation analysis showed that differences in predictive CS (i.e. the CS statistics produced from sites not used in defining the classifications) were often significant. However, these differences were generally small. Gains in predictive performance of classifications appear to be small relative to the increase in complexity in the manner in which environmental variables are combined to define classes.

Key questions dominating contemporary ecological research and management concern interactions between biodiversity, ecosystem processes, and ecosystem services provision in the face of global change. This is particularly salient for freshwater biodiversity and in the context of river drying and flow-regime change. Rivers that stop flowing and dry, herein intermittent rivers, are globally prevalent and dynamic ecosystems on which the body of research is expanding rapidly, consistent with the era of big data. However, the data encapsulated by this work remain largely fragmented, limiting our ability to answer the key questions beyond a case-by-case basis. To this end, the Intermittent River Biodiversity Analysis and Synthesis (IRBAS; http://irbas.cesab.org) project has collated, analyzed, and synthesized data from across the world on the biodiversity and environmental characteristics of intermittent rivers. The IRBAS database integrates and provides free access to these data, contributing to the growing, and global, knowledge base on these ubiquitous and important river systems, for both theoretical and applied advancement. The IRBAS database currently houses over 2000 data samples collected from six countries across three continents, primarily describing aquatic invertebrate taxa inhabiting intermittent rivers during flowing hydrological phases. As such, there is room to expand the biogeographic and taxonomic coverage, for example, through addition of data collected during nonflowing and dry hydrological phases. We encourage contributions and provide guidance on how to contribute and access data. Ultimately, the IRBAS database serves as a portal, storage, standardization, and discovery tool, enabling collation, synthesis, and analysis of data to elucidate patterns in river biodiversity and guide management. Contribution creates high visibility for datasets, facilitating collaboration.The IRBAS database will grow in content as the study of intermittent rivers continues and data retrieval will allow for networking, meta-analyses, and testing of generalizations across multiple systems, regions, and taxa.

Patients with myocardial infarction and a high-sensitivity CRP level of 2 mg or more per liter were assigned to one of three canakinumab doses or placebo. The 150-mg dose, but not the 50-mg or 300-mg dose, led to a lower incidence of recurrent cardiovascular events.

Lowering the cost of assessing clinicians' competence could promote the scalability of evidence-based treatments such as cognitive behavioral therapy (CBT). This study examined the concordance between clinicians', supervisors' and independent observers' session-specific ratings of clinician competence in school-based CBT and treatment as usual (TAU). It also investigated the association between clinician competence and supervisory session observation and rater agreement. Fifty-nine school-based clinicians (90% female, 73% Caucasian) were randomly assigned to implement TAU or modular CBT for youth anxiety. Clinicians rated their confidence after each therapy session (n = 1898), and supervisors rated clinicians' competence after each supervision session (n = 613). Independent observers rated clinicians' competence from audio recordings (n = 395). Patterns of rater discrepancies differed between the TAU and CBT groups. Correlations with independent raters were low across groups. Clinician competence and session observation were associated with higher agreement among TAU, but not CBT, supervisors and clinicians. These results support the gold standard practice of obtaining independent ratings of adherence and competence in implementation contexts. Further development of measures and/or rater training methods for clinicians and supervisors is needed.

Background Investigator-initiated clinical studies (IITs) are crucial to generate reliable evidence that answers questions of day-to-day clinical practice. Many challenges make IITs a complex endeavour, for example, IITs often need to be multinational in order to recruit a sufficient number of patients. Recent studies highlighted that well-trained study personnel are a major factor to conduct such complex IITs successfully. As of today, however, no overview of the European training activities, requirements and career options for clinical study personnel exists. Methods To fill this knowledge gap, a survey was performed in all 11 member and observer countries of the European Clinical Research Infrastructure Network (ECRIN), using a standardised questionnaire. Three rounds of data collection were performed to maximize completeness and comparability of the received answers. The survey aimed to describe the landscape of academic training opportunities, to facilitate the exchange of expertise and experience among countries and to identify new fields of action. Results The survey found that training for Good Clinical Practice (GCP) and investigator training is offered in all but one country. A specific training for study nurses or study coordinators is also either provided or planned in ten out of eleven countries. A majority of countries train in monitoring and clinical pharmacovigilance and offer specific training for principal investigators but only few countries also train operators of clinical research organisations (CRO) or provide training for methodology and quality management systems (QMS). Minimal requirements for study-specific functions cover GCP in ten countries. Only three countries issued no requirements or recommendations regarding the continuous training of study personnel. Yet, only four countries developed a national strategy for training in clinical research and the career options for clinical researchers are still limited in the majority of countries. Conclusions There is a substantial and impressive investment in training and education of clinical research in the individual ECRIN countries. But so far, a systematic approach for (top-down) strategic and overarching considerations and cross-network exchange is missing. Exchange of available curricula and sets of core competencies between countries could be a starting point for improving the situation.

Statins are one of the most commonly prescribed drugs in clinical practice. They are usually well tolerated and effectively prevent cardiovascular events. Most adverse effects associated with statin therapy are muscle-related. The recent statement of the European Atherosclerosis Society (EAS) has focused on statin associated muscle symptoms (SAMS), and avoided the use of the term 'statin intolerance'. Although muscle syndromes are the most common adverse effects observed after statin therapy, excluding other side effects might underestimate the number of patients with statin intolerance, which might be observed in 10-15% of patients. In clinical practice, statin intolerance limits effective treatment of patients at risk of, or with, cardiovascular disease. Knowledge of the most common adverse effects of statin therapy that might cause statin intolerance and the clear definition of this phenomenon is crucial to effectively treat patients with lipid disorders. Therefore, the aim of this position paper was to suggest a unified definition of statin intolerance, and to complement the recent EAS statement on SAMS, where the pathophysiology, diagnosis and the management were comprehensively presented.