On the visual detection of non-natural records in streamflow time series: challenges and impacts

Large datasets of long-term streamflow measurements are widely used to infer and model hydrological processes. However, streamflow measurements may suffer from what users can consider anomalies, i.e. non-natural records that may be erroneous streamflow values or anthropogenic influences that can lead to misinterpretation of actual hydrological processes. Since identifying anomalies is time consuming for humans, no study has investigated their proportion, temporal distribution, and influence on hydrological indicators over large datasets. This study summarizes the results of a large visual inspection campaign of 674 streamflow time series in France made by 43 evaluators, who were asked to identify anomalies falling under five categories, namely, linear interpolation, drops, noise, point anomalies, and other. We examined the evaluators' individual behaviour in terms of severity and agreement with other evaluators, as well as the temporal distributions of the anomalies and their influence on commonly used hydrological indicators. We found that inter-evaluator agreement was surprisingly low, with an average of 12 % of overlapping periods reported as anomalies. These anomalies were mostly identified as linear interpolation and noise, and they were more frequently reported during the low-flow periods in summer. The impact of cleaning data from the identified anomaly values was higher on low-flow indicators than on high-flow indicators, with change rates lower than 5 % most of the time. We conclude that the identification of anomalies in streamflow time series is highly dependent on the aims and skills of each evaluator, which raises questions about the best practices to adopt for data cleaning.</p

airGR et airGRteaching : deux outils Open-Source pour la modélisation hydrologique et l'enseignement de l'hydrologie

HIC 2018. 13th International Conference on Hydroinformatics, Palermo, ITA, 01-/07/2018 - 05/07/2018International audienceIn this paper, we present two R packages, airGR and airGRteaching, which are aimed at hydrological modeling. These two open-source packages allow for undertaking simplified simulations of surface flows on river catchments, based on lumped rainfall-runoff models that require few input data. airGR can be used for engineering, research and education purposes and is well indicated for experiments on large sample datasets. airGRteaching is an add-on to airGR and is especially dedicated to education, since one of its functionalities provides an interface on which parameters and models fluxes can be easily understood through an interactive visualization. airGRteaching also contains simplified functions that require less programming from users but do not allow for some more advanced experiments

<span style="" class="text typewriter">airGRteaching</span>: an open-source tool for teaching hydrological modeling with R

Hydrological modeling is at the core of most studies related to water, especially for anticipating disasters, managing water resources, and planning adaptation strategies. Consequently, teaching hydrological modeling is an important, but difficult, matter. Teaching hydrological modeling requires appropriate software and teaching material (exercises, projects); however, although many hydrological modeling tools exist today, only a few are adapted to teaching purposes. In this article, we present the airGRteaching package, which is an open-source R package. The hydrological models that can be used in airGRteaching are the GR rainfall-runoff models, i.e., lumped processed-based models, allowing streamflows to be simulated, including the GR4J model. In this package, thanks to a graphical user interface and a limited number of functions, numerous hydrological modeling exercises representing a wide range of hydrological applications are proposed. To ease its use by students and teachers, the package contains several vignettes describing complete projects that can be proposed to investigate various topics such as streamflow reconstruction, hydrological forecasting, and assessment of climate change impact.</p

Antiproliferative and proapoptotic actions of okra pectin on B16F10 melanoma cells

The proliferation and apoptosis of metastatic melanoma cells are often abnormal. We have evaluated the action of a pectic rhamnogalacturonan obtained by hot buffer extraction of okra pods (okra RG-I) on melanoma cell growth and survival in vitro. We added okra RG-I containing an almost pure RG-I carrying very short galactan side chains to 2D (on tissue culture polystyrene, tPS) and 3D (on poly(2-hydroxyethylmethacrylate), polyHEMA) cultures of highly metastatic B16F10 mouse melanoma cells. We then analyzed cell morphology, proliferation index, apoptosis, cell cycle progression and the expression of adhesion molecules. Immunostaining and western blotting were used to assay galectin-3 (Gal-3) protein. Incubation with okra RG-I altered the morphology of B16F10 cells and significantly reduced their proliferation on both tPS and polyHEMA. The cell cycle was arrested in G2/M, and apoptosis was induced, particularly in cells on polyHEMA. The expression of N-cadherin and 5 integrin subunit was reduced and that of the multifunctional carbohydrate-binding protein, Gal-3, at the cell membrane increased. These findings suggest that okra RG-I induces apoptosis in melanoma cells by interacting with Gal-3. As these interactions might open the way to new melanoma therapies, the next step will be to determine just how they occu

Using R in hydrology: a review of recent developments and future directions

The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of a typical hydrological workflow based on reproducible principles and packages for retrieval of hydro-meteorological data, spatial analysis, hydrological modelling, statistics, and the design of static and dynamic visualizations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation, and vignettes (long-form guides that illustrate how to use packages); the role of integrated development environments (IDEs); and the challenges of big data and parallel computing in hydrology. Lastly, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, application programming interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events

New guidelines for the prevention of imported malaria in France

International audienc

Technical note: Hydrology modelling R packages - A unified analysis of models and practicalities from a user perspective

Following the rise of R as a scientific programming language, the increasing requirement for more transferable research and the growth of data availability in hydrology, R packages containing hydrological models are becoming more and more available as an open-source resource to hydrologists. Corresponding to the core of the hydrological studies workflow, their value is increasingly meaningful regarding the reliability of methods and results. Despite package and model distinctiveness, no study has ever provided a comparison of R packages for conceptual rainfall-runoff modelling from a user perspective by contrasting their philosophy, model characteristics and ease of use. We have selected eight packages based on our ability to consistently run their models on simple hydrology modelling examples. We have uniformly analysed the exact structure of seven of the hydrological models integrated into these R packages in terms of conceptual storages and fluxes, spatial discretisation, data requirements and output provided. The analysis showed that very different modelling choices are associated with these packages, which emphasises various hydrological concepts. These specificities are not always sufficiently well explained by the package documentation. Therefore a synthesis of the package functionalities was performed from a user perspective. This synthesis helps to inform the selection of which packages could/should be used depending on the problem at hand. In this regard, the technical features, documentation, R implementations and computational times were investigated. Moreover, by providing a framework for package comparison, this study is a step forward towards supporting more transferable and reusable methods and results for hydrological modelling in R

Systèmes d'aide à la décision clinique pour la pédiatrie en santé

Effective, scalable and sustainable strategies to improve quality of care are needed to address the substantial burden of preventable deaths of children under-five in resource-constrained settings. Clinical decision support systems (CDSS), digital tools which generate recommendations for healthcare providers based on patient-specific information, show promise. By strengthening adherence to evidence-based assessment, diagnosis and management and generating high-quality data, CDSS can improve quality care - care that is effective, safe, people-centered, timely, equitable, integrated and efficient. Designing and implementing CDSS that deliver this impact is a complex and iterative process. We advocate for collaboration on developing and evaluating these tools to guide their implementation for maximal impact

Systèmes d’aide à la décision clinique pour la pédiatrie en santé globale [Paediatric digital clinical decision support for global health]

Effective, scalable and sustainable strategies to improve quality of care are needed to address the substantial burden of preventable deaths of children under-five in resource-constrained settings. Clinical decision support systems (CDSS), digital tools which generate recommendations for healthcare providers based on patient-specific information, show promise. By strengthening adherence to evidence-based assessment, diagnosis and management and generating high-quality data, CDSS can improve quality care - care that is effective, safe, people-centered, timely, equitable, integrated and efficient. Designing and implementing CDSS that deliver this impact is a complex and iterative process. We advocate for collaboration on developing and evaluating these tools to guide their implementation for maximal impact