Résumé
FARADI est un protocole multicentrique national qui évalue l’efficacité et la tolérance de l’utilisation des fentanyls transmuqueux lors de la réalisation d’examen diagnostique (imagerie, ...TEP-scan) ou thérapeutique (radiothérapie). Ces examens peuvent induire, lors du placement des patients, des accès douloureux (AD) violents qui empêchent souvent la réalisation complète du programme. Ces AD peuvent être gérés par les citrates de fentanyl, mais ceux-ci n’ont, pour l’instant, l’autorisation de mise sur le marché (AMM) que pour les patients ayant déjà eu un traitement morphinique de fond, suffisant, depuis au moins une semaine. Nous souhaitons par cet essai de phase II, randomisé, démontrer que l’indication maitrisée et justifiée, l’évaluation et la surveillance permettent aux patients de réaliser leurs examens dans de bonnes conditions de tolérance. Une étude ancillaire inclura les patients qui répondent aux critères de l’AMMde ces produits.
The social impact of research has usually been analysed through the scientific outcomes produced under the auspices of the research. The growth of scholarly content in social media and the use of ...altmetrics by researchers to track their work facilitate the advancement in evaluating the impact of research. However, there is a gap in the identification of evidence of the social impact in terms of what citizens are sharing on their social media platforms. This article applies a social impact in social media methodology (SISM) to identify quantitative and qualitative evidence of the potential or real social impact of research shared on social media, specifically on Twitter and Facebook. We define the social impact coverage ratio (SICOR) to identify the percentage of tweets and Facebook posts providing information about potential or actual social impact in relation to the total amount of social media data found related to specific research projects. We selected 10 projects in different fields of knowledge to calculate the SICOR, and the results indicate that 0.43% of the tweets and Facebook posts collected provide linkages with information about social impact. However, our analysis indicates that some projects have a high percentage (4.98%) and others have no evidence of social impact shared in social media. Examples of quantitative and qualitative evidence of social impact are provided to illustrate these results. A general finding is that novel evidences of social impact of research can be found in social media, becoming relevant platforms for scientists to spread quantitative and qualitative evidence of social impact in social media to capture the interest of citizens. Thus, social media users are showed to be intermediaries making visible and assessing evidence of social impact.
The rise in wildfire frequency and severity across the globe has increased interest in secondary succession. However, despite the role of soil microbial communities in controlling biogeochemical ...cycling and their role in the regeneration of post‐fire vegetation, the lack of measurements immediately post‐fire and at high temporal resolution has limited understanding of microbial secondary succession. To fill this knowledge gap, we sampled soils at 17, 25, 34, 67, 95, 131, 187, 286, and 376 days after a southern California wildfire in fire‐adapted chaparral shrublands. We assessed bacterial and fungal biomass with qPCR of 16S and 18S and richness and composition with Illumina MiSeq sequencing of 16S and ITS2 amplicons. Fire severely reduced bacterial biomass by 47%, bacterial richness by 46%, fungal biomass by 86%, and fungal richness by 68%. The burned bacterial and fungal communities experienced rapid succession, with 5–6 compositional turnover periods. Analogous to plants, turnover was driven by “fire‐loving” pyrophilous microbes, many of which have been previously found in forests worldwide and changed markedly in abundance over time. Fungal secondary succession was initiated by the Basidiomycete yeast Geminibasidium, which traded off against the filamentous Ascomycetes Pyronema, Aspergillus, and Penicillium. For bacteria, the Proteobacteria Massilia dominated all year, but the Firmicute Bacillus and Proteobacteria Noviherbaspirillum increased in abundance over time. Our high‐resolution temporal sampling allowed us to capture post‐fire microbial secondary successional dynamics and suggest that putative tradeoffs in thermotolerance, colonization, and competition among dominant pyrophilous microbes control microbial succession with possible implications for ecosystem function.
Domoic acid was identified as the toxin responsible for an outbreak of human poisoning that occurred in Canada in 1987 following consumption of contaminated blue mussels Mytilus edulis. The poisoning ...was characterized by a constellation of clinical symptoms and signs. Among the most prominent features described was memory impairment which led to the name Amnesic Shellfish Poisoning ASP. Domoic acid is produced by certain marine organisms, such as the red alga Chondria armata and planktonic diatom of the genus Pseudo-nitzschia. Since 1987, monitoring programs have been successful in preventing other human incidents of ASP. However, there are documented cases of domoic acid intoxication in wild animals and outbreaks of coastal water contamination in many regions world-wide. Hence domoic acid continues to pose a global risk to the health and safety of humans and wildlife. Several mechanisms have been implicated as mediators for the effects of domoic acid. Of particular importance is the role played by glutamate receptors as mediators of excitatory neurotransmission and the demonstration of a wide distribution of these receptors outside the central nervous system, prompting the attention to other tissues as potential target sites. The aim of this document is to provide a comprehensive review of ASP, DOM induced pathology including ultrastructural changes associated to subchronic oral exposure, and discussion of key proposed mechanisms of cell/tissue injury involved in DOM induced brain pathology and considerations relevant to food safety and human health.
Domoic acid was identified as the toxin responsible for an outbreak of human poisoning that occurred in Canada in 1987 following consumption of contaminated blue mussels Mytilus edulis. The poisoning ...was characterized by a constellation of clinical symptoms and signs. Among the most prominent features described was memory impairment which led to the name Amnesic Shellfish Poisoning ASP. Domoic acid is produced by certain marine organisms, such as the red alga Chondria armata and planktonic diatom of the genus Pseudo-nitzschia. Since 1987, monitoring programs have been successful in preventing other human incidents of ASP. However, there are documented cases of domoic acid intoxication in wild animals and outbreaks of coastal water contamination in many regions world-wide. Hence domoic acid continues to pose a global risk to the health and safety of humans and wildlife. Several mechanisms have been implicated as mediators for the effects of domoic acid. Of particular importance is the role played by glutamate receptors as mediators of excitatory neurotransmission and the demonstration of a wide distribution of these receptors outside the central nervous system, prompting the attention to other tissues as potential target sites. The aim of this document is to provide a comprehensive review of ASP, DOM induced pathology including ultrastructural changes associated to subchronic oral exposure, and discussion of key proposed mechanisms of cell/tissue injury involved in DOM induced brain pathology and considerations relevant to food safety and human health.
Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. ...Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.
Many regions around the world are transitioning out of open access to groundwater resources in order to tackle over extraction by irrigated agriculture. However, the state has limited capacities to ...regulate effectively agricultural groundwater use. This paper evaluates how users and public authorities can co‐manage groundwater extraction by agriculture. Based on Schlager and Ostrom’s “bundle of rights” framework, the paper examines how decisions over access and use of groundwater resources are made in France, Spain and California. The three cases share a common strive to involve groundwater users in decisions over how to reduce over extraction of groundwater resources. However, different choices were made regarding the institutional set‐up for user involvement in allocation decisions. The paper presents the diversity of institutional arrangements influencing groundwater allocations in the three cases, and the relative involvement and power of users and public authorities over these institutions. The papers show the different ways in which “comanagement” may be made operational for managing agricultural groundwater use.
Key Points
The paper presents institutional mechanisms influencing allocation decisions in agriculture in France, Spain, and California
An evolution is observed toward greater oversight over operational rights of accessing and withdrawing groundwater
A system of check‐and‐balances are needed to ensure that all three sustainability pillars (economic, social, environmental) are met
Specification and tuning of errors from dynamical models are important issues in data assimilation. In this work, we propose an iterative expectation–maximization (EM) algorithm to estimate the ...model‐error covariances using classical extended and ensemble versions of the Kalman smoother. We show that, for additive model errors, the estimate of the error covariance converges. We also investigate other forms of model error, such as parametric or multiplicative errors. We show that additive Gaussian model error is able to compensate for non‐additive sources of error in the algorithms we propose. We also demonstrate the limitations of the extended version of the algorithm and recommend the use of the more robust and flexible ensemble version. This article is a proof of concept of the methodology with the Lorenz‐63 attractor. We developed an open‐source Python library to enable future users to apply the algorithm to their own nonlinear dynamical models.
We propose an iterative expectation–maximization algorithm to estimate the model‐error covariances using classical extended and ensemble versions of the Kalman smoother. We show convergence of the algorithm on the Lorenz‐63 model, including for non‐Gaussian model errors. We have developed an open‐source Python library to enable future users to apply the algorithm to their own nonlinear dynamical models.
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