Analysis of the uncertainty in the estimates of regional PV power generation evaluated with the upscaling method

International audienceThe estimation of the regional photovoltaic (PV) power generation is an important step prior to the forecast of the PV power generation and its integration into the energy supply system. The large majority of PV plants not being measured in Germany, the total PV power generated in a region is commonly estimated by upscaling the power production of a set of reference PV plants to the entireness of the plants installed in the considered area. A given uncertainty can be expected in the estimation of the power generation of a PV plant with the upscaling method when the reference plants used have different configurations or weather conditions. To gain better insight into the performance of the upscaling method, its error has been analysed using power measurements of a set of 366 PV plants. The analysis allows an understanding of the mechanisms underlying the uncertainty of the upscaling method and quantifies its error for the test study considered. In the case study analysed, it could be shown that the quarter hourly RMSE 1 value decreases with an increasing number of reference plants and a decreasing number of un-metered plants. It could also be shown that even for a large number of reference plants, a variation of the RMSE between 0.01 and 0.025 kW/kWp can be observed, depending on the choice of the reference plants. It is shown that the average distance between a reference and unknown plant constitutes a good indicator of the performance of a set of reference plants, but that the match between the characteristics of the reference and unknown plant also plays an important role, which could not be quantified with the available dataset

Congenital hyperinsulinism: current trends in diagnosis and therapy

Congenital hyperinsulinism (HI) is an inappropriate insulin secretion by the pancreatic β-cells secondary to various genetic disorders. The incidence is estimated at 1/50, 000 live births, but it may be as high as 1/2, 500 in countries with substantial consanguinity. Recurrent episodes of hyperinsulinemic hypoglycemia may expose to high risk of brain damage. Hypoglycemias are diagnosed because of seizures, a faint, or any other neurological symptom, in the neonatal period or later, usually within the first two years of life. After the neonatal period, the patient can present the typical clinical features of a hypoglycemia: pallor, sweat and tachycardia. HI is a heterogeneous disorder with two main clinically indistinguishable histopathological lesions: diffuse and focal. Atypical lesions are under characterization. Recessive ABCC8 mutations (encoding SUR1, subunit of a potassium channel) and, more rarely, recessive KCNJ11 (encoding Kir6.2, subunit of the same potassium channel) mutations, are responsible for most severe diazoxide-unresponsive HI. Focal HI, also diazoxide-unresponsive, is due to the combination of a paternally-inherited ABCC8 or KCNJ11 mutation and a paternal isodisomy of the 11p15 region, which is specific to the islets cells within the focal lesion. Genetics and 18F-fluoro-L-DOPA positron emission tomography (PET) help to diagnose diffuse or focal forms of HI. Hypoglycemias must be rapidly and intensively treated to prevent severe and irreversible brain damage. This includes a glucose load and/or a glucagon injection, at the time of hypoglycemia, to correct it. Then a treatment to prevent the recurrence of hypoglycemia must be set, which may include frequent and glucose-enriched feeding, diazoxide and octreotide. When medical and dietary therapies are ineffective, or when a focal HI is suspected, surgical treatment is required. Focal HI may be definitively cured when the partial pancreatectomy removes the whole lesion. By contrast, the long-term outcome of diffuse HI after subtotal pancreatectomy is characterized by a high risk of diabetes, but the time of its onset is hardly predictable

Dix ans d'histoire culturelle

L'association pour le développement de l'histoire culturelle (ADHC) est née, en 1999, du constat de la place croissante, en même temps que problématique, de l'histoire culturelle dans l'historiographie contemporaine. Revendiquée par les uns, dénoncée par les autres, cette place méritait l'institution d'un lieu de rencontres où tous ceux qui se reconnaissent dans cette qualification pourraient échanger sur le fond et sur la forme de leur travail. L'association a tenu son premier congrès en 2000. Au terme d'une décennie et plus d'activité, il était temps de tirer le bilan et, comme il se doit, de tracer de nouvelles perspectives. Cette anthologie des conférences et tables rondes organisées dans le cadre du congrès annuel de l'association propose un panorama unique en son genre des propositions avancées par l'histoire culturelle en France et, dans une moindre mesure, à l'étranger depuis dix ans. Regroupés en sections thématiques (définitions et frontières, objets, regards et transferts, débats), ces textes rédigés par d'éminents spécialistes venus de divers horizons (historiens, sociologues, philosophes, historiens de l'art ou de la littérature) donnent à voir à la fois la permanence de certains questionnements et leur renouvellement

Conservation assessments and Red Listing of the endemic Moroccan flora (monocotyledons)

Morocco constitutes an important centre of plant diversity and speciation in the Mediterranean Basin. However, numerous species are threatened by issues ranging from human activities to global climatic change. In this study, we present the conservation assessments and Red Listing of the endemic Moroccan monocotyledons according to International Union for Conservation of Nature (IUCN) criteria and categories. For each species, we include basic taxonomic information, local names and synonyms, uses, a distribution map, extent of occurrence, area of occupancy, population size and trend, a description of habitats and ecological requirements, and a discussion of the threats affecting the species and habitats. We assessed the threatened status of the endemic Moroccan monocotyledons at the species level (59 species) using the IUCN Red List criteria and categories (Version 3.1). This study shows the high extinction risk to the Moroccan monocotyledon flora, with 95% of threatened species (20% Critically Endangered, 50% Endangered, 25% Vulnerable) and only 5% not threatened (2% Near Threatened and 3% Least Concern). The flora is thus of conservation concern, which is poorly recognized, both nationally and internationally. The study presents the first part and so far the only national IUCN Red Data List for a large group of Moroccan plants, and thus provides an overview of the threatened Moroccan flora. This IUCN Red List is an important first step towards the recognition of the danger to Moroccan biodiversity hotspots, conservation of threatened species and the raising of public awareness at national and international levels

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Association of the PHACTR1/EDN1 genetic locus with spontaneous coronary artery dissection

Background: Spontaneous coronary artery dissection (SCAD) is an increasingly recognized cause of acute coronary syndromes (ACS) afflicting predominantly younger to middle-aged women. Observational studies have reported a high prevalence of extracoronary vascular anomalies, especially fibromuscular dysplasia (FMD) and a low prevalence of coincidental cases of atherosclerosis. PHACTR1/EDN1 is a genetic risk locus for several vascular diseases, including FMD and coronary artery disease, with the putative causal noncoding variant at the rs9349379 locus acting as a potential enhancer for the endothelin-1 (EDN1) gene. Objectives: This study sought to test the association between the rs9349379 genotype and SCAD. Methods: Results from case control studies from France, United Kingdom, United States, and Australia were analyzed to test the association with SCAD risk, including age at first event, pregnancy-associated SCAD (P-SCAD), and recurrent SCAD. Results: The previously reported risk allele for FMD (rs9349379-A) was associated with a higher risk of SCAD in all studies. In a meta-analysis of 1,055 SCAD patients and 7,190 controls, the odds ratio (OR) was 1.67 (95% confidence interval [CI]: 1.50 to 1.86) per copy of rs9349379-A. In a subset of 491 SCAD patients, the OR estimate was found to be higher for the association with SCAD in patients without FMD (OR: 1.89; 95% CI: 1.53 to 2.33) than in SCAD cases with FMD (OR: 1.60; 95% CI: 1.28 to 1.99). There was no effect of genotype on age at first event, P-SCAD, or recurrence. Conclusions: The first genetic risk factor for SCAD was identified in the largest study conducted to date for this condition. This genetic link may contribute to the clinical overlap between SCAD and FMD