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60. Combined use of environmental and spectral variables with vegetation archives for large-scale modeling of grassland habitats

Author

Laurence Hubert-Moy, Léa Panhelleux, Sébastien Rapinel, Arnault Lalanne, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), and Université de Nantes (UN)-Université de Nantes (UN)

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Geography, Planning and Development, 010603 evolutionary biology, 01 natural sciences, Grassland, Ecosystem services, Urbanization, Earth and Planetary Sciences (miscellaneous), 0105 earth and related environmental sciences, 2. Zero hunger, geography, geography.geographical_feature_category, business.industry, Environmental resource management, [SHS.GEO]Humanities and Social Sciences/Geography, Vegetation, 15. Life on land, Habitat, 13. Climate action, Remote sensing (archaeology), [SDE]Environmental Sciences, Threatened species, General Earth and Planetary Sciences, Environmental science, business, Scale model
Abstract

International audience; Grassland habitats provide many ecosystem services but are threatened by agricultural intensification and urbanization. While the lack of accurate and comprehensive inventories at the national scale makes them difficult to manage, advances in spatial modeling using open remote sensing data and open-source software, as well as the increasing use of ecological archives, provide new perspectives for mapping natural habitats. In this context, this study evaluated the contribution of spectral and environmental variables to discriminate and then map grassland habitats throughout France. To this end, 92 spectral variables derived from moderate-resolution imaging spectroradiometer data, 19 bioclimatic variables derived from WorldClim data, 4 topographic variables derived from the European Union Digital Elevation Model (DEM), and 8 soil variables derived from SoilGrids data were combined at a spatial resolution of 250 m. Reference plots that characterized 6 and 21 grassland ecosystems at European Nature Information System (EUNIS) levels 2 (broad habitats) and 3 (habitats), respectively, were collected from vegetation archives. We first performed descriptive analysis that included habitat description, ordination, and pairwise separability. We then performed predictive analysis of grassland habitats using a cross-validated random forest model that included a spatial constraint. While environmental and spectral variables characterized most grassland habitats well and consistently, some confusion occurred between habitats with similar abiotic conditions. The main grassland habitat types were correctly mapped at EUNIS level 2 ( F1 score = 0.68), but not at EUNIS level 3 ( F1 score = 0.52). In addition, the two variables that contributed most to the model were the near-infrared spectral band in spring and the minimum temperature of the coldest month. The model’s prediction at EUNIS level 2 for mainland France provides the map of grassland habitats at a new spatial scale.

Published
2021

71. Role of land use and land cover in residential exposures to agricultural pesticide models

Author

Clémence Vannier, Cécile Chevrier, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Department of Geography, College of Science, University of Canterbury - Te Whare Wananga O Waitaha, Christchurch, New Zealand, EHESP-Irset (EHESP-Irset), École des Hautes Études en Santé Publique [EHESP] (EHESP), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), and University of Canterbury [Christchurch]

Subjects
medicine.medical_specialty, Pesticide drift, Health, Toxicology and Mutagenesis, Population, Land cover, 010501 environmental sciences, 01 natural sciences, mitigation, remote sensing, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, rural population, 030212 general & internal medicine, Pesticides, education, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, education.field_of_study, Land use, business.industry, Public health, Environmental resource management, Public Health, Environmental and Occupational Health, Agriculture, [SHS.GEO]Humanities and Social Sciences/Geography, General Medicine, 15. Life on land, Pesticide, Pollution, 3. Good health, 13. Climate action, Remote sensing (archaeology), [SDE]Environmental Sciences, Environmental science, business, residential exposure
Abstract

Exposure of the general population to pesticides, especially in agricultural areas, is a major public health concern. This review analyses the role of Land Use and Land Cover (LULC) in Residential Exposure to Agricultural Pesticides (REAP) and how it is measured and modelled. Some epidemiological studies have shown that basic LULC variables, such as distance to a crop and field size, are relevant for explaining REAP. However, the potential of LULC mitigation elements, such as vegetation barriers, grassy strips and buffer zones, to mitigate REAP has been poorly studied. The availability of recent low-cost and high-quality geospatial data enables REAP models to include alternative and more precise LULC variables. This review also highlights the need for (i) generic environmental sampling protocols, (ii) exposure and spraying datasets and (iii) assessment of the mitigation capacity of LULC to improve REAP modelling significantly.

Published
2020

72. Evaluation of Sentinel-1 & 2 time series for predicting wheat and rapeseed phenological stages

Author

Jacques Baudry, Audrey Mercier, Vincent Le Roux, Julie Betbeder, Jérôme Lacoux, Laurence Hubert-Moy, Fabien Spicher, David Roger, Signalisation, radiobiologie et cancer, Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Centre National de la Recherche Scientifique (CNRS)-Université de Picardie Jules Verne (UPJV), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Rapeseed, F40 - Écologie végétale, 010504 meteorology & atmospheric sciences, Télédétection, Triticum aestivum, 0211 other engineering and technologies, Context (language use), 02 engineering and technology, 01 natural sciences, Normalized Difference Vegetation Index, Crop, Computers in Earth Sciences, Leaf area index, Engineering (miscellaneous), ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, 2. Zero hunger, Phenology, Brassica napus, food and beverages, [SHS.GEO]Humanities and Social Sciences/Geography, Spectral bands, Vegetation, 15. Life on land, technique de prévision, Atomic and Molecular Physics, and Optics, Computer Science Applications, Agronomy, 13. Climate action, [SDE]Environmental Sciences, Environmental science, U30 - Méthodes de recherche, Phénologie
Abstract

In the global context of population growth and climate change, monitoring crops is necessary to sustain agriculture and conserve natural resources. While many studies have demonstrated the ability of optical and SAR remotely sensed data to estimate crop parameters, these data have not been compared or combined to predict crop phenological stages. Despite the high sensitivity of SAR polarimetric data to crop phenological stages, no study has used high temporal resolution data. The freely available SAR Sentinel-1 (S-1) and optical Sentinel-2 (S-2) time series provide a unique opportunity to monitor crop phenology at a high spatial resolution on a weekly basis. The objective of this study was to evaluate the potential of S-1 data alone, S-2 data alone, and their combined use to predict wheat and rapeseed phenological stages. We first analyzed temporal profiles of spectral bands, vegetation indices and leaf area index (LAI) derived from S-2 data, and backscattering coefficients and polarimetric indicators derived from S-1 data. Then, an incremental procedure was used to estimate the contribution of S-1 and S-2 features to the classification of principal and secondary phenological stages of wheat and rapeseed. Results for both crops showed that the classification obtained with combined S-1 & 2 data (mean kappa = 0.53–0.82 and 0.74–0.92 for wheat and rapeseed, respectively) was more accurate than those obtained with S-2 data alone (mean kappa = 0.54–0.75 and 0.67–0.86 for wheat and rapeseed, respectively) or S-1 data alone (mean kappa = 0.48–0.61 and 0.61–0.64 for wheat and rapeseed, respectively). Combining S-1 & 2 data allowed better identification of the beginning and end of tillering for wheat and the beginning and end of ripening for rapeseed. Among S-2 features, the most important were LAI for wheat and the NDVI for rapeseed. For both crops, the S2REP index was one of the most important vegetation indices, while MCARI was less important. For S-1 features, results highlighted the large contribution of the backscatter ratio (σ◦VH:σ◦VV) and the value of using polarimetric indicators (Shannon entropy and span) to monitor rapeseed and wheat phenology. The main novelties of this work are the use of S-1 polarimetric indicators to identify phenological stages of wheat and rapeseed and the mapping of wheat and rapeseed secondary phenological stages using remotely sensed data.

Published
2020

73. Recurrent de novo mutations in CLDN5 induce an anion-selective blood-brain barrier and alternating hemiplegia

Author

Yosuke Hashimoto, Karine Poirier, Nathalie Boddaert, Laurence Hubert, Melodie Aubart, Anna Kaminska, Marianne Alison, Isabelle Desguerre, Arnold Munnich, and Matthew Campbell

Subjects
Anions, Tight Junction Proteins, Blood-Brain Barrier, Mutation, Humans, Neurology (clinical), Claudin-5, Tight Junctions
Abstract

Claudin-5 is the most enriched tight junction protein at the blood–brain barrier. Perturbations in its levels of expression have been observed across numerous neurological and neuropsychiatric conditions; however, pathogenic variants in the coding sequence of the gene have never been reported previously. Here, we report the identification of a novel de novo mutation (c.178G>A) in the CLDN5 gene in two unrelated cases of alternating hemiplegia with microcephaly. This mutation (G60R) lies within the first extracellular loop of claudin-5 and based on protein modelling and sequence alignment, we predicted it would modify claudin-5 to become an anion-selective junctional component as opposed to a purely barrier-forming protein. Generation of stably transfected cell lines expressing wild-type or G60R claudin-5 showed that the tight junctions could still form in the presence of the G60R mutation but that the barrier against small molecules was clearly attenuated and displayed higher Cl− ion permeability and lower Na+ permeability. While this study strongly suggests that CLDN5 associated alternating hemiplegia is a channelopathy, it is also the first study to identify the conversion of the blood–brain barrier to an anion-selective channel mediated by a dominant acting variant in CLDN5.

Published
2022
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77. Vingt ans de consultations de génétique clinique sur site dans les hôpitaux de jour pour les personnes atteintes de troubles du spectre autistique de la région parisienne

Author

Jean-Pierre Malen, Moise Assouline, Caroline Demily, Gilles Roland-Manuel, Claude Besmond, Lisa Frugère, Emeline Throo, Laurence Hubert, Mélanie Ferreri, Sylvain Hanein, Valérie Malan, Nathalie Boddaert, Céline Vidal, Charlyne Duwime, Giulia Barcia, and Arnold Munnich

Subjects
Psychiatry and Mental health, Clinical Psychology, Political science, General Medicine, Humanities, Applied Psychology, General Biochemistry, Genetics and Molecular Biology
Abstract

Malgré les avancées de la recherche, un grand nombre de patients atteints de troubles du spectre autistique (TSA) n’ont pas accès aux explorations aujourd’hui disponibles, du fait d’idées reçues, de l’insuffisance des structures à même de les explorer et de l’inadaptation des consultations hospitalières à leurs troubles du comportement. Pour améliorer l’accès aux soins et au progrès des connaissances, nous avons inversé le paradigme et offrons depuis 20 ans des consultations de génétique clinique sur site dans les hôpitaux de jour et les institutions spécialisées de la région parisienne. Depuis 1998, une équipe mobile de génétique médicale propose aux patients et à leurs familles des consultations dans leur environnement habituel. L’unité mobile opère sous l’égide de l’hôpital universitaire Necker Enfants-Malades, qui leur donne accès aux services de biochimie, de cytogénétique moléculaire et de séquençage de nouvelle génération (NGS). En vingt ans, 502 patients appartenant à 26 institutions ont bénéficié de consultations sur site et d’un accès aux plateformes de génétique moléculaire. Moins de 1 % des parents ont décliné la proposition. Des affections génétiques ont été identifiées chez 71 patients présentant un TSA : anomalies cytogénétiques causales (34/388 : 8,8 % ; de novo : 19, héritées : 4), X Fragile (4/312 : 1,3 %) et mutations monogéniques reconnues responsables de TSA (33/141 ; 23,4 % : de novo : 23 ; héritées : 10, dont 5 liées à l’X et 5 récessives autosomiques). L’IRM cérébrale a été possible chez 347 patients et considérée comme anormale chez 42 % d’entre eux (146/347). Tous les patients diagnostiqués présentaient un TSA atypique ou syndromique, avec déficience intellectuelle modérée à sévère. Grâce à ce mode d’intervention, un grand nombre de consultations manquantes ont été rattrapées et les familles ont pu bénéficier d’une consultation de génétique. Eu égard aux contraintes imposées par les troubles du comportement dans les TSA, les consultations sur site constituent, pour les patients et leurs apparentés, un moyen d’améliorer l’accès aux soins et de réduire le risque de méconnaissance d’une pathologie organique à présentation psychiatrique.

Published
2019

80. Biallelic variants in

Author

Kyle, Thompson, Lucas, Bianchi, Francesca, Rastelli, Florence, Piron-Prunier, Sophie, Ayciriex, Claude, Besmond, Laurence, Hubert, Magalie, Barth, Inês A, Barbosa, Charu, Deshpande, Manali, Chitre, Sarju G, Mehta, Eric J M, Wever, Pascale, Marcorelles, Sandra, Donkervoort, Dimah, Saade, Carsten G, Bönnemann, Katherine R, Chao, Chunyu, Cai, Susan T, Iannaccone, Andrew F, Dean, Robert, McFarland, Frédéric M, Vaz, Agnès, Delahodde, Robert W, Taylor, and Agnès, Rötig

Abstract

Mitochondrial disorders are clinically and genetically heterogeneous, with variants in mitochondrial or nuclear genes leading to varied clinical phenotypes.

Published
2021

81. Sentinel-2 images reveal functional biophysical heterogeneities in crop mosaics

Author

Audrey Mercier, Laurence Hubert-Moy, Jacques Baudry, Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), and Université de Nantes (UN)-Université de Nantes (UN)

Subjects
2. Zero hunger, Future studies, 010504 meteorology & atmospheric sciences, Ecology, Phenology, Ecology (disciplines), Geography, Planning and Development, Biodiversity, 04 agricultural and veterinary sciences, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, 01 natural sciences, Field (geography), Crop, Geography, 13. Climate action, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Physical geography, Landscape ecology, Scale (map), ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Nature and Landscape Conservation
Abstract

Identifying landscape structure and understanding its functions are crucial for biological control. However, the relationship between the crop mosaic phenological heterogeneity and crop phenology at the field scale remains a blind spot. This hinders the understanding of crop dynamics and associated biodiversity. Remote sensing images are commonly used in landscape ecology because they allow for regular fine-scale monitoring of large areas. The objective of this study was to understand the influence of biophysical heterogeneity of the crop mosaic on crop phenology and biodiversity using optical satellite images. Indicators of wheat phenology and biophysical heterogeneity were derived from Sentinel-2 images using the Weighted Difference Vegetation Index (WDVI). A landscape gradient was studied in 2017 and 2018 using six study sites in Brittany, Picardy (France) and Wallonia (Belgium). First, we analyzed relationships among the crop mosaic, landscape grain and biophysical heterogeneity. Second, we studied effects of biophysical heterogeneity on wheat phenology. Last, we used WDVI to estimate the distribution of two carabid beetle species. The biophysical heterogeneity derived from WDVI correlated strongly with the crop mosaic gradient and landscape grain. Biophysical heterogeneity appeared to benefit wheat growth in fine-grain landscapes but disadvantage it in coarse-grain landscapes during the stem-extension and ripening periods. Biophysical heterogeneity estimated the distribution of carabid beetle species accurately. The biophysical heterogeneity metric is continuous, consistent across locations and crop types and enables to address ecological issues using freely available satellite images covering the Earth. Future studies could use this metric to study the dynamics of species.

Published
2021

84. Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images

Author

Corgne, Amit Kumar Sharma, Laurence Hubert-Moy, Sriramulu Buvaneshwari, Muddu Sekhar, Laurent Ruiz, Hemanth Moger, Soumya Bandyopadhyay, and Samuel

Subjects
groundwater irrigation, optical remote sensing, NDVI, support vector machine classifier, Kabini critical zone observatory
Abstract

Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study’s main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014–2016 using ground observations. Kappa coefficients ranged from 0.81–0.96 for 2014–2015 and 0.62–0.89 for 2015–2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions.

Published
2021
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86. Riparian trees genera identification based on leaf-on/leaf-off airborne laser scanner data and machine learning classifiers in northern France

Author

Marianne Laslier, A. Ba, Laurence Hubert-Moy, Simon Dufour, Institut d'Economie Rurale du Mali, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), and Université de Nantes (UN)-Université de Nantes (UN)

Subjects
geography, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Laser scanning, 0211 other engineering and technologies, Forestry, [SHS.GEO]Humanities and Social Sciences/Geography, 02 engineering and technology, 15. Life on land, 01 natural sciences, Natural (archaeology), Ecosystem services, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Riparian forest, Identification (biology), [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Riparian zone
Abstract

Riparian forests are valuable environments delivering multiples ecological services. Because they face both natural and anthropogenic constraints, riparian forests need to be accurately mapped in t...

Published
2019

87. De novo SCAMP5 mutation causes a neurodevelopmental disorder with autistic features and seizures

Author

Karine Poirier, Laure Villoing-Gaudé, Laurence Hubert, Marlène Rio, Nathalie Boddaert, Magda Cannata Serio, Claude Besmond, Anna Kaminska, Matias Simons, Arnold Munnich, and Stanislas Lyonnet

Subjects
0301 basic medicine, Genetics, Mutation, Genetic heterogeneity, Biology, medicine.disease_cause, medicine.disease, biology.organism_classification, Phenotype, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Neurodevelopmental disorder, Membrane protein, Tetraspanin, medicine, Missense mutation, Drosophila melanogaster, 030217 neurology & neurosurgery, Genetics (clinical)
Abstract

BackgroundAutistic spectrum disorders (ASDs) with developmental delay and seizures are a genetically heterogeneous group of diseases caused by at least 700 different genes. Still, a number of cases remain genetically undiagnosed.ObjectiveThe objective of this study was to identify and characterise pathogenic variants in two individuals from unrelated families, both of whom presented a similar clinical phenotype that included an ASD, intellectual disability (ID) and seizures.MethodsWhole-exome sequencing was used to identify pathogenic variants in the two individuals. Functional studies performed in the Drosophila melanogaster model was used to assess the protein function in vivo.ResultsProbands shared a heterozygous de novo secretory carrier membrane protein (SCAMP5) variant (NM_001178111.1:c.538G>T) resulting in a p.Gly180Trp missense variant. SCAMP5 belongs to a family of tetraspanin membrane proteins found in secretory and endocytic compartments of neuronal synapses. In the fly SCAMP orthologue, the p.Gly302Trp genotype corresponds to human p.Gly180Trp. Western blot analysis of proteins overexpressed in the Drosophila fat body showed strongly reduced levels of the SCAMP p.Gly302Trp protein compared with the wild-type protein, indicating that the mutant either reduced expression or increased turnover of the protein. The expression of the fly homologue of the human SCAMP5 p.Gly180Trp mutation caused similar eye and neuronal phenotypes as the expression of SCAMP RNAi, suggesting a dominant-negative effect.ConclusionOur study identifies SCAMP5 deficiency as a cause for ASD and ID and underscores the importance of synaptic vesicular trafficking in neurodevelopmental disorders.

Published
2019

88. Impact of on-site clinical genetics consultations on diagnostic rate in children and young adults with autism spectrum disorder

Author

Caroline Demily, Emeline Throo, Mélanie Ferreri, Laurence Hubert, Nathalie Boddaert, Lisa Frugère, Sylvain Hanein, Valérie Malan, Arnold Munnich, Jean-Christophe Thalabard, Jean-Pierre Malen, Claude Besmond, Charlyne Duwime, Céline Vidal, Gilles Roland-Manuel, Moise Assouline, and Giulia Barcia

Subjects
Adult, Male, Pediatrics, medicine.medical_specialty, Gene panel, Adolescent, DNA Copy Number Variations, Genetic counseling, Genetics, Medical, Neurogenetics, Microarray, Compound heterozygosity, Copy number variant, lcsh:RC346-429, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Developmental Neuroscience, Intellectual disability, Medicine, Humans, Autism spectrum disorder, Child, Molecular Biology, Referral and Consultation, lcsh:Neurology. Diseases of the nervous system, 030304 developmental biology, 0303 health sciences, business.industry, Research, medicine.disease, Magnetic Resonance Imaging, Human genetics, Fragile X syndrome, Psychiatry and Mental health, Sequence variant, Genetic diagnosis, Next-generation sequencing, Medical genetics, Female, business, 030217 neurology & neurosurgery, Developmental Biology
Abstract

Background Neurogenetics investigations and diagnostic yield in patients with autism spectrum disorder (ASD) have significantly improved over the last few years. Yet, many patients still fail to be systematically investigated. Methods To improve access to services, an ambulatory team has been established since 1998, delivering on-site clinical genetics consultations and gradually upgrading services to 502 children and young adults with ASD in their standard environment across 26 day-care hospitals and specialized institutions within the Greater Paris region. The evaluation included a clinical genetics consultation, screening for fragile X syndrome, metabolic workup, chromosomal microarray analysis, and, in a proportion of patients, next-generation sequencing of genes reported in ASD and other neurodevelopmental disorders. Results Fragile X syndrome and pathogenic copy number variants (CNVs) accounted for the disease in 10% of cases, including 4/312 (1.3%) with fragile X syndrome and 34/388 (8.8%) with pathogenic CNVs (19 de novo and 4 inherited). Importantly, adding high-throughput resequencing of reported intellectual disability/ASD genes to the screening procedure had a major impact on diagnostic yield in the 141 patients examined most recently. Pathogenic or likely pathogenic sequence variants in 27 disease genes were identified in 33/141 patients (23.4%; 23 were de novo and 10 inherited, including five X-linked and five recessive compound heterozygous variants). Diagnosed cases presented atypical and/or syndromic ASD with moderate to severe intellectual disability. The diagnostic yield of fragile X syndrome and array CGH testing combined with next-generation sequencing was significantly higher than fragile X syndrome and array CGH alone (p value 0.009). No inborn errors of metabolism were detected with the metabolic screening. Conclusion Based on the diagnostic rate observed in this cohort, we suggest that a stepwise procedure be considered, first screening pathogenic CNVs and a limited number of disease genes in a much larger number of patients, especially those with syndromic ASD and intellectual disability. Electronic supplementary material The online version of this article (10.1186/s13229-019-0284-2) contains supplementary material, which is available to authorized users.

Published
2019

89. Agriculture and Wetland Applications

Author

J.D. Ballester-Berman, Irena Hajnsek, Shane R. Cloude, R. Brooks, Thomas Jagdhuber, Juan M. Lopez-Sanchez, Henning Skriver, Gulab Singh, G. Gosselin, Samuel Corgne, Shimon Wdowinski, Eric Pottier, Jiali Shang, Ridha Touzi, Yoshio Yamaguchi, H. McNairn, Fernando Vicente-Guijalba, Cecile Marechal, Laurence Hubert-Moy, Hajnsek, Irena, Desnos, Yves-Louis, UNIVERSITY OF ALICANTE ESP, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Carleton University, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut d'Electronique et de Télécommunications de Rennes (IETR), Centre National de la Recherche Scientifique (CNRS)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Institut d'Électronique et des Technologies du numéRique (IETR), Université de Nantes (UN)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université (NU), Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Ecole Supérieure d'Electricité - SUPELEC (FRANCE)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN)

Subjects
Min–max scaling, Fagus sylvatica, 0208 environmental biotechnology, 0211 other engineering and technologies, Polarimetry, Classification scheme, Wetland, Chloroplast markers, 02 engineering and technology, Genetic diversity, Nuclear markers, Microsatellites, Water content, ComputingMilieux_MISCELLANEOUS, Demography, 021101 geological & geomatics engineering, Remote sensing, geography, Heterozygosity, geography.geographical_feature_category, business.industry, Single nucleotide polymorphisms (SNPs), Vegetation, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, 020801 environmental engineering, Agriculture, Environmental science, Stage (hydrology), [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, Relevant information
Abstract

Based on experimental results, this chapter describes applications of SAR polarimetry to extract relevant information on agriculture and wetland scenarios by exploiting differences in the polarimetric signature of different scatterers, crop types and their development stage depending on their physical properties. Concerning agriculture, crop type mapping, soil moisture estimation and phenology estimation are reviewed, as they are ones with a clear benefit of full polarimetry over dual or single polarimetry. For crop type mapping, supervised or partially unsupervised classification schemes are used. Phenology estimation is treated as a classification problem as well, by regarding the different stages as different classes. Soil moisture estimation makes intensive use of scattering models, in order to separate soil and vegetation scattering and to invert for soil moisture from the isolated ground component. Then, applications of SAR polarimetry to wetland monitoring are considered that include the delineation of their extent and their characterisation by means of polarimetric decompositions. In the last section of the chapter, the use of a SAR polarimetric decomposition is shown for the assessment of the damages consequential to earthquakes and tsunamis.

Published
2021

90. SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates

Author

Sébastien Rapinel, Elodie Fabre, Julien Denize, Julie Betbeder, Eric Pottier, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-Université de Nantes (UN)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Institut d'Électronique et des Technologies du numéRique (IETR), Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Nantes (UN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Nantes Université (NU), Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche, Centre National d’Etudes Spatiales, Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-CentraleSupélec-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Université de Rennes (UNIV-RENNES), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Rennes 1 (UR1), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

Subjects
Synthetic aperture radar, Marsh, 010504 meteorology & atmospheric sciences, Radar à synthèse d'ouverture, 0211 other engineering and technologies, Polarimetry, Wetland, 02 engineering and technology, Conservation, RADARSAT-2, terre humide, 01 natural sciences, conservation des forêts, conservation des terres, Wetland conservation, Ecosystem, Computers in Earth Sciences, Engineering (miscellaneous), Polarisation, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, geography, geography.geographical_feature_category, ANOVA, Vegetation, Préservation de l'écosystème, [SDE.IE]Environmental Sciences/Environmental Engineering, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Polarization (waves), [SDE.ES]Environmental Sciences/Environmental and Society, Atomic and Molecular Physics, and Optics, Computer Science Applications, Random forest, Écosystème forestier, Environmental science, P01 - Conservation de la nature et ressources foncières, U30 - Méthodes de recherche, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, TerraSAR-X
Abstract

International audience; The availability of high spatial resolution synthetic aperture radar (SAR) sensors with a wide range of acquisition modes has increased greatly over the past decade and contributed significantly to the study of wetland ecosystems. However, the relative influence of acquisition configurations (i.e. band frequency, polarization mode, number of acquisition dates) in wetland analysis remains poorly explored. This article investigated the relative influence of X-/C-band frequency, dual-/quad-polarization and single-/multi-acquisition features on discrimination of vegetation types in 632 ha Ramsar-protected temperate riverine marshes (Mont-Saint-Michel Bay, France). Three SAR datasets (i.e. quad-pol/C-band, dual-pol/C-band and dual-pol/X-band) were generated from five pairs of TerraSAR-X and RADARSAT-2 images. First, a set of 25 SAR features, including backscattering coefficients and polarimetric parameters, was extracted from the SAR datasets. Second, correlation between each pair of images was calculated using the polarimetric parameter Shannon entropy to select the most similar pairs in the time series. Third, the importance of each SAR feature and modeling accuracy were calculated using a conditional random forest model for each of the three datasets. Finally, analysis of variance was performed to assess the impact of band frequency, polarization mode and number of acquisition dates on the classification of vegetation types. The results highlighted that although the time-shift of each pair of TerraSAR-X and RADARSAT-2 images was short (3-11 days), only three pairs were sufficiently similar, highlighting the high variability in wetland ecosystems. The polarimetric parameter Shannon entropy was the most discriminating feature, regardless of the frequency or polarization. Most variance in the model accuracy was explained by the number of acquisition dates (68%), followed by the frequency (23%), while polarization explained little. This article will help select the most suitable SAR sensor acquisition modes for wetland conservation.

Published
2020

91. Developmental and epilepsy spectrum of KCNB1 encephalopathy with long‐term outcome

Author

Rima Nabbout, Patrick Berquin, Sylvie Odent, Mathieu Kuchenbuch, Gwenaël Le Guyader, Marieke F. van Dooren, Jamel Chelly, Edor Kabashi, Melanie Jennesson, Giulia Barcia, Cyril Mignot, Tayeb Sekhara, Alexandra Afenjar, Marlène Rio, Anne Rolland, Claude Besmond, Andrés Rodríguez-Sacristán Cascajo, Gaetano Terrone, Isabelle Marey, Boris Keren, Alice Goldenberg, A.S. Lebre, Heather C Mefford, Gaetan Lesca, Anne de Saint Martin, Susanna Negrin, Nathalie Dorison, Hélène Maurey, Agnès Guët, David Geneviève, Marie Claire Y. de Wit, Jeremy L. Freeman, Pierre Meyer, Thierry Billette de Villemeur, Ingrid E. Scheffer, Katherine B. Howell, Anca Nica, Raphael Levy, Martino Montomoli, Renzo Guerrini, Elena Parrini, Candace T. Myers, Bertrand Isidor, Alice Poisson, Marion Gérard, Salima El Chehadeh, Lynette G. Sadleir, Julien Durigneux, Pascal Vrielynck, Amy L Schneider, Emmanuel Scalais, Laurence Hubert, Sophie Dupont, Vesna Brankovic, Damien Lederer, Hervé Isnard, Delphine Breuillard, Claire Bar, Alberto Danieli, Diane Doummar, Arnold Munnich, CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Unité neurovasculaire et troubles cognitifs (Neuvacod), Université de Poitiers, Institut NeuroMyoGène (INMG), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre hospitalier universitaire de Nantes (CHU Nantes), CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), CHU Strasbourg, Centre for the Diagnosis and management of genetic psychiatric disorders [Bron] (GénoPsy), Centre Hospitalier le Vinatier [Bron], Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pontchaillou [Rennes], Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), CHU Rouen, Normandie Université (NU), CHU Amiens-Picardie, Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] (PhyMedExp), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Département de génétique médicale, maladies rares et médecine personnalisée [CHRU Montpellier], Cellules Souches, Plasticité Cellulaire, Médecine Régénératrice et Immunothérapies (IRMB), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN), CHU Pitié-Salpêtrière [AP-HP], FP7 Ideas: European Research Council, National Health and Medical Research Council, Health Research Council of New Zealand, ANR‐10-IAHU‐01, Agence Nationale de la Recherche, Fondation Bettencourt Schueller, 602531, Seventh Framework Programme, ANR-10-IAHU-0001,Imagine,Institut Hospitalo-Universitaire Imagine(2010), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), ANR‐10IAHU‐01, Agence Nationale de la Recherche, Clinical Genetics, Neurology, and Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1)

Subjects
0301 basic medicine, Adult, Male, Pediatrics, medicine.medical_specialty, Time Factors, Adolescent, autism spectrum disorders, [SDV]Life Sciences [q-bio], Encephalopathy, Severe epilepsy, Imaging data, Cohort Studies, 03 medical and health sciences, Epilepsy, Young Adult, 0302 clinical medicine, Shab Potassium Channels, drug-resistant epilepsy, Intellectual disability, medicine, Missense mutation, Humans, In patient, developmental and epileptic encephalopathy, Child, ComputingMilieux_MISCELLANEOUS, Retrospective Studies, Brain Diseases, sudden unexpected death in epilepsy, developmental encephalopathy, business.industry, Genetic Variation, Infant, Cognition, Electroencephalography, medicine.disease, potassium channels, 3. Good health, 030104 developmental biology, Treatment Outcome, Neurology, Child, Preschool, Female, Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

Objective: We aimed to delineate the phenotypic spectrum and long-term outcome of individuals with KCNB1 encephalopathy. Methods: We collected genetic, clinical, electroencephalographic, and imaging data of individuals with KCNB1 pathogenic variants recruited through an international collaboration, with the support of the family association “KCNB1 France.” Patients were classified as having developmental and epileptic encephalopathy (DEE) or developmental encephalopathy (DE). In addition, we reviewed published cases and provided the longterm outcome in patients older than 12 years from our series and from literature. Results: Our series included 36 patients (21 males, median age = 10 years, range = 1.6 months-34 years). Twenty patients (56%) had DEE with infantile onset seizures (seizure onset = 10 months, range = 10 days-3.5 years), whereas 16 (33%) had DE with late onset epilepsy in 10 (seizure onset = 5 years, range = 18 months-25 years) and without epilepsy in six. Cognitive impairment was more severe in individuals with DEE compared to those with DE. Analysis of 73 individuals with KCNB1 pathogenic variants (36 from our series and 37 published individuals in nine reports) showed developmental delay in all with severe to profound intellectual disability in 67% (n = 41/61) and autistic features in 56% (n = 32/57). Long-term outcome in 22 individuals older than 12 years (14 in our series and eight published individuals) showed poor cognitive, psychiatric, and behavioral outcome. Epilepsy course was variable. Missense variants were associated with more frequent and more severe epilepsy compared to truncating variants. Significance: Our study describes the phenotypic spectrum of KCNB1 encephalopathy, which varies from severe DEE to DE with or without epilepsy. Although cognitive impairment is worse in patients with DEE, long-term outcome is poor for most and missense variants are associated with more severe epilepsy outcome. Further understanding of disease mechanisms should facilitate the development of targeted therapies, much needed to improve the neurodevelopmental prognosis.

Published
2020

92. Heterogeneity of PNPT1 neuroimaging: mitochondriopathy, interferonopathy or both?

Author

Laurence Hubert, Marlène Rio, Nathalie Boddaert, Uluç Yiş, Sulin Wu, Laura L Konczal, Giulia Barcia, Claude Besmond, Fatma Ceren Sarioglu, Arnold Munnich, Manuel Schiff, Alessandra Pennisi, Pelin Teke Kisa, Charles-Joris Roux, Pauline Gaignard, Raphael Levy, Jutta Gärtner, Deepika D Burkardt, Marco Henneke, and Agnès Rötig

Subjects
Adult, Pathology, medicine.medical_specialty, Mitochondrial Diseases, Mitochondrial disease, Neuroimaging, Disease, Mitochondrion, Leukoencephalopathy, Mitochondrial Proteins, 03 medical and health sciences, 0302 clinical medicine, Immune system, Interferon, Leukoencephalopathies, Genetics, medicine, Humans, Child, Genetics (clinical), 030304 developmental biology, 0303 health sciences, Whole Genome Sequencing, business.industry, RNA, Brain, medicine.disease, Pathophysiology, Child, Preschool, Exoribonucleases, Interferon Type I, Leigh Disease, business, 030217 neurology & neurosurgery, medicine.drug
Abstract

BackgroundBiallelic variants in PNPT1 cause a mitochondrial disease of variable severity. PNPT1 (polynucleotide phosphorylase) is a mitochondrial protein involved in RNA processing where it has a dual role in the import of small RNAs into mitochondria and in preventing the formation and release of mitochondrial double-stranded RNA into the cytoplasm. This, in turn, prevents the activation of type I interferon response. Detailed neuroimaging findings in PNPT1-related disease are lacking with only a few patients reported with basal ganglia lesions (Leigh syndrome) or non-specific signs.Objective and methodsTo document neuroimaging data in six patients with PNPT1 highlighting novel findings.ResultsTwo patients exhibited striatal lesions compatible with Leigh syndrome; one patient exhibited leukoencephalopathy and one patient had a normal brain MRI. Interestingly, two unrelated patients exhibited cystic leukoencephalopathy resembling RNASET2-deficient patients, patients with Aicardi-Goutières syndrome (AGS) or congenital CMV infection.ConclusionWe suggest that similar to RNASET2, PNPT1 be searched for in the setting of cystic leukoencephalopathy. These findings are in line with activation of type I interferon response observed in AGS, PNPT1 and RNASET2 deficiencies, suggesting a common pathophysiological pathway and linking mitochondrial diseases, interferonopathies and immune dysregulations.

Published
2020

93. Large dam removal and early spontaneous riparian vegetation recruitment on alluvium in a former reservoir: Lessons learned from the pre‐removal phase of the Sélune River project (France)

Author

Laurence Hubert-Moy, Marianne Laslier, Charlotte Ravot, Simon Dufour, Ivan Bernez, Didier Le Coeur, Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Écologie et santé des écosystèmes (ESE), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Seine Normandy Water Agency (AESN), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA), Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-École pratique des hautes études (EPHE)-Université de Nantes (UN)-Université d'Angers (UA)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 2 (UR2), and Normandie Université (NU)-Normandie Université (NU)

Subjects
0106 biological sciences, alluvium stabilization, 0208 environmental biotechnology, Dam removal, ecological restoration, 02 engineering and technology, riparian communities, 01 natural sciences, dam removal, Environmental Chemistry, Restoration ecology, General Environmental Science, Water Science and Technology, Riparian zone, Hydrology, geography, geography.geographical_feature_category, [SDE.IE]Environmental Sciences/Environmental Engineering, 010604 marine biology & hydrobiology, Plant community, Vegetation, 15. Life on land, 020801 environmental engineering, Spatial heterogeneity, Environmental science, Alluvium, Species richness, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, river management, [SDE.ER]Environmental Sciences/domain_sde.er
Abstract

International audience; The restoration of ecological continuity along the Sélune River (Normandy, France) involves the removal of two tall hydroelectric dams (36 m removed in 2019 and 16 m in 2021), a project without precedent in Europe. During the pre‐removal phase (2014–2018), we performed scientific monitoring of the vegetation that was colonizing alluvium in the former dam reservoir (length: 19 km; surface area: 151 ha).Our study aimed to analyse if spontaneous vegetation could ecologically restore the riparian zone and help maintain fine sediment after dam removal. We used colonization indicators related to vegetation structure, taxonomic richness and diversity, and composition. These indicators were calculated at two spatial scales (local, at a single site, and broad, along the reservoir). The aim was to (a) characterize the spontaneously established species pool; (b) analyse longitudinal patterns in vegetation colonization; and (c) assess temporal changes in the species community.Our results show that diverse plant communities have developed. Slight differences in longitudinal and lateral patterns existed; they were linked with habitat heterogeneity and the reservoir's slow pace of draining. We observed fast spontaneous terrestrialization, which has resulted in cover stabilization, decreased diversity, and the development of herbaceous riverbank communities, with very few invasive species. This finding suggests stabilization potential is high and passive ecological restoration could occur, at least locally. Further analyses focusing on functional traits could help inform future management decisions regarding revegetation on reservoir alluvium.

Published
2020

94. Mapping the functional dimension of vegetation series in the Mediterranean region using multitemporal MODIS data

Author

Laurence Hubert-Moy, Pauline Delbosc, Sébastien Rapinel, Frédéric Bioret, Alban Thomas, Jan-Bernard Bouzillé, Damien Arvor, Clémence Rozo, Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-Université de Nantes (UN)-École pratique des hautes études (EPHE)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Géoarchitecture, Institut Brestois des Sciences de l'Homme et de la Société (IBSHS), Université de Brest (UBO)-Université de Brest (UBO)-Université de Brest (UBO)-Université de Bretagne Sud (UBS), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Economie des Ressources Naturelles (LERNA), Université Toulouse 1 Capitole (UT1)-Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Centre National de la Recherche Scientifique (CNRS), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Institut Brestois des Sciences de l'Homme et de la Société (IBSHS), Université de Brest (UBO)-Université de Brest (UBO), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), This work was supported by the Ministère de la Transition écologique et Solidaire (CarHAB program) [grant 2101606295], Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)

Subjects
Mediterranean climate, Series (stratigraphy), 010504 meteorology & atmospheric sciences, [SDE.IE]Environmental Sciences/Environmental Engineering, 0211 other engineering and technologies, Biodiversity, Effective management, 02 engineering and technology, Vegetation, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, 01 natural sciences, [SDE.ES]Environmental Sciences/Environmental and Society, Random forest, Geography, Dimension (vector space), General Earth and Planetary Sciences, Habitats Directive, Physical geography, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences
Abstract

Monitoring the structural and functional dimensions of natural vegetation is a critical issue to ensure effective management of biodiversity. While coarse-resolution satellite image time-se...

Published
2020

95. Evaluation of sentinel-1 and -2 time series for estimating LAI and biomass of wheat and rapeseed crop types

Author

Jacques Baudry, Julie Betbeder, Sébastien Rapinel, Audrey Mercier, Nicolas Jégou, Laurence Hubert-Moy, Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES), Centro Agronómico Tropical de Investigación y Enseñanza - Tropical Agricultural Research and Higher Education Center (CATIE), Université de Montpellier (UM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Biodiversité agroécologie et aménagement du paysage (UMR BAGAP), AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Kalideos project - CNESZone Atelier Armorique projectMinistry of Research and Technology of the Republic of Indonesia (RISTEK)2015-2016 BiodivERsA COFUNDFrench National Research Agency (ANR)MINECOBelgian Federal Science Policy Office, and AGROCAMPUS OUEST-Ecole supérieure d'Agricultures d'Angers (ESA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Synthetic aperture radar, Rapeseed, 010504 meteorology & atmospheric sciences, Télédétection, Radar à synthèse d'ouverture, 0211 other engineering and technologies, Biomass, 02 engineering and technology, 01 natural sciences, Crop, F01 - Culture des plantes, Biomasse, Water content, ComputingMilieux_MISCELLANEOUS, Triticum, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing, 2. Zero hunger, Modélisation des cultures, Indice de surface foliaire, Brassica napus, Vegetation, Spectral bands, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Environmental science, U30 - Méthodes de recherche, Scale (map), Analyse de séries chronologiques
Abstract

Monitoring crops at a fine scale is critical because it provides information crucial for assessing the influence of increased food production on sustainable management of agricultural landscapes. We assessed the potential of synthetic aperture radar (SAR) Sentinel-1 (S-1) and optical Sentinel-2 (S-2) images to derive crop biophysical parameters of wheat and rapeseed in Brittany, France. We generated a dataset of 29 features, including spectral bands and vegetation indices derived from S-2 images, and backscattering coefficients and polarimetric indicators derived from S-1 images. Then, we compared the respective value of S-1 and S-2 features to estimate crop LAI, biomass, and water content (WC) using a Gaussian processes regression. The results show that best S-2-based models were achieved using the green band, NIR bands, and vegetation indices for the wheat WC, LAI, and biomass, respectively, and the shortwave-infrared bands for rapeseed biomass. Concerning S-1-based models, the σ0VH : σ0VV ratio was the most relevant feature for wheat LAI and rapeseed biomass, and the Shannon entropy polarization contribution best performed for wheat WC. We highlighted not only the value of optical S-2 images but also the importance of polarimetric indicators derived from SAR S-1 images for estimating crop biophysical parameters.

Published
2020

96. Contribution of free satellite time-series images to mapping plant communities in the Mediterranean Natura 2000 site: the example of Biguglia Pond in Corse (France)

Author

Pauline Delbosc, Jean-Bernard Bouzillé, Laurence Hubert-Moy, Frédéric Bioret, Sébastien Rapinel, Clémence Rozo, Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN), Université de Rennes (UNIV-RENNES), Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), Géoarchitecture : Territoires, Urbanisation, Biodiversité, Environnement, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), This study was funded by the French Ministry of Ecology (CarHAB project, grant no. 2101606295)., Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Rennes (UR), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), and Université de Rennes (UR)-Université de Brest (UBO)

Subjects
0106 biological sciences, Mediterranean climate, 010504 meteorology & atmospheric sciences, Ecology, Corsica, Plant community, Plant Science, Vegetation, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Spatial distribution, Coastal lagoon, 010603 evolutionary biology, 01 natural sciences, Field (geography), Geography, Habitat, [SDE]Environmental Sciences, Conservation status, multispectral images, Physical geography, Sentinel-2, Natura 2000, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences
Abstract

International audience; Mapping plant communities, which is essential to assess the conservation status of natural habitats, is currently based mainly on time-consuming field surveys without the use of satellite data. However, free image time-series with high spatial and temporal resolution have been available since 2015. This study assessed the contribution of Sentinel-2 time-series images to mapping the spatial distribution of 18 plant communities within a Natura 2000 site (1978 ha) located on the Mediterranean biogeographical region (Corsica, France). The method was based on random forest modeling of six Sentinel-2 images acquired from 26 February to 24 October 2017, which were calibrated and validated using a field vegetation map. The results showed that the 18 plant communities were modeled correctly, with 72% overall accuracy. The uncertainty map associated with the model indicated areas that required additional field observations.

Published
2020

97. Long-term forest mapping from classification of MODIS time series: best practices

Author

Emmanuelle Cano, Jean-Philippe Denux, Laurence Hubert-Moy, Véronique Chéret, Marie Parrens, Dynamiques et écologie des paysages agriforestiers (DYNAFOR), École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Service de pathologie [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Groupe hospitalier Pellegrin, and Pyrenees Climate Change Observatory (OPCC) program - Ecole d'Ingenieurs de Purpan

Subjects
010504 meteorology & atmospheric sciences, Computer science, 0211 other engineering and technologies, 02 engineering and technology, 01 natural sciences, support vector machine, maximum likelihood, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, accuracy assessment, Contextual image classification, business.industry, Pattern recognition, Spectral bands, Vegetation, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Random forest, Term (time), Support vector machine, Compositing, [SDE]Environmental Sciences, General Earth and Planetary Sciences, Artificial intelligence, time series, Scale (map), business, random forest
Abstract

International audience; Coarse spatial resolution (CSR) time series have been successfully used at regional scale to produce homogeneous and up-to-date forest cover maps. This study aims to classify CSR time series using a nomenclature as detailed as national forest inventories nomenclatures. To identify best practices for classifying time series, three algorithms are compared: maximum likelihood, support vector machine, and random forest. For each algorithm, training, temporal compositing, and selection of input features have been optimized Results establish a clear improvement in classification accuracy when red, near-infrared, and short-wave infrared spectral bands are used instead of vegetation indices. Temporal compositing has a major impact when the whole phenological cycle is used for 3 consecutive years. Random forest produces the best classification, support vector machine appears to be sensitive to overtraining, and maximum likelihood is unable to deal with the complex characteristics of forest and natural vegetation classes.

Published
2019

98. Monitoring the colonization of alluvial deposits using multitemporal UAV RGB ‐imagery

Author

Laurence Hubert-Moy, Thomas Corpetti, Marianne Laslier, Simon Dufour, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), and Université de Nantes (UN)-Université de Nantes (UN)

Subjects
0106 biological sciences, Ecology, Correlation coefficient, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Management, Monitoring, Policy and Law, Vegetation dynamics, 010603 evolutionary biology, 01 natural sciences, [SDE]Environmental Sciences, medicine, RGB color model, Image acquisition, Alluvium, Colonization, medicine.symptom, Vegetation (pathology), Scale (map), Geology, ComputingMilieux_MISCELLANEOUS, 010606 plant biology & botany, Nature and Landscape Conservation, Remote sensing
Abstract

AIMS: (a) Evaluate the potential of Unmanned Aerial Vehicle (UAV) technology for very high resolution monitoring vegetation dynamics. (b) Describe and explain the colonization pattern of dewatered alluvial deposits by vegetation during early successional stages at an intra‐annual scale. LOCATION: Selune River, Normandy, France. METHODS: We assessed vegetation cover using models based on UAV imagery and field samples at very fine spatial (cm) and temporal (intra‐annual) scales. A UAV flight was conducted every two months from January to July 2015, and vegetation was measured during image acquisition phases. Vegetation cover was mapped for each image acquisition date (four UAV orthomosaics) using a nonlinear regression model (Support Vector Regression algorithm). Then, the maps of vegetation cover were compared to evaluate the colonization process. RESULTS: Vegetation cover was predicted from UAV with high accuracy (mean correlation coefficient: 0.90). Analysis of the maps revealed that colonization of the alluvial deposits by vegetation was rapid in spring. CONCLUSIONS: This study shows that intra‐annual vegetation dynamics on alluvial deposits is rapid and that the colonization pattern can be observed in early successional stages. Very high spatial resolution images acquired by UAV can be used to create detailed maps to evaluate vegetation cover development.

Published
2019

99. The use of time series of remote sensing images for a sustainable management of territories: Application to the study of grasslands in the West of France

Author

Thomas Corpetti, Pauline Dusseux, Laurence Hubert-Moy, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Département Image et Traitement Information (ITI), and Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)

Subjects
010504 meteorology & atmospheric sciences, 0211 other engineering and technologies, lcsh:G1-922, 02 engineering and technology, image satellite, satellite imagery, imágenes de satélite, 01 natural sciences, medio ambiente, agricultura, land cover, imágenes de alta resolución, high resolution images, occupation et utilisation du sol, ComputingMilieux_MISCELLANEOUS, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, agriculture, 2. Zero hunger, images très haute résolution, land use, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, environnement, 13. Climate action, [SDE]Environmental Sciences, environment, Social Sciences (miscellaneous), lcsh:Geography (General), cobertura y uso de suelo
Abstract

La diminution du nombre de prairies, que l’on observe à l’échelle mondiale depuis plusieurs décennies, s’est accompagnée de l’évolution de leur mode de gestion dans un contexte d’intensification de l’usage des terres. Face aux enjeux que ces changements impliquent, tant sur le plan environnemental qu’économique, il est nécessaire d’identifier et de caractériser les dynamiques spatiotemporelles des prairies, afin notamment d’évaluer les impacts du changement climatique sur ces dernières et leur capacité à s’y adapter. La télédétection, qui est un outil d’observation couramment utilisé pour étudier la surface terrestre, permet de répondre aux problématiques de gestion des territoires. Cet article présente un exemple de l’utilisation de séries temporelles d’images à haute résolution spatiale pour d’une part étudier la dynamique spatiotemporelle des prairies depuis les années 50, et d’autre part les caractériser en déterminant leur mode de gestion et leur productivité. Les informations dérivées de ces séries d’images peuvent être intégrées dans des modèles agro-climatiques afin de pouvoir simuler le fonctionnement des agro-écosystèmes pour évaluer leur évolution face aux changements climatiques in fine et proposer des scénarios d’adaptation à ces changements. The decrease of grasslands, which has been observed on a global scale for several decades, occurred with changes in their management in the context of land use intensification. Faced with the environmental and economic implications of these changes, it is necessary to identify and characterize the spatial and temporal dynamics of grasslands, in particular to assess the impacts of climate change on these grasslands and their capacity to adapt. Remote sensing, which is an observation tool commonly used to study the Earth's surface, makes it possible to address territorial management issues. This article presents an example of the use of time series of high spatial resolution images to study the spatial and temporal dynamics of grasslands since the 1950s and to characterize them by determining their management and their productivity. Information derived from these images series can be integrated into agro-climatic models in order to simulate the agro-ecosystems functioning to evaluate their evolution regarding climate change and to propose adaptation scenarios. La disminución del número de praderas, observada a escala mundial desde hace varias décadas, ha ido acompañada de cambios en su modo de gestión en el contexto de una intensificación del uso de la tierra. Ante los desafíos que implican estos cambios, tanto sobre el plano ambiental como económico, resulta necesario identificar y caracterizar las dinámicas espacio temporales de las praderas, con la finalidad de evaluar los impactos en ellas producto del cambio climático y su capacidad para adaptarse a este. La teledetección, que es una herramienta de observación comúnmente utilizada para estudiar la superficie de la tierra, permite responder a los problemas de gestión de los territorios. Este artículo, presenta un ejemplo del uso de series temporales de imágenes con alta resolución espacial para estudiar la dinámica espacio temporal de los prados desde 1950, y, además, caracterizar y determinar su modo de gestión y productividad. La información derivada de esta serie de imágenes puede ser integrada en modelos agroclimáticos, con la finalidad de poder simular el funcionamiento de los agroecosistemas y evaluar así su evolución en detalle frente al cambio climático, proponiendo así escenarios para la adaptación a tales cambios.

Published
2019

100. IQSEC2-related encephalopathy in males and females: a comparative study including 37 novel patients

Author

Darina Prchalova, Philippe Pm Campeau, Bénédicte Duban-Bedu, Jacques L. Michaud, Marije Koopmans, Arnold Munnich, Christel Depienne, Marilyn Tallot, Fadi F. Hamdan, Eveline Hagebeuk, Marie-Laure Moutard, Anna Kaminska, Petra Laššuthová, Kathryn G. Miller, Ange Line Bruel, Xilma Xr Ortiz-Gonzalez, Shoji Ichikawa, Ingo Helbig, Ethan Em Goldberg, Sarah Weckhuysen, Daphné Lehalle, Elena Gardella, Marie-Bertille Dehouck, Claude Besmond, Patrick Edery, Christine Ioos, Pauline Marzin, Christine Coubes, Julien Buratti, Rima Nabbout, Hubert Journel, Audrey Putoux, Giulia Barcia, Laurence Hubert, Claire Davidson, Berten Ceulemans, Ana Ag Cristancho, Fiona Cunningham, Chloé Quélin, Christèle Dubourg, Aoife Ac McMahon, Thomas Smol, Delphine Héron, Katalin Štěrbová, Katherine Kl Helbig, Boris Keren, Ivan Shelihan, Damien Lederer, Rikke Rs Møller, Emílie Vyhnálková, Alyssa R. Rosen, Natasha Shur, Julie Gauthier, Dragan Marjanovic, Berge Ba Minassian, Marleen Simon, Ledia Brunga, Guillaume Smits, Sandra Janssens, Catheline Vilain, Gaetan Lesca, Caroline Nava, Jasper J. van der Smagt, Laurent Villard, Cyril Mignot, Samuel P. Yang, Joelle Roume, Julie Soblet, JM Pinard, Stéphanie Gobin-Limballe, Bobby P. C. Koeleman, Miroslava Hancarova, Elizabeth J. Donner, Nienke Ne Verbeek, Marie-Line Jacquemont, Marjan J. A. van Kempen, Julia Metreau, David Geneviève, Joannella Morales, Peter M. van Hasselt, Christine Barnerias, Caroline Lacoste, Claire Bar, Thierry Bienvenu, Mathieu Milh, Elsa Rossignol, Marseille medical genetics - Centre de génétique médicale de Marseille (MMG), Institut National de la Santé et de la Recherche Médicale (INSERM)-Aix Marseille Université (AMU), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Imagine - Institut des maladies génétiques (IMAGINE - U1163), Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), CHU Necker - Enfants Malades [AP-HP], Université Paris Descartes - Paris 5 (UPD5), CHU Sainte Justine [Montréal], Centre Hospitalier Universitaire de La Réunion (CHU La Réunion), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de recherche en neurosciences de Lyon - Lyon Neuroscience Research Center (CRNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon (HCL), Service de Génétique Médicale [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Service de neurologie pédiatrique [CHU Necker], Institut de psychiatrie et neurosciences (U894 / UMS 1266), Service de biochimie et de génétique moléculaire [CHU Cochin], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Cochin [AP-HP], Hôpital Cochin [AP-HP], Génétique des Anomalies du Développement (GAD), Université de Bourgogne (UB)-IFR100 - Structure fédérative de recherche Santé-STIC, The Hospital for sick children [Toronto] (SickKids), University of Antwerp (UA), Département de génétique médicale, maladies rares et médecine personnalisée [CHRU Montpellier], Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Children’s Hospital of Philadelphia (CHOP ), Hôpital Saint-Vincent de Paul, Institut de Génétique et Développement de Rennes (IGDR), Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), University of Southern Denmark (SDU), Cellules Souches, Plasticité Cellulaire, Médecine Régénératrice et Immunothérapies (IRMB), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), University Hospital Motol [Prague], Hôpital Raymond Poincaré [AP-HP], Center for Medical Genetics [Ghent], Ghent University Hospital, University Medical Center [Utrecht], Institut de Pathologie et Génétique [Gosselies] (I.P.G.), Lipides - Nutrition - Cancer [Dijon - U1231] (LNC), Université de Bourgogne (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, CHI Créteil, AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), CHU Trousseau [APHP], Université de Lyon, CHI Poissy-Saint-Germain, Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Université libre de Bruxelles (ULB), Hôpital Erasme [Bruxelles] (ULB), Faculté de Médecine [Bruxelles] (ULB), Université libre de Bruxelles (ULB)-Université libre de Bruxelles (ULB), Antwerp University Hospital [Edegem] (UZA), Département de génétique médicale [Hôpital de la Timone - APHM], Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire [Rennes], Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Génétique Cytogénétique et Embryologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université de Lyon-Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Service de Neuropédiatrie [CHU Trousseau], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre de recherche en neurosciences de Lyon (CRNL), IFR100 - Structure fédérative de recherche Santé-STIC-Université de Bourgogne (UB), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU), and Centre de Psychiatrie et Neurosciences (U894)

Subjects
0301 basic medicine, Male, Génétique clinique, [SDV]Life Sciences [q-bio], Medizin, Physiology, 030105 genetics & heredity, Seizures/epidemiology, Epilepsy, Brain Diseases/epidemiology, X-linked inheritance, Intellectual disability, Guanine Nucleotide Exchange Factors, Protein Isoforms, Missense mutation, Genetics(clinical), 10. No inequality, Non-U.S. Gov't, Genetics (clinical), X-linked recessive inheritance, ComputingMilieux_MISCELLANEOUS, Brain Diseases, Sex Characteristics, Research Support, Non-U.S. Gov't, Brain, Sciences bio-médicales et agricoles, 3. Good health, Pedigree, Phenotype, intellectual disability, Female, Brain/growth & development, Sex characteristics, Génétique moléculaire, Guanine Nucleotide Exchange Factors/genetics, Encephalopathy, Research Support, X-inactivation, Article, 03 medical and health sciences, Seizures, Protein Isoforms/genetics, medicine, Journal Article, IQSEC2, Humans, Intellectual Disability/epidemiology, [SDV.GEN]Life Sciences [q-bio]/Genetics, business.industry, Infant, Newborn, isoforms, Correction, Infant, medicine.disease, Newborn, Human genetics, 030104 developmental biology, Mutation, epilepsy, Human medicine, business, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Variants in IQSEC2, escaping X inactivation, cause X-linked intellectual disability with frequent epilepsy in males and females. We aimed to investigate sex-specific differences., SCOPUS: ar.j, info:eu-repo/semantics/published

Published
2019

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