Validation de la BREV dans les troubles des apprentissages chez 115 enfants

REIMS-BU Santé (514542104) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Étude du coup d'État en fait et en droit : thèse pour le doctorat / présentée... par Olivier Brichet... ; Université de Paris, Faculté de droit

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An image-based automated pipeline for maize ear and silk detection in a highthroughput phenotyping platform

Water deficit strongly impacts silk growth and silk emergence in maize (Zea mays L.), which in turn determines the final number of ovaries developing grains (Turc et al. 2016, Oury et al. 2016). However, phenotyping silk growth and silk expansion is difficult at throughput needed for genetic analyses. We have developed an image-based automated pipeline for maize ear and silk detection in a high-throughput phenotyping platform. The first step consists of selecting the best whole plant side images containing maximum information for each plant and day as that containing the most leaves and whole stem, based on top view images. In the second step, the best side images are segmented and skeletonized, and potential ear positions are determined based on changes in stem widths. The x, y, z ear position identified in this way serves to pilot the movement of a mobile camera able to take a detailed picture taken at 30 cm from the ear, with the final aim of determining silk emergence and silk growth duration. These methods were tested at the PhenoArch plant phenotyping platform (www6.montpellier.inra.fr/lepse/M3P) in a panel of 300 maize hybrids. First results showed that in >80% of cases, ears were successfully detected before silking and duration of silk expansion significantly correlated with visual scores. The image pipeline presented here opens up the way for large-scale genetic analyses of control of reproductive growth to changes in environmental conditions in reproductive structures

A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

International audienceBackground: In maize, silks are hundreds of filaments that simultaneously emerge from the ear for collecting pollen over a period of 1–7 days, which largely determines grain number especially under water deficit. Silk growth is a major trait for drought tolerance in maize, but its phenotyping is difficult at throughputs needed for genetic analyses.Results: We have developed a reproducible pipeline that follows ear and silk growths every day for hundreds of plants, based on an ear detection algorithm that drives a robotized camera for obtaining detailed images of ears and silks. We first select, among 12 whole‑plant side views, those best suited for detecting ear position. Images are seg‑mented, the stem pixels are labelled and the ear position is identified based on changes in width along the stem. A mobile camera is then automatically positioned in real time at 30 cm from the ear, for a detailed picture in which silks are identified based on texture and colour. This allows analysis of the time course of ear and silk growths of thousands of plants. The pipeline was tested on a panel of 60 maize hybrids in the PHENOARCH phenotyping platform. Over 360 plants, ear position was correctly estimated in 86% of cases, before it could be visually assessed. Silk growth rate, estimated on all plants, decreased with time consistent with literature. The pipeline allowed clear identification of the effects of genotypes and water deficit on the rate and duration of silk growth.Conclusions: The pipeline presented here, which combines computer vision, machine learning and robotics, provides a powerful tool for large‑scale genetic analyses of the control of reproductive growth to changes in environ‑mental conditions in a non‑invasive and automatized way. It is available as Open Source software in the OpenAlea platform

PHENODYN: a high throughput platform for measurement of organ elongation rate and plant transpiration with high temporal resolution

Leaf elongation rate (LER) is the first trait affected by water deficit or high evaporative demand, with typical time constants of 30 min for change in LER upon rapid changes in soil water content or air vapour pressure deficit (VPD). The same applies to other organs such as maize silks. Phenodyn (https://www6.montpellier.inra.fr/lepse/M3P/plateforme-PHENODYN) measures organ elongation rate and transpiration rate of hundreds of plants with a temporal resolution of 3 min (or more if required) in order to follow the changes in LER and transpiration in fluctuating conditions of soil water content, evaporative demand and temperature. Phenodyn imposes known soil water potentials to each plant via independent automatic irrigation. Climatic conditions are either imposed in the growth chamber or left to naturally fluctuate in the greenhouse. Elongation rate is measured with 500 rotational displacement transducers with a precision of 0.2 mm. Transpiration and soil water content are measured with scales; changes in weight are attributed to changes in soil water content after correction for the increase in plant biomass. A set of sensors measures meristem temperature, incident light, air temperature and VPD every minute. Phenodyn is associated to an information system for real time monitoring of experiments, post-analysis of large datasets (around 700.000 data points are generated in each experiment) and identification of genotypic parameters such as rates or time constants. It has been used (i) for analyzing the response of LER to soil water potential and to VPD in mapping populations, diversity panel for association genetics or insertion lines, (ii) for establishing response curves to temperature in different species and genotypes, (iii) for following jointly changes in transpiration and in elongation rates of leaves or silks together with hydraulic variables. It has been used in maize, rice, wheat, sorghum, millet, apple tree and vine. Phenodyn is part of the M3P facility (https://www6.montpellier.inra.fr/lepse/M3P). It is accessible to public or private scientists via the website of the national project Phenome-FPPN (https://www.phenome-fppn.fr/)

Predictive Factor for COVID-19 Worsening: Insights for High-Sensitivity Troponin and D-Dimer and Correlation With Right Ventricular Afterload

International audienceBackground: Coronavirus disease 2019 (COVID-19) has been associated with cardiovascular complications and coagulation disorders. Objectives: To explore clinical and biological parameters of COVID-19 patients with hospitalization criteria that could predict referral to intensive care unit (ICU). Methods: Analyzing the clinical and biological profiles of COVID-19 patients at admission. Results: Among 99 consecutive patients that fulfilled criteria for hospitalization, 48 were hospitalized in the medicine department, 21 were first admitted to the medicine ward department and referred later to ICU, and 30 were directly admitted to ICU from the emergency department. At admission, patients requiring ICU were more likely to have lymphopenia, decreased SpO2, a D-dimer level above 1,000 ng/mL, and a higher high-sensitivity cardiac troponin (Hs-cTnI) level. A receiver operating characteristic curve analysis identified Hs-cTnI above 9.75 pg/mL as the best predictive criteria for ICU referral [area under the curve (AUC), 86.4; 95% CI, 76.6-96.2]. This cutoff for Hs-cTnI was confirmed in univariate [odds ratio (OR), 22.8; 95% CI, 6.0-116.2] and multivariate analysis after adjustment for D-dimer level (adjusted OR, 20.85; 95% CI, 4.76-128.4). Transthoracic echocardiography parameters subsequently measured in 72 patients showed an increased right ventricular (RV) afterload correlated with Hs-cTnI (r = 0.42, p = 0.010) and D-dimer (r = 0.18, p = 0.047). Conclusion: Hs-cTnI appears to be the best relevant predictive factor for referring COVID-19 patients to ICU. This result associated with the correlation of D-dimer with RV dilatation probably reflects a myocardial injury due to an increased RV wall tension. This reinforces the hypothesis of a COVID-19-associated microvascular thrombosis inducing a higher RV afterload

The 2q37-deletion syndrome: an update of the clinical spectrum including overweight, brachydactyly and behavioural features in 14 new patients

International audienceThe 2q37 locus is one of the most commonly deleted subtelomeric regions. Such a deletion has been identified in >100 patients by telomeric fluorescence in situ hybridization (FISH) analysis and, less frequently, by array-based comparative genomic hybridization (array-CGH). A recognizable ‘2q37-deletion syndrome’ or Albright’s hereditary osteodystrophy-like syndrome has been previously described. To better map the deletion and further refine this deletional syndrome, we formed a collaboration with the Association of French Language Cytogeneticists to collect 14 new intellectually deficient patients with a distal or interstitial 2q37 deletion characterized by FISH and array-CGH. Patients exhibited facial dysmorphism (13/14) and brachydactyly (10/14), associated with behavioural problems, autism or autism spectrum disorders of varying severity and overweight or obesity. The deletions in these 14 new patients measured from 2.6 to 8.8 Mb. Although the major role of HDAC4 has been demonstrated, the phenotypic involvement of several other genes in the deleted regions is unknown. We further refined the genotype–phenotype correlation for the 2q37 deletion. To do this, we examined the smallest overlapping deleted region for candidate genes for skeletal malformations (facial dysmorphism and brachydactyly), overweight, behavioural problems and seizures, using clinical data, a review of the literature, and the Manteia database. Among the candidate genes identified, we focus on the roles of PRLH, PER2, TWIST2, CAPN10, KIF1A, FARP2, D2HGDH and PDCD1