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Dans cette étude, la mise en place d'un banc de métrologie optique permettant la mesure d'extinction et de diffusion angulaire à différentes longueurs d'ondes par des nanoparticules de suie issus d'une flamme de laboratoire est présentée. Le couplage de ces mesures optiques, associé à une théorie analytique de l'interaction lumière-nanoparticules (Rayleigh Debye Gans for Fractal Aggregate RDG-FA) permet l'estimation inédite de champs 2D de différentes mesurandes au sein de particules telles que la fraction volumique, la taille ou des variations de l'indice optique induites par la modification de leur composition., In this study, the implementation of an optical bench allowing the measurement of extinction and angular scattering at different wavelengths on soot nanoparticles from a laboratory flame is presented. The coupling of these optical measurements, associated with an analytical theory of the light-nanoparticle interaction (Rayleigh Debye Gans for Fractal Aggregate RDG-FA), allows an original estimation of 2D fields of different measurands within particles such as volume fraction, size, or variations of the optical index caused by a change of composition of the particles.

High-k materials are needed to minimise the gate leakage current in high-speed and high-power switching applications. In this regard, aluminium oxide (Al2O3) deposited by plasma enhanced atomic layer deposition (PEALD) is gaining extensive attention to be used as high-k material in microelectronics. In this work, we studied the effect of substrate biasing during the oxidizing plasma step on physical, chemical and electrical properties of Al2O3 thin films grown by PEALD on silicon substrate. We show that the structural and electrical properties such as the flat band voltage, and chemical composition can be tuned with the applied substrate bias. Indeed, we highlight that the dielectric constant of the MIS capacitor decreases from 8.5 to 6.5 and the charge polarity of the film is modulated from negative to positive when the applied substrate bias is increased. Using morphological and structural characterisations, we show that the substrate bias significantly affects the chemical composition of Al2O3 thin film layer. Moreover, we highlight by cross-sectional transmission electron the presence of an interfacial layer between Si and Al2O3 which could significantly influence the electrical properties of the deposited thin film. The chemical composition of this interfacial layer can be controlled by the applied substrate bias. Using a series of energy dispersive x-ray experiments, we further confirm the formation of aluminosilicate under low substrate bias condition while silicon oxide is formed under high bias. These findings show that the substrate biasing plays a critical role in defining physical, chemical as well as electrical properties of the PEALD Al2O3 thin films.

Dans cette étude, une cavité optique multispectrale (405, 450, 520 nm) permettant la mesure d'extinction et de diffusion à 90° est développée. Cette cavité a pour but d'être miniaturisée et finalement embarquée sur un drone pour des mesures résolues temporellement et spatialement afin d'étudier les panaches de fumées ou pour le monitoring de la qualité de l'air. L'une des premières étapes de ce développement, et qui est l'objet de cette étude, est la mise en place de la cavité et son étalonnage. En effet, la détermination des coefficients de réflexion des miroirs constituant la cavité et la calibration des capteurs sont une étape cruciale pour la qualité des mesures. Pour ce faire, nous avons utilisé la génération d'un aérosol « étalon » (miniCAST) en laboratoire couplée à la théorie de Rayleigh-Debye-Gans pour les agrégats fractals (RDG-FA). A termes, cette cavité permettra d'obtenir des informations sur la fraction volumique, leur taille ainsi que des informations sur leurs propriétés optiques via la dépendance spectrale de l'indice optique., In this study, a multispectral optical cavity (405, 450, 520 nm) allowing the measurement of extinction and scattering at 90° is developed. This cavity is intended to be miniaturized and eventually mounted on a UAV for temporally and spatially resolved measurements to study smoke plumes or for air quality monitoring. One of the first steps in this development, which is the subject of this study, is the design and commissioning of the cavity and its calibration. Indeed, the determination of the reflection coefficients of the mirrors constituting the cavity and the calibration of the sensors are a crucial step for the quality of the measurements. For this purpose, we used the generation of a "standard" aerosol (miniCAST) in the laboratory coupled with the Rayleigh Debye Gans for Fractal Aggregates (RDG-FA) theory. In the future, this cavity will provide information on the soot volume fraction, size and optical properties through the spectral dependence of the optical index.

Background With recent advances in microscopy, recordings of cell behaviour can result in terabyte-size datasets. The lattice light sheet microscope (LLSM) images cells at high speed and high 3D resolution, accumulating data at 100 frames/second over hours, presenting a major challenge for interrogating these datasets. The surfaces of vertebrate cells can rapidly deform to create projections that interact with the microenvironment. Such surface projections include spike-like filopodia and wave-like ruffles on the surface of macrophages as they engage in immune surveillance. LLSM imaging has provided new insights into the complex surface behaviours of immune cells, including revealing new types of ruffles. However, full use of these data requires systematic and quantitative analysis of thousands of projections over hundreds of time steps, and an effective system for analysis of individual structures at this scale requires efficient and robust methods with minimal user intervention. Results We present LLAMA, a platform to enable systematic analysis of terabyte-scale 4D microscopy datasets. We use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, generating detailed object level statistics. Our system is designed to run on high-performance computing to achieve high throughput, with outputs suitable for visualisation and statistical analysis. Advanced visualisation is a key element of LLAMA: we provide a specialised tool which supports interactive quality control, optimisation, and output visualisation processes to complement the processing pipeline. LLAMA is demonstrated in an analysis of macrophage surface projections, in which it is used to i) discriminate ruffles induced by lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1) and ii) determine the autonomy of ruffle morphologies. Conclusions LLAMA provides an effective open source tool for running a cell microscopy analysis pipeline based on semantic segmentation, object analysis and tracking. Detailed numerical and visual outputs enable effective statistical analysis, identifying distinct patterns of increased activity under the two interventions considered in our example analysis. Our system provides the capacity to screen large datasets for specific structural configurations. LLAMA identified distinct features of LPS and CSF-1 induced ruffles and it identified a continuity of behaviour between tent pole ruffling, wave-like ruffling and filopodia deployment.

Conservation efforts often focus on umbrella species whose distributions overlap with many other flora and fauna. However, because biodiversity is affected by different threats that are spatially variable, focusing only on the geographic range overlap of species may not be sufficient in allocating the necessary actions needed to efficiently abate threats. We developed a problem‐based method for prioritizing conservation actions for umbrella species that maximizes the total number of flora and fauna benefiting from management while considering threats, actions, and costs. We tested our new method by assessing the performance of the Australian federal government's umbrella prioritization list, which identifies 73 umbrella species as priorities for conservation attention. Our results show that the federal government priority list benefits only 6% of all Australia's threatened terrestrial species. This could be increased to benefit nearly half (or 46%) of all threatened terrestrial species for the same budget of AU$550 million/year if more suitable umbrella species were chosen. This results in a 7‐fold increase in management efficiency. We believe nations around the world can markedly improve the selection of prioritized umbrella species for conservation action with this transparent, quantitative, and objective prioritization approach., Article impact statement: Considering spatially overlapping species, threats, actions, and costs in conservation prioritization yields 7‐fold increase in efficiency.

Isolated right ventricular acute myocardial infarction is rare and its presentation can sometimes mimic an anterior ST-segment elevation myocardial infarction. We reported two cases of isolated right ventricular acute myocardial infarction presenting with a ST-elevation in anterior leads. The first case was admitted for an out-of-hospital cardiac arrest due to ventricular fibrillation. The patient died from neurologic consequences of the cardiac arrest, despite a successful prehospital thrombolysis, followed by a percutaneous angioplasty of the right coronary artery. The second case occurred after a complex percutaneous angioplasty of the right coronary artery, complicated by a total occlusion of a right marginal branch. These two cases illustrate the limits of the ECG for the diagnosis of isolated right ventricular acute infarction, and the difficulties of the differential diagnosis with anterior infarction, which may determine the treatment and the prognosis.

Hypo- and hypercalcemia are common and some causes require urgent diagnosis and treatment. Measurement of ionized calcium is the reference test to diagnose calcium disorders but total calcium adjusted for protein or albumin concentration is more often used.Patients hospitalised in a general internal medicine department from September 2013 to December 2015 who had a total plasma calcium concentration and a serum albumin or protein concentration measured within 24h of a ionized calcium blood measurement were included. Total calcium was adjusted for protein or albumin concentration using widely used formulas and compared to ionized calcium as the gold standard.Among 210 included patients, 46 (22%) had hypocalcemia, 124 (59%) normocalcemia and 40 (19%) hypercalcemia according to ionized calcium concentration. Total calcium had 50% sensitivity and 95% specificity to diagnose hypocalcemia and a 93% sensitivity and 89% specificity to diagnose hypercalcemia. Adjusting total calcium for protein or albumin concentrations did not increase and sometimes decreased diagnostic accuracy.Total calcium, with or without albumin/protein adjustment, is poorly sensitive to screen for hypocalcemia. Unadjusted total calcium is as sensitive as protein- or albumin-adjusted total calcium to screen for hypercalcemia. These data argue against the use of albumin- or protein-adjusted calcium. Ionized calcium measurement should be performed to confirm dyscalcemia in patients with abnormal total calcium concentration and to rule out hypocalcemia in patients with total calcium concentration in the lower range of normal values.

The promise of machine learning methods to act as decision support systems for pathologists continues to grow. However, central to their successful adoption must be interpretable implementations so that people can trust and learn from them effectively. Generative modeling, most notable in the form of adversarial generative models, is a naturally interpretable technique because the quality of the model is explicit from the quality of images it generates. Such a model can be further assessed by exploring its latent space, using human-meaningful concepts by defining concept vectors. Motivated by these ideas, we apply for the first time generative methods to histological images of basal cell carcinoma (BCC). By simultaneously learning to generate and encode realistic image patches, we extract feature rich latent vectors that correspond to various tissue morphologies, namely BCC, epidermis, keratin, papillary dermis and inflammation. We show that a logistic regression model trained on these latent vectors can achieve high classification accuracies across 6 binary tasks (86–98%). Further, by projecting the latent vectors onto learned concept vectors we can generate a score for the absence or degree of presence for a given concept, providing semantically accurate “conceptual summaries” of the various tissues types within a patch. This can be extended to generate multi-dimensional heat maps for whole-image specimens, which characterizes the tissue in a similar way to a pathologist. We additionally find that accurate concept vectors can be defined using a small labeled dataset.

Les émissions particulaires de suie, issues des moteurs, sont l'un des principaux contributeurs aux émissions de particules fines dans les zones urbaines. Afin contrôler ces émissions, savoir mesurer la taille des particules et leur concentration en conditions réelles de roulage est nécessaire, requérant de nouvelles technologies de capteur. Dans ce but, disposer de générateurs de suie représentatifs revêt un grand intérêt. Le générateur de suie miniCAST à base de propane a longtemps été utilisé à ces fins et a été largement caractérisé. Cependant, le dispositif est limité à un seul carburant, avec la faiblesse inhérente de n'imiter que partiellement les particules de suie pour des carburants liquides tels que le Diesel et le Kérosène. Le but de cette étude et de caractériser le miniCAST 5201 D à combustible liquide en termes de gamme de fonctionnement, de stabilité de production et de morphologie des particules qu'il génère dans des conditions de laboratoire contrôlées., Engine combustion is one of the main contributors to fine particle emissions in urban areas. In order to control these emissions, new sensor technologies are necessary in order to measure size and number of particulate under on road driving conditions. For this purpose, representative laboratory soot generators are needed. The propane fuel miniCAST soot generator has been the standard for these development purposes and has been extensively characterized. Yet, the device is limited to a single fuel, coming with the inherent weakness of mimicking partially particles for real customer grade liquid fuels such as Diesel and Kerosene. This study aims to characterize the liquid fueled miniCAST 5201 D in terms of operating range, production stability and the morphological properties of the particles produced under controlled laboratory conditions.

International audience; In this work, we study the structural and electrical properties of Hafnium Zirconium Oxide (HZO) thin films deposited by Hf0.5Zr0.5O2 single-target sputtering to fabricate a TiN/(14-/22 nm-thick) HZO/TiN stack. The structural analysis of the HZO thin films performed by in situ x-ray diffraction upon thermal annealing shows the formation of the HZO orthorhombic phase at annealing temperatures as low as 370 °C. X-ray photoelectron spectroscopy interestingly reveals an identical chemical composition of the deposited HZO thin films and the sputtered target, i.e., an Hf:Zr ratio of 1:1. The current–voltage characteristic of the TiN/HZO/TiN stack shows a current density of 10⁻⁵ A/cm² at an applied electric field of 1 MV/cm, which, being rather low, gives a strong indication of the good electrical quality of the HZO layer. Finally, a butterfly-like capacitance–voltage loop is obtained on the TiN/HZO/TiN stack, indicating a ferroelectric behavior of the HZO layer.

We present LLAMA, a pipeline for systematic analysis of terabyte scale 4D microscopy datasets. Analysis of individual biological structures in imaging at this scale requires efficient and robust methods which do not require human micromanagement or editing of outputs. To meet this challenge, we use a machine learning method for semantic segmentation, followed by a robust and configurable object separation and tracking algorithm, and the generation of detailed object level statistics. Advanced visualisation is a key element of LLAMA: we provide a specialised software tool which supports quality control and optimisation as well as visualisation of outputs. LLAMA was used in a quantitative analysis of macrophage surface membrane projections (filopodia, ruffles, tent-pole ruffles) examining the differential effects of two interventions: lipopolysaccharide (LPS) and macrophage colony stimulating factor (CSF-1). Distinct patterns of increased activity were identified. In addition, a continuity of behaviour was found between tent pole ruffling and wave-like ruffling, further defining the role of filopodia in ruffling.

Densely labelled segmentation data for digital pathology images is costly to produce but is invaluable to training effective machine learning models. We make available 290 hand-annotated histopathology tissue sections of the 3 most common skin cancers; basal cell carcinoma (BCC), squamous cell carcinoma (SCC) and intraepidermal carcinoma (IEC). These non-melanoma skin cancers constitute over 90% of all skin cancer diagnoses and hence this dataset gives an opportunity to the scientific community to benchmark analytic methodologies on a significant portion of the dermatopathology workflow. The data represents typical cases of the three cancer types (not requiring a differential diagnosis) across shave, punch and excision biopsy contexts. Each image is accompanied with a segmentation mask which characterizes the section into 12 tissue types, specifically: keratin, epidermis, papillary dermis, reticular dermis, hypodermis, inflammation, glands, hair follicles and background, as well as BCC, SCC and IEC. Included also are cancer margin measurements to work towards automated assessment of surgical margin clearance and tumour invasion. This leaves open many opportunities for researchers to utilize or extend the dataset, building upon recent work on image analysis problems in skin cancer (Thomas et al., 2021).

Introduction La pandemie a COVID-19 est responsable de pres de 3 millions de deces. En l’absence de traitement curatif a ce jour, la prevention est la seule strategie efficace pour la controler. La vaccination est un des piliers de cette prevention. Elle a commence par les sujets les plus fragiles, les residents d’etablissement d’hebergement pour personnes âgees dependantes (EHPAD). Ils ont beneficie des premiers vaccins disponibles a ARN messager depuis le 28 decembre 2020. Ils presentent une immunosenescence physiologique a l’origine habituellement d’une immunite innee moins efficiente et ou d’une immunite adaptative moins durable. Les donnees concernant cette population âgee particuliere sont absentes que ce soit sur la reponse immunitaire naturelle versus l’immunite vaccinale et les facteurs (âge, comorbidites, statut nutritionnel) pouvant modifier les reponses du systeme immunitaire. L’objectif de ce travail est de comparer la qualite de la reponse vaccinale anti-SARS-CoV-2 versus l’immunite naturelle. Materiels et methodes Etude observationnelle prospective monocentrique d’une cohorte de residents d’EHPAD et d’unites de soins de longue duree de plus de 65 ans d’un centre hospitalo-universitaire francais. Les residents beneficient de la vaccination par COMINARTY (BNT162b2), 2 doses a 21 jours d’intervalle. Les objectifs sont de comparer la reponse immunitaire specifique 3 mois apres COVID-19 et 3 mois apres la vaccination. Elle evalue le nombre de lymphocytes T totaux specifiques anti-S et d’anticorps neutralisants (Virus neutralization test) 3 mois apres une infection COVID-19 prouvee avant toute vaccination et 3 mois apres la vaccination chez les sujets « naifs ». La qualite de reponse immunitaire postvaccinale est evaluee en comparant l’amplitude, le nombre de lymphocytes T, d’anticorps totaux anti-S en fonction de la presence d’anticorps prevaccinaux (infection anterieure). La qualite de la reponse immunitaire est comparee en fonction de l’etat nutritionnel des sujets âges et des indicateurs d’immunosenescence sur l’immunite adaptative (nombre de lymphocytes T, taux d’anticorps totaux anti-S et d’anticorps neutralisants en fonction des taux basals de cytokines proinflammatoires, du pourcentage et du nombre absolu de lymphocytes CD4+ naifs, lymphocytes CD8+ de differenciation terminale). Resultats Seules les donnees pour les reponses a 3 mois seront disponibles pour cette communication. Description de la population : EHPAD de 104 lits et USDL de 96 lits avec 162 residents presents avec une moyenne d’âge de 83 ans et des comorbidites lourdes avec un GIR moyen pondere de 761 en EHPAD et de 865 en USLD. Quatre-vingt-sept residents ont presente une infection a COVID-19 documentee entre mars et decembre 2020. Quatre-vingt-quinze pour cent des residents ont beneficie d’une vaccination anti-SARS-CoV-2 et 90 % ont accepte la participation a l’etude. Les premieres donnees a 3 mois en Elispot apres immunisation naturelle anti-S : – sujets > 65 ans : mediane 33 spots (n = 34) ; – sujets > 75 ans : mediane 22 spots (n = 13). Conclusion A suivre.

Introduction La pandémie à COVID-19 est responsable de près de 3 millions de décès. En l’absence de traitement curatif à ce jour, la prévention est la seule stratégie efficace pour la contrôler. La vaccination est un des piliers de cette prévention. Elle a commencé par les sujets les plus fragiles, les résidents d’établissement d’hébergement pour personnes âgées dépendantes (EHPAD). Ils ont bénéficié des premiers vaccins disponibles à ARN messager depuis le 28 décembre 2020. Ils présentent une immunosénescence physiologique à l’origine habituellement d’une immunité innée moins efficiente et ou d’une immunité adaptative moins durable. Les données concernant cette population âgée particulière sont absentes sur la réponse immunitaire vaccinale et les facteurs (âge, comorbidités, statut nutritionnel) pouvant modifier les réponses du système immunitaire. L’objectif de ce travail est de comparer la qualité de la réponse vaccinale anti-SARS-CoV-2 par rapport à des sujets immunocompétents de 18 à 65 ans. Matériels et méthodes Étude observationnelle prospective monocentrique d’une cohorte de résidents d’EHPAD et d’unités de soins de longue durée de plus de 65 ans d’un centre hospitalo-universitaire français par rapport à des professionnels de santé et du médicosocial de 18 à 65 ans. Les 2 groupes bénéficient de la vaccination par COMINARTY(BNT162b2), 2 doses à 21 jours d’intervalle. Les objectifs sont : – évaluer l’amplitude de la réponse lymphocytaire T totale anti-S à 3 mois : nombre de cellules sécrétrices d’interféron gamma ; – évaluer l’amplitude de la réponse lymphocytaire T CD4+ et T CD8+ spécifique anti-S à 3 et 9 mois (nombre de lymphocytes T spécifiques anti-S détectés à 3 et 9 mois, après enrichissement en T CD4+ et en T CD8+) ; – évaluer l’amplitude de la réponse humorale spécifique anti-S à 3 et 9 mois (taux d’anticorps totaux anti-S et d’anticorps neutralisants). Seules les données pour les réponses à 3 mois seront disponibles pour cette communication. Résultats Description de la population: EHPAD de 104 lits et USDL de 96 lits avec 162 résidents présents avec une moyenne d’âge de 83 ans et des comorbidités lourdes avec un GIR moyen pondéré de 761 en EHPAD et de 865 en USLD. Quatre-vingt-sept résidents ont présenté une infection à COVID-19 documentée entre mars et décembre 2020. Quatre-vingt-quinze pour cent des résidents ont bénéficié d’une vaccination anti-SARS-CoV-2 et 90 % ont accepté la participation à l’étude. Pour le groupe contrôle 125 professionnels du CHU ont été inclus à l’étude en collaboration avec l’équipe de la médecine du travail et le centre de vaccination. Les premières données à 3 mois en Elispot après immunisation naturelle anti-S : – sujets 65 ans : médiane 33 spots (n = 34) ; – sujets > 75 ans : médiane 22 spots (n = 13). Conclusion À suivre.

Isolated right ventricular acute myocardial infarction is rare and its presentation can sometimes mimic an anterior ST-segment elevation myocardial infarction. We reported two cases of isolated right ventricular acute myocardial infarction presenting with a ST-elevation in anterior leads. The first case was admitted for an out-of-hospital cardiac arrest due to ventricular fibrillation. The patient died from neurologic consequences of the cardiac arrest, despite a successful prehospital thrombolysis, followed by a percutaneous angioplasty of the right coronary artery. The second case occurred after a complex percutaneous angioplasty of the right coronary artery, complicated by a total occlusion of a right marginal branch. These two cases illustrate the limits of the ECG for the diagnosis of isolated right ventricular acute infarction, and the difficulties of the differential diagnosis with anterior infarction, which may determine the treatment and the prognosis.

Progenitor self-renewal and differentiation is often regulated by spatially restricted cues within a tissue microenvironment. Here, we examine how progenitor cell migration impacts regionally induced commitment within the nephrogenic niche in mice. We identify a subset of cells that express Wnt4, an early marker of nephron commitment, but migrate back into the progenitor population where they accumulate over time. Single cell RNA-seq and computational modelling of returning cells reveals that nephron progenitors can traverse the transcriptional hierarchy between self-renewal and commitment in either direction. This plasticity may enable robust regulation of nephrogenesis as niches remodel and grow during organogenesis.

We apply for the first-time interpretable deep learning methods simultaneously to the most common skin cancers (basal cell carcinoma, squamous cell carcinoma and intraepidermal carcinoma) in a histological setting. As these three cancer types constitute more than 90% of diagnoses, we demonstrate that the majority of dermatopathology work is amenable to automatic machine analysis. A major feature of this work is characterising the tissue by classifying it into 12 meaningful dermatological classes, including hair follicles, sweat glands as well as identifying the well-defined stratified layers of the skin. These provide highly interpretable outputs as the network is trained to represent the problem domain in the same way a pathologist would. While this enables a high accuracy of whole image classification (93.6-97.9%), by characterising the full context of the tissue we can also work towards performing routine pathologist tasks, for instance, orientating sections and automatically assessing and measuring surgical margins. This work seeks to inform ways in which future computer aided diagnosis systems could be applied usefully in a clinical setting with human interpretable outcomes.