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Background and study aims Esophageal stricture is the most frequent adverse event after endoscopic resection for early esophageal neoplasia. Currently available treatments for the prevention of esophageal stricture are poorly effective and associated with major adverse events. Our aim was to identify transcripts specifically overexpressed or repressed in patients who have developed a post-endoscopic esophageal stricture, as potential targets for stricture prevention. Patients and methods We conducted a prospective single-center study in a tertiary endoscopy center. Patients scheduled for an endoscopic resection and considered at risk of esophageal stricture were offered inclusion in the study. The healthy mucosa and resection bed were biopsied on Days 0, 14, and 90. A transcriptomic analysis by microarray was performed, and the differences in transcriptomic profile compared between patients with and without esophageal strictures. Results Eight patients, four with esophageal stricture and four without, were analyzed. The mean ± SD circumferential extension of the mucosal defect was 85 ± 11 %. The transcriptomic analysis in the resection bed at day 14 found an activation of the interleukin (IL)-1 group (Z score = 2.159, P = 0.0137), while interferon-gamma (INFγ) and NUPR1 were inhibited (Z score = –2.375, P = 0.0022 and Z score = –2.333, P = 0.00131) in the stricture group. None of the activated or inhibited transcripts were still significantly so in any of the groups on Day 90. Conclusions Our data suggest that IL-1 inhibition or INFγ supplementation could constitute promising targets for post-endoscopic esophageal stricture prevention.

The objectives of this exploratory paper are to propose and implement original urban design strategies, but also to highlight the links between cultural and natural heritage at the city–land–sea interface and, in particular, in coastal areas with historical value. For the moment, the tools for evaluating the social and economic “value” of this heritage are extremely limited. Moreover, design and environmental parameters may also come into play in this context, and it is worth considering the creation of an evaluation toolkit. The proposed methodology includes three phases: a documentary analysis, an on-site analysis consisting of a sensory mapping and an analysis by questionnaires. The main result of this work is to demonstrate that the methodology of sensory mapping techniques allows obtaining a holistic global approach where all human senses are considered in the analytical phase of pre-design of a coastal site, thus integrating this approach in a more sustainable design strategy. This methodology was evaluated in a significant natural and historic area. This case study and the literature review demonstrate that it is possible to provide a scheme toolkit that integrates all the parameters of importance identified in urban analysis, sensory mapping, environmental analysis and natural and cultural heritage analysis.

The retrofit of the most energy-intensive buildings represents an opportunity to improve their energy efficiency or to reduce their energy demand. This paper proposes combining computer-aided design (CAD) modeling and the use of energy efficiency software to build a methodology for calculating, visualizing and analyzing building parameters in order to provide retrofit scenarios. Five retrofit scenarios were implemented using the energy software, including the initial operating cost, capital cost and payback period to be evaluated. At the same time, a three-dimensional CAD model was created to perform daylighting and shading simulations to visualize and design the role of building orientation under actual use conditions. These retrofit scenarios were evaluated individually and then combined to examine their performance in terms of cost-effectiveness and energy efficiency. The simulation results show the importance of the building’s orientation, as this directly affects the thermal properties of the walls and openings, as well as the daylighting areas. The simulation results were also used to define the parameters that affect the interoperability of the retrofit solutions. Finally, in addition to the significant reduction in calculation time, the coupling of the CAD software with the energy efficiency software allowed access to information that was not available at the outset.

International audience; This paper proposes an efficient methodology to monitor the formation of cracks in concrete after non-destructive ultrasonic testing of a structure. The objective is to be able to automatically detect the initiation of cracks early enough, i.e. well before they are visible on the concrete surface, in order to implement adequate maintenance actions on civil engineering structures. The key element of this original approach is the wavelet-based multiresolution analysis of the ultrasonic signal received from a sample or a specimen of the studied material subjected to several types of solicitation. This analysis is finally coupled to an automatic identification scheme of the types of cracks based on artificial neural networks (ANNs), and in particular deep learning by convolutional neural networks (CNNs); a technology today at the cutting edge of machine learning, in particular for all applications of pattern recognition. Wavelet-based multiresolution analysis does not add any value in detecting fractures in concrete visible by optical inspection. However, the results of its implementation coupled with different CNN architectures show cracks in concrete can be identified at an early stage with a very high accuracy, i.e. around 98%, and a loss function of less than 0.1, regardless of the implemented learning architecture.

International audience; Cooperative machine learning has many applications, such as data annotation, where an initial model trained with partially labeled data is used to predict labels for unseen data continuously. Predicted labels with a low confidence value are manually revised to allow the model to be retrained with the predicted and revised data. In this paper, we propose an alternative to this approach: an initial training process called Deep Unsupervised Active Learning. Using the proposed training scheme, a classification model can incrementally acquire new knowledge during the testing phase without manual guidance or correction of decision making. The training process consists of two stages: the first stage of supervised training using a classification model, and an unsupervised active learning stage during the test phase. The labels predicted during the test phase, with high confidence, are continuously used to extend the knowledge base of the model. To optimize the proposed method, the model must have a high initial recognition rate. To this end, we exploited the Visual Geometric Group (VGG16) pre-trained model applied to three datasets: Mathematical Image Analysis (AMI), University of Science and Technology Beijing (USTB2), and Annotated Web Ears (AWE). This approach achieved impressive performance that shows a significant improvement in the recognition rate of the USTB2 dataset by coloring its images using a Generative Adversarial Network (GAN). The obtained performances are interesting compared to the current methods: the recognition rates are 100.00%, 98.33%, and 51.25% for the USTB2, AMI, and AWE datasets, respectively.

International audience; Segmentation of brain tumor images, to refine the detection and understanding of abnormal masses in the brain, is an important research topic in medical imaging. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed, to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied, to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block, to classify it as a tumor or not. A thorough evaluation of the proposed algorithm was performed, using: (1) a private MRI database provided by the Kouba imaging center—Algiers (KICA); (2) the multimodal brain tumor segmentation challenge (BraTS) 2015 database. Estimates of the selected fitness function were first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion, to demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm was then compared to the results of several state-of-the-art techniques. The results obtained, by using the Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements, demonstrated the superiority of the proposed optimized segmentation algorithm over equivalent techniques

Pen-holding postures (PHPs) can significantly affect the speed and quality of writing, and incorrect postures can lead to health problems. This paper presents and experimentally implements a methodology for quickly recognizing and correcting poor writing postures using a digital dot matrix pen. The method first extracts basic handwriting information, including page number, handwriting coordinates, movement trajectory, pen tip pressure, stroke sequence, and pen handling time. This information is then used to generate writing features that are fed into our proposed fusion classification model, which combines a simple parameter-free attention module for convolutional neural networks (CNNs) called NetworkSimAM, CNNs, and an extension of the well-known long short-term memory (LTSM) called Mogrifier LSTM or MLSTM. Finally, the method ends with a classification step (Softmax) to recognize the type of PHP. The implemented method achieves significant results through receiver operating characteristic (ROC) curves and loss functions, including a recognition accuracy of 72%, which is, for example, higher than that of the single-stroke model (i.e., TabNet incorporating SimAM). The obtained results show that a promising solution is provided for accurate and efficient PHP recognition and has the potential to improve writing speed and quality while reducing health problems induced by incorrect postures.

The COVID-19 pandemic, which has been destabilizing the world since the end of 2019, has significant repercussions on not only health but also the economy, finances, society, politics, the environments, culture and education [...]

IntroductionAlthough the physiological role of the C-terminal hydrolase domain of the soluble epoxide hydrolase (sEH-H) is well investigated, the function of its N-terminal phosphatase activity (sEH-P) remains unknown.ObjectivesThis study aimed to assess in vivo the physiological role of sEH-P.MethodsCRISPR/Cas9 was used to generate a novel knock-in (KI) rat line lacking the sEH-P activity.ResultsThe sEH-P KI rats has a decreased metabolism of lysophosphatidic acids to monoacyglycerols. KI rats grew almost normally but with less weight and fat mass gain while insulin sensitivity was increased compared to wild-type rats. This lean phenotype was more marked in males than in female KI rats and mainly due to decreased food consumption and enhanced energy expenditure. In fact, sEH-P KI rats had an increased lipolysis allowing to supply fatty acids as fuel to potentiate brown adipose thermogenesis under resting condition and upon cold exposure. The potentiation of thermogenesis was abolished when blocking PPARγ, a nuclear receptor activated by intracellular lysophosphatidic acids, but also when inhibiting simultaneously sEH-H, showing a functional interaction between the two domains. Furthermore, sEH-P KI rats fed a high-fat diet did not gain as much weight as the wild-type rats, did not have increased fat mass and did not develop insulin resistance or hepatic steatosis. In addition, sEH-P KI rats exhibited enhanced basal cardiac mitochondrial activity associated with an enhanced left ventricular contractility and were protected against cardiac ischemia-reperfusion injury.ConclusionOur study reveals that sEH-P is a key player in energy and fat metabolism and contributes together with sEH-H to the regulation of cardiometabolic homeostasis. The development of pharmacological inhibitors of sEH-P appears of crucial importance to evaluate the interest of this promising therapeutic strategy in the management of obesity and cardiac ischemic complications.

SUMMARYEscalation of drug use is a hallmark stage in the transition to addiction and uncovering its underlying brain molecular mechanisms constitutes a considerable challenge. Here, we report in rats with extended access to heroin for self-administration that miR-3594-5p was upregulated in the dorsal striatum at late, but not early, stages during escalation when excessive heroin intake plateaued. Striatal miR-3594-5p bound directly to the 3’UTR region of Sez6 transcript and inhibited its expression, thereby decreasing the mature form of the translated SEZ6 protein. This miR-3594-5p/Sez6 interaction was specific to heroin, as it was not observed with cocaine, and correlated with the severity of heroin intake escalation. Our findings reveal that miRNA alterations during escalation of drug self-administration are spatially and temporally regulated and drug-specific.

Fanconi anemia (FA) is a rare human genetic disorder characterized by bone marrow failure, predisposition to cancer and developmental defects including hypogonadism. Reproductive defects leading to germ cell aplasia are the most consistent phenotypes seen in FA mouse models. We examined the role of the nuclear FA core complex gene Fancg in the development of primordial germ cells (PGCs), the embryonic precursors of adult gametes, during fetal development. PGC maintenance was severely impaired in Fancg−/− embryos. We observed a defect in the number of PGCs starting at E9.5 and a strong attrition at E11.5 and E13.5. Remarkably, we observed a mosaic pattern reflecting a portion of testicular cords devoid of PGCs in E13.5 fetal gonads. Our in vitro and in vivo data highlight a potential role of Fancg in the proliferation and in the intrinsic cell motility abilities of PGCs. The random migratory process is abnormally activated in Fancg−/− PGCs, altering the migration of cells. Increased cell death and PGC attrition observed in E11.5 Fancg−/− embryos are features consistent with delayed migration of PGCs along the migratory pathway to the genital ridges. Moreover, we show that an inhibitor of RAC1 mitigates the abnormal migratory pattern observed in Fancg−/− PGCs.

International audience; In this paper, we propose a new methodology for crack detection and monitoring in concrete structures. This approach is based on a multiresolution analysis of a sample or a specimen of concrete material subjected to several types of solicitation. The image obtained by ultrasonicinvestigation and processed by a customized wavelet is analyzed at various scales in order to detect internal cracks and crack initiation. The ultimate objective of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible. This is achieved through the combination of two major data analysis tools which are wavelets and deep learning. This original procedure is shown to yield a high accuracyclose to 90%. In order to evaluate the performance of the proposed CNN architectures, we also used an open access database, SDNET2018, for the automatic detection of external cracks.

In this paper, we propose a new methodology for crack monitoring in concrete structures. This approach is based on a n this paper, we propose a new methodology for monitoring cracks in concrete structures. This approach is based on a multi-resolution analysis of a sample or a specimen of the studied material subjected to several types of solicitation. The image obtained by ultrasonic investigation and processing by a dedicated wavelet will be analyzed according to several scales in order to detect internal cracks and crack initiation. The ultimate goal of this work is to propose an automatic crack type identification scheme based on convolutional neural networks (CNN). In this context, crack propagation can be monitored without access to the concrete surface and the goal is to detect cracks before they are visible on the concrete surface. The key idea allowing such a performance is the combination of two major data analysis tools which are wavelets and Deep Learning. This original procedure allows to reach a high accuracy close to 0.90. In this work, we have also implemented another approach for automatic detection of external cracks by deep learning from publicly available datasets.

This paper focuses on the development of a predictive cycle life model of all-solid-state thin film Cu/Li/LiPON/LiCoO2 microbatteries based on experimental accelerated aging tests. More than 120 samples were tested in 8 different conditions including several electrochemical cycling modes and different temperature levels. Hence, an original reliability model, based on an exponential function especially designed to describe the degradation mechanism occurring at the positive electrode, is proposed to describe the observed capacity loss evolution of microbatteries. An extended mathematical model, which includes a large set of accelerated factors, physicochemical considerations and statistical parameters is then built around such an exponential function. This model thus enables to simulate a wide variety of battery aging profiles from input parameters such as temperature, depth of charge (DoC), discharge rate, failure rate and capacity fade. We believe that the development of such a reliability model is of most importance not only from an industrial point of view, since it enables to determine precisely the expected behavior of microbatteries in various mission profile conditions, but it represents also a key tool that enables to further understand the observed degradation mechanisms, and to identify any technological optimization opportunities.

With the immersion of a plethora of technological tools in the early post-COVID-19 era in university education, instructors around the world have been at the forefront of implementing hybrid learning spaces for knowledge delivery. The purpose of this experimental study is not only to divert the primary use of a YouTube channel into a tool to support asynchronous teaching; it also aims to provide feedback to instructors and suggest steps and actions to implement in their teaching modules to ensure students’ access to new knowledge while promoting their engagement and satisfaction, regardless of the learning environment, i.e., face-to-face, distance and hybrid. Learners’ viewing habits were analyzed in depth from the channel’s 37 instructional videos, all of which were related to the completion of a computer-aided mechanical design course. By analyzing and interpreting data directly from YouTube channel reports, six variables were identified and tested to quantify the lack of statistically significant changes in learners’ viewing habits. Two time periods were specifically studied: 2020–2021, when instruction was delivered exclusively via distance education, and 2021–2022, in a hybrid learning mode. The results of both parametric and non-parametric statistical tests showed that “Number of views” and “Number of unique viewers” are the two variables that behave the same regardless of the two time periods studied, demonstrating the relevance of the proposed concept for asynchronous instructional support regardless of the learning environment. Finally, a forthcoming instructor’s manual for learning CAD has been developed, integrating the proposed methodology into a sustainable academic educational process.

International audience; Since the beginning of the COVID-19 pandemic, students around the world have seen their schooling completely disrupted. Their teachers had to reorganize in a hurry to be able to give their classes synchronously but at a distance. Thanks to strong political incentives, both at the national and university levels, many tools have been massively deployed: digital learning management systems (e.g. Moodle), collaborative digital platforms (e.g. Google Meets, Microsoft Teams and Zoom), social networks (e.g. Facebook and Twitter) and even the telephone. While the COVID-19 pandemic has highlighted the essential role of digital technologies in higher education, major issues arise regarding the quality of distance education, the learning process itself and the assessment of knowledge and skills. In this study, 81 engineering students in France were followed during several periods of containment in order to provide some answers to these questions. The results of the various knowledge tests carried out at a distance show that the students obtained local scores similar to those expected from face-to-face teaching. According to the results of the satisfaction surveys, for 91.4% of students who had sufficient hardware and software resources, the synchronous approach to e-learning presented few barriers. For the 8.6% of students affected by the digital divide, telephone communications and social networks played a major role in the learning process.

International audience; Limiting inrush currents is usually necessary when AC-DC conversion is used to supply DC loads such as battery chargers in particular, it must comply with IEC 61000-3-3. This document discusses the suitability of an active inrush current limiter that can be used to replace traditional thermistors and NTC relays. This strategy is based on the control of the phase shift of thyristor type power components. It has been implemented in a totem-pole bridgeless power factor corrector (PFC). Experimental results show that this solution differs from traditional solutions to ensure high energy efficiency (higher than 95%) while limiting inrush currents. K e y w o r d s: totem-pole-bridgeless PFC topology, inrush current limitation, control of the phase shift of thyristor

International audience; Since the beginning of the COVID-19 pandemic, students around the world have seen their schooling completely disrupted. Their teachers have had to reorganize in a hurry and even develop digital skills to be able to teach their classes synchronously but at a distance. Thanks to strong political incentives, both at national and university level, many tools have been massively deployed: digital learning management systems (e.g. Moodle), collaborative digital platforms (e.g. Google Meets, Microsoft Teams and Zoom) and even social networks (e.g. Facebook and Twitter). While this unprecedented health crisis has demonstrated the essential role of digital technologies in higher education, major questions arise regarding the quality of distance education, the learning process itself and the evaluation of knowledge and skills acquired at a distance. In this policy paper, about a hundred students from engineering schools and master's degrees in France were followed during several periods of confinement in order to provide some answers to these questions. The performance of the students in this very particular context is analyzed. The feelings of students, but also those of teachers, are also discussed, both on the use of digital technologies and on the digital transformation of higher education

Male infertility is responsible for approximately half of all cases of reproductive issues. Spermatogenesis originates in a small pool of spermatogonial stem cells (SSCs), which are of interest for therapy of infertility but remain not well defined in humans. Using multiparametric analysis of the side population (SP) phenotype and the α-6 integrin, THY1, and β-2 microglobulin cell markers, we identified a population of human primitive undifferentiated spermatogonia with the phenotype β-2 microglobulin (β-2M)

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex & Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

International audience; On 16 March 2020, as a result of the unprecedented global health crisis linked to the emergence of a new form of coronavirus (COVID-19), the 74 universities of France closed their doors, forcing nearly 1.6 million students, as well as their teachers, to find solutions and initiatives that could ensure continuity in teaching. In the reliance on videoconferencing tools, chat, the sharing of documents/tutorials/videos/podcasts, and the use of social networks, many ideas have emerged, but no consensus has developed nor has a common way of doing things been adopted by a majority of teachers. Some software tools, such as Zoom, have also been questioned over data security issues or excessive intrusion into the student learning process. Nevertheless, in these uncertain times, much had to be done so that students can acquire the requisite knowledge, develop skills, and build on what they have learned. How can we ensure that the learning process is as smooth as possible for everyone involved? How can we evaluate knowledge and skills learned at a distance, and their relevance? Four groups of electronic and electrical engineering students in France were monitored during the containment period in order to provide answers to these questions. Lectures, tutorials, practical work, and projects were carried out using the Microsoft Teams and Zoom video conferencing and chat tools to complement activities made available through the digital work environment. In order to ensure equity among all students, especially in view of the digital divide, open access tools/software/applications have been promoted. In the various surveys completed, the engineering students asserted their complete satisfaction with the learning process, the use of distance tools, and the level of mastery of these tools by their teachers. The results of the various knowledge tests show that, for the same course, distance learning does not reduce the performance of the engineering students. Indeed, they obtained local grades similar to those expected in face-to-face teaching. The results presented in this article are not intended to highlight the virtues of distance education, but rather to open up a debate and reflect more widely on the sustainability of this transformation of education in universities.

International audience; Since mid-March 2020, due to the COVID-19 pandemic, higher education has been facing a very uncertain situation, despite the hasty implementation of information and communication technologies for distance and online learning. Hybrid learning, i.e., the mixing of distance and face-to-face learning, seems to be the rule in most universities today. In order to build a post-COVID-19 university education, i.e., one that is increasingly digital and sustainable, it is essential to learn from these years of health crisis. In this context, this paper aims to identify and quantify the main factors affecting mechanical engineering student performance in order to build a generalized linear autoregressive (GLAR) model. This model, which is distinguished by its simplicity and ease of implementation, is responsible for predicting student grades in online learning situations in hybrid environments. The thirty or so variables identified by a previously tested model in 2020–2021, in which distance learning was the exclusive mode of learning, were evaluated in blended learning spaces. Given the low predictive power of the original model, about ten new factors, specific to blended learning, were then identified and tested. The refined version of the GLAR model predicts student grades to within ±1 with a success rate of 63.70%, making it 28.08% more accurate than the model originally created in 2020–2021. Special attention was also given to students whose grade predictions were underestimated and who failed. The methodology presented is applicable to all aspects of the academic process, including students, instructors, and decisionmakers.

Two major obstetric diseases, preeclampsia (PE), a pregnancy-induced endothelial dysfunction leading to hypertension and proteinuria, and intra-uterine growth-restriction (IUGR), a failure of the fetus to acquire its normal growth, are generally triggered by placental dysfunction. Many studies have evaluated gene expression deregulations in these diseases, but none has tackled systematically the role of alternative splicing. In the present study, we show that alternative splicing is an essential feature of placental diseases, affecting 1060 and 1409 genes in PE vs controls and IUGR vs controls, respectively, many of those involved in placental function. While in IUGR placentas, alternative splicing affects genes specifically related to pregnancy, in preeclamptic placentas, it impacts a mix of genes related to pregnancy and brain diseases. Also, alternative splicing variations can be detected at the individual level as sharp splicing differences between different placentas. We correlate these variations with genetic variants to define splicing Quantitative Trait Loci (sQTL) in the subset of the 48 genes the most strongly alternatively spliced in placental diseases. We show that alternative splicing is at least partly piloted by genetic variants located either in cis (52 QTL identified) or in trans (52 QTL identified). In particular, we found four chromosomal regions that impact the splicing of genes in the placenta. The present work provides a new vision of placental gene expression regulation that warrants further studies. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-020-02248-x.

International audience; This chapter deals with a simplified, meaningful thermal model to calculate photovoltaic (PV) cell temperature, which is of utmost importance in determining the electrical energy efficiency of PV modules. This model, which has been applied to standard PV modules (or Glass/Backsheet solar panels), takes into consideration ambient temperature, incident irradiance, and wind speed. A complete experimental database was built to calibrate this model. The results of the thermal modelling provide a heat transfer coefficient law, which may be implemented in a PV simulation tool. Two key elements of this approach can be highlighted. The results enable better calculation of the electrical energy production of PV modules and help to predict the energy efficiency of PV units.