Rosai-Dorfman Disease With Pure and Multifocal Cutaneous Lesions: A Case Report

A 52‐year‐old woman developed progressive infiltrated purple and hyperpigmented cutaneous lesions in the face, thighs, armpits, chest, and abdomen evolving forone year. Histopathological examination showed large histiocytes exhibiting intact inflammatory cells in their cytoplasm (emperipolesis). Immunohistochemical analyses showed that the histiocyte population was positive for S100 and CD68, but negative for CD1a. Based on the clinical, histopathological, and immunohistochemical findings, we made the diagnosis of Rosai Dorfman disease (RDD). Our patient didn’t manifest any other extra-cutaneous involvement and all the biological and radiological investigations were normal. This form of pure cutaneous RDD (P-CRDD) with multifocal lesions has been rarely reported. RDD is very rare and hardly recognized in the absence of lymphadenopathy. The diagnosis of this entity involves a combination of histology and immunohistochemistry. To date, there is no standard treatment

Models of Big data storage and analysis in the field of remote sensing

Notre thèse s’inscrit dans le cadre spatiotemporel des images satellitaires, l’analyse du gros volume d'images devient de plus en plus difficile avec l'apparition des capteurs à très hautes résolutions spatiales, spectrales et temporelles. Afin de pouvoir situer notre thèse en rapport avec la littérature, nous avons étudié les principales étapes du pipeline de grand volume de données et nous avons travaillé sur deux contributions principales qui sont le stockage et le traitement des données. Parmi les objectifs de notre thèse est de développer une architecture adaptée pour notre système du point de vue stockage et traitement. Pour la mise en place de cette plateforme nous avons développé un cluster local maître-esclave. Dans la première contribution, il s’agit de la proposition d’un système de stockage physique intelligent et qui tient compte des données hétérogènes, on a étudié plusieurs méthodes de stockage de données massives et des méthodes de représentation des données en se basant sur le système de fichier distribué de hadoop hdfs et les avantages de Nosql permettant de stocker, récupérer et interroger les données massives. Nous avons essayé de les adapter à notre contexte des images satellitaires en se basant sur notre architecture physique ensuite les tester avec une collection de données satellitaires. La deuxième contribution de notre thèse est le traitement des images satellitaires massives après les avoir stockés dans le but de les classifier, où il s’agit de développer une approche de classifications des images satellitaires par apprentissage des labels existants en utilisant les techniques de l’apprentissage profond et les plateformes Spark et Tensorflow.Our work forming part of the spatiotemporal remote sensing images, the analysis of the large volume of images is becoming more difficult with the appearance of sensors with very high spatial, spectral and temporal resolutions. In order to be able to situate our thesis in relation to the literature, we studied the main stages of the large volume data pipeline and we focused on two main contributions which are data storage and data processing. Among the objectives of our thesis is to develop a suitable architecture for our system from the perspective of storage and processing. For the implementation of this platform we developed a local master-slave cluster with several machines including one dedicated for the master node and the others for the slave nodes. The first contribution is the idea of a physical storage system that is intelligent and takes into account heterogeneous data. For this, several methods of big data storage and data representation methods based on the hadoop distributed file system (HDFS) and the benefits of Nosql allowing to store, retrieve and query massive data were investigated. We tried to adapt them to our context of satellite images based on our physical architecture and then test them with in-house satellite data collection.The second contribution of our thesis is the processing of massive satellite images after having stored them in order to classify them, where the aim is to develop an approach to classify satellite images by learning the existing truth-labels. We used deep learning techniques and more particularly the adaptation of the Unet and Vggnet algorithms based on the Apache Spark and Tensorflow platform

Big Remote Sensing Image Classification Based on Deep Learning Extraction Features and Distributed Spark Frameworks

Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification. The obtained results prove that our approach outperforms other methods

Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia

In the context of a changing climate, monitoring agricultural systems is becoming increasingly important. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This plugin was designed to manage large Earth observation (EO) data. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. This result was obtained by support vector machine (SVM). As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool

Hand hygiene and biomedical waste management among medical students: a quasi-experimental study evaluating two training methods

Abstract Background Several studies revealed that medical students have low performance levels of hand hygiene (HH) and biomedical waste management (BMWM). However, there have been limited interventions directed at young students targeting HH and BMWM enhancement. Given these data, we aimed at assessing HH and BMWM among medical students after two training methods. Methods We performed a quasi-experimental study from September 2021 to May 2022, which included fifth-year medical students enrolled in the faculty of Medicine of Monastir (Tunisia). We relied on a conventional training based on presentations and simulations guided by the teacher and a student-centred training method based on courses and simulated exercises prepared by students. We used the WHO HH Knowledge Questionnaire and the “BMWM audit” validated by The Nosocomial Infection Control Committee in France. Results A total of 203 medical students were included (105 in the control group and 98 in the experimental group) with a mean age of 23 ± 0.7 years. Regarding HH, we found a statistically significant increase in post-test scores for both training methods. A higher post-test mean score was noted for student-centred method (14.1 ± 1.9 vs. 13.9 ± 2.3). The overall improvement in good HH knowledge rates was greater after student-centred method compared to conventional training (40.5% vs. 25%). Concerning infectious waste, mean scores were higher after student-centred learning in all hazardous waste management steps (25 ± 3.3 vs. 23.6 ± 5.5). Results Coupling student-centred teaching and continuous supervision could improve HH and BMWM knowledge and practices among medical students

Mutation Spectrum of <i>RB1</i> Gene in Unilateral Retinoblastoma Cases from Tunisia and Correlations with Clinical Features

<div><p>Retinoblastoma, an embryonic neoplasm of retinal origin, is the most common primary intraocular malignancy in children. Somatic inactivation of both alleles of the <i>RB1</i> tumor suppressor gene in a retinal progenitor cell through diverse mechanisms including genetic and epigenetic modifications, is the crucial event in initiation of tumorigenesis in most cases of isolated unilateral retinoblastoma. We analyzed DNA from tumor tissue and from peripheral blood to determine the <i>RB1</i> mutation status and seek correlations with clinical features of 37 unrelated cases of Tunisian origin with sporadic retinoblastoma. All cases were unilateral except one who presented with bilateral disease, in whom no germline coding sequence alteration was identified. A multi-step mutation scanning protocol identified bi-allelic inactivation of <i>RB1</i> gene in 30 (81%) of the samples tested. A total of 7 novel mutations were identified. There were three tumors without any detectable mutation while a subset contained multiple mutations in <i>RB1</i> gene. The latter group included tumors collected after treatment with chemotherapy. There were seven individuals with germline mutations and all presented with advanced stage of tumor. There was no difference in age of onset of RB based on the germline mutation status. Thus 20% of the individuals with sporadic unilateral RB in this series carried germline mutations and indicate the importance of genetic testing all children with sporadic retinoblastoma. These findings help to characterize the spectrum of mutations present in the Tunisian population and can improve genetic diagnosis of retinoblastoma.</p></div

Correlation between tumor stage and presence or absence of germline RB1 mutation.

<p>Correlation between tumor stage and presence or absence of germline RB1 mutation.</p

Frequency of somatic RB1 mutations in RB Tunisian patients (not including LOH).

<p>Frequency of somatic RB1 mutations in RB Tunisian patients (not including LOH).</p