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This paper presents an adaptation of the harmony search algorithm to solve the storage allocation problem for inbound and outbound containers. This problem is studied considering multiple container type (regular, open side, open top, tank, empty and refrigerated) which lets the situation more complicated, as various storage constraints appeared. The objective is to find an optimal container arrangement which respects their departure dates, and minimize the re-handle operations of containers. The performance of the proposed approach is verified comparing to the results generated by genetic algorithm and LIFO algorithm., 7 pages

International audience; Deep learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space. In this paper, we propose a geometric deep learning approach using rigid and non-rigid transformations, named KShapenet, for 2D and 3D landmark-based human motion analysis. Landmark configuration sequences are first modeled as trajectories on Kendall's shape space and then mapped to a linear tangent space. The resulting structured data are then input to a deep learning architecture, which includes a layer that optimizes over rigid and non-rigid transformations of landmark configurations, followed by a CNN-LSTM network. We apply KShapenet to 3D human landmark sequences for action and gait recognition, and 2D facial landmark sequences for expression recognition, and demonstrate the competitiveness of the proposed approach with respect to state-of-the-art.

Compliant mechanisms (CMs), as a novel type of mechanical systems, utilize the elastic deformation of the builtin elementary flexible members to transfer motion, force and energy. Due to their inherent advantages, CMs have been widely used in various mechanical applications. However, in the current literature, researchers still suffer from the lack of efficient tools and a comprehensive analysis framework for designing CMs. To more systematically and efficiently analyze CMs for application-oriented purposes, this thesis aims to propose a comprehensive methodology of designing, modeling, optimizing and controlling CMs. Detailed derivations have been presented on the aforementioned perspectives, followed by both numerical and experimental validation.; Les mécanismes compliants (CM), en tant que nouveau type de systèmes mécaniques, utilisent la déformation élastique des éléments flexibles intégrés pour transférer le mouvement, la force et l'énergie. En raison de leurs avantages inhérents, les CM sont largement utilisés dans diverses applications mécaniques. Cependant, dans la littérature actuelle, les chercheurs souffrent toujours du manque d'outils efficaces et d'un cadre d'analyse complet pour la conception des CM. Afin d'analyser de manière plus systématique et efficace les CM, cette thèse vise à proposer une méthodologie complète de conception, de modélisation, d'optimisation et de contrôle des CM. Des dérivations détaillées ont été présentées sur les perspectives mentionnées ci-dessus, suivies de validations numériques et expérimentales.

International audience; A photon structure is advanced based on the experimental evidence and the vector potential quantization at a single photon level. It is shown that the photon is neither a point particle nor an infinite wave but behaves rather like a local "wave-corpuscle" extended over a wavelength, occupying a minimum quantization volume and guided by a non-local vector potential real wave function. The quantized vector potential oscillates over a wavelength with circular left or right polarization giving birth to orthogonal magnetic and electric fields whose amplitudes are proportional to the square of the frequency. The energy $\hbar$$\omega$ and momentum $\hbar$$\bar{k}$ are carried by the local wavecorpuscle guided by the non-local vector potential wave function suitably normalized.

International audience; In this technology report we present how to design functionalprototypes of smart home systems, based on Augmented Reality(AR) and Brain-Computer Interfaces (BCI). A prototype was designedand integrated into a home automation platform, aiming to illustrate the potential of combining EEG-based interaction with Augmented Reality interfaces for operating home appliances. Our proposed solution enables users to interact with different types of appliances from ”on-off”-based objects like lamps, to multiple command objects like televisions. This technology report presents the different steps of the design and implementation of the system, and proposes general guidelines regarding the future development of such solutions. These guidelines start with the description of the functional and technical specifications that should be met, before the introduction of a generic and modular software architecture that can be maintained and adapted for different types of BCI, AR displays and connected objects. Overall this technology report paves the way to the development of a new generation of smart home systems, exploiting brain activity and Augmented Reality for direct interaction with multiple home appliances.

International audience; The use of low-level spatial information, in addition to the rich spectral information of Hyperspectral Image (HSI) with the potential of deep learning-based methods, has provided high performance in improving the HSI classification. With this regard, Gabor Filtering has been successfully applied for HSI pixels analysis due to its ability in extracting representative spatial features. Therefore, a novel methodology based on Gabor filtering and multi-view Convolutional Neural Networks (multi-view CNNs) was proposed in this paper. Firstly, Gabor texture features at different scales and orientations performed on the first three principal components of HSI were extracted. Then, spectral and spatial features are introduced separately through spectral, and spatial CNN, respectively. Afterward, their learned feature maps are combined and fed into our spectral-spatial CNN (SSCNN) to learn a fused multi-view representation. Experiments applied on two real HSI datasets, including Indian Pines, and Salinas yield competitive classification performances compared to other state-of-the-art deep learning methods.

Multidrug resistance in Enterobacteriaceae has become a major concern worldwide that is increasingly observed in human, animals, and also cockroaches. In our study, we found that cockroaches may play an important role as a potential vector of multidrug-resistant Enterobacteriaceae in the hospital environment and collective catering.

International audience; Deprecations are a common way to indicate that a given feature or API will not be available in subsequent versions of a library or framework. While raising deprecation warnings lets developers of libraries evolve their APIs, the developers of client applications often have to manually rewrite their applications to adapt to the deprecation (removal, new APIs.. .). Some may use static analysis tools to support the rewriting. However, dynamically-typed languages or the use of reflective features often produce incorrect rewrite candidates. This is a costly activity that can lead to bug introductions. In this article, we present a method deprecation approach and a tool called DEPREWRITER that can automatically rewrite the callers of deprecated methods during program execution. Clients of a deprecated API execute their program and associated tests, and DEPREWRITER dynamically rewrites the source code of methods that called a deprecated API to use the new API. The implementation of DEPREWRITER is based on dynamic program transformation: when a deprecated method is executed, a program transformation engine rewrites and recompiles the caller's code before continuing the execution. The approach presented in this article has been developed by the Pharo consortium. Since 2016, DEPREWRITER is used in production in multiple distributions of the Pharo programming language: Pharo 6, 7, 8, and 9 alpha. This article presents and validates this approach. The validation is done in two steps: first with an analysis of deprecations available in Pharo 8 and second with an open survey of software developers about DEPREWRITER. We studied 367 Pharo 8 deprecations, among which we analyzed the 218 rewriting deprecations that use transformation rules. We identified the validity conditions and reported defects to the community. We also proposed 33 transformation rules to be added to the non-rewriting deprecations. Both contributions were accepted into Pharo 9 alpha. We classified the rules and identified possible points of improvement. In addition, we performed a user survey and collected information from 46 software developers: some of them used existing DEPREWRITER' rules and executed them on their code, others used DEPREWRITER to create rewriting deprecations, and finally, some were not aware of DEPREWRITER. 28 of 46 developers (60%) reported that the rewriting deprecations helped them, while 10 stated the inverse and 8 were uncertain. After discussing the current implementation, we sketch possible implementations for other languages than Pharo, showing that the approach is general enough to be applied to other languages.

International audience

International audience; The synthesis and the optical properties of a family of new fluorenyl phthalocyanines substituted with water-solubilizing triethyleneglycol chains is described. These biocompatible molecules exhibit strong one- and two-photon absorptions, together with fluorescence and photosensitization properties similar to those of their lipophilic analogues, thus outperforming unsubstituted zinc phthalocyanine in terms of brightness and singlet oxygen production. They were subsequently probed in vitro on cancer cells under one and two-photon excitation in both fluorescence imaging and photodynamic therapy experiments. The metal-free compound was shown to be more biocompatible and thus more promising than the zinc complex for theranostic applications.

International audience; This paper proposes a new method to solve the monitoring and anomaly detection problems of Low-power Internet of Things (IoT) devices. However, their performances are constrained by limited processing, memory, and communication, usually using battery-powered energy. Polling driven mechanisms for monitoring the security, performance, and quality of service of these networks should be efficient and with low overhead, which makes it particularly challenging. The present work proposes the design of a novel method based on a Deep Reinforcement Learning (DRL) algorithm coupled with an Unsupervised Learning reward technique to build a pooling monitoring of IoT networks. This combination makes the network more secure and optimizes predictions of the DRL agent in adaptive environments.

International audience; Nowadays, many research studies and industrial investigations have allowed the integration of the Internet of Things (IoT) in current and future networking applications by deploying a diversity of wireless-enabled devices ranging from smartphones, wearables, to sensors, drones, and connected vehicles. The growing number of IoT devices, the increasing complexity of IoT systems, and the large volume of generated data have made the monitoring and management of these networks extremely difficult. Numerous research papers have applied Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques to overcome these difficulties by building IoT systems with effective and dynamic decision-making mechanisms, dealing with incomplete information related to their environments. The paper first reviews pre-existing surveys covering the application of RL and DRL techniques in IoT communication technologies and networking. The paper then analyzes the research papers that apply these techniques in wireless IoT to resolve issues related to routing, scheduling, resource allocation, dynamic spectrum access, energy, mobility, and caching. Finally, a discussion of the proposed approaches and their limits is followed by the identification of open issues to establish grounds for future research directions proposal.

Rift Valley fever (RVF) has been reported in the sub-Saharan region of Africa, Egypt and Arabian Peninsula - Yemen and Saudi Arabia, over the past 20 years and is a threat to both the animal and human populations in Tunisia. Tunisia is considered as a high-risk country for the introduction of RVF due to the informal movements of diseased animals already reported in the neighboring countries. The objective of this study was to assess the status of RVF in small ruminants and camels in Tunisia. A risk-based serological survey was conducted to evaluate the presence of RVF based on spatial qualitative risk analysis (SQRA). Samples were collected from small ruminants (sheep and goats) (n = 1,114), and camels (n = 173) samples, belonging to 18 breeders in 14 governorates between November 2017 and January 2018. Samples were tested using an RVF specific multispecies competitive ELISA. Out of the 1,287 samples tested for the presence of RVF IgG antibodies by ELISA, only one positive sample 0.07% (1/1 287) was detected but not confirmed with the virus neutralization test (VNT) used for confirmation. So far, no RVF outbreaks have been reported in Tunisia and our study confirmed the absence of RVF in livestock up to January 2018. Further investigations are needed to confirm the RVF-free status of Tunisia today., QRA methodology, Risk mapping, Survey, Tunisia, Rift valley fever, Small ruminants, Camels

International audience; In the last decades, under technological progress in electronic and avionics systems, Unmanned Aerial Vehicles (UAVs) have known an increasing use in several military and civilian missions. Multiple UAVs can cooperatively carry out dangerous applications for human operators or missions where human intervention is not needed, such as surveillance and monitoring of areas of interest and physical infrastructures. Due to the lack of proper communication rules and standards, communication remains one of the most crucial design issues for UAVs swarm. Furthermore, UAVs generally operate in frequency bands shared with other users. Hence, these spectral bands become overcrowded and UAVs may face a spectrum scarcity issue. To address the above challenges, it is essential to define a reliable communication architecture for multiple UAVs coordination and efficient bandwidth sharing. A promising technology that can satisfy the spectrum requirements of the emerging UAVs Networks is Cognitive Radio Network (CRN). The goal is to opportunistically use spectral bands with minimum interference to other users or applications, i.e., primary users. In this paper, we consider the problem of spectrum scarcity encountered by UAVs. We present a centralized CRN-based communication approach for UAVs-Ground Control Station (GCS) communication. The GCS is used as a central coordinator to handle bandwidth usage for the UAVs swarm in its coverage zone. It selects, allocates, and shares available frequencies using CRN and Software Defined Radio (SDR). To share the available CRN-frequency between the different UAVs in its scope, the GCS uses dynamic Time Division Multiple Access (TDMA) technique. The performance of the proposed communication approach is evaluated in a surveillance context in terms of the total time required to transfer UAVs data, the total number of packets sent, and the achieved throughput.

CNRS 2, FNEGE 2, HCERES A; International audience; Available technologies in organizations can be exploited for creating useful innovative solutions in a rather quick, efficient and economical way. Despite the huge benefits that can be derived by proactively exploiting available technologies and systems for innovation, there is relatively less academic research devoted to the subject. Motivated by this perceptible gap in the digital innovation literature, we theorize and examine the role of one possible structured creative mechanism (design thinking approach) for exploiting existing digital technologies to facilitate innovation. Grounding our arguments in the 'effective use of IS' and 'technology affordance' literature, we conceptualize innovation agent attributes and actualized innovation affordance achieved through the use of ICT, as the two salient facilitators for digital innovation. Innovation agent attributes are operationalized via domain generic and domain specific attributes of 'creative self-efficacy' and 'affect towards ICT use' and actualized innovation affordance is operationalized as the effective use of available technologies through an 'ICT enabled design-thinking approach'. The hypothesized model is empirically tested via a quasi-experimental method comprising data from a two-wave temporally separated survey. Results establish the significant influence of both-innovation agent attributes and actualized innovation affordance for facilitating digital innovation. Moreover, actualized innovation affordance, operationalized through ICT-DT, has a stronger association with digital innovation compared to the innovation agent attributes. This result underlines the need for organizations to proactively focus on using ICT effectively through structured creativity approaches (such as DT) for facilitating innovative outcomes. The study also offers several implications for research and practice.; Les technologies de l’information (TIC) disponibles dans les organisations peuvent être utilisées pour créer des solutions innovantes d’une manière rapide, efficace et économique. Malgré la nécessité de mieux comprendre les modalités permettant d’utiliser les TIC dans les organisations pour favoriser l’innovation, la littérature sur l’innovation numérique/digitale se concentre principalement sur l’étude de la mise en œuvre de TIC existantes dans de nouveaux contextes ou sur la conception et le développement de nouvelles technologies de l’information. Cet article enrichit ces travaux de recherche en étudiant un mécanisme créatif alternatif mobilisant les technologies digitales existantes pour l’innovation. En ancrant nos arguments dans la littérature sur l’usage efficace des SI et « l’affordance de la technologie », nous conceptualisons les caractéristiques des agents d’innovation et l’affordance actualisée de l’innovation basée sur l’utilisation des TIC, comme les deux principaux facilitateurs de l’innovation digitale. Les caractéristiques des agents d’innovation sont opérationnalisées par le biais de caractéristiques génériques et spécifiques au domaine de « l’auto-efficacité créative » et de « l’affect envers l’utilisation des TIC ». L’affordance de l’innovation actualisée est opérationnalisée comme l’utilisation efficace des technologies disponibles grâce à une approche de conception basée sur les TIC ou encore « ICT enabled design-thinking » (ICT-DT). Le modèle proposé est testé empiriquement via une méthode quasi-expérimentale comprenant des données provenant d’une enquête en deux temps de collecte. Les résultats montrent une influence significative à la fois des caractéristiques de l’agent d’innovation et de l’affordance actualisée de l’innovation pour faciliter l’innovation digitale. Cependant, l’affordance actualisée de l’innovation, opérationnalisée par l’approche ICT-DT, a une relation plus forte avec l’innovation digitale par rapport aux caractéristiques de l’agent d’innovation. Cela souligne la nécessité pour les organisations d’intégrer l’utilisation des TIC pour permettre des approches de créativité structurées (telles que le DT) et encourager les productions innovantes. L’étude offre également plusieurs implications pour la recherche et pour la pratique.

International audience; 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.

The isolation and molecular typing of Toxoplasma gondii strains provides an essential basis for a better understanding of the distribution of genetic diversity and associated human health risks. In this context, we report, the molecular characterization of strains of T. gondii isolated from domestic animals infected with T. gondii in Southern and coastal area of Tunisia. This diversity is compared to that known for the Western European and Mediterranean sub-region.Blood, hearts and/or brains were collected from 766 domestic animals (630 sheep and 136 free-range chickens). Strain isolation from these samples were performed using mouse bioassay. The strains genotyping was carried out with a multiplex PCR technique using 15 microsatellite markers. Viable strains of T. gondii were isolated from 13.4% sheep and 33.3% chickens. Furthermore, the parasite was also detected in three DNA extracts from animal tissue digestates.This study showed a large predominance of type II strains (87.9%) from which two samples were type II variants for W35 locus. The other genotypes were three type III sheep isolates (9.1%) and, for the first time in Tunisia, an isolate of sheep origin of the Africa 4 genotype (3.0%). The comparison of microsatellite alleles of type II strains shows the recent migration of strains between Tunisia and other countries of the world.

13 páginas, 6 figuras, 3 tablas, Background: Cystic echinococcosis (CE) afects predominantly young patients in highly endemic areas. Improved serological methods are needed for the follow-up of CE cases, especially given the high rates of post-surgical relapse that require detection as soon as possible., Methods: We designed a study to investigate the value of antigenic proteins extracted from Echinococcus granu losus (E. granulosus) protoscoleces, and of recombinant B2t and 2B2t proteins, for assessing the efcacy of surgical treatment carried out on CE-afected children. This study was performed on 278 plasma samples collected from 59 Tunisian children surgically treated for CE and monitored for 3 years post-surgery. The patients were classifed according to post-surgical outcomes into a “non-relapsed” (NRCE) and a “relapsed” (RCE) group. We performed in-house ELISAs to measure anti-B2t and anti-2B2t IgG and immunoblotting for the detection of IgG against SDS-PAGE-resolved E. granulosus protoscoleces-specifc antigens. The Wilcoxon test was applied to assess anti-B2t and anti-2B2t IgG levels. We applied the Cochran Q test to compare the distribution of immunoblotting antigenic bands between 1-month and 1-year post-surgery., Results: The probability of being “relapse-free” when a decrease in antibody titers occurred between 1 month and 1 year post-surgery was 81% and 75%, respectively, for anti-B2t and anti-2B2t IgG. We identifed fve protoscolex protein bands of 20, 26/27, 30, 40 and 46 kDa as highly immunoreactive by immunoblot for both RCE and NRCE patients at 1 month post-surgery, and signifcantly lower immunoreactivity after 1 year (p< 10–4) for NRCE compared to RCE patients. The proteins at 26/27 and 40 kDa displayed the best performance in predicting the outcome, with an 84% probability of being relapse-free when the reactivity against the 40 kDa antigen, the doublet at 26/27 kDa, or both was absent or disappeared between 1 month and 1 year post-surgery, and a 93% probability of being relapsed when both bands remained reactive or increased in intensity between the two time points., Conclusions: The B2t protein could be useful for the prediction of CE early post-surgical outcomes. The proteins of E. granulosus protoscoleces, especially the doublet P26/27 and P40, could be promising predictive biomarkers for the post-surgical follow-up of CE cases as well., This study was supported by the Ministry of Higher Education and Scientifc Research, Tunisia, and National Reference Center for Echinococcosis, Besançon, France. EBS received a 3-month fellowship from the University of Monastir to carry out the current study in collaboration with the UMR/CNRS 6249 Chrono-Environnement Research Team, University of Bourgogne Franche-Comté, France.

Aims Following treatment, cystic echinococcosis (CE) exhibits a relatively high relapse rate. Here, we evaluated the value of soluble programmed death-1 (sPD-1), sPD-1 ligand (sPD-L1) and anti-recP29 antibody concentrations, as predictors of early surgical treatment outcome in young CE-affected patients. Methods and results This prospective study included 59 Tunisian children (177 plasmas ), where CE was surgically treated and monitored for 3 post-operative years. Based on CE post-surgical development, patients were clustered into a "No relapsed" CE (NRCE; n = 39) and a "Relapsed" CE (RCE; n = 20) group. Serum levels of sPD-1, sPD-L1 and anti-recP29 IgG were measured using ELISA. In the NRCE group, sPD-1, sPD-L1 and anti-recP29 IgG concentrations were significantly lower at D365 than at D30. By contrast, in the RCE group, no significant difference was observed between D0, D30 and D365.When considering individual variations, the probability to be "relapse-free" was 67% and 73% when anti-recP29 IgG and sPD-L1 level, respectively, decreased between D30 and D365. The probability to be "relapse-free" was 86% when the sPD-1 level decreased between D30 and D365 (p= 0.003; Chi-square test). Conclusion sPD-1 may be a useful biomaker for the early evaluation of surgical procedure efficacy in pediatric CE cases.

Spatial quantification of soil attributes is needed to assess and monitor soil resources. Our objective was to map soil total carbon (TC) over 580 ha bare soils, in Djerid arid area (SW Tunisia). One hundred and forty-four soil samples were collected in nodes of 200 m square grid and their spectra acquired in the laboratory at 400–2500 nm have served to radiometrically correct ASTER image using the empirical line method. TC was predicted using partial least squares regression-kriging model, based on the 144 spectra extracted from the nine visible-near infrared ASTER bands. Residual interpolation has improved prediction efficiency. Indeed, PLSR-kriging achieved higher R2 and lower RMSE than PLSR, respectively: 0.78 against 0.53 and 0.52% against 0.16%. Furthermore, spatial distribution of these quantifications has a physical significance. Our results offer relevant method for soil attribute mapping on large scale.

International audience

International audience; In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.

Drought disasters significantly affected human life and water resources. Therefore, forecasting methods like statistical models, machine learning, and deep learning architectures help scientists to take effective decisions to decrease the effects of natural disasters by providing decision-making plans. Droughts can be forecasted using meteoro-logical indices like the standardized precipitation evapotranspiration index (SPEI), which aid governments in taking drought-prevention steps. In this paper, we present a big drought architecture for drought modeling and forecasting. The proposed architecture is composed of 5 layers: Data collection, data preprocessing, data storage, data processing and interpretation, and decision making. Besides, we present a comparative study between three different methods ARIMA, PROPHET, and LSTM for drought forecasting. Three different metrics are used for the performance evaluation Root Mean Squared Error (RMSE), coefficient of determination (R2), and Mean Squared Error (MAE). Experiments are carried out using data from the province of Jiangsu. Results revealed that LSTM outperformed the other models, and ARIMA outperformed the PROPHET model.

4 tablas y 4 figuras., A novel strain of the genus Promicromonospora, designated PT9T, was recovered from irradiated roots of the xerophyte Panicum turgidum collected from the Ksar Ghilane oasis in southern Tunisia. Strain PT9T is aerobic, non-spore-forming, Gram- positive actinomycete that produces branched hyphae and forms white to yellowish-white colonies. Chemotaxonomic features, including fatty acids, whole cell sugars and polar lipid profles, support the assignment of PT9T to the genus Promicromonospora. The genomic relatedness indexes based on DNA-DNA hybridization and average nucleotide identity values revealed a signifcant genomic divergence between strain PT9T and all sequenced type strains of the taxon. Phylogenomic analysis showed that isolate PT9T was most closely related to Promicromonospora soli CGMCC 4.7398T. Phenotypic and phylogenomic analyses suggest that isolate PT9T represents a novel species of the genus Promicromonospora, for which the name Promicromonospora panici sp. nov. is proposed. The type strain is PT9T (LMG 31103T= DSM 108613T).The isolate PT9T is an ionizing-radiation-resistant actinobacterium (D10 value=2.6 kGy), with resistance to desiccation and hydrogen peroxide. The complete genome sequence of PT9T consists of 6,582,650 bps with 71.2% G+C content and 6291 protein-coding sequences. This genome will help to decipher the microbial genetic bases for ionizing-radiation resistance mechanisms including the response to oxidative stress., This work was performed under the auspices of Université Lyon 1, CNRS UMR 5557, Écologie Microbienne (France) and the National Center for Nuclear Sciences and Technology CNSTN (Tunisia) and in the ambit of the BIODESERT research program of the LR11-ES31 (BVBGR, ISBST, University of Manouba). The LABGeM (CEA/Genoscope & CNRS UMR8030), the France Génomique and French Bioinformatics Institute national infrastructures (funded as part of Investissement d'Avenir program managed by Agence Nationale pour la Recherche, contracts ANR-10-INBS-09 and ANR-11-INBS-0013) are acknowledged for support within them MicroScope annotation platform.

International audience; Physical-layer network coding (PNC) can effectively improve wireless networks by extracting superimposed signals and transforming collision’s dilemma into an advantage. The proposition of higher layer methods (such as Data Link and Network Layer) explicitly designed for PNC can better enhance wireless network performances. Since the field of PNC is very vast and has several applications, the researchers of the higher layers find it very difficult to propose a suitable PNC system for their research, which has led to the scarcity of research works of this type. This article proposes a new classification method designed to help researchers choose the optimal method that suits their system. This paper is a complete guide for researchers of higher layers to discover the PNC field and improve wireless network performances.

In this era of big data, as relational databases are inefficient, NoSQL databases are a workable solution for data storage. In this context, one of the key issues is the veracity and therefore the data quality. Indeed, as with classic data, geospatial big data are generally fuzzy even though they are stored as crisp data (perfect data). Hence, if data are geospatial and fuzzy, additional complexities appear because of the complex syntax and semantic features of such data. The NoSQL databases do not offer strict data consistency. Therefore, new challenges are needed to be overcome to develop efficient methods that simultaneously ensure the performance and the consistency in storing fuzzy geospatial big data. This paper presents a new methodology that tackles the storage issues and validates the fuzzy spatial entities’ consistency in a document-based NoSQL system. Consequently, first, to better express the structure of fuzzy geospatial data in such a system, we present a logical model called Fuzzy GeoJSON schema. Second, for consistent storage, we implement a schema-driven pipeline based on the Fuzzy GeoJSON schema and semantic constraints.

Dealing with missing values is an important issue in data observation or data recording process. In this paper, we consider a functional linear regression model, where some observations of the real response and the functional covariate are affected by missing data. We use a reconstruction operator that aims to recover the missing parts of the explanatory curves, then we are interested in regression imputation method of missing data on the response variable, using functional principal component regression to estimate the functional coefficient of the model. We study the asymptotic behavior of the prediction error we commit when missing data are replaced by the imputed values in the original dataset. The practical behavior of the method is also studied on simulated data and a real dataset.

International audience; This paper deals with the problem of prescribedtime stabilization of controllable linear systems with input delay. The problem is reformulated under a cascade PDE-ODE setting from which a prescribed-time predictor feedback is designed based on the backstepping approach, and whose transformation makes use of time-varying kernels. The bounded invertibility of the transformation is guaranteed. It is proved that the solution converges to the equilibrium in a prescribed-time. A simulation example is presented to illustrate the results.

International audience; The nowadays CPPS (Cyber Physical Production System) depends essentially on Operational Technologies (OTs) that have been acquired from a wide range of Information Technologies (ITs). In order to guarantee the CPPSs requirements, several of ITs have been adjusted or adapted. As a result, new issues appeared, in the design of this production system, such as the integration of safety, security and vulnerability risks. The greater part of CPPSs vulnerabilities have generally not been considered in the design phase. However, this dependence on ITs makes CPPSs increasingly vulnerable to cyber-attacks and security threats, which affect their global performance. The focus of this research is to address the cyber security challenges that these systems have to cope. It discusses the features of the CPPSs threat environment and align safety and security risk analysis. In fact, the first problem in security risk analyzes is determining the likelihood of the cyber-attacks. The use likelihood in security analysis is not practical and sometimes does not make sense. Therefore, this prevents the research community from using a panoply of methods that exist in the field of safety because they are based on the use of probabilities. In this paper it is introduced a new vulnerability scoring system for industrial systems, called ICVSS. The proposed approach based on the calculation of variety of metrics distributed into two groups of metrics inherited from the classical CVSS metrics. The ICVSS not only make possible to assess the vulnerability and to ease the communication between the safety and security teams, but also it is a good alternative to replace the likelihood in order to be able to use these safety methods. Next, a new approach to co-analysis and co-assessment of safety and security called FVMEARA (Failure and Vulnerability Modes, and Effect Analysis and Risk Assessment) is presented. Actually, FVMEARA is a use case that shows the effectiveness of our methodology in which we have been able to reuse the FMEA method, known as the most widely used in the field of safety, to carry out a co-analysis of hazards and threats and to assess their risks. Even more, the proposed solution complies with the standards IEC 60812 for safety and the standard IEC 62443 for security.

International audience; Although sheep are known to be seasonal breeders and give birth in winter, not all of them follow this trend. A few breeds can be mated and give birth all year round, meaning that mothers and neonates will have to face contrasting climatic conditions. The aim of this study was to test whether lambing season affects maternal and neonatal behaviors in D’man sheep. During four different lambing seasons (winter, autumn, summer, spring), periparturient ewes (n = 111) and their lambs (n = 213) were kept under 24-h-video surveillance in order to record post-partum behaviors. Mother-young preference was tested around 48 h after parturition. Lamb vigor was studied by the determination of birth weight, early postnatal behavior and rectal temperature at birth and 48 h later. Litter expulsion time was not affected by lambing season but birth weight was biased against summer and winter born lambs. Ewes provided a higher intensity of care to their offspring in winter: latency for grooming was shorter and time spent grooming was longer compared to lambing in spring and summer (P = 0.01 in all cases). On the other hand, lambs were the most active in spring as they were faster to extend their hind legs (P = 0.01), stand up (P = 0.04) and reach the udder (P = 0.04). Rectal temperature at 48 h was affected by season of birth (P < 0.001) with higher values observed in summer. Glycaemia variation between birth and 48 h was the lowest in spring born lambs and plasma levels increased less in spring born lambs than in winter (P < 0.0001), autumn (P < 0.0001) and summer born lambs (P < 0.0001). In the choice test, mothers clearly preferred their own young and no season effect was detected except that in the first minute of the test they spent less time near their own young in winter than in the other seasons (P = 0.04). Lambs also chose their mother successfully without any major effect of the season however, but winter born lambs were the least vocal (P = 0.01). Overall, this study show that maternal care, lamb behavior and vigor vary lightly according to seasons, albeit not in a consistent manner. In conclusion, a season is no more detrimental than another for the onset of mother-young relationships. Keywords: parturition, maternal behavior, neonate behavior, bonding, glycaemia

International audience; Low-power Internet of Things (IoT) networks are widely deployed in various environments with resource constrained devices, making their states monitoring particularly challenging. In this paper, we propose an adaptive monitoring mechanism for low-power IoT devices, by using a reinforcement learning (RL) method to automatically adapt the polling frequencies of the collected attributes. Our goal is to minimize the number of monitoring packets while keeping accurate and timely detection of threshold crossings associated to supervised attributes. We study the various RL parameter settings under different monitoring attribute behaviors using OpenAi Gym simulator. We implement the RL based adaptive polling in Contiki OS and we evaluate its performance using Cooja simulator. Our results show that our approach converges to optimal polling frequencies and outperforms static periodic notification-based methods by reducing the number of monitoring packets, with a percentage of correctly detected threshold crossings exceeding 80%.

International audience; Surrogate modeling is an increasingly popular tool for engineering design as it enables to model the performance of very complex systems with a limited computational cost. A large number of techniques exists for the surrogate modeling of continuous functions, however, only a very few methods for the surrogate modeling of mixed continuous/discrete functions have been developed. In this chapter, existing adaptations and variants of Gaussian process-based surrogate modeling techniques for mixed continuous/discrete variables are described, discussed and compared on several analytical test-cases and aerospace design problems.

International audience; In just a few years, social networking sites have become the most popular landmarks on the Internet. They revolutionized the way we communicate, and socialized the Web. However, while it is now impossible to deny their impact, it can take a variety of forms, not all of them are positive. As a result, the detection of anomalies on social networks is a topic of current research that has attracted researchers since the 2000s. This problem is of crucial importance to prevent abnormal activities. So far, all existing works have been devoted to one-dimensional networks. Our approach attempts to provide a new anomaly detection method based on examining relationships between OSN users using multidimensional networks.

Monitoring the epidemiological situation of public health remains a challenge, given the exponential growth of health data. Indeed, these latter come from various sources such as, health professionals, biologists, experts in health organizations, etc. In this context, reconciling the search for the greatest amount of appropriate signals and the way of treating them, is the ultimate challenge.

International audience; Business Process (BP) development can be defined as the process of constructing a workflow application by composing a set of services performing BP's activities. In this respect, Cloud Services (CSs) are being increasingly used in BP development to ensure a high level of performance with a low operating cost. Although large companies may benefit from CSs' advantages, Small and Medium-sized Enterprises (SMEs) and startups are falling behind in cloud usage due to missing Information Technology competence, (IT-competence). The crucial challenge facing SMEs and startups in cloud-based BP development is to effectively address the so-called business and IT alignment issue. It represents the alignment between two different domains; one that entails technical cloud resource requirements and another comprising business-level. Formerly, we present this issue as a discovery challenge of suitable CSs performing abstract BP's activities. To address this challenge, firstly, we introduce the concept of cloud-aware BP by proposing a Domain-Specific Language (DSL) named "BP4Cloud" to enrich BP modeling and cover both business and technical requirements. Secondly, we propose an Activity-Services Matching algorithm that automates the discovery of CSs performing BP's activities. As a part of the evaluation, we set up by clarifying the specification of BP4Cloud elements through a proof of concept implementation applied on a real BP. Then, we proceed by evaluating the precision and recall of our Activity-Service Matching algorithm.

With serious concerns on environmental problems related to CO\(_{2}\) emissions and energy crisis, special attention has recently been paid to Electric Vehicles (EVs). Specifically, an accurate estimation of energy consumption for EV is still a big issue. Therefore, quantifying the impact of various external factors on EV energy consumption, such as road topology, driver profile and traffic conditions is a necessity. In this paper, we review the energy consumption models for EVs. Then, we analyze the impact of the road gradient on EV energy consumption through a set of real world experimentations. The obtained results, allow us to better understand the impact of upward and downward gradient on both energy consumption and energy recuperation processes.

International audience; Airborne laser scanning (ALS) data with regression models measure height, diameter and crown tree at an individual tree level to estimate inventory data, including volume, biomass, basal area, and diameter at breast height. The optical images provide high spatial resolution and multispectral reflectance, which improves the accuracy of tree identification. Fusion of multispectral and light detection and ranging data decrease segmentation errors. Many researches demonstrate that fusion reduced RMSE (root mean squared error) by 7% to 8%. In this paper, we study the feasibility of fusing of LIDAR and Multispectral data sets for forest structure parameter assessment. This fusion provides many features such as identifying individual trees, species, measuring tree height and crown diameter. To assess the benefit of fusing ALS and Sentinel images, 4 regression algorithms (Linear Model, Partial least Squares Regression, Random Forest and Support Vector Machine) were compared to model basel area (G) and Gini index (GINI) as a function of ALS variables alone and both ALS and Sentinel 2 variables. Resulting models were compared based on both RMSE (Root Mean Square Error) and R 2 values.

International audience; Rapid and accurate detection of proteins in biological fluids is increasingly required in the biomedical environment. Actually, it is performed with conventional techniques, which are generally run by robotized platforms at centralized laboratories. In this work, molecular dynamics calculations and an experimental procedure were conducted to set up electrochemical sensors based on polypyrrol (PPy) molecular imprinted polymers (MIP) for proteins detection. Here, prostate-specific antigen (PSA) was selected as a template model. Computational calculations indicate that for any PPy conformation and any amino-acid location in the protein, PSA molecules remain strongly inserted in the PPy polymer without biological alterations. One from possible orientations, appeared to be most probable as it presents the lowest absorption energy (-363 kcal mol-1) and largest contact area (4034.1 Å2). The device was then elaborated by in situ electropolymerization of PPy films. MIP's thickness and extraction duration were optimized by chronoamperometry. Square wave voltammetry technique was investigated for PSA detection in standard solution in the concentration range of 3x10 -8 ng.ml-1- 300 ng ml-1. According to the Hill equation, the equilibrium dissociation constant Kdbetween PSA and its imprint was estimated at Kd = (1.02 ± 0.54) × 10-14 M, confirming the strong binding between the designed MIP and the protein as predicted by the computational study. PSA concentration values directly measured in 35 human serum samples were found closely correlated to those measured by the ELISA technique. The promising fast and low-cost sensor might be used successfully for proteins detection at low concentrations with high selectivity and reproducibility.

Toxoplasma gondii is a protozoon parasite that can cause severe clinical problems such as congenital toxoplasmosis. The distribution of T. gondii genotypes varies from one geographic area to another. So far, little is known about the parasite genotypes in Tunisia, North Africa. The present study aimed isolating and genotyping T. gondii from the amniotic fluid (AF) and placenta of pregnant women in Monastir, Tunisia. Amniotic fluid and/or placenta from 80 women who acquired toxoplasma infection during pregnancy were tested by PCR and/or mouse bioassay. Genotyping of T. gondii isolates from these samples was performed with 15 microsatellite markers. Four viable T. gondii strains were isolated from either the AF or placenta of four women. Specifically, strains TUN001-MON1 and TUN002-MON2 were isolated from both the AF and placenta, TUN003-AHA from only the placenta, and TUN004-NEL from only the AF. The four viable strains were not virulent for mice. Genotyping revealed that the four strains were type II strains. This is the first report on isolation and genotyping of T. gondii from AF human samples in Tunisia. Further studies focused on T. gondii genotyping on a larger number of human cases and on animals in Tunisia are needed to improve the knowledge and epidemiology of toxoplasmosis.

A robust nonlinear control is designed for stabilizing linear multi-input–multi-output systems. The presented control law homogenizes a linear system (without its transformation to a canonical form) with a specified degree and stabilizes it in a finite time (or with a fixed-time attraction to any compact set containing the origin) if the degree of homogeneity is negative (positive). The tuning procedure is formalized in an linear matrix inequalities (LMI) form. Performance of the approach is illustrated by numerical and experimental examples.

International audience; “The world is changing” according to Michel Serres in his book “Petite Poucette” (March 2012) and in his detailed speech at the French Academy (November 2017), he argues by: “It should then draw the consequences of this change of the space that affects the human species with the emergence of the “new technologies” of information and communication – in particular by trying to establish a new balance between the material element and the intangible element. “. Historically for Humanity, two major inventions that have profoundly changed the knowledge organization (KO) while associating culture and the transmission of knowledge: it is about writing (around the fourth millennium BC), then printing (in the fifteenth century by the invention of Johannes Gutenberg). Today, with digital technology, it is a major new invention that is imposing and having profound repercussions on the knowledge organization system (KOS) and its transmission of knowledge, on teaching and pedagogy, and on society and its world economy oriented towards the intangible.At the dawn of the twenty-first century, we are witnessing the emergence of a young science called “digital sciences” to designate the information and communication sciences on their hardware and software components. The digital sciences revolutionize knowledge in relation to its technological means, as well as social links (or humanity) with its relationship to knowledge and its organization.To the changes brought about by digital sciences, society as a whole must adapt and consider skills that reinvent tomorrow’s social relationships and its relation to knowledge: what we call the dynamic and interactive Web (web2.0 and web3.0), the Social networks that allow the creation of educational, professional and societal communities, the Collective Production of Knowledge and its relation to the collective intelligence, the risks of the Proliferation of the information and its consequences like the misinformation, the “fake news” or invasion of privacy. All of these changes have a potential and profound impact on education, culture, society and knowledge organization systems (KOS).Faced with the advent of the digital sciences, the governance of knowledge seems to be the scientific policy best suited to the creation of value with regard to Man and his evolution in cultures and civilizations. The task of this governance is to take into consideration the transmission of knowledge related to scientific, technological and communication progress. Intrinsically, this process of knowledge transfer requires a system of knowledge organization and management that implements the production of knowledge, its actors and the digital sciences for its influence in society.The objective of the ISKO-Maghreb chapter continues to contribute to understanding the factors that organize knowledge and the phenomena that affect the information society. The actions to be undertaken by the scholarly society ISKO will have to take into account socio-cultural, cognitive and economic aspects in the strategic management of knowledge. Towards the knowledge society, knowledge must be seen in its dynamics, content and scientific and technological interactions with academics, business and politics (actors and institutions).In this context, a first orientation is pedagogical to try to answer the question “what do we know about knowledge, its organization and its mutation?”. Then, the question evolves towards the societal challenges of knowledge, in theory and in practice, to provide clarifications to “what convergence of KO (Knowledge Organization) and KM (Knowledge Management) approaches that organize knowledge and know-how? “. Education, Digital Sciences, Culture, Information and Communication Sciences, remain the major themes covered by ISKO-Maghreb, for the establishment of knowledge organization, management skills, collective intelligence and Digital Humanities.

International audience; In a global context, more aware than ever, about climate change and challenges, from one hand and, shortages of current energy resources, from the other hand. It remains an undeniable fact today to observe that advanced technologies for industrial purposes and the implementation of efficient use of renewable energies, will be the pillars of modern economies and future challenges.Shumpeter 1912, highlighted the relationship between the innovation factor and economic conjuncture; pattern of economic transition; i.e, theory of economic evolution. Analyzed in the context of economic cycles, the horizons of 2020-2030, appear according to the cycles of Kondratiev 1926, as the beginning of the transition to a new era of production, industrialization and means, which can now be explained by the rise of the green economy theme.The international symposium CITED’2019, in its second edition, aims to bring together the work on concerted and reflective research on the establishment of sustainable economic development based on technological advances, the optimal use of means and resources, and on renewable energies.For researchers, technologists and R&D professionals, both public and private, in CITED’2019 int. event, will be the opportunity to present, share and evaluate scientific research, R&D and innovation, towards the rise of the green economy.

International audience; Excessive reactive oxygen species (ROS) production can induce tissue injury involved in a variety of neurodegenerative disorders such as neurodegeneration observed in pilocarpine-induced temporal lobe epilepsy. Ketamine, a noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonist has beneficial effects in pilocarpine-induced temporal lobe epilepsy when administered within minutes of seizure to avoid the harmful neurological lesions induced by pilocarpine. However, the enzymes involved in ROS productions and the effect of ketamine on this process remain less documented. Here we show that during pilocarpine-induced epilepsy in mice, the expression of the phagocyte NADPH oxidase NOX2 subunits (NOX2/gp91 phox , p22 phox , and p47 phox) and the expression of myeloperoxidase (MPO) were dramatically increased in mice brain treated with pilocarpine. Interestingly, treatment of mice with ketamine before or after pilocarpine administration decreased this process mainly when injected before pilocarpine. Finally, our results showed that pilocarpine induced p47 phox phosphorylation and H 2 O 2 production in mice brain and ketamine was able to inhibit these processes. Our results show that pilocarpine-induced NOX2 activation to produce ROS in mice brain and that administration of ketamine before or after the induction of temporal lobe epilepsy by pilocarpine inhibited this activation in mice brain. These results suggest a key role of the phagocyte NADPH oxidase NOX2 and MPO in epilepsy and identify a novel effect of ketamine.

International audience; High accuracy air pollution monitoring in a smart city requires the deployment of a huge number of sensors in this city. One of the most appropriate wireless technologies expected to support high density deployment is LoRaWAN which belongs to the Low Power Wide Area Network (LPWAN) family and offers long communication range, multi-year battery lifetime and low cost end devices. It has been designed for End Devices (EDs) and applications that need to send small amounts of data a few times per hour. However, a high number of end devices breaks the orthogonality of LoRaWAN transmissions, which was one of the main advantages of LoRaWAN. Hence, network performances are strongly impacted. To solve this problem, we propose a solution called OAPM (Orthogonal Air Pollution Monitoring) which ensures the orthogonality of LoRaWAN transmissions and provides accurate air pollution monitoring. In this paper, we show how to organize EDs into clusters and sub-clusters, assign transmission times to EDs, configurate and synchronize them, taking into account the specificities of LoRaWAN and the features of the air pollution monitoring application. Simulation results corroborate the very good behavior of OAPM.

The ultimate goal of this project is to develop a recommender system for the SmartVideo platform. The platform streams different content of local channels for the Grand Est Region of France to a large public. So, we aim to propose a solution to alleviate the data representation and data collection issue of recommender systems by adopting and adjusting the xAPI standard to fit our case of study and to be able to represent our usage data in a formal and consistent format. Then, we will propose and implement a bunch of recommendation algorithms that we are going to test in order to evaluate our developed recommender system., Le but ultime de ce projet est de développer un système de recommandation dédié à la plateforme SmartVideo de diffusion de vidéo en ligne. En effet, la plateforme met à disposition diverses contenus des chaînes locales de la région Grand Est du France. Alors, nous allons présenter une solution pour alléger le problème de représentation et de collecte de données d’usages par adopter et ajuster le standard xAPI pour représenter et collecter les données de façon simple et formelle. Ensuite, nous allons proposer et implanter des algorithmes de recommandation que nous allons les tester pour évaluer notre système de recommandation.

With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.

Toxoplasma gondii is a protozoan parasite that can be transmitted to humans through a variety of routes including blood transfusion. This study aimed to investigate the seroprevalence of T . gondii infection and associated risk factors in healthy blood donors in Tunisia. A total of 800 healthy blood donors from two blood centers in south and coastal Tunisia were analyzed for anti- T. gondii IgG and IgM antibodies by indirect immunofluorescence assay (IFA) and enzyme-linked immunoassays (ELISA), respectively. Structured questionnaires were used to gather information on risk factors for T . gondii infection during collection. The overall seroprevalence was 44.4% of which 352 (44%) and 3 (0.4%) were positive for IgG and both IgG and IgM anti- T. gondii antibodies, respectively. Multivariate analysis showed that T. gondii seropositivity was significantly associated with the birth place (adjusted odds ratio [OR] = 2.72; 95% confidence interval [CI]: 1.49–4.94) and the age of the donors (adjusted OR = 4.98; 95% CI: 1.50–16.58) which are independent risk factors. In addition, the variables of hand washing before eating (adjusted OR = 0.52; 95% CI: 0.37–0.74) and living in an urban environment (adjusted OR = 0.30; 95% CI: 0.13–0.71) are two protective factors. This study provided the first data on the seroprevalence and epidemiology of T. gondii infection in healthy blood donors in Tunisia., Toxoplasma gondii est un parasite protozoaire qui peut être transmis à l’homme par diverses voies, dont la transfusion sanguine. Cette étude vise à étudier la séroprévalence de l’infection à T. gondii et les facteurs de risque associés chez les donneurs de sang sains en Tunisie. Au total, huit cents donneurs de sang sains de deux centres de transfusion sanguine du sud et de la côte tunisienne ont été analysés respectivement pour la recherche des anticorps IgG et IgM anti- T. gondii par immunofluorescence indirecte (IFA) et par dosage immuno-enzymatique (ELISA). Des questionnaires structurés ont été utilisés pour recueillir des informations sur les facteurs de risque d’infection à T. gondii pendant la collecte. La séroprévalence globale était de 44,4 % dont 352 (44 %) et 3 (0,4 %) étaient respectivement positifs pour les anticorps IgG et IgG/IgM anti- T. gondii . Une analyse multivariée a montré que la séropositivité à T. gondii était significativement associée au lieu de naissance (rapport de côtes ajusté [OR] = 2,72 ; intervalle de confiance à 95 % [IC] : 1,49–4,94) et à l’âge des donneurs (OR ajusté = 4,98 ; IC 95 % : 1,50–16,58) qui sont des facteurs de risque indépendants. De plus, le lavage des mains avant de manger (OR ajusté = 0,52 ; IC 95 % : 0,37–0,74) et vivre dans un milieu urbain (OR ajusté = 0,30 ; IC 95 % : 0,13–0,71) sont deux facteurs de protection. Cette étude a fourni les premières données sur la séroprévalence et l’épidémiologie de l’infection à T. gondii chez les donneurs de sang sains en Tunisie.