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23 results on '"Atoui M"'

1. Negative moments as the signature of the radial density at small distances

Author

Atoui, M., Hoballah, M., Lassaut, M., and Van de Wiele, J.

Subjects
Nuclear Theory, Nuclear Experiment
Abstract

The present paper proposes a robust evaluation of any radial density at small distances using negative-order radial moments evaluated in momentum space. This evaluation provides a valuable insight into the behavior of a given radial density in the vicinity of $r=0$, and puts strong emphasis on the importance of measuring form factors at large squared four-momentum transfer, a domain essential for the determination of negative order moments. A specific attention is paid to the regularization scheme directly affecting the numerical determination of the radial density's parametrization. The proposed method is applied to non-relativistic study cases of the nucleon electric ($G_{En}, G_{Ep}$), and proton magnetic $G_{Mp}$ form factors. The validation is performed through comparison of the results of the approach to the analytically determined Maclaurin expansion - in the vicinity of $r=0$ - of the radial density function. The method is also applied to the relativistic Dirac form factor $F_1$ of the proton. In such a non-trivial case, the Maclaurin development might not exist for the radial density, rendering the determination from the proposed method extremely important., Comment: 11 pages, 8 plots

Published
2024

2. Double Gradient Reversal Network for Single-Source Domain Generalization in Multi-mode Fault Diagnosis

Author

Li, Guangqiang, Atoui, M. Amine, and Li, Xiangshun

Subjects
Computer Science - Machine Learning
Abstract

Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and only single-mode fault data can be obtained. Extracting domain-invariant fault features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. Therefore, double gradient reversal network (DGRN) is proposed. First, the model is pre-trained to acquire fault knowledge from the single seen mode. Then, pseudo-fault feature generation strategy is designed by Adaptive instance normalization, to simulate fault features of unseen mode. The dual adversarial training strategy is created to enhance the diversity of pseudo-fault features, which models unseen modes with significant distribution differences. Subsequently, domain-invariant feature extraction strategy is constructed by contrastive learning and adversarial learning. This strategy extracts common features of faults and helps multi-mode fault diagnosis. Finally, the experiments were conducted on Tennessee Eastman process and continuous stirred-tank reactor. The experiments demonstrate that DGRN achieves high classification accuracy on unseen modes while maintaining a small model size.

Published
2024

3. Spatial Clustering Approach for Vessel Path Identification

Author

Abuella, Mohamed, Atoui, M. Amine, Nowaczyk, Slawomir, Johansson, Simon, and Faghan, Ethan

Subjects
Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, 68T10 (Primary), 68T20 (Secondary), G.1.3, H.3.4
Abstract

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five classes achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation., Comment: Keywords: Spatial clustering, vessel path identification, maritime transportation, average nearest neighbor distance, hierarchical clustering, likelihood estimation. This preprint has 12 pages, 14 figures, 2 tables

Published
2024
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4. Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications

Author

Lou, Chuyue and Atoui, M. Amine

Subjects
Computer Science - Machine Learning
Abstract

At present, decision making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, Convolutional Neural Network (CNN) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health state recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a One-vs-Rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRN learn state-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based decision schemes incorporating OVRN have outstanding recognition ability for samples of unknown heath states, while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms conventional CNNs, and the one based on residual and multi-scale learning has the best overall performance.

Published
2023

5. Determination of the moments of the proton charge density: is there a proton radius puzzle?

Author

Atoui, M., Barbaro, M. B., Hoballah, M., Keyrouz, C., Kunne, R., Lassaut, M., Marchand, D., Quemener, G., and Voutier, E.

Subjects
Nuclear Theory, High Energy Physics - Phenomenology, Nuclear Experiment
Abstract

The charge radius of the proton can be determined using two different kinds of experiments: the spectroscopy technique, measuring the hyperfine structure of hydrogen atoms, and the scattering technique, deducing the radius from elastic lepton scattering off a proton target. These two methods lead to quite different results, a discrepancy known as the "proton radius puzzle ". To shed light on this problem, we have proposed a novel method for the determination of spatial moments from densities expressed in the momentum space. This method provides a direct access not only to the second order moment, directly related to the proton radius, but to all moments of any real order larger than -3. The method is applied to the global analysis of proton electric form factor experimental data from Rosenbluth separation and low-$Q^2$ experiments, paying specific attention to the evaluation of the systematic errors. Within this analysis, the integer order moments of the proton charge density are evaluated, the moment of second order leading to a new determination of the proton charge radius., Comment: 12 pages, 2 figures, Proceedings of the 40th International Workshop on Nuclear Theory (IWNT), Rila Mountains, July 2023

Published
2023

6. Beam Charge Asymmetries for Deeply Virtual Compton Scattering on the Proton at CLAS12

Author

Voutier, E., Burkert, V., Niccolai, S., Paremuzyan, R., Afanasev, A., Alvarado-Galeano, J. -S., Atoui, M., Barion, L., Battaglieri, M., Bernauer, J., Bianconi, A., Bondi, M., Briscoe, W., Camsonne, A., Capobianco, R., Celentano, A., Chatagnon, P., Chetry, T., Ciullo, G., Cole, P., Contalbrigo, M., Costantini, G., Defurne, M., Deur, A., De Vita, R., Diehl, S., Dupré, R., Elouadrhiri, L., Fernando, I., Filippi, A., Forest, T., Girod, F. -X., Gosta, G., Grames, J., Gueye, P., Habet, S., Hattawy, M., Higinbotham, D., Hobart, A., Hyde, C., Joo, K., Kim, A., Klimenko, V., Kubarovsky, V., Leali, M., Lenisa, P., Marchand, D., Marsicano, L., Mascagna, V., Matamoros, D., McCaughan, M., McKinnon, B., Migliorati, S., Mitra, H. S., Movsisyan, A., Muñoz-Camacho, C., Osipenko, M., Ouillon, M., Pappalardo, L., Pasquini, B., Pasyuk, E., Pilleux, N., Poelker, M., Raue, B., Ripani, M., Santos, R., Schmidt, A., Singh, R., Sokhan, D., Spreafico, M., Stoler, P., Strakovsky, I., Taiuti, M., Venturelli, L., Wang, P. -K., Wei, X., Zhao, Z., Collaboration, the CLAS, and Group, the Jefferson Lab Positron Working

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

The parameterization of the nucleon structure through Generalized Parton Distributions (GPDs) shed a new light on the nucleon internal dynamics. For its direct interpretation, Deeply Virtual Compton Scattering (DVCS) is the golden channel for GPDs investigation. The DVCS process interferes with the Bethe-Heitler (BH) mechanism to constitute the leading order amplitude of the $eN \to eN\gamma$ process. The study of the $ep\gamma$ reaction with polarized positron and electron beams gives a complete set of unique observables to unravel the different contributions to the $ep \gamma$ cross section. This separates the different reaction amplitudes, providing a direct access to their real and imaginary parts which procures crucial constraints on the model dependences and associated systematic uncertainties on GPDs extraction. The real part of the BH-DVCS interference amplitude is particularly sensitive to the $D$-term which parameterizes the Gravitational Form Factors of the nucleon. The separation of the imaginary parts of the interference and DVCS amplitudes provides insights on possible higher-twist effects. We propose to measure the unpolarized and polarized Beam Charge Asymmetries (BCAs) of the $\vec{e}^{\pm}p \to e^{\pm}p \gamma$ process on an unpolarized hydrogen target with {\tt CLAS12}, using polarized positron and electron beams at 10.6 GeV. The azimuthal and $t$-dependences of the unpolarized and polarized BCAs will be measured over a large $(x_B,Q^2)$ phase space using a 100 day run with a luminosity of 0.66$\times 10^{35}$cm$^{-2}\cdot$s$^{-1}$., Comment: Proposal to the Jefferson Lab Program Advisory Committee (PAC51)

Published
2023

7. Determination of the moments of the proton charge density

Author

Atoui, M., Barbaro, M. B., Hoballah, M., Keyrouz, C., Lassaut, M., Marchand, D., Quéméner, G., and Voutier, E.

Subjects
Nuclear Experiment, Nuclear Theory
Abstract

A global analysis of proton electric form factor experimental data from Rosenbluth separation and low squared four-momentum transfer experiments is discussed for the evaluation of the spatial moments of the proton charge density based on the recently published integral method~\cite{Hob20}. Specific attention is paid to the evaluation of the systematic errors of the method, particularly the sensitivity to the choice of the mathematical expression of the form factor fitting function. Within this comprehensive analysis of proton electric form factor data, the moments of the proton charge density are determined for integer order moments, particularly: $\langle r^2 \rangle$=0.682(02)$_{Sta.}$(11)$_{Sys.}$~fm$^2$, $\langle r^3 \rangle$=0.797(10)$_{Sta.}$(58)$_{Sys.}$~fm$^3$, and $\langle r^4 \rangle$=1.02(05)$_{Sta.}$(31)$_{Sys.}$~fm$^4$., Comment: 10 pages, 3 figures

Published
2023

8. Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping

Author

Abuella, Mohamed, Atoui, M. Amine, Nowaczyk, Slawomir, Johansson, Simon, Faghani, Ethan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, De Francisci Morales, Gianmarco, editor, Perlich, Claudia, editor, Ruchansky, Natali, editor, Kourtellis, Nicolas, editor, Baralis, Elena, editor, and Bonchi, Francesco, editor

Published
2023
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13. Recent deep learning models for diagnosis and health monitoring: A review of research works and future challenges.

Author

Lou, Chuyue, Atoui, M. Amine, and Li, Xiangshun

Subjects
*LITERATURE reviews, *FAULT diagnosis, *INDUSTRIALISM, *MACHINE learning, *BIG data
Abstract

As an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions developed based on DL models have received extensive attention in academia and industry along with the rapid improvement of computing power. Therefore, this paper focuses on a comprehensive review of DL model–based FDD and health monitoring schemes in view of common problems of industrial systems. First, brief theoretical backgrounds of basic DL models are introduced. Then, related publications are discussed about the development of DL and graphical models in the industrial context. Afterwards, public data sets are summarized, which are associated with several research papers. More importantly, suggestions on DL model–based diagnosis and health monitoring solutions and future developments are given. Our work will have a positive impact on the selection and design of FDD solutions based on DL and graphical models in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Unknown Health States Recognition with Collective-Decision-Based Deep Learning Networks in Predictive Maintenance Applications

Author

Lou, Chuyue, Atoui, M. Amine, Lou, Chuyue, and Atoui, M. Amine

Abstract

At present, decision-making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, convolutional neural networks (CNNs) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health states recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a one-vs.-rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRNs learn class-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of the Tennessee Eastman process (TEP), the proposed CNN-based decision schemes incorporating an OVRN have outstanding recognition ability for samples of unknown heath states while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms state-of-the-art CNNs, and the one based on residual and multi-scale learning has the best overall performance. © 2023 by the authors.

Published
2024
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20. Recent deep learning models for diagnosis and health monitoring : a review of researches and future challenges

Author

Lou, Chuyue, Atoui, M. Amine, Li, Xiangshun, Lou, Chuyue, Atoui, M. Amine, and Li, Xiangshun

Abstract

As an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions developed based on DL models have received extensive attention in academia and industry along with the rapid improvement of computing power. Therefore, this paper focuses on a comprehensive review of DL model–based FDD and health monitoring schemes in view of common problems of industrial systems. First, brief theoretical backgrounds of basic DL models are introduced. Then, related publications are discussed about the development of DL and graphical models in the industrial context. Afterwards, public data sets are summarized, which are associated with several research papers. More importantly, suggestions on DL model–based diagnosis and health monitoring solutions and future developments are given. Our work will have a positive impact on the selection and design of FDD solutions based on DL and graphical models in the future. © The Author(s) 2023.

Published
2023
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21. No increased risk of Kaposi sarcoma relapse in patients with controlled HIV‐1 infection after switching protease inhibitor‐based antiretroviral therapy

Author

Lajaunie, Rébecca, Cuzin, Lise, Palich, Romain, Makinson, Alain, Bani-Sadr, Firouzé, Duvivier, Claudine, Arvieux, Cedric, Rey, David, Poizot-Martin, Isabelle, Delpierre, Cyril, Delobel, Pierre, Martin-Blondel, Guillaume, Chirouze, C., Drobacheff-Thiébaut, C., Foltzer, A., Bouiller, K., Hustache- Mathieu, L., Lepiller, Q., Bozon, F., Babre, O, Brunel, As., Muret, P., Chevalier, E., Jacomet, C., Laurichesse, H., Lesens, O., Vidal, M., Mrozek, N., Aumeran, C., Baud, O., Corbin, V., Goncalvez, E., Mirand, A, Brebion, A, Henquell, C, Lamaury, I., Fabre, I., Curlier, E., Ouissa, R., Herrmann-Storck, C., Tressieres, B., Receveur, Mc., Boulard, F., Daniel, C., Clavel, C., Roger, Pm., Markowicz, S., Chellum Rungen, N., Merrien, D., Perré, P., Guimard, T., Bollangier, O., Leautez, S., Morrier, M., Laine, L., Boucher, D., Point, P., Cotte, L., Ader, F., Becker, A., Boibieux, A., Brochier, C., Brunel-Dalmas, F., Cannesson, O., Chiarello, P., Chidiac, C., Degroodt, S., Ferry, T., Godinot, M., Livrozet, J.M., Makhloufi, D., Miailhes, P., Perpoint, T., Perry, M., Pouderoux, C., Roux, S., Triffault-Fillit, C., Valour, F., Charre, C., Icard, V., Tardy, J.C., Trabaud, M.A., Ravaux, I., Ménard, A., Belkhir, Ay., Colson, P., Dhiver, C., Madrid, A., Martin-Degioanni, M., Meddeb, L., Mokhtari, M., Motte, A., Raoux, A., Toméi, C., Tissot-Dupont, H., Poizot-Martin, I., Brégigeon, S., Zaegel-Faucher, O., Obry-Roguet, V., Laroche, H, Orticoni, M., Soavi, M.J., Ressiot, E., Ducassou, M.J., Jaquet, I., Galie, S., Colson, H., Ritleng, A.S., Ivanova, A., Debreux, C., Lions, C., Rojas-Rojas, T, Cabié, A., Abel, S., Bavay, J., Bigeard, B., Cabras, O., Cuzin, L., Dupin de Majoubert, R., Fagour, L., Guitteaud, K., Marquise, A., Najioullah, F., Pierre-François, S., Pasquier, J., Richard, P., Rome, K., Turmel, Jm, Varache, C., Atoui, N., Bistoquet, M., Delaporte, E, Le Moing, V., Makinson, A., Meftah, N., Merle de Boever, C., Montes, B., Montoya Ferrer, A., Tuaillon, E., Reynes, J., Lefèvre, B., Jeanmaire, E., Hénard, S., Frentiu, E., Charmillon, A., Legoff, A., Tissot, N., André, M., Boyer, L., Bouillon, Mp., Delestan, M., Goehringer, F., Bevilacqua, S., Rabaud, C., May, T., Raffi, F., Allavena, C., Aubry, O., Billaud, E., Biron, C., Bonnet, B., Bouchez, S., Boutoille, D., Brunet-Cartier, C., Deschanvres, C., Gaborit, B.J., Grégoire, A., Grégoire, M., Grossi, O., Guéry, R., Jovelin, T., Lefebvre, M., Le Turnier, P., Lecomte, R., Morineau, P., Reliquet, V., Sécher, S., Cavellec, M., Paredes, E., Soria, A., Ferré, V., André-Garnier, E., Rodallec, A., Pugliese, P., Breaud, S., Ceppi, C., Chirio, D., Cua, E., Dellamonica, P., Demonchy, E., de Monte, A., Durant, J., Etienne, C., Ferrando, S., Garraffo, R., Michelangeli, C., Mondain, V., Naqvi, A., Oran, N., Perbost, I., Carles, M., Klotz, C., Maka, A., Pradier, C., Prouvost-Keller, B., Risso, K., Rio, V., Rosenthal, E., Touitou, I., Wehrlen-Pugliese, S., Zouzou, G., Hocqueloux, L., Prazuck, T., Gubavu, C., Sève, A., Giaché, S., Rzepecki, V., Colin, M., Boulard, C., Thomas, G., Cheret, A., Goujard, C., Quertainmont, Y., Teicher, E., Lerolle, N., Jaureguiberry, S., Colarino, R., Deradji, O., Castro, A., Barrail-Tran, A., Yazdanpanah, Y., Landman, R., Joly, V., Ghosn, J., Rioux, C., Lariven, S., Gervais, A., Lescure, Fx., Matheron, S., Louni, F., Julia, Z., Le Gac, S., Charpentier, C., Descamps, D., Peytavin, G., Duvivier, C., Aguilar, C., Alby-Laurent, F., Amazzough, K., Benabdelmoumen, G., Bossi, P., Cessot, G., Charlier, C., Consigny, P.H., Jidar, K., Lafont, E., Lanternier, F., Leporrier, J., Lortholary, O., Louisin, C., Lourenco, J., Parize, P., Pilmis, B., Rouzaud, C., Touam, F., Valantin, Ma., Tubiana, R., Agher, R., Seang, Sophie, Schneider, L., Palich, R., Blanc, C., Katlama, C., Bani-Sadr, F., Berger, Jl., N’guyen, Y., Lambert, D., Kmiec, I., Hentzien, M., Brunet, A., Romaru, J., Marty, H., Brodard, V., Arvieux, C., Tattevin, P., Revest, M., Souala, F., Baldeyrou, M., Patrat-Delon, S., Chapplain, J.M., Benezit, F., Dupont, M., Poinot, M., Maillard, A., Pronier, C., Lemaitre, F., Morlat, C., Poisson-Vannier, M., Sinteff, Jp., Gagneux-Brunon, A., Botelho-Nevers, E., Frésard, A., Ronat, V., Lucht, F., Rey, D., Fischer, P., Partisani, M., Cheneau, C., Priester, M., Batard, Ml., Mélounou, C, Bernard-Henry, C., de Mautort, E., Fafi-Kremer, S., Delobel, P., Alvarez, M., Biezunski, N., Debard, A., Delpierre, C., Gaube, G., Lansalot, P., Lelièvre, L., Marcel, M., Martin-Blondel, G., Piffaut, M., Porte, L., Saune, K., Robineau, O., Ajana, F., Aïssi, E., Alcaraz, I., Alidjinou, E., Baclet, V., Bocket, L., Boucher, A., Digumber, M., Huleux, T., Lafon-Desmurs, B., Meybeck, A., Pradier, M., Tetart, M., Thill, P., Viget, N., Valette, M., Service Maladies infectieuses et tropicales [CHU Toulouse], Pôle Inflammation, infection, immunologie et loco-moteur [CHU Toulouse] (Pôle I3LM Toulouse), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), CHU de la Martinique [Fort de France], Epidémiologie et analyses en santé publique : risques, maladies chroniques et handicaps (LEASP), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Département Maladies Infectieuses et Tropicales, Hôpital Universitaire, Montpellier, France, Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Centre Hospitalier Universitaire de Reims (CHU Reims), Service des Maladies infectieuses et tropicales [CHU Necker], CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Imagine - Institut des maladies génétiques (IHU) (Imagine - U1163), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Institut Cochin (IC UM3 (UMR 8104 / U1016)), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Centre Médical de l'Institut Pasteur (CMIP), Institut Pasteur [Paris] (IP)-Université Paris Cité (UPCité), CHU Pontchaillou [Rennes], CHU Strasbourg, Sciences Economiques et Sociales de la Santé & Traitement de l'Information Médicale (SESSTIM - U1252 INSERM - Aix Marseille Univ - UMR 259 IRD), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Toulouse III Paul Sabatier - Faculté de médecine Purpan (UTPS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut Toulousain des Maladies Infectieuses et Inflammatoires (Infinity), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), And The Dat’AIDS study group: C Chirouze, C Drobacheff-Thiébaut, A Foltzer, K Bouiller, L Hustache-Mathieu, Q Lepiller, F Bozon, O Babre, A S Brunel, P Muret, E Chevalier, C Jacomet, H Laurichesse, O Lesens, M Vidal, N Mrozek, C Aumeran, O Baud, V Corbin, E Goncalvez, A Mirand, A Brebion, C Henquell, I Lamaury, I Fabre, E Curlier, R Ouissa, C Herrmann-Storck, B Tressieres, M C Receveur, F Boulard, C Daniel, C Clavel, P M Roger, S Markowicz, N Chellum Rungen, D Merrien, P Perré, T Guimard, O Bollangier, S Leautez, M Morrier, L Laine, D Boucher, P Point, L Cotte, F Ader, A Becker, A Boibieux, C Brochier, F Brunel-Dalmas, O Cannesson, P Chiarello, C Chidiac, S Degroodt, T Ferry, M Godinot, J M Livrozet, D Makhloufi, P Miailhes, T Perpoint, M Perry, C Pouderoux, S Roux, C Triffault-Fillit, F Valour, C Charre, V Icard, J C Tardy, M A Trabaud, I Ravaux, A Ménard, A Y Belkhir, P Colson, C Dhiver, A Madrid, M Martin-Degioanni, L Meddeb, M Mokhtari, A Motte, A Raoux, C Toméi, H Tissot-Dupont, I Poizot-Martin, S Brégigeon, O Zaegel-Faucher, V Obry-Roguet, H Laroche, M Orticoni, M J Soavi, E Ressiot, M J Ducassou, I Jaquet, S Galie, H Colson, A S Ritleng, A Ivanova, C Debreux, C Lions, T Rojas-Rojas, A Cabié, S Abel, J Bavay, B Bigeard, O Cabras, L Cuzin, R Dupin de Majoubert, L Fagour, K Guitteaud, A Marquise, F Najioullah, S Pierre-François, J Pasquier, P Richard, K Rome, J M Turmel, C Varache, N Atoui, M Bistoquet, E Delaporte, V Le Moing, A Makinson, N Meftah, C Merle de Boever, B Montes, A Montoya Ferrer, E Tuaillon, J Reynes, B Lefèvre, E Jeanmaire, S Hénard, E Frentiu, A Charmillon, A Legoff, N Tissot, M André, L Boyer, M P Bouillon, M Delestan, F Goehringer, S Bevilacqua, C Rabaud, T May, F Raffi, C Allavena, O Aubry, E Billaud, C Biron, B Bonnet, S Bouchez, D Boutoille, C Brunet-Cartier, C Deschanvres, B J Gaborit, A Grégoire, M Grégoire, O Grossi, R Guéry, T Jovelin, M Lefebvre, P Le Turnier, R Lecomte, P Morineau, V Reliquet, S Sécher, M Cavellec, E Paredes, A Soria, V Ferré, E André-Garnier, A Rodallec, P Pugliese, S Breaud, C Ceppi, D Chirio, E Cua, P Dellamonica, E Demonchy, A De Monte, J Durant, C Etienne, S Ferrando, R Garraffo, C Michelangeli, V Mondain, A Naqvi, N Oran, I Perbost, M Carles, C Klotz, A Maka, C Pradier, B Prouvost-Keller, K Risso, V Rio, E Rosenthal, I Touitou, S Wehrlen-Pugliese, G Zouzou, L Hocqueloux, T Prazuck, C Gubavu, A Sève, S Giaché, V Rzepecki, M Colin, C Boulard, G Thomas, A Cheret, C Goujard, Y Quertainmont, E Teicher, N Lerolle, S Jaureguiberry, R Colarino, O Deradji, A Castro, A Barrail-Tran, Y Yazdanpanah, R Landman, V Joly, J Ghosn, C Rioux, S Lariven, A Gervais, F X Lescure, S Matheron, F Louni, Z Julia, S Le Gac, C Charpentier, D Descamps, G Peytavin, C Duvivier, C Aguilar, F Alby-Laurent, K Amazzough, G Benabdelmoumen, P Bossi, G Cessot, C Charlier, P H Consigny, K Jidar, E Lafont, F Lanternier, J Leporrier, O Lortholary, C Louisin, J Lourenco, P Parize, B Pilmis, C Rouzaud, F Touam, M A Valantin, R Tubiana, R Agher, S Seang, L Schneider, R Palich, C Blanc, C Katlama, F Bani-Sadr, J L Berger, Y N'Guyen, D Lambert, I Kmiec, M Hentzien, A Brunet, J Romaru, H Marty, V Brodard, C Arvieux, P Tattevin, M Revest, F Souala, M Baldeyrou, S Patrat-Delon, J M Chapplain, F Benezit, M Dupont, M Poinot, A Maillard, C Pronier, F Lemaitre, C Morlat, M Poisson-Vannier, T Jovelin, J P Sinteff, A Gagneux-Brunon, E Botelho-Nevers, A Frésard, V Ronat, F Lucht, D Rey, P Fischer, M Partisani, C Cheneau, M Priester, M L Batard, C Mélounou, C Bernard-Henry, E de Mautort, S Fafi-Kremer, P Delobel, M Alvarez, N Biezunski, A Debard, C Delpierre, G Gaube, P Lansalot, L Lelièvre, M Marcel, G Martin-Blondel, M Piffaut, L Porte, K Saune, O Robineau, F Ajana, E Aïssi, I Alcaraz, E Alidjinou, V Baclet, L Bocket, A Boucher, M Digumber, T Huleux, B Lafon-Desmurs, A Meybeck, M Pradier, M Tetart, P Thill, N Viget, M Valette, Malbec, Odile, Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Toulouse (UT)-Université de Toulouse (UT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Service de Maladies Infectieuses et Tropicales [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Hospitalier Universitaire de Rennes (CHU Rennes), Laboratoire de Physique des Lasers (LPL), Université Paris 13 (UP13)-Centre National de la Recherche Scientifique (CNRS)-Université Sorbonne Paris Nord, Service d'Immuno-hématologie clinique [Hôpital Sainte Marguerite - APHM], Assistance Publique - Hôpitaux de Marseille (APHM)-Hôpital Sainte-Marguerite [CHU - APHM] (Hôpitaux Sud ), Institut des sciences de la santé publique [Marseille] (ISSPAM), European Infective Endocarditis Registry (Euro-Endo), EMERGEN consortium, Stratégies thérapeutiques contre l'infection VIH et les maladies virales associées [iPLesp] (THERAVIR), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Laboratoire Microorganismes : Génome et Environnement (LMGE), and Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)

Subjects
medicine.medical_specialty, MESH: CD4 Lymphocyte Count, [SDV]Life Sciences [q-bio], antiretroviral therapy, Human immunodeficiency virus (HIV), protease inhibitors, HIV Infections, medicine.disease_cause, MESH: HIV-1, Acquired immunodeficiency syndrome (AIDS), MESH: Neoplasm Recurrence, Local / complications, Internal medicine, medicine, Humans, HHV8, MESH: HIV Infections* / complications, MESH: Protease Inhibitors / adverse effects, Pharmacology (medical), Protease inhibitor (pharmacology), Sarcoma, Kaposi, Retrospective Studies, MESH: Humans, business.industry, Health Policy, Kaposi sarcoma, MESH: Retrospective Studies, Viral Load, MESH: HIV Infections* / drug therapy, medicine.disease, Antiretroviral therapy, switch, CD4 Lymphocyte Count, AIDS, [SDV] Life Sciences [q-bio], Regimen, Infectious Diseases, Increased risk, MESH: Sarcoma, Kaposi* / drug therapy, HIV-1, Sarcoma, Neoplasm Recurrence, Local, business, MESH: Viral Load, Viral load
Abstract

International audience; Objectives: Our aim was to assess if switching from a protease inhibitors (PI)-based regimen to a PI-free one is associated with an increased risk of Kaposi Sarcoma (KS) relapse among patients living with HIV (PLHIV) with history of KS and controlled HIV replication.Methods: In a retrospective analysis of the prospectively collected Dat'AIDS database we selected patients who both had a past KS history and a HIV-1 viral load below 200 copies/mL while being PI-treated. We searched for KS relapses while persistent virological success was maintained for at least 6 months, whether patients kept taking the PI, or switched to PI-free regimen.Results: Among the 216 patients with past KS event and a history of HIV-1 infection efficiently treated by a PI-based regimen, 148 patients (68.5%) later switched to a PI-sparing regimen. Their baseline characteristics were not different from non-switching patients. We described 7 cases of relapse (3.2% of the 216 patients). Five cases of relapse occurred in switching patients (3.4%). The remaining two relapses occurred in PI-treated patients (2.9%). At KS relapse, CD4 cell count was 459 cells/μL (range 225-560) for switching patients, compared with 362 and 136 cells/μL for the other two patients.Conclusions: In this large cohort of PLHIV with a history of KS and ART-controlled HIV replication, KS relapses were described in 3.2% of the patients, and were not more frequent when a PI-containing ART regimen has been switched to a PI-free regimen. Our results do not support a specific effect of PI on KS.

Published
2022

22. Psychosocial impacts, preventive behaviours, and concerns associated with the COVID-19 pandemic for pregnant and non-pregnant women: A matched analysis from the International iCARE Study.

Author

Séguin K, Suarthana E, Okun ML, Atoui M, Lavoie KL, and Herba CM

Subjects
Matched-Pair Analysis, Linear Models, Logistic Models, Chronic Disease, Mental Disorders complications, Mental Disorders psychology, Humans, Female, Pregnancy, Adolescent, Young Adult, Adult, Middle Aged, Psychosocial Functioning, Health Behavior, COVID-19 complications, COVID-19 psychology
Abstract

Background:  The COVID-19 pandemic negatively affected physical and psychological health worldwide. Pregnant women were likely more vulnerable to mental health difficulties due to the significant social, psychological, and hormonal changes they experience. During the pandemic, higher rates of antenatal depression and anxiety were observed compared to pre-pandemic rates. Increased mental health symptoms in pregnancy have been associated with adverse outcomes for child development. Understanding pandemic-specific preventive behaviours (i.e., mask use, physical distancing) and concerns may also be linked to maternal psychological well-being., Purpose: To compare matched pregnant and non-pregnant women (N = 474) to assess COVID-19 psychosocial impacts, preventive behaviours, and concerns., Methods: This study used a matched analysis of data collected by the International COVID-19 Awareness and Responses Evaluation (iCARE) Study. Participants were matched on several demographic factors and analyses were adjusted for chronic illness and psychiatric disorder., Results: Linear regression analyses indicated that pregnant women did not significantly differ from matched non-pregnant women for psychosocial impacts (B = 0.11, SE = 0.08, p = 0.178). Those who reported a chronic illness (B=-0.19, SE = 0.09, p = 0.036) or a psychiatric disorder (B=-0.28, SE = 0.09, p = 0.003) were more likely to report more significant psychosocial impacts. Logistic regression analyses indicated that pregnant women were more likely to report staying at home rather than going to work (OR = 2.01, 95% CI 1.31-3.08, p = 0.002) and being concerned about being infected (OR = 1.61, 95% CI 1.05-2.46, p = 0.028)., Conclusions: Our findings in the context of the COVID-19 pandemic highlight the need to consider interventions targeting women, with chronic illnesses or psychiatric disorders, as they are often the most vulnerable., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.)

Published
2024
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23. Assessment of groundwater vulnerability and pollution risk using AVI, SPI, and RGPI indexes: applied to southern Gabes aquifer system, Tunisia.

Author

Atoui M and Agoubi B

Subjects
Tunisia, Water Pollution, Water Supply, Environmental Monitoring methods, Groundwater chemistry
Abstract

Southern Gabes aquifer is part of coastal Jeffara plain located in southeastern Tunisia. It consists a semi-arid area in which groundwater is the main source to water supply for several socio-economic sectors. Southern Gabes aquifer suffers from excessive abstraction and heavy anthropogenic pressures that make local groundwater resources threatened by pollution risks. This study aims to assess groundwater vulnerability, evaluate, and delineate groundwater risk regions. For this, a 17 water samples were carried out in the study area and chemical compositions were analyzed. A well-known AVI model has been used to assess aquifer vulnerability and new algorithms of sensitivity to pollution index (PSI) and risk groundwater to pollution index (RGPI) were implemented and used to assess, classify, and map groundwater pollution risk. Results reveal that study area suffers from high risk. Forty one percent of the total surface of study area has a very high risk. Nonetheless, only 30% of study area has a low to insignificant risk to pollution which necessitates taking severe precautions to protect the southern Gabes aquifer system. The method used in this study seems giving more precise results compared to conventional approaches. Moreover, this method allows assessing the pollution risk with flexible and reliable algorithm even with limited dataset. Hence, the poor natural protective capacity of study area needs a rapid intervention by local authorities in order to develop proactive solutions to protect and preserve groundwater resources from pollution risks and establish a long-term program for groundwater resources sustainable development., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Published
2022
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