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2,430 results on '"Neves, João"'

1. Zero-Shot Action Recognition in Surveillance Videos

Author

Pereira, Joao, Lopes, Vasco, Semedo, David, and Neves, Joao

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract

The growing demand for surveillance in public spaces presents significant challenges due to the shortage of human resources. Current AI-based video surveillance systems heavily rely on core computer vision models that require extensive finetuning, which is particularly difficult in surveillance settings due to limited datasets and difficult setting (viewpoint, low quality, etc.). In this work, we propose leveraging Large Vision-Language Models (LVLMs), known for their strong zero and few-shot generalization, to tackle video understanding tasks in surveillance. Specifically, we explore VideoLLaMA2, a state-of-the-art LVLM, and an improved token-level sampling method, Self-Reflective Sampling (Self-ReS). Our experiments on the UCF-Crime dataset show that VideoLLaMA2 represents a significant leap in zero-shot performance, with 20% boost over the baseline. Self-ReS additionally increases zero-shot action recognition performance to 44.6%. These results highlight the potential of LVLMs, paired with improved sampling techniques, for advancing surveillance video analysis in diverse scenarios.

Published
2024

2. Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-Resolution

Author

Santos, Marcelo dos, Laroca, Rayson, Ribeiro, Rafael O., Neves, João C., and Menotti, David

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Super-resolution algorithms often struggle with images from surveillance environments due to adverse conditions such as unknown degradation, variations in pose, irregular illumination, and occlusions. However, acquiring multiple images, even of low quality, is possible with surveillance cameras. In this work, we develop an algorithm based on diffusion models that utilize a low-resolution image combined with features extracted from multiple low-quality images to generate a super-resolved image while minimizing distortions in the individual's identity. Unlike other algorithms, our approach recovers facial features without explicitly providing attribute information or without the need to calculate a gradient of a function during the reconstruction process. To the best of our knowledge, this is the first time multi-features combined with low-resolution images are used as conditioners to generate more reliable super-resolution images using stochastic differential equations. The FFHQ dataset was employed for training, resulting in state-of-the-art performance in facial recognition and verification metrics when evaluated on the CelebA and Quis-Campi datasets. Our code is publicly available at https://github.com/marcelowds/fasr, Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2024

Published
2024
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3. A Laplacian-based Quantum Graph Neural Network for Semi-Supervised Learning

Author

Gholipour, Hamed, Bozorgnia, Farid, Hambarde, Kailash, Mohammadigheymasi, Hamzeh, Mancilla, Javier, Sequeira, Andre, Neves, Joao, and Proença, Hugo

Subjects
Computer Science - Machine Learning, Quantum Physics
Abstract

Laplacian learning method is a well-established technique in classical graph-based semi-supervised learning, but its potential in the quantum domain remains largely unexplored. This study investigates the performance of the Laplacian-based Quantum Semi-Supervised Learning (QSSL) method across four benchmark datasets -- Iris, Wine, Breast Cancer Wisconsin, and Heart Disease. Further analysis explores the impact of increasing Qubit counts, revealing that adding more Qubits to a quantum system doesn't always improve performance. The effectiveness of additional Qubits depends on the quantum algorithm and how well it matches the dataset. Additionally, we examine the effects of varying entangling layers on entanglement entropy and test accuracy. The performance of Laplacian learning is highly dependent on the number of entangling layers, with optimal configurations varying across different datasets. Typically, moderate levels of entanglement offer the best balance between model complexity and generalization capabilities. These observations highlight the crucial need for precise hyperparameter tuning tailored to each dataset to achieve optimal performance in Laplacian learning methods.

Published
2024

4. Unsupervised Contrastive Analysis for Salient Pattern Detection using Conditional Diffusion Models

Author

Patrício, Cristiano, Barbano, Carlo Alberto, Fiandrotti, Attilio, Renzulli, Riccardo, Grangetto, Marco, Teixeira, Luis F., and Neves, João C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Contrastive Analysis (CA) regards the problem of identifying patterns in images that allow distinguishing between a background (BG) dataset (i.e. healthy subjects) and a target (TG) dataset (i.e. unhealthy subjects). Recent works on this topic rely on variational autoencoders (VAE) or contrastive learning strategies to learn the patterns that separate TG samples from BG samples in a supervised manner. However, the dependency on target (unhealthy) samples can be challenging in medical scenarios due to their limited availability. Also, the blurred reconstructions of VAEs lack utility and interpretability. In this work, we redefine the CA task by employing a self-supervised contrastive encoder to learn a latent representation encoding only common patterns from input images, using samples exclusively from the BG dataset during training, and approximating the distribution of the target patterns by leveraging data augmentation techniques. Subsequently, we exploit state-of-the-art generative methods, i.e. diffusion models, conditioned on the learned latent representation to produce a realistic (healthy) version of the input image encoding solely the common patterns. Thorough validation on a facial image dataset and experiments across three brain MRI datasets demonstrate that conditioning the generative process of state-of-the-art generative methods with the latent representation from our self-supervised contrastive encoder yields improvements in the generated image quality and in the accuracy of image classification. The code is available at https://github.com/CristianoPatricio/unsupervised-contrastive-cond-diff., Comment: 18 pages, 11 figures

Published
2024

5. A Multilevel Strategy to Improve People Tracking in a Real-World Scenario

Author

de Oliveira, Cristiano B., Neves, Joao C., Ribeiro, Rafael O., and Menotti, David

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The Pal\'acio do Planalto, office of the President of Brazil, was invaded by protesters on January 8, 2023. Surveillance videos taken from inside the building were subsequently released by the Brazilian Supreme Court for public scrutiny. We used segments of such footage to create the UFPR-Planalto801 dataset for people tracking and re-identification in a real-world scenario. This dataset consists of more than 500,000 images. This paper presents a tracking approach targeting this dataset. The method proposed in this paper relies on the use of known state-of-the-art trackers combined in a multilevel hierarchy to correct the ID association over the trajectories. We evaluated our method using IDF1, MOTA, MOTP and HOTA metrics. The results show improvements for every tracker used in the experiments, with IDF1 score increasing by a margin up to 9.5%., Comment: Accepted for presentation at the International Conference on Computer Vision Theory and Applications (VISAPP) 2024

Published
2024
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8. Towards Concept-based Interpretability of Skin Lesion Diagnosis using Vision-Language Models

Author

Patrício, Cristiano, Teixeira, Luís F., and Neves, João C.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Concept-based models naturally lend themselves to the development of inherently interpretable skin lesion diagnosis, as medical experts make decisions based on a set of visual patterns of the lesion. Nevertheless, the development of these models depends on the existence of concept-annotated datasets, whose availability is scarce due to the specialized knowledge and expertise required in the annotation process. In this work, we show that vision-language models can be used to alleviate the dependence on a large number of concept-annotated samples. In particular, we propose an embedding learning strategy to adapt CLIP to the downstream task of skin lesion classification using concept-based descriptions as textual embeddings. Our experiments reveal that vision-language models not only attain better accuracy when using concepts as textual embeddings, but also require a smaller number of concept-annotated samples to attain comparable performance to approaches specifically devised for automatic concept generation., Comment: Accepted for publication in IEEE ISBI 2024

Published
2023

9. Prediabetes remission after bariatric surgery: a 4-years follow-up study

Author

Borges-Canha, Marta, Neves, João Sérgio, Silva, Maria Manuel, Mendonça, Fernando, Moreno, Telma, Ribeiro, Sara, Vale, Catarina, Gonçalves, Juliana, Ferreira, Helena Urbano, Gil-Santos, Sara, Calheiros, Raquel, Meira, Inês, Menino, João, Guerreiro, Vanessa, Pedro, Jorge, Sande, Ana, Souto, Selma B., da Costa, Eduardo Lima, Carvalho, Davide, and Freitas, Paula

Published
2024
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11. Embedding Aggregation for Forensic Facial Comparison

Author

Ribeiro, Rafael Oliveira, Neves, João C. R., Ruifrok, Arnout C. C., and Vidal, Flavio de Barros

Subjects
Computer Science - Computer Vision and Pattern Recognition, Statistics - Applications, I.4, I.5
Abstract

In forensic facial comparison, questioned-source images are usually captured in uncontrolled environments, with non-uniform lighting, and from non-cooperative subjects. The poor quality of such material usually compromises their value as evidence in legal matters. On the other hand, in forensic casework, multiple images of the person of interest are usually available. In this paper, we propose to aggregate deep neural network embeddings from various images of the same person to improve performance in facial verification. We observe significant performance improvements, especially for very low-quality images. Further improvements are obtained by aggregating embeddings of more images and by applying quality-weighted aggregation. We demonstrate the benefits of this approach in forensic evaluation settings with the development and validation of score-based likelihood ratio systems and report improvements in Cllr of up to 95% (from 0.249 to 0.012) for CCTV images and of up to 96% (from 0.083 to 0.003) for social media images., Comment: 13 pages, 8 figures, submitted to Forensic Science International

Published
2023

12. Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis

Author

Patrício, Cristiano, Neves, João C., and Teixeira, Luís F.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and acceptability of these diagnostic methods. Attempts to provide concept-based explanations are based on post-hoc approaches, which depend on an additional model to derive interpretations. In this paper, we propose an inherently interpretable framework to improve the interpretability of concept-based models by incorporating a hard attention mechanism and a coherence loss term to assure the visual coherence of concept activations by the concept encoder, without requiring the supervision of additional annotations. The proposed framework explains its decision in terms of human-interpretable concepts and their respective contribution to the final prediction, as well as a visual interpretation of the locations where the concept is present in the image. Experiments on skin image datasets demonstrate that our method outperforms existing black-box and concept-based models for skin lesion classification., Comment: Under IEEE Copyright. Accepted for publication at CVPR 2023 Workshop Safe Artificial Intelligence for All Domains (SAIAD)

Published
2023

13. DIABETES MELLITUS NO IDOSO

Author

DONIZETTI MORAES SILVA, RAUL, primary, REIS OLEJ, GABRIEL, additional, AMANDA MARTINS PILLAR, MARIA, additional, CIDACO DE OLIVEIRA COUTINHO, MARIANA, additional, MAGGINI SERAPHIM, NICOLI, additional, GARCIA MELO, FERNANDA, additional, SILVA DANTAS, GABRIEL, additional, GOMES GARCIA, GABRIELLE, additional, DALLA VALLE CONSTANTINO MIGUEL, ISADORA, additional, MORAES DOS SANTOS NEVES, JOÃO, additional, HOEPERS BAASCH, LETICIA, additional, RAFAEL DE SIQUEIRA, LINIKER, additional, LUNA DE CASTRO, LUANA, additional, RODOLPHO BATISTA DE OLIVEIRA, PAULO, additional, and APARECIDA BALARINI LIMA, GIOVANNA, additional

Published
2024
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14. WildFruiP: Estimating Fruit Physicochemical Parameters from Images Captured in the Wild

Author

Paulo, Diogo J., Neves, Cláudia M. B., Wessel, Dulcineia Ferreira, Neves, João C., 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, Vasconcelos, Verónica, editor, Domingues, Inês, editor, and Paredes, Simão, editor

Published
2024
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15. Face Super-Resolution Using Stochastic Differential Equations

Author

Santos, Marcelo dos, Laroca, Rayson, Ribeiro, Rafael O., Neves, João, Proença, Hugo, and Menotti, David

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Diffusion models have proven effective for various applications such as images, audio and graph generation. Other important applications are image super-resolution and the solution of inverse problems. More recently, some works have used stochastic differential equations (SDEs) to generalize diffusion models to continuous time. In this work, we introduce SDEs to generate super-resolution face images. To the best of our knowledge, this is the first time SDEs have been used for such an application. The proposed method provides an improved peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and consistency than the existing super-resolution methods based on diffusion models. In particular, we also assess the potential application of this method for the face recognition task. A generic facial feature extractor is used to compare the super-resolution images with the ground truth and superior results were obtained compared with other methods. Our code is publicly available at https://github.com/marcelowds/sr-sde, Comment: Accepted for presentation at the Conference on Graphics, Patterns and Images (SIBGRAPI) 2022

Published
2022
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18. Explainable Deep Learning Methods in Medical Image Classification: A Survey

Author

Patrício, Cristiano, Neves, João C., and Teixeira, Luís F.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are also discussed., Comment: Accepted for publication in ACM Computing Surveys

Published
2022

24. Iconography Design for Digital Applications Developed for the Elderly or Senior User

Author

Pereira, Leonardo, Martins, Nuno, Ferreira, Sérgio Dominique, Neves, João, Silva, José, Brandão, Daniel, Tosi, Francesca, Editor-in-Chief, Germak, Claudio, Series Editor, Zurlo, Francesco, Series Editor, Jinyi, Zhi, Series Editor, Pozzatti Amadori, Marilaine, Series Editor, Caon, Maurizio, Series Editor, Raposo, Daniel, editor, Neves, João, editor, Silva, Ricardo, editor, Correia Castilho, Luísa, editor, and Dias, Rui, editor

Published
2023
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25. Generative Adversarial Graph Convolutional Networks for Human Action Synthesis

Author

Degardin, Bruno, Neves, João, Lopes, Vasco, Brito, João, Yaghoubi, Ehsan, and Proença, Hugo

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Synthesising the spatial and temporal dynamics of the human body skeleton remains a challenging task, not only in terms of the quality of the generated shapes, but also of their diversity, particularly to synthesise realistic body movements of a specific action (action conditioning). In this paper, we propose Kinetic-GAN, a novel architecture that leverages the benefits of Generative Adversarial Networks and Graph Convolutional Networks to synthesise the kinetics of the human body. The proposed adversarial architecture can condition up to 120 different actions over local and global body movements while improving sample quality and diversity through latent space disentanglement and stochastic variations. Our experiments were carried out in three well-known datasets, where Kinetic-GAN notably surpasses the state-of-the-art methods in terms of distribution quality metrics while having the ability to synthesise more than one order of magnitude regarding the number of different actions. Our code and models are publicly available at https://github.com/DegardinBruno/Kinetic-GAN., Comment: Published as a conference paper at WACV 2022. Code and pretrained models available at https://github.com/DegardinBruno/Kinetic-GAN

Published
2021

26. ZSpeedL -- Evaluating the Performance of Zero-Shot Learning Methods using Low-Power Devices

Author

Patrício, Cristiano and Neves, João

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

The recognition of unseen objects from a semantic representation or textual description, usually denoted as zero-shot learning, is more prone to be used in real-world scenarios when compared to traditional object recognition. Nevertheless, no work has evaluated the feasibility of deploying zero-shot learning approaches in these scenarios, particularly when using low-power devices. In this paper, we provide the first benchmark on the inference time of zero-shot learning, comprising an evaluation of state-of-the-art approaches regarding their speed/accuracy trade-off. An analysis to the processing time of the different phases of the ZSL inference stage reveals that visual feature extraction is the major bottleneck in this paradigm, but, we show that lightweight networks can dramatically reduce the overall inference time without reducing the accuracy obtained by the de facto ResNet101 architecture. Also, this benchmark evaluates how different ZSL approaches perform in low-power devices, and how the visual feature extraction phase could be optimized in this hardware. To foster the research and deployment of ZSL systems capable of operating in real-world scenarios, we release the evaluation framework used in this benchmark (https://github.com/CristianoPatricio/zsl-methods)., Comment: 8 pages. Accepted at the 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2021

Published
2021

27. Efficacy of T-cell assays for the diagnosis of primary defects in cytotoxic lymphocyte exocytosis

Author

Abboud, Miguel R, Aytac, Sevkiye Selin, Bosse, Franziskus Johannes, Choo, Sharon, Drabko, Katarzyna, Onkologii, Klinika, Elfeky, Reem, El-Ghoneimy, Dalia Helmy, Fadoo, Zehra, Greenwood, Tatiana, Gustafsson, Britt, Hagelberg, Stefan, Hasle, Henrik, Hästbacka, Johanna, Jadrešin, Oleg, Jädersten, Martin, Kaya, Zuhre, Lecumberri, Ranon, Marques, Laura, Mushtaq, Naureen, Naqvi, Ahmed, Neves, João Farela, Nunes, Susana, Paucar, Martin, Payne, Jeanette H., Rascon, Jelena, Ruuska, Terhi Susanna, Saribeyoglu, Ebru Tugrul, Sundin, Mikael C., Svedenkrans, Jenny, Świderska, Natalia, Tedgård, Ulf, Tvedt, Tor Henrik, Ünüvar, Ayşegül, Van Laar, Jan A. M., Weitzman, Sheila, Winiarski, Jacek, Yaseen, Muhamma Zohaib, Yildiz, Mehmet, Zantomio, Daniela, Øra, Ingrid, Øverland, Torstein, Chiang, Samuel C. C., Covill, Laura E., Tesi, Bianca, Campbell, Tessa M., Schlums, Heinrich, Nejati-Zendegani, Jelve, Mördrup, Karina, Wood, Stephanie, Theorell, Jakob, Sekine, Takuya, Al-Herz, Waleed, Akar, Himmet Haluk, Belen, Fatma Burcu, Chan, Mei Yoke, Devecioglu, Omer, Aksu, Tekin, Ifversen, Marianne, Malinowska, Iwona, Sabel, Magnus, Unal, Ekrem, Unal, Sule, Introne, Wendy J., Krzewski, Konrad, Gilmour, Kimberly C., Ehl, Stephan, Ljunggren, Hans-Gustaf, Nordenskjöld, Magnus, Horne, AnnaCarin, Henter, Jan-Inge, Meeths, Marie, and Bryceson, Yenan T.

Published
2024
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29. Lack of association between classical HLA genes and asymptomatic SARS-CoV-2 infection

Author

Abel, Laurent, Aiuti, Alessandro, Al-Muhsen, Saleh, Al-Mulla, Fahd, Amara, Ali, Anderson, Mark S., Andreakos, Evangelos, Arias, Andrés A., Arkin, Lisa M., Feldman, Hagit Baris, Bastard, Paul, Belot, Alexandre, Biggs, Catherine M., Bogunovic, Dusan, Bolze, Alexandre, Bondarenko, Anastasiia, Borghesi, Alessandro, Bousfiha, Ahmed A., Brodin, Petter, Bryceson, Yenan, Butte, Manish J., Casanova, Jean-Laurent, Casari, Giorgio, Christodoulou, John, Cobat, Aurélie, Colobran, Roger, Condino-Neto, Antonio, Constantinescu, Stefan N., Cooper, Megan A., Dalgard, Clifton L., Desai, Murkesh, Drolet, Beth A., Duval, Xavier, El Baghdadi, Jamila, Eloy, Philippine, Espinosa-Padilla, Sara, Fellay, Jacques, Flores, Carlos, Franco, José Luis, Froidure, Antoine, Gorochov, Guy, Gregersen, Peter K., Grimbacher, Bodo, Haerynck, Filomeen, Hagin, David, Halwani, Rabih, Hammarström, Lennart, Heath, James R., Hsieh, Elena W.Y., Husebye, Eystein, Imai, Kohsuke, Itan, Yuval, Jouanguy, Emmanuelle, Kaja, Elżbieta, Karamitros, Timokratis, Kisand, Kai, Ku, Cheng-Lung, Lau, Yu-Lung, Ling, Yun, Lucas, Carrie L., Maniatis, Tom, Mansouri, Davood, Maródi, László, Mentré, France, Meyts, Isabelle, Milner, Joshua D., Mironska, Kristina, Mogensen, Trine H., Morio, Tomohiro, Ng, Lisa F.P., Notarangelo, Luigi D., Novelli, Antonio, Novelli, Giuseppe, O'Farrelly, Cliona, Okada, Satoshi, Okamoto, Keisuke, Ozcelik, Tayfun, Pan-Hammarström, Qiang, Pape, Jean W., Perez de Diego, Rebeca, Perez-Tur, Jordi, Perlin, David S., Pesole, Graziano, Planas, Anna M., Prando, Carolina, Pujol, Aurora, Puel, Anne, Quintana-Murci, Lluis, Ramaswamy, Sathishkumar, Renia, Laurent, Resnick, Igor, Rodríguez-Gallego, Carlos, Sancho-Shimizu, Vanessa, Sediva, Anna, Seppänen, Mikko R.J., Shahrooei, Mohammad, Shcherbina, Anna, Slaby, Ondrej, Snow, Andrew L., Soler-Palacín, Pere, Soumelis, Vassili, Spaan, András N., Tancevski, Ivan, Tangye, Stuart G., Tayoun, Ahmad Abou, Temel, Şehime Gülsün, Thorball, Christian, Tiberghien, Pierre, Trouillet-Assant, Sophie, Turvey, Stuart E., Uddin, K. M. Furkan, Uddin, Mohammed J., van de Beek, Diederik, Vinh, Donald C., von Bernuth, Horst, Wauters, Joost, Zatz, Mayana, Zawadzki, Pawel, Zhang, Qian, Zhang, Shen-Ying, Bureau, Serge, Vacher, Yannick, Gysembergh-Houal, Anne, Demerville, Lauren, Benleulmi-Chaachoua, Abla, Abad, Sebastien, Abassi, Radhiya, Abdellaoui, Abdelrafie, Abdelmalek, Abdelkrim, Abdoul, Hendy, Abergel, Helene, Abeud, Fariza, Abgrall, Sophie, Abisror, Noemie, Adechian, Marylise, Aderdour, Nordine, Admane, Hakeem Farid, Adnet, Frederic, Afritt, Sara, Agostini, Helene, Aguilar, Claire, Agut, Sophie, Aiello, Tommaso Francesco, Kaci, Marc Ait, Oufella, Hafid Ait, Ajeenthiravasan, Gokula, Alauzy, Virginie, Alby-Laurent, Fanny, Allard, Lucie, Alyanakian, Marie-Alexandra, Borrero, Blanca Amador, Amam, Sabrina, Amrouche, Lucile, Andronikof, Marc, Anglicheau, Dany, Anguel, Nadia, Annane, Djillali, Aounzou, Mohammed, Aparicio, Caroline, Aratus, Gladys, Arlet, Jean-Benoit, Arzoine, Jeremy, Aslangul, Elisabeth, Assefi, Mona, Aubry, Adeline, Audiffred, Laetitia, Audureau, Etienne, Auger, Christelle Nathalie, Auregan, Jean-Charles, Awotar, Celine, Milla, Sonia Ayllon, Azan, Delphine, Azemar, Laurene, Azzouguen, Billal, Elrufaai, Marwa Bachir, Badsi, Aïda, Bakouboula, Prissile, Balcerowiak, Coline, Balde, Fanta, Baldivia, Elodie, Bangamingo, Eliane-Flore, Baptiste, Amandine, Baran-Marszak, Fanny, Barau, Caroline, Barget, Nathalie, Baronnet, Flore, Barthelemy, Romain, Baudel, Jean-Luc, Baudry, Camille, Baudry, Elodie, Beaugerie, Laurent, Belamri, Adel, Belaube, Nicolas, Belilita, Rhida, Bellassen, Pierre, Belmokhtar, Rawan, Beltran, Isabel, Benainous, Ruben, Benallaoua, Mourad, Benamouzig, Robert, Benbara, Amélie, Benhida, Jaouad, Benkhelouf, Anis, Benlagha, Jihene, Benmostafa, Chahinez, Benothmane, Skander, Bentifraouine, Miassa, Berard, Laurence, Bernier, Quentin, Berti, Enora, Bertier, Astrid, Berton, Laure, Bessis, Simon, Beurton, Alexandra, Bianco, Celine, Bianquis, Clara, Bidar, Frank, Blanche, Philippe, Blayau, Clarisse, Bleibtreu, Alexandre, Blin, Emmanuelle, Bloch-Queyrat, Coralie, Boissier, Marie-Christophe, Bollens, Diane, Bolzoni, Marion, Bompard, Rudy pierre, Bonnet, Nicolas, Bonnouvrier, Justine, Botha, Shirmonecrystal, Boucenna, Wissam, Bouchama, Fatiha, Bouchaud, Olivier, Bouchghoul, Hanane, Boudjebla, Taoueslylia, Boudjema, Noel, Bouffard, Catherine, Bougle, Adrien, Bouguerra, Meriem, Bouras, Leila, Bourcier, Agnes, Durand, Anne Bourgarit, Bourrier, Anne, Bouscarat, Fabrice, Bouvry, Diane, Bouziri, Nesrine, Bouzrara, Ons, Bribier, Sarah, Brugier, Delphine, Brunel, Melanie, Bui, Eida, Buisson, Anne, Bukreyeva, Iryna, Bureau, Côme, Cadranel, Jacques, Cailhol, Johann, Calin, Ruxandra, Vega, Clara Campos, Canavaggio, Pauline, Cancella, Marta, Cantin, Delphine, Cao, Albert, Carbillon, Lionel, Carlier, Nicolas, Cassard, Clementine, Castor, Guylaine, Cauchy, Marion, Cha, Olivier, Chaigne, Benjamin, Challal, Salima, Champion, Karine, Chariot, Patrick, Chas, Julie, Chauveau, Simon, Chauvin, Anthony, Chauvin, Clement, Chavarot, Nathalie, Chebbout, Kamélia, Cherai, Mustapha, Cherubini, Ilaria, Chevalier, Amelie, Chiarabini, Thibault, Chinet, Thierry, Chocron, Richard, Choinier, Pascaline, Chommeloux, Juliette, Choquet, Christophe, Choupeaux, Laure, Chousterman, Benjamin, Ciocan, Dragosmarius, Clarke, Ada, Clavere, Gaëlle, Clavier, Florian, Clement, Karine, Clerc, Sebastien, Cohen, Yves, Cohen, Fleur, Cohen, Adrien, Coilly, Audrey, Colboc, Hester, Colin, Pauline, Collet, Magalie, Comarmond, Chloé, Combacon, Emeline, Combes, Alain, Comparon, Celine, Constantin, Jean-Michel, Cordel, Hugues, Cordier, Anne-Gael, Costantini, Adrien, Chalumeau, Nathalie Costedoat, Couffignal, Camille, Coupeau, Doriane, Creange, Alain, Lamarre, Yannie Cuvillier, Da Silveira, Charlène, Guibal El Kayani, Sandrine Dautheville, De Castro, Nathalie, De Rycke, Yann, Del Pozo, Lucie, Delannoy, Quentin, Delay, Mathieu, Deleris, Robin, Delforge, Juliette, Delphine, Laëtitia, Demare, Noemie, Demeret, Sophie, Demoule, Alexandre, Deniau, Aurore, Depret, François, Derolez, Sophie, Derradji, Ouda, Derridj, Nawal, Descamps, Vincent, Deschamps, Lydia, Desconclois, Celine, Desnos, Cyrielle, Desongins, Karine, Dhote, Robin, Diallo, Benjamin, Didier, Morgane, Diemer, Myriam, Diez, Stephane, Djadi-Prat, Juliette, Djamouri Monnory, Fatima-Zohra, Djebara, Siham, Djebra, Naoual, Djietcheu, Minette, Djillali, Hadjer, Djouadi, Nouara, Donneger, Severine, Santos, Catarina Dos, Dournon, Nathalie, Dres, Martin, Droctove, Laura, Drogrey, Marie, Dropy, Margot, Drouet, Elodie, Dubosq, Valérie, Dubreucq, Evelyne, Dubus, Estelle, Duchemann, Boris, Duchenoy, Thibault, Dudoignon, Emmanuel, Dufau, Romain, Dumas, Florence, Duran, Clara, Duron, Emmanuelle, Durrbach, Antoine, Duvivier, Claudine, Ebstein, Nathan, El Khalifa, Jihane, Elabbadi, Alexandre, Elie, Caroline, Ernotte, Gabriel, Esling, Anne, Etienne, Martin, Eyer, Xavier, Fartoukh, Muriel Sarah, Fayali, Takoua, Fermaut, Marion, Fiorentino, Arianna, Fliss, Souha, Fournier, Marie-Céline, Fournier, Benjamin, Francois, Hélène, Freynet, Olivia, Frigout, Yvann, Fromont, Isaure, Fuentes, Axelle, Furet, Thomas, Galand, Joris, Garnier, Marc, Gaubert, Agnes, Gaudry, Stéphane, Gaugain, Samuel, Gauthier, Damien, Gautier, Maxime, Georgin-Lavialle, Sophie, Geromin, Daniela, Ghalayini, Mohamed, Ghaleh, Bijan, Ghezal, Myriam, Gibelin, Aude, Gimeno, Linda, Girard, Benoit, Leprieur, Bénédicte Giroux, Gomes, Doryan, Gomes-Pires, Elisabete, Gouge, Anne, Gouja, Amel, Goulet, Helene, Goupil, Sylvain, De Bouille, Jeanne Goupil, Gras, Julien, Greffe, Segolene, Grimaldi, Lamiae, Guedeney, Paul, Guidet, Bertrand, Guillo, Matthias, Gulczynski, Mariechristelle, Hadjam, Tassadit, Haguenauer, Didier, Hammal, Soumeya, Hammoudi, Nadjib, Hanon, Olivier, Harrois, Anarole, Hausfater, Pierre, Hautem, Coraline, Hekimian, Guillaume, Heming, Nicholas, Hermine, Olivier, Ho, Sylvie, Houllier, Marie, Huot, Benjamin, Huscenot, Tessa, Saied, Wafa Ibn, Ikherbane, Ghilas, Imarazene, Meriem, Ingiliz, Patrick, Iratni, Lina, Jaureguiberry, Stephane, Jean-Marc, Jean-Francois, Jeyarajasingham, Deleena, Jouany, Pauline, Jouis, Veronique, Jourdaine, Clement, Kafif, Ouifiya, Kallala, Rim, Katsahian, Sandrine, Kelesyan, Lilit, Keo, Vixra, Ketz, Flora, Khamis, Warda, Khelili, Enfel, Khellaf, Mehdi, Kotokpo Youkou, Christy Gaëlla, Kounis, Ilias, Kpalma, Gaelle, Krause, Jessica, Labbe, Vincent, Lacombe, Karine, Lacorte, Jean-Marc, Lafont, Anne Gaelle, Lafont, Emmanuel, Lagha, Lynda, Lamhaut, Lionel, Lancelot, Aymeric, Landman, Cecilia, Lanternier, Fanny, Larcheveque, Cecile, Combe, Caroline Lascoux, Lassel, Ludovic, Laverdant, Benjamin, Lavergne, Christophe, Lavillegrand, Jean-Rémi, Lazureanu, Pompilia, Le Guennec, Loïc, Leberre, Lamia, Leblanc, Claire, Leboyer, Marion, Lecomte, Francois, Lecorre, Marine, Leenhardt, Romain, Lefebvre, Marylou, Lefebvre, Bénédicte, Legendre, Paul, Leger, Anne, Legros, Laurence, Legrosse, Justyna, Lehuunghia, Sébastien, Lemarec, Julien, Leporrier-Ext, Jeremie, Lesein, Manon, Lesur, Hubert, Levy, Vincent, Levy, Albert, Lopes, Edwige, Lopes, Amanda, Lopez, Vanessa, Lopinto, Julien, Lortholary, Olivier, Louadah, Badr, Loze, Bénédicte, Lucas, Marie-Laure, Lucasamichi, Axelle, Luong, Liem Binh, Magazimama-Ext, Arouna, Maingret, David, Mameri, Lakhdar, Manivet, Philippe, Mansouri, Cylia, Marcault, Estelle, Marey, Jonathan, Marin, Nathalie, Marois, Clémence, Martin, Olivier, Martineau, Lou, Martinez-Lopez, Cannelle, Martyniuck, Pierre, De Farcy, Pauline Mary, Marzouk, Nessrine, Masmoudi, Rafik, Mebazaa, Alexandre, Mechai, Frédéric, Mecozzi, Fabio, Mediouni, Chamseddine, Megarbane, Bruno, Meghadecha, Mohamed, Mejean, Élodie, Mekinian, Arsene, Abdelhadi, Nour Mekki, Mekni, Rania, Meliti, Thinhinan Sabrina, Lima, Breno Melo, Meng, Paris, Merbah, Soraya, Messani, Fadhila, Messaoudi, Yasmine, Mewasing, Baboo-Irwinsingh, Meziane, Lydia, Michelot-Burger, Carole, Mignot, Françoise, Minka, Fadi Hillary, Miyara, Makoto, Moine, Pierre, Molina, Jean-Michel, Montegnies-Boulet, Anaïs, Monti, Alexandra, Montlahuc, Claire, Montout, Anne-Lise, Moores, Alexandre, Morbieu, Caroline, Mortelette, Helene, Mouly, Stéphane, Muzaffar, Rosita, Nacerddine, Cherifa Iness, Nadal, Marine, Nadif, Hajer, Nassarmadji, Kladoum, Natella, Pierre, Ndingamondze, Sandrine, Neraal, Stefan, Nguyen, Caroline, N'Guyen, Bao, Larmurier, Isabelle Nion, Nlomenyengue, Luc, Noel, Nicolas, Nunes, Hilario, Omar, Edris, Ouazene, Zineb, Ouedraogo, Elise, Ouelaa, Wassila, Oukhedouma, Anissa, Amara, Yasmina Ould, Oya, Herve, Oziel, Johanna, Padilla, Thomas, Paillaud, Elena, Paiva, Solenne, Parfait, Beatrice, Parize, Perrine, Parizot, Christophe, Parrot, Antoine, Pavot, Arthur, Peaudecerf, Laetitia, Pene, Frédéric, Pepin, Marion, Pernet, Julie, Pernin, Claire, Petit, Mylène, Peyrony, Olivier, Pietri, Marie-Pierre, Pietri, Olivia, De Chambrun, Marc Pineton, Pinson, Michelle, Pintado, Claire, Piquard, Valentine, Pires, Christine, Planquette, Benjamin, Poirier, Sandrine, Pomel, Anne-Laure, Pons, Stéphanie, Ponscarme, Diane, Pourcelot, Annegaelle, Pourcher, Valérie, Pouvaret, Anne, Prever, Florian, Previlon, Miresta, Prevost, Margot, Provoost, Marie-Julie, Quemeneur, Cyril, Rafat, Cédric, Rami, Agathe, Ranque, Brigitte, Raphael, Maurice, Raphalen, Jean Herle, Rastoin, Anna, Raux, Mathieu, Rebai, Amani, Reby, Michael, Regent, Alexis, Regrag, Asma, Resche-Rigon, Matthieu, Ressaire, Quentin, Richard, Christian, Richard, Mariecaroline, Robert, Maxence, Rohaut, Benjamin, Rolland-Debord, Camille, Ropers, Jacques, Roque-Afonso, Anne-Marie, Rosso, Charlotte, Rousseaux, Mélanie, Rousseaux, Nabila, Roux, Swasti, Roux, Lorène, Rouzaud, Claire, Rozes, Antoine, Rubenstein, Emma, Sabate, Jean-Marc, Sabet, Sheila, Sacleux, Sophie-Caroline, Kermanach, Nathalie Saidenberg, Saliba, Faouzi, Salmon, Dominique, Savale, Laurent, Savary, Guillaume, Sberro, Rebecca, Scemla, Anne, Schlemmer, Frederic, Schwartz, Mathieu, Sedfi, Saïd, Sefir-Kribel, Samia, Seksik, Philippe, Sellier, Pierre, Selves, Agathe, Sembach, Nicole, Semerano, Luca, Senat, Marie-Victoire, Sene, Damien, Serris, Alexandra, Sese, Lucile, Sghiouar, Naima, Sigaux, Johanna, Siguier, Martin, Silvain, Johanne, Simon, Noémie, Simon, Tabassome, Skandri, Lina Innes, Slimani, Miassa, Snauwaert, Aurélie, Sokol, Harry, Soliman, Heithem, Soltani, Nisrine, Soyer, Benjamin, Steg, Gabriel, Suarez, Lydia, Szwebel, Tali-Anne, Taffame, Kossi, Tandjaoui-Lambiotte, Yacine, Tantet, Claire, Tateo, Mariagrazia, Theodose, Igor, Thiebaud, Pierre clement, Thomas, Caroline, Tiercelet, Kelly, Tisserand, Julie, Tomczak, Carole, Torelino, Krystel, Touam-Ext, Fatima, Toumi, Lilia, Toury, Gustave, Toy-Miou, Mireille, Dinh Thanh Lien, Olivia Tran, Trandinh, Alexy, Treluyer, Jean-Marc, Trinque, Baptiste, Truchot, Jennifer, Tubach, Florence, Tubiana, Sarah, Tunesi, Simone, Turpin, Matthieu, Turpin, Agathe, Urbina, Tomas, Narvaez, Rafael Usubillaga, Uzunhan, Yurdagul, Vaittinadaayar, Prabakar, Valent, Arnaud, Valentian, Maelle, Valin, Nadia, Vallet, Hélène, Vaz, Marina, Vazquezibarra, Miguel-Alejandro, Vedie, Benoit, Velly, Laetitia, Verstuyft, Celine, Viallette, Cedric, Vicaut, Eric, Vignes, Dorothee, Vimpere, Damien, Virlouvet, Myriam, Voiriot, Guillaume, Voisot, Lena, Weiss, Emmanuel, Weiss, Nicolas, Winchenne, Anaïs, Yordanov, Youri, Zafrani, Lara, Zaidan, Mohamad, Zaidi, Wissem, Zak, Cathia, Zarhrate-Ghoul, Aida, Zatout, Ouassila, Zeino, Suzanne, Zeitouni, Michel, Zemirli, Naïma, Zerah, Lorene, Zia, Ounsa, Ziol, Marianne, Zolario, Oceane, Zuber, Julien, Andrejak, Claire, Angoulvant, François, Bachelet, Delphine, Bartoli, Marie, Basmaci, Romain, Behillil, Sylvie, Beluze, Marine, Benkerrou, Dehbia, Bhavsar, Krishna, Bouadma, Lila, Bouchez, Sabelline, Bouscambert, Maude, Cervantes-Gonzalez, Minerva, Chair, Anissa, Chirouze, Catherine, Coelho, Alexandra, Couffin-Cadiergues, Sandrine, d’Ortenzio, Eric, Debray, Marie-Pierre, Deconinck, Laurene, Deplanque, Dominique, Descamps, Diane, Desvallée, Mathilde, Diallo, Alpha, Diouf, Alphonsine, Dorival, Céline, Dubos, François, Elharrar, Brigitte, Enouf, Vincent, Esperou, Hélène, Esposito-Farese, Marina, Etienne, Manuel, Devouge, Eglantine Ferrand, Gault, Nathalie, Gaymard, Alexandre, Ghosn, Jade, Gigante, Tristan, Gilg, Morgane, Guedj, Jérémie, Hoctin, Alexandre, Hoffmann, Isabelle, Houas, Ikram, Hulot, Jean-Sébastien, Jaafoura, Salma, Kaguelidou, Florentia, Kali, Sabrina, Khalil, Antoine, Khan, Coralie, Laouénan, Cédric, Laribi, Samira, Le, Minh, Le Hingrat, Quentin, Le Mestre, Soizic, Le Nagard, Hervé, Lescure, François-Xavier, Letrou, Sophie, Levy, Yves, Lina, Bruno, Lingas, Guillaume, Lucet, Jean-Christophe, Malvy, Denis, Mambert, Marina, Meziane, Amina, Mouquet, Hugo, Mullaert, Jimmy, Neant, Nadège, Nguyen, Duc, Noret, Marion, Nseir, Saad, Papadopoulos, Aurélie, Paul, Christelle, Peiffer-Smadja, Nathan, Perpoint, Thomas, Petrov-Sanchez, Ventzislava, Peytavin, Gilles, Pham, Huong, Picone, Olivier, Puéchal, Oriane, Rabaud, Christian, Rosa-Calatrava, Manuel, Rossignol, Bénédicte, Rossignol, Patrick, Roy, Carine, Schneider, Marion, Su, Richa, Tardivon, Coralie, Tellier, Marie-Capucine, Téoulé, François, Terrier, Olivier, Timsit, Jean-François, Tual, Christelle, Van Der Werf, Sylvie, Vanel, Noémie, Veislinger, Aurélie, Visseaux, Benoit, Wiedemann, Aurélie, Yazdanpanah, Yazdan, Alavoine, Loubna, Burdet, Charles, Charpentier, Charlotte, Dechanet, Aline, Ecobichon, Jean-Luc, Frezouls, Wahiba, Houhou, Nadhira, Lehacaut, Jonathan, Manchon, Pauline, Nouroudine, Mariama, Quintin, Caroline, Thy, Michael, van der Werf, Sylvie, Vignali, Valérie, Chahine, Abir, Waucquier, Nawal, Migaud, Maria-Claire, Djossou, Félix, Mergeay-Fabre, Mayka, Lucarelli, Aude, Demar, Magalie, Bruneau, Léa, Gérardin, Patrick, Maillot, Adrien, Payet, Christine, Laviolle, Bruno, Laine, Fabrice, Paris, Christophe, Desille-Dugast, Mireille, Fouchard, Julie, Pistone, Thierry, Perreau, Pauline, Gissot, Valérie, Goas, Carole L.E., Montagne, Samatha, Richard, Lucie, Bouiller, Kévin, Desmarets, Maxime, Meunier, Alexandre, Bourgeon, Marilou, Lefévre, Benjamin, Jeulin, Hélène, Legrand, Karine, Lomazzi, Sandra, Tardy, Bernard, Gagneux-Brunon, Amandine, Bertholon, Frédérique, Botelho-Nevers, Elisabeth, Kouakam, Christelle, Nicolas, Leturque, Roufai, Layidé, Amat, Karine, Espérou, Hélène, Hendou, Samia, Foti, Giuseppe, Citerio, Giuseppe, Contro, Ernesto, Pesci, Alberto, Valsecchi, Maria Grazia, Cazzaniga, Marina, Bellani, Giacomo, Abad, Jorge, Accordino, Giulia, Angelini, Micol, Aguilera-Albesa, Sergio, Aguiló-Cucurull, Aina, Özkan, Esra Akyüz, Darazam, Ilad Alavi, Roblero Albisures, Jonathan Antonio, Aldave, Juan C., Ramos, Miquel Alfonso, Khan, Taj Ali, Aliberti, Anna, Nadji, Seyed Alireza, Alkan, Gulsum, AlKhater, Suzan A., Allardet-Servent, Jerome, Allende, Luis M., Alonso-Arias, Rebeca, Alshahrani, Mohammed S., Alsina, Laia, Amoura, Zahir, Antolí, Arnau, Arrestier, Romain, Aubart, Mélodie, Auguet, Teresa, Avramenko, Iryna, Aytekin, Gökhan, Azot, Axelle, Bahram, Seiamak, Bajolle, Fanny, Baldanti, Fausto, Baldolli, Aurélie, Ballester, Maite, Barrou, Benoit, Barzaghi, Federica, Basso, Sabrina, Bayhan, Gulsum Iclal, Bezrodnik, Liliana, Bilbao, Agurtzane, Blanchard-Rohner, Geraldine, Blanco, Ignacio, Blandinières, Adeline, Blázquez-Gamero, Daniel, Bloomfield, Marketa, Bolivar-Prados, Mireia, Borie, Raphael, Botdhlo-Nevers, Elisabeth, Bousquet, Aurore, Boutolleau, David, Bouvattier, Claire, Boyarchuk, Oksana, Bravais, Juliette, Briones, M. Luisa, Brunner, Marie-Eve, Bruno, Raffaele, Bueno, Maria Rita P., Bukhari, Huda, Bustamante, Jacinta, Cáceres Agra, Juan José, Capra, Ruggero, Carapito, Raphael, Carrabba, Maria, Casasnovas, Carlos, Caseris, Marion, Cassaniti, Irene, Castelle, Martin, Castelli, Francesco, Castillo de Vera, Martín, Castro, Mateus V., Catherinot, Emilie, Celik, Jale Bengi, Ceschi, Alessandro, Chalumeau, Martin, Charbit, Bruno, Boulanger, Cécile, Clavé, Père, Clotet, Bonaventura, Codina, Anna, Comarmond, Cloé, Comoli, Patrizia, Corsico, Angelo G., Coşkuner, Taner, Cvetkovski, Aleksandar, Cyrus, Cyril, Dalmau, David, Danion, François, Darley, David Ross, Das, Vincent, Dauby, Nicolas, Dauger, Stéphane, De Munte, Paul, de Pontual, Loic, Dehban, Amin, Delplancq, Geoffroy, Desguerre, Isabelle, Di Sabatino, Antonio, Diehl, Jean-Luc, Dobbelaere, Stephanie, Domínguez-Garrido, Elena, Dubost, Clément, Ekwall, Olov, Bozdemir, Şefika Elmas, Elnagdy, Marwa H., Emiroglu, Melike, Endo, Akifumi, Erdeniz, Emine Hafize, Aytekin, Selma Erol, Etxart Lasa, Maria Pilar, Euvrard, Romain, Fabio, Giovanna, Faivre, Laurence, Falck, Antonin, Fartoukh, Muriel, Faure, Morgane, Arquero, Miguel Fernandez, Ferrer, Ricard, Ferreres, Jose, Francois, Bruno, Fumadó, Victoria, Fung, Kitty S.C., Fusco, Francesca, Gagro, Alenka, Solis, Blanca Garcia, Garçon, Pierre, Gaussem, Pascale, Gayretli, Zeynep, Gil-Herrera, Juana, Gilardin, Laurent, Gatineau, Audrey Giraud, Girona-Alarcón, Mònica, Cifuentes Godínez, Karen Alejandra, Goffard, Jean-Christophe, Gonzales, Nacho, Gonzalez-Granado, Luis I., González-Montelongo, Rafaela, Guerder, Antoine, Gülhan, Belgin, Gumucio, Victor Daniel, Hanitsch, Leif Gunnar, Gunst, Jan, Gut, Marta, Hadjadj, Jérôme, Hancerli, Selda, Hariyan, Tetyana, Hatipoglu, Nevin, Heppekcan, Deniz, Hernandez-Brito, Elisa, Ho, Po-ki, Holanda-Peña, María Soledad, Horcajada, Juan P., Hraiech, Sami, Humbert, Linda, Hung, Ivan F.N., Iglesias, Alejandro D., Íñigo-Campos, Antonio, Jamme, Matthieu, Arranz, María Jesús, Jimeno, Marie-Thérèse, Jordan, Iolanda, Kanık-Yüksek, Saliha, Kara, Yalcin, Karahan, Aydın, Karbuz, Adem, Yasar, Kadriye Kart, Kasapcopur, Ozgur, Kashimada, Kenichi, Keles, Sevgi, Demirkol, Yasemin Kendir, Kido, Yasutoshi, Kizil, Can, Kılıç, Ahmet Osman, Klocperk, Adam, Koutsoukou, Antonia, Król, Zbigniew J., Ksouri, Hatem, Kuentz, Paul, Kwan, Arthur M.C., Kwan, Yat Wah M., Kwok, Janette S.Y., Lagier, Jean-Christophe, Lam, David S.Y., Lampropoulou, Vicky, Le Bourgeois, Fleur, Leo, Yee-Sin, Lopez, Rafael Leon, Leung, Daniel, Levin, Michael, Levy, Michael, Lévy, Romain, Li, Zhi, Lilleri, Daniele, Adrian Bolanos Lima, Edson Jose, Linglart, Agnes, López-Collazo, Eduardo, Lorenzo-Salazar, José M., Louapre, Céline, Lubetzki, Catherine, Lung, Kwok-Cheung, Luyt, Charles-Edouard, Lye, David C., Magnone, Cinthia, Marchioni, Enrico, Marioli, Carola, Marjani, Majid, Marques, Laura, Pereira, Jesus Marquez, Martín-Nalda, Andrea, Pueyo, David Martínez, Martinez-Picado, Javier, Marzana, Iciar, Mata-Martínez, Carmen, Mathian, Alexis, Matos, Larissa R.B., Matthews, Gail V., Mayaux, Julien, McLaughlin-Garcia, Raquel, Meersseman, Philippe, Mège, Jean-Louis, Mekontso-Dessap, Armand, Melki, Isabelle, Meloni, Federica, Meritet, Jean-François, Merlani, Paolo, Akcan, Özge Metin, Mezidi, Mehdi, Migeotte, Isabelle, Millereux, Maude, Million, Matthieu, Mirault, Tristan, Mircher, Clotilde, Mirsaeidi, Mehdi, Mizoguchi, Yoko, Modi, Bhavi P., Mojoli, Francesco, Moncomble, Elsa, Melián, Abián Montesdeoca, Martinez, Antonio Morales, Morandeira, Francisco, Morange, Pierre-Emmanuel, Mordacq, Clémence, Morelle, Guillaume, Mouly, Stéphane J., Muñoz-Barrera, Adrián, Nafati, Cyril, Nagashima, Shintaro, Nakagama, Yu, Neven, Bénédicte, Neves, João Farela, Ng, Yuk-Yung, Hubert Nielly, Medina, Yeray Novoa, Cuadros, Esmeralda Nuñez, Karabela, Semsi Nur, Ocejo-Vinyals, J. Gonzalo, Oualha, Mehdi, Ouedrani, Amani, Özçelik, Tayfun, Ozkaya-Parlakay, Aslinur, Pagani, Michele, Papadaki, Maria, Parola, Philippe, Pascreau, Tiffany, Paul, Stéphane, Paz-Artal, Estela, Pedraza, Sigifredo, González Pellecer, Nancy Carolina, Pellegrini, Silvia, Pérez de Diego, Rebeca, Pérez-Fernández, Xosé Luis, Philippe, Aurélien, Philippot, Quentin, Picod, Adrien, Pineton de Chambrun, Marc, Piralla, Antonio, Planas-Serra, Laura, Ploin, Dominique, Poissy, Julien, Poncelet, Géraldine, Poulakou, Garyphallia, Pouletty, Marie S., Pourshahnazari, Persia, Qiu-Chen, Jia Li, Quentric, Paul, Rambaud, Thomas, Raoult, Didier, Raoult, Violette, Rebillat, Anne-Sophie, Redin, Claire, Resmini, Léa, Ricart, Pilar, Richard, Jean-Christophe, Rigo-Bonnin, Raúl, Rivet, Nadia, Rivière, Jacques G., Rocamora-Blanch, Gemma, Rodero, Mathieu P., Rodrigo, Carlos, Rodriguez, Luis Antonio, Rodriguez-Gallego, Carlos, Rodriguez-Palmero, Agustí, Romero, Carolina Soledad, Rothenbuhler, Anya, Roux, Damien, Rovina, Nikoletta, Rozenberg, Flore, Ruch, Yvon, Ruiz, Montse, Ruiz del Prado, Maria Yolanda, Ruiz-Rodriguez, Juan Carlos, Sabater-Riera, Joan, Saks, Kai, Salagianni, Maria, Sanchez, Oliver, Sánchez-Montalvá, Adrián, Sánchez-Ramón, Silvia, Schidlowski, Laire, Schluter, Agatha, Schmidt, Julien, Schmidt, Matthieu, Schuetz, Catharina, Schweitzer, Cyril E., Scolari, Francesco, Seijo, Luis, Seminario, Analia Gisela, Seng, Piseth, Senoglu, Sevtap, Seppänen, Mikko, Llovich, Alex Serra, Siguret, Virginie, Siouti, Eleni, Smadja, David M., Smith, Nikaia, Sobh, Ali, Solanich, Xavier, Solé-Violán, Jordi, Soler, Catherine, Sözeri, Betül, Stella, Giulia Maria, Stepanovskiy, Yuriy, Stoclin, Annabelle, Taccone, Fabio, Taupin, Jean-Luc, Tavernier, Simon J., Tello, Loreto Vidaur, Terrier, Benjamin, Thiery, Guillaume, Thorn, Karolina, Thumerelle, Caroline, Tipu, Imran, Tolstrup, Martin, Tomasoni, Gabriele, Toubiana, Julie, Alvarez, Josep Trenado, Triantafyllia, Vasiliki, Troya, Jesús, Tsang, Owen T.Y., Tserel, Liina, Tso, Eugene Y.K., Tucci, Alessandra, Tüter Öz, Şadiye Kübra, Ursini, Matilde Valeria, Utsumi, Takanori, Vabres, Pierre, Valencia-Ramos, Juan, Van Den Rym, Ana Maria, Vandernoot, Isabelle, Velez-Santamaria, Valentina, Zuniga Veliz, Silvia Patricia, Vidigal, Mateus C., Viel, Sébastien, Villain, Cédric, Vilaire-Meunier, Marie E., Villar-García, Judit, Vincent, Audrey, Van der Linden, Dimitri, Volokha, Alla, Vuotto, Fanny, Wauters, Els, Wu, Alan K.L., Wu, Tak-Chiu, Yahşi, Aysun, Yesilbas, Osman, Yildiz, Mehmet, Young, Barnaby E., Yükselmiş, Ufuk, Zecca, Marco, Zuccaro, Valentina, Van Praet, Jens, Lambrecht, Bart N., Van Braeckel, Eva, Bosteels, Cédric, Hoste, Levi, Hoste, Eric, Bauters, Fré, De Clercq, Jozefien, Heijmans, Catherine, Slabbynck, Hans, Naesens, Leslie, Florkin, Benoit, Young, Mary-Anne, Willis, Amanda, Lapuente-Suanzes, Paloma, de Andrés-Martín, Ana, Berkell, Matilda, Carelli, Valerio, Fiorentino, Alessia, Malhotra, Surbhi, Mattiaccio, Alessandro, Pippucci, Tommaso, Seri, Marco, Tacconelli, Evelina, van Agtmael, Michiel, Algera, Anne Geke, Appelman, Brent, van Baarle, Frank, Bax, Diane, Beudel, Martijn, Bogaard, Harm Jan, Bomers, Marije, Bonta, Peter, Bos, Lieuwe, Botta, Michela, de Brabander, Justin, de Bree, Godelieve, de Bruin, Sanne, Buis, David T.P., Bugiani, Marianna, Bulle, Esther, Chouchane, Osoul, Cloherty, Alex, Dijkstra, Mirjam, Dongelmans, Dave A., Dujardin, Romein W.G., Elbers, Paul, Fleuren, Lucas, Geerlings, Suzanne, Geijtenbeek, Theo, Girbes, Armand, Goorhuis, Bram, Grobusch, Martin P., Hafkamp, Florianne, Hagens, Laura, Hamann, Jorg, Harris, Vanessa, Hemke, Robert, Hermans, Sabine M., Heunks, Leo, Hollmann, Markus, Horn, Janneke, Hovius, Joppe W., de Jong, Menno D., Koning, Rutger, Lim, Endry H.T., van Mourik, Niels, Nellen, Jeaninne, Nossent, Esther J., Paulus, Frederique, Peters, Edgar, Pina-Fuentes, Dan A.I., van der Poll, Tom, Preckel, Bennedikt, Prins, Jan M., Raasveld, Jorinde, Reijnders, Tom, de Rotte, Maurits C.F. J., Schinkel, Michiel, Schultz, Marcus J., Schrauwen, Femke A.P., Schuurmans, Alex, Schuurmans, Jaap, Sigaloff, Kim, Slim, Marleen A., Smeele, Patrick, Smit, Marry, Stijnis, Cornelis S., Stilma, Willemke, Teunissen, Charlotte, Thoral, Patrick, Tsonas, Anissa M., Tuinman, Pieter R., van der Valk, Marc, Veelo, Denise P., Volleman, Carolien, de Vries, Heder, Vught, Lonneke A., van Vugt, Michèle, Wouters, Dorien, Zwinderman, A.H., Brouwer, Matthijs C., Wiersinga, W. Joost, Vlaar, Alexander P.J., Tompkins, Miranda F., Alba, Camille, Hupalo, Daniel N., Rosenberger, John, Sukumar, Gauthaman, Wilkerson, Matthew D., Zhang, Xijun, Lack, Justin, Oler, Andrew J., Dobbs, Kerry, Delmonte, Ottavia M., Danielson, Jeffrey J., Biondi, Andrea, Bettini, Laura Rachele, D’Angiò, Mariella, Beretta, Ilaria, Imberti, Luisa, Sottini, Alessandra, Quaresima, Virginia, Quiros-Roldan, Eugenia, Rossi, Camillo, Castagnoli, Riccardo, Montagna, Daniela, Licari, Amelia, Marseglia, Gian Luigi, Marchal, Astrid, Cirulli, Elizabeth T., Neveux, Iva, Bellos, Evangelos, Thwaites, Ryan S., Schiabor Barrett, Kelly M., Zhang, Yu, Nemes-Bokun, Ivana, Kalinova, Mariya, Catchpole, Andrew, Lack, Justin B., Chiu, Christopher, and Grzymski, Joseph J.

Published
2024
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31. Temporal validation of the MMCD score to predict kidney replacement therapy and in-hospital mortality in COVID-19 patients

Author

das Graças José Ventura, Vanessa, Pereira, Polianna Delfino, Pires, Magda Carvalho, Asevedo, Alisson Alves, de Oliveira Jorge, Alzira, dos Santos, Ana Carolina Pitanga, de Moura Costa, André Soares, dos Reis Gomes, Angélica Gomides, Lima, Beatriz Figueiredo, Pessoa, Bruno Porto, Cimini, Christiane Corrêa Rodrigues, de Andrade, Claudio Moisés Valiense, Ponce, Daniela, Rios, Danyelle Romana Alves, Pereira, Elayne Crestani, Manenti, Euler Roberto Fernandes, de Almeida Cenci, Evelin Paola, Costa, Felício Roberto, Anschau, Fernando, Aranha, Fernando Graça, Vigil, Flavia Maria Borges, Bartolazzi, Frederico, Aguiar, Gabriella Genta, Grizende, Genna Maira Santos, Batista, Joanna d’Arc Lyra, Neves, João Victor Baroni, Ruschel, Karen Brasil, do Nascimento, Letícia, de Oliveira, Lucas Moyses Carvalho, Kopittke, Luciane, de Castro, Luís César, Sacioto, Manuela Furtado, Carneiro, Marcelo, Gonçalves, Marcos André, Bicalho, Maria Aparecida Camargos, da Paula Sordi, Mônica Aparecida, da Cunha Severino Sampaio, Natália, Paraíso, Pedro Gibson, Menezes, Rochele Mosmann, Araújo, Silvia Ferreira, de Assis, Vivian Costa Morais, de Paula Farah, Katia, and Marcolino, Milena Soriano

Published
2023
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32. 2023 UPDATE: Luso-Brazilian evidence-based guideline for the management of antidiabetic therapy in type 2 diabetes

Author

Bertoluci, Marcello Casaccia, Silva Júnior, Wellington S., Valente, Fernando, Araujo, Levimar Rocha, Lyra, Ruy, de Castro, João Jácome, Raposo, João Filipe, Miranda, Paulo Augusto Carvalho, Boguszewski, Cesar Luiz, Hohl, Alexandre, Duarte, Rui, Salles, João Eduardo Nunes, Silva-Nunes, José, Dores, Jorge, Melo, Miguel, de Sá, João Roberto, Neves, João Sérgio, Moreira, Rodrigo Oliveira, Malachias, Marcus Vinícius Bolívar, Lamounier, Rodrigo Nunes, Malerbi, Domingos Augusto, Calliari, Luis Eduardo, Cardoso, Luis Miguel, Carvalho, Maria Raquel, Ferreira, Hélder José, Nortadas, Rita, Trujilho, Fábio Rogério, Leitão, Cristiane Bauermann, Simões, José Augusto Rodrigues, dos Reis, Mónica Isabel Natal, Melo, Pedro, Marcelino, Mafalda, and Carvalho, Davide

Published
2023
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34. The GAIN Registry — a New Prospective Study for Patients with Multi-organ Autoimmunity and Autoinflammation

Author

Staus, Paulina, Rusch, Stephan, El-Helou, Sabine, Müller, Gabriele, Krausz, Máté, Geisen, Ulf, Caballero-Oteyza, Andrés, Krüger, Renate, Bakhtiar, Shahrzad, Lee-Kirsch, Min Ae, Fasshauer, Maria, Baumann, Ulrich, Hoyer, Bimba Franziska, Farela Neves, João, Borte, Michael, Carrabba, Maria, Hauck, Fabian, Ehl, Stephan, Bader, Peter, von Bernuth, Horst, Atschekzei, Faranaz, Seppänen, Mikko R. J., Warnatz, Klaus, Nieters, Alexandra, Kindle, Gerhard, and Grimbacher, Bodo

Published
2023
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38. Western outcomes of circumferential endoscopic submucosal dissection for early esophageal squamous cell carcinoma

Author

Rodríguez de Santiago, Enrique, van Tilburg, Laurelle, Deprez, Pierre H., Pioche, Mathieu, Pouw, Roos E., Bourke, Michael J., Seewald, Stefan, Weusten, Bas L.A.M., Jacques, Jeremie, Leblanc, Sara, Barreiro, Pedro, Lemmers, Arnaud, Parra-Blanco, Adolfo, Küttner-Magalhães, Ricardo, Libânio, Diogo, Messmann, Helmut, Albéniz, Eduardo, Kaminski, Michal F., Mohammed, Noor, Ramos-Zabala, Felipe, Herreros-de-Tejada, Alberto, Huchima Koecklin, Hugo, Wallenhorst, Timothée, Santos-Antunes, João, Cunha Neves, João A., Koch, Arjun D., Ayari, Myriam, Garces-Duran, Rodrigo, Ponchon, Thierry, Rivory, Jérôme, Bergman, Jacques J.G.H.M., Verheij, Eva P.D., Gupta, Sunil, Groth, Stefan, Lepilliez, Vincent, Franco, Ana Rita, Belkhir, Sanaa, White, Jonathan, Ebigbo, Alanna, Probst, Andreas, Legros, Romain, Pilonis, Nastazja Dagny, de Frutos, Diego, Muñoz González, Raquel, and Dinis-Ribeiro, Mario

Published
2024
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41. Documenting the Recovery of Vascular Services in European Centres Following the Initial COVID-19 Pandemic Peak: Results from a Multicentre Collaborative Study

Author

collaborative, The VERN COVER study, Ruffino, Maria Antonella, Chan, Sharon, Coughlin, Patrick, Awopetu, Ayoola, Stather, Philip, Lane, Tristan, Theodosiou, Dimitrios, Ahmed, Mohamed Abozeid, Vasudevan, Thodur, Ibrahim, Mohammed, Maadany, Faraj Al, Eljareh, Mohamed, Alkhafeefi, Fatimah Saad, Coscas, Raphael, Ünal, Ertekin Utku, Pulli, Raffaele, Zacà, Sergio, Angiletta, Domenico, Kotsis, Thomas, Moawad, Magdy, Tozzi, Matteo, Patelis, Nikolaos, Lazaris, Andreas M, Chuen, Jason, Croo, Alexander, Tsolaki, Elpiniki, Zenunaj, Gladiol, Kamal, Dhafer, Tolba, Mahmoud MH, Maresch, Martin, Khetarpaul, Vipul, Mills, Joseph, Gangwani, Gaurav, Elahwal, Mohamed, Khalil, Rana, Azab, Mohammed A, Mahomed, Anver, Whiston, Richard, Contractor, Ummul, Esposito, Davide, Pratesi, Carlo, Giacomelli, Elena, Troncoso, Martín Veras, Elkouri, Stephane, Johansson, Flavia Gentile, Dodos, Ilias, Benezit, Marie, Vidoedo, José, Rocha-Neves, João, Pereira-Neves, António Henrique, Dias-Neto, Marina Felicidade, Jácome, Ana Filipa Campos, Loureiro, Luis, Silva, Ivone, Garza-Herrera, Rodrigo, Canata, Victor, Bezard, Charlotte, Bowser, Kathryn, Tobar, Jorge Felipe, Vera, Carlos Gomez, Parra, Carolina Salinas, Lopez, Eugenia, Serra, Yvis Gadelha, Varela, Juan, Rubio, Vanessa, Victoria, Gerardo, Johnson, Adam, O’Banion, Leigh Ann, Makar, Ragai, Tantawy, Tamer Ghatwary, Storck, Martin, Jongkind, Vincent, falah, Orwa, McBride, Olivia, Isik, Arda, Papaioannou, Athanasios, Reis, Paulo Eduardo Ocke, Bracale, Umberto Marcello, Atkins, Ellie, Tinelli, Giovanni, Scott, Emma, Wales, Lucy, Sivaharan, Ashwin, Priona, Georgia, Nesbitt, Craig, Grainger, Tabitha, Shelmerdine, Lauren, Chong, Patrick, Bajwa, Adnan, Arwynck, Luke, Hadjievangelou, Nancy, Elbasty, Ahmed, Rubio, Oscar, Ricardo, Michael, Ulloa, Jorge H, Tarazona, Marcos, Pabon, Manuel, Pitoulias, Georgios, and Corless, Kevin

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Dentistry, Pediatric Research Initiative, Prevention, 6.4 Surgery, Evaluation of treatments and therapeutic interventions, VERN COVER study collaborative, AAA, COVID-19, PAD, Survey, Vascular surgery, Cardiovascular medicine and haematology, Clinical sciences
Abstract

ObjectiveTo document the recovery of vascular services in Europe following the first COVID-19 pandemic peak.MethodsAn online structured vascular service survey with repeated data entry between 23 March and 9 August 2020 was carried out. Unit level data were collected using repeated questionnaires addressing modifications to vascular services during the first peak (March - May 2020, "period 1"), and then again between May and June ("period 2") and June and July 2020 ("period 3"). The duration of each period was similar. From 2 June, as reductions in cases began to be reported, centres were first asked if they were in a region still affected by rising cases, or if they had passed the peak of the first wave. These centres were asked additional questions about adaptations made to their standard pathways to permit elective surgery to resume.ResultsThe impact of the pandemic continued to be felt well after countries' first peak was thought to have passed in 2020. Aneurysm screening had not returned to normal in 21.7% of centres. Carotid surgery was still offered on a case by case basis in 33.8% of centres, and only 52.9% of centres had returned to their normal aneurysm threshold for surgery. Half of centres (49.4%) believed their management of lower limb ischaemia continued to be negatively affected by the pandemic. Reduced operating theatre capacity continued in 45.5% of centres. Twenty per cent of responding centres documented a backlog of at least 20 aortic repairs. At least one negative swab and 14 days of isolation were the most common strategies used for permitting safe elective surgery to recommence.ConclusionCentres reported a broad return of services approaching pre-pandemic "normal" by July 2020. Many introduced protocols to manage peri-operative COVID-19 risk. Backlogs in cases were reported for all major vascular surgeries.

Published
2022

42. Development of the first Portuguese radar tracking sensor for Space Debris

Author

Pandeirada, João, Bergano, Miguel, Neves, João, Marques, Paulo, Barbosa, Domingos, Coelho, Bruno, and Ribeiro, Valério

Subjects
Physics - Space Physics, Astrophysics - Instrumentation and Methods for Astrophysics, Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

Currently, space debris represents a threat for satellites and space-based operations, both in-orbit and during the launching process. The yearly increase in space debris represents a serious concern to major space agencies leading to the development of dedicated space programs to deal with this issue. Ground-based radars can detect Earth orbiting debris down to a few square centimeters and therefore constitute a major building block of a space debris monitoring system. New radar sensors are required in Europe to enhance capabilities and availability of its small radar network capable of tracking and surveying space objects and to respond to the debris increase expected from the New Space economy activities. This article presents ATLAS, a new tracking radar system for debris detection located in Portugal. It starts by an extensive technical description of all the system components followed by a study that estimates its future performance. A section dedicated to waveform design is also presented, since the system allows the usage of several types of pulse modulation schemes such as LFM and phase coded modulations while enabling the development and testing of more advanced ones. By presenting an architecture that is highly modular with fully digital signal processing, ATLAS establishes a platform for fast and easy development, research and innovation. The system follows the use of Commercial-Off-The-Shelf technologies and Open Systems which is unique among current radar systems., Comment: Reviewed; Accepted for Publication at Signals, MDPI, ISSN 2624-6120, February 2021; 16 pages, 8 Figures

Published
2021
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43. Accident Prediction Model Applied to Motorway A29 in Portugal

Author

Ferreira, Sara, Couto, António, Lobo, António, Souza, Suellen, De Santos-Berbel, César, Neves, João, Chastre, Carlos, editor, Neves, José, editor, Ribeiro, Diogo, editor, Pinho, Fernando F. S., editor, Biscaia, Hugo, editor, Neves, Maria Graça, editor, Faria, Paulina, editor, and Micaelo, Rui, editor

Published
2023
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44. Simulation of the Implementation of Domestic Solar Systems Using Multi-agent Systems from Web Scraping

Author

Neves, João Emmanuel D.’Alkmin, Pedro, Paulo Sérgio Martins, de Freitas Gomes Hernandez, Marli, Junior, Luiz Ariovaldo Fabri, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Iano, Yuzo, editor, Saotome, Osamu, editor, Kemper Vásquez, Guillermo Leopoldo, editor, Cotrim Pezzuto, Claudia, editor, Arthur, Rangel, editor, and Gomes de Oliveira, Gabriel, editor

Published
2023
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50. Design Of Drug-Like Protein-Protein Interaction Stabilizers Guided By Chelation-Controlled Bioactive Conformation Stabilization

Author

Bosica, Francesco, Andrei, Sebastian, Neves, João Filipe, Brandt, Peter, Gunnarsson, Anders, Landrieu, Isabelle, Ottmann, Christian, and O'Mahony, Gavin

Subjects
Quantitative Biology - Biomolecules
Abstract

The protein-protein interactions (PPIs) of 14-3-3 proteins are a model system for studying PPI stabilization. The complex natural product Fusicoccin A stabilizes many 14-3-3 PPIs but is not amenable for use in SAR studies, motivating the search for more drug-like chemical matter. However, drug-like 14-3-3 PPI stabilizers enabling such study have remained elusive. An X-ray crystal structure of a PPI in complex with an extremely low potency stabilizer uncovered an unexpected non-protein interacting, ligand-chelated Mg 2+ leading to the discovery of metal ion-dependent 14-3-3 PPI stabilization potency. This originates from a novel chelation-controlled bioactive conformation stabilization effect. Metal chelation has been associated with pan-assay interference compounds (PAINS) and frequent hitter behavior, but chelation can evidently also lead to true potency gains and find use as a medicinal chemistry strategy to guide compound optimization. To demonstrate this, we exploited the effect to design the first potent, selective and drug-like 14-3-3 PPI stabilizers.

Published
2020
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