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1. VDAC2 primes myeloma cells for BAK-dependent apoptosis and represents a novel therapeutic target: MULTIPLE MYELOMA, GAMMOPATHIES

Author

Champion, Ophélie, Maïga, Sophie, Antier, Chloé, Dousset, Christelle, Moreau-Aubry, Agnès, Bellanger, Céline, Guillonneau, François, Descamps, Géraldine, Moreno, Jose Antonio, Kwon, Ohyun, Lilli, Nicoletta Libera, Moreau, Philippe, Chiron, David, Pellat-Deceunynck, Catherine, Touzeau, Cyrille, and Gomez-Bougie, Patricia

Published
2025
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2. Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

Author

Aamir, M., Adamov, G., Adams, T., Adloff, C., Afanasiev, S., Agrawal, C., Ahmad, A., Ahmed, H. A., Akbar, S., Akchurin, N., Akgul, B., Akgun, B., Akpinar, R. O., Aktas, E., Kadhim, A. Al, Alexakhin, V., Alimena, J., Alison, J., Alpana, A., Alshehri, W., Dominguez, P. Alvarez, Alyari, M., Amendola, C., Amir, R. B., Andersen, S. B., Andreev, Y., Antoszczuk, P. D., Aras, U., Ardila, L., Aspell, P., Avila, M., Awad, I., Aydilek, O., Azimi, Z., Pretel, A. Aznar, Bach, O. A., Bainbridge, R., Bakshi, A., Bam, B., Banerjee, S., Barney, D., Bayraktar, O., Beaudette, F., Beaujean, F., Becheva, E., Behera, P. K., Belloni, A., Bergauer, T., Besancon, M., Bylund, O. Bessidskaia, Bhatt, L., Bhattacharya, S., Bhowmil, D., Blekman, F., Blinov, P., Bloch, P., Bodek, A., Boger, a., Bonnemaison, A., Bouyjou, F., Brennan, L., Brondolin, E., Brusamolino, A., Bubanja, I., Perraguin, A. Buchot, Bunin, P., Misura, A. Burazin, Butler-nalin, A., Cakir, A., Callier, S., Campbell, S., Candemir, Y. B., Canderan, K., Cankocak, K., Cappati, A., Caregari, S., Carron, S., Carty, C., Cauchois, A., Ceard, L., Cerci, S., Chang, P. J., Chatterjee, R. M., Chatterjee, S., Chattopadhyay, P., Chatzistavrou, T., Chaudhary, M. S., Chen, J. A., Chen, J., Chen, Y., Cheng, K., Cheung, H., Chhikara, J., Chiron, A., Chiusi, M., Chokheli, D., Chudasama, R., Clement, E., Mendez, S. Coco, Coko, D., Coskun, K., Couderc, F., Crossman, B., Cui, Z., Cuisset, T., Cummings, G., Curtis, E. M., D'Alfonso, M., Döhler-Ball, J., Dadazhanova, O., Damgov, J., Das, I., Gupta, S. Das, Dauncey, P., Mendes, A. David Tinoco, Davies, G., Davignon, O., de Barbaro, P., De La Taille, C., De Silva, M., De Wit, A., Debbins, P., Defranchis, M. M., Delagnes, E., Devouge, P., Di Guglielmo, G., Diehl, L., Dilsiz, K., Dincer, G. G., Dittmann, J., Dragicevic, M., Du, D., Dubinchik, B., Dugad, S., Dulucq, F., Dumanoglu, I., Duran, B., Dutta, S., Dutta, V., Dychkant, A., Dünser, M., Edberg, T., Ehle, I. T., Berni, A. El, Elias, F., Eno, S. C., Erdogan, E. N., Erkmen, B., Ershov, Y., Ertorer, E. Y., Extier, S., Eychenne, L., Fedar, Y. E., Fedi, G., De Almeida, J. P. Figueiredo De Sá Sousa, Alves, B. A. Fontana Santos, Frahm, E., Francis, K., Freeman, J., French, T., Gaede, F., Gandhi, P. K., Ganjour, S., Garcia-Bellido, A., Gastaldi, F., Gazi, L., Gecse, Z., Gerwig, H., Gevin, O., Ghosh, S., Gill, K., Gingu, C., Gleyzer, S., Godinovic, N., Goettlicher, P., Goff, R., Gok, M., Golunov, A., Gonultas, B., Martínez, J. D. González, Gorbounov, N., Gouskos, L., Gray, A., Gray, L., Grieco, C., Groenroos, S., Groner, D., Gruber, A., Grummer, A., Grönroos, S., Guerrero, D., Guilloux, F., Guler, Y., Gungordu, A. D., Guo, J., Guo, K., Guler, E. Gurpinar, Gutti, H. K., Guvenli, A. A., Gülmez, E., Hacisahinoglu, B., Halkin, Y., Machado, G. Hamilton Ilha, Hare, H. S., Hatakeyama, K., Heering, A. H., Hegde, V., Heintz, U., Hinton, N., Hinzmann, A., Hirschauer, J., Hitlin, D., Hoff, J., Hos, İ., Hou, B., Hou, X., Howard, A., Howe, C., Hsieh, H., Hsu, T., Hua, H., Hummer, F., Imran, M., Incandela, J., Iren, E., Isildak, B., Jackson, P. S., Jackson, W. J., Jain, S., Jana, P., Jaroslavceva, J., Jena, S., Jige, A., Jordano, P. P., Joshi, U., Kaadze, K., Kachanov, V., Kafizov, A., Kalipoliti, L., Tharayil, A. Kallil, Kaluzinska, O., Kamble, S., Kaminskiy, A., Kanemura, M., Kanso, H., Kao, Y., Kapic, A., Kapsiak, C., Karjavine, V., Karmakar, S., Karneyeu, A., Kaya, M., Topaksu, A. Kayis, Kaynak, B., Kazhykarim, Y., Khan, F. A., Khudiakov, A., Kieseler, J., Kim, R. S., Klijnsma, T., Kloiber, E. G., Klute, M., Kocak, Z., Kodali, K. R., Koetz, K., Kolberg, T., Kolcu, O. B., Komaragiri, J. R., Komm, M., Kopsalis, I., Krause, H. A., Krawczyk, M. A., Vinayakam, T. R. Krishnaswamy, Kristiansen, K., Kristic, A., Krohn, M., Kronheim, B., Krüger, K., Kudtarkar, C., Kulis, S., Kumar, M., Kumar, N., Kumar, S., Verma, R. Kumar, Kunori, S., Kunts, A., Kuo, C., Kurenkov, A., Kuryatkov, V., Kyre, S., Ladenson, J., Lamichhane, K., Landsberg, G., Langford, J., Laudrain, A., Laughlin, R., Lawhorn, J., Dortz, O. Le, Lee, S. W., Lektauers, A., Lelas, D., Leon, M., Levchuk, L., Li, A. J., Li, J., Li, Y., Liang, Z., Liao, H., Lin, K., Lin, W., Lin, Z., Lincoln, D., Linssen, L., Litomin, A., Liu, G., Liu, Y., Lobanov, A., Lohezic, V., Loiseau, T., Lu, C., Lu, R., Lu, S. Y., Lukens, P., Mackenzie, M., Magnan, A., Magniette, F., Mahjoub, A., Mahon, D., Majumder, G., Makarenko, V., Malakhov, A., Malgeri, L., Mallios, S., Mandloi, C., Mankel, A., Mannelli, M., Mans, J., Mantilla, C., Martinez, G., Massa, C., Masterson, P., Matthewman, M., Matveev, V., Mayekar, S., Mazlov, I., Mehta, A., Mestvirishvili, A., Miao, Y., Milella, G., Mirza, I. R., Mitra, P., Moccia, S., Mohanty, G. B., Monti, F., Moortgat, F., Murthy, S., Music, J., Musienko, Y., Nabili, S., Nelson, J. W., Nema, A., Neutelings, I., Niedziela, J., Nikitenko, A., Noonan, D., Noy, M., Nurdan, K., Obraztsov, S., Ochando, C., Ogul, H., Olsson, J., Onel, Y., Ozkorucuklu, S., Paganis, E., Palit, P., Pan, R., Pandey, S., Pantaleo, F., Papageorgakis, C., Paramesvaran, S., Paranjpe, M. M., Parolia, S., Parsons, A. G., Parygin, P., Pastika, J., Paulini, M., Paus, C., Castillo, K. Peñaló, Pedro, K., Pekic, V., Peltola, T., Peng, B., Perego, A., Perini, D., Petrilli, A., Pham, H., Podem, S. K., Popov, V., Portales, L., Potok, O., Pradeep, P. B., Pramanik, R., Prosper, H., Prvan, M., Qasim, S. R., Qu, H., Quast, T., Trivio, A. Quiroga, Rabour, L., Raicevic, N., Rao, M. A., Rapacz, K., Redjeb, W., Reinecke, M., Revering, M., Roberts, A., Rohlf, J., Rosado, P., Rose, A., Rothman, S., Rout, P. K., Rovere, M., Roy, A., Rubinov, P., Rumerio, P., Rusack, R., Rygaard, L., Ryjov, V., Sadivnycha, S., Sahin, M. Ö., Sakarya, U., Salerno, R., Saradhy, R., Saraf, M., Sarbandi, K., Sarkisla, M. A., Satyshev, I., Saud, N., Sauvan, J., Schindler, G., Schmidt, A., Schmidt, I., Schmitt, M. H., Sculac, A., Sculac, T., Sedelnikov, A., Seez, C., Sefkow, F., Selivanova, D., Selvaggi, M., Sergeychik, V., Sert, H., Shahid, M., Sharma, P., Sharma, R., Sharma, S., Shelake, M., Shenai, A., Shih, C. W., Shinde, R., Shmygol, D., Shukla, R., Sicking, E., Silva, P., Simsek, C., Simsek, E., Sirasva, B. K., Sirois, Y., Song, S., Song, Y., Soudais, G., Sriram, S., Jacques, R. R. St, Leiton, A. G. Stahl, Steen, A., Stein, J., Strait, J., Strobbe, N., Su, X., Sukhov, E., Suleiman, A., Cerci, D. Sunar, Suryadevara, P., Swain, K., Syal, C., Tali, B., Tanay, K., Tang, W., Tanvir, A., Tao, J., Tarabini, A., Tatli, T., Taylor, R., Taysi, Z. C., Teafoe, G., Tee, C. Z., Terrill, W., Thienpont, D., Thomas, P. E., Thomas, R., Titov, M., Todd, C., Todd, E., Toms, M., Tosun, A., Troska, J., Tsai, L., Tsamalaidze, Z., Tsionou, D., Tsipolitis, G., Tsirigoti, M., Tu, R., Polat, S. N. Tural, Undleeb, S., Usai, E., Uslan, E., Ustinov, V., Uzunian, A., Vernazza, E., Viahin, O., Viazlo, O., Vichoudis, P., Vijay, A., Virdee, T., Voirin, E., Vojinovic, M., Vámi, T. Á., Wade, A., Walter, D., Wang, C., Wang, F., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Wanlin, E., Wayne, M., Wetzel, J., Whitbeck, A., Wickwire, R., Wilmot, D., Wilson, J., Wu, H., Xiao, M., Yang, J., Yazici, B., Ye, Y., Yerli, B., Yetkin, T., Yi, R., Yohay, R., Yu, T., Yuan, C., Yuan, X., Yuksel, O., YushmanoV, I., Yusuff, I., Zabi, A., Zareckis, D., Zehetner, P., Zghiche, A., Zhang, C., Zhang, D., Zhang, H., Zhang, J., Zhang, Z., Zhao, X., Zhong, J., Zhou, Y., and Zorbilmez, Ç.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Physics - Data Analysis, Statistics and Probability
Abstract

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Published
2024
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3. Weakly Supervised Training for Hologram Verification in Identity Documents

Author

Pouliquen, Glen, Chiron, Guillaume, Chazalon, Joseph, Géraud, Thierry, and Awal, Ahmad Montaser

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We propose a method to remotely verify the authenticity of Optically Variable Devices (OVDs), often referred to as ``holograms'', in identity documents. Our method processes video clips captured with smartphones under common lighting conditions, and is evaluated on two public datasets: MIDV-HOLO and MIDV-2020. Thanks to a weakly-supervised training, we optimize a feature extraction and decision pipeline which achieves a new leading performance on MIDV-HOLO, while maintaining a high recall on documents from MIDV-2020 used as attack samples. It is also the first method, to date, to effectively address the photo replacement attack task, and can be trained on either genuine samples, attack samples, or both for increased performance. By enabling to verify OVD shapes and dynamics with very little supervision, this work opens the way towards the use of massive amounts of unlabeled data to build robust remote identity document verification systems on commodity smartphones. Code is available at https://github.com/EPITAResearchLab/pouliquen.24.icdar, Comment: Accepted at the International Conference on Document Analysis and Recognition (ICDAR 2024)

Published
2024

5. Synthetic dataset of ID and Travel Document

Author

Boned, Carlos, Talarmain, Maxime, Ghanmi, Nabil, Chiron, Guillaume, Biswas, Sanket, Awal, Ahmad Montaser, and Terrades, Oriol Ramos

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

This paper presents a new synthetic dataset of ID and travel documents, called SIDTD. The SIDTD dataset is created to help training and evaluating forged ID documents detection systems. Such a dataset has become a necessity as ID documents contain personal information and a public dataset of real documents can not be released. Moreover, forged documents are scarce, compared to legit ones, and the way they are generated varies from one fraudster to another resulting in a class of high intra-variability. In this paper we trained state-of-the-art models on this dataset and we compare them to the performance achieved in larger, but private, datasets. The creation of this dataset will help to document image analysis community to progress in the task of ID document verification.

Published
2024

6. Mining telemonitored physiological data and patient-reported outcomes of congestive heart failure patients.

Author

Miha Mlakar, Paolo Emilio Puddu, Maja Somrak, Silvio Bonfiglio, Mitja Luštrek, and Chiron and HeartMan research projects

Subjects
Medicine, Science
Abstract

This paper addresses patient-reported outcomes (PROs) and telemonitoring in congestive heart failure (CHF), both increasingly important topics. The interest in CHF trials is shifting from hard end-points such as hospitalization and mortality, to softer end-points such health-related quality of life. However, the relation of these softer end-points to objective parameters is not well studied. Telemonitoring is suitable for collecting both patient-reported outcomes and objective parameters. Most telemonitoring studies, however, do not take full advantage of the available sensor technology and intelligent data analysis. The Chiron clinical observational study was performed among 24 CHF patients (17 men and 7 women, age 62.9 ± 9.4 years, 15 NYHA class II and 9 class III, 10 of ishaemic, aetiology, 6 dilated, 2 valvular, and 6 of multiple aetiologies or cardiomyopathy) in Italy and UK. A large number of physiological and ambient parameters were collected by wearable and other devices, together with PROs describing how well the patients felt, over 1,086 days of observation. The resulting data were mined for relations between the objective parameters and the PROs. The objective parameters (humidity, ambient temperature, blood pressure, SpO2, and sweeting intensity) could predict the PROs with accuracies up to 86% and AUC up to 0.83, making this the first report providing evidence for ambient and physiological parameters to be objectively related to PROs in CHF patients. We also analyzed the relations in the predictive models, gaining some insights into what affects the feeling of health, which was also generally not attempted in previous investigations. The paper strongly points to the possibility of using PROs as primary end-points in future trials.

Published
2018
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8. Quantitative CT imaging and radiation-absorbed dose estimations of 166Ho microspheres: paving the way for clinical application

Author

Chiron Morsink, Nienke Klaassen, Gerrit van de Maat, Milou Boswinkel, Alexandra Arranja, Robin Bruggink, Ilva van Houwelingen, Irene Schaafsma, Jan Willem Hesselink, Frank Nijsen, and Bas van Nimwegen

Subjects
Brachytherapy, Dosimetry, Holmium-166, Monte Carlo method, Tomography (x-ray computed), Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Abstract Background Microbrachytherapy enables high local tumor doses sparing surrounding tissues by intratumoral injection of radioactive holmium-166 microspheres (166Ho-MS). Magnetic resonance imaging (MRI) cannot properly detect high local Ho-MS concentrations and single-photon emission computed tomography has insufficient resolution. Computed tomography (CT) is quicker and cheaper with high resolution and previously enabled Ho quantification. We aimed to optimize Ho quantification on CT and to implement corresponding dosimetry. Methods Two scanners were calibrated for Ho detection using phantoms and multiple settings. Quantification was evaluated in five phantoms and seven canine patients using subtraction and thresholding including influences of the target tissue, injected amounts, acquisition parameters, and quantification volumes. Radiation-absorbed dose estimation was implemented using a three-dimensional 166Ho specific dose point kernel generated with Monte Carlo simulations. Results CT calibration showed a near-perfect linear relation between radiodensity (HU) and Ho concentrations for all conditions, with differences between scanners. Ho detection during calibration was higher using lower tube voltages, soft-tissue kernels, and without a scanner detection limit. The most accurate Ho recovery in phantoms was 102 ± 11% using a threshold of mean tissue HU + (2 × standard deviation) and in patients 98 ± 31% using a 100 HU threshold. Thresholding allowed better recovery with less variation and dependency on the volume of interest compared to the subtraction of a single HU reference value. Corresponding doses and histograms were successfully generated. Conclusion CT quantification and dosimetry of 166Ho should be considered for further clinical application with on-site validation using radioactive measurements and intra-operative Ho-MS and dose visualizations. Relevance statement Image-guided holmium-166 microbrachytherapy currently lacks reliable quantification and dosimetry on CT to ensure treatment safety and efficacy, while it is the only imaging modality capable of quantifying high in vivo holmium concentrations. Key Points Local injection of 166Ho-MS enables high local tumor doses while sparing surrounding tissue. CT enables imaging-based quantification and radiation-absorbed dose estimation of concentrated Ho in vivo, essential for treatment safety and efficacy. Two different CT scanners and multiple acquisition and reconstruction parameters showed near-perfect linearity between radiodensity and Ho concentration. The most accurate Ho recoveries on CT were 102 ± 11% in five phantoms and 98 ± 31% in seven canine patients using thresholding methods. Dose estimations and volume histograms were successfully implemented for clinical application using a dose point kernel based on Monte Carlo simulations. Graphical Abstract

Published
2024
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11. Identification and multimodal characterization of a specialized epithelial cell type associated with Crohn’s disease

Author

Li, Jia, Simmons, Alan J., Hawkins, Caroline V., Chiron, Sophie, Ramirez-Solano, Marisol A., Tasneem, Naila, Kaur, Harsimran, Xu, Yanwen, Revetta, Frank, Vega, Paige N., Bao, Shunxing, Cui, Can, Tyree, Regina N., Raber, Larry W., Conner, Anna N., Pilat, Jennifer M., Jacobse, Justin, McNamara, Kara M., Allaman, Margaret M., Raffa, Gabriella A., Gobert, Alain P., Asim, Mohammad, Goettel, Jeremy A., Choksi, Yash A., Beaulieu, Dawn B., Dalal, Robin L., Horst, Sara N., Pabla, Baldeep S., Huo, Yuankai, Landman, Bennett A., Roland, Joseph T., Scoville, Elizabeth A., Schwartz, David A., Washington, M. Kay, Shyr, Yu, Wilson, Keith T., Coburn, Lori A., Lau, Ken S., and Liu, Qi

Published
2024
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14. Cyclin dependent kinase 4/6 inhibitor palbociclib synergizes with BCL2 inhibitor venetoclax in experimental models of mantle cell lymphoma without RB1 deletion

Author

Malarikova, Diana, Jorda, Radek, Kupcova, Kristyna, Senavova, Jana, Dolnikova, Alexandra, Pokorna, Eva, Kazantsev, Dmitry, Nozickova, Kristina, Sovilj, Dana, Bellanger, Celine, Chiron, David, Andera, Ladislav, Krystof, Vladimir, Strnad, Miroslav, Helman, Karel, Klanova, Magdalena, Trneny, Marek, Havranek, Ondrej, and Klener, Pavel

Published
2024
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15. Deployment of a Matrix Element Method code for the ttH channel analysis on GPU’s platform

Author

Grasseau Gilles, Beaudette Florian, Perez Cristina Martin, Zabi Alexandre, Chiron Arnaud, Strebler Thomas, and Hautreux Gabriel

Subjects
Physics, QC1-999
Abstract

The observation of the associated production of the Higgs boson with two top quarks in proton-proton collisions is one of the highlights of the LHC Run 2. Driven by the theoretical description of the physics processes, the Matrix Element Method (MEM) consists in computing a probability that an event is compatible with the signal hypothesis (ttH) or with one of the background hypotheses. It is a powerful classifying tool requiring high dimensional integral computations. The deployment of our MEM production code on GPU’s platform will be described. What follows will focus on the adaptation of the main components of the computations in OpenCL kernels, namely the Magraph matrix element code generator, VEGAS, and LHAPDF. Finally, the gain obtained on GPU’s platforms compared with classical CPU’s platforms will be assessed.

Published
2019
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16. Cross-scale Multi-instance Learning for Pathological Image Diagnosis

Author

Deng, Ruining, Cui, Can, Remedios, Lucas W., Bao, Shunxing, Womick, R. Michael, Chiron, Sophie, Li, Jia, Roland, Joseph T., Lau, Ken S., Liu, Qi, Wilson, Keith T., Wang, Yaohong, Coburn, Lori A., Landman, Bennett A., and Huo, Yuankai

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

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Published
2023

18. Impacts of Excreta Exposure and Age on Ileal Microbial Communities, Intestinal Permeability, and Corticosterone in Hens Housed in Enriched Colonies and Cage-Free Housing Systems

Author

Benjamin J. Altendorf, Chiron J. Anderson, Isabella von Seggern, Maddison L. Wiersema, Stephan Schmitz-Esser, and Dawn A. Koltes

Subjects
laying hen, housing system, intestinal permeability, corticosterone, microbial community, Animal culture, SF1-1100
Abstract

To tease apart differences between conventional cage (CC) and cage-free (CF) housing systems, this study focuses on the effects of excreta exposure and age by comparing microbial communities, intestinal permeability, and corticosterone in hens in enriched colonies (EC) and CF housing systems during early- and late-lay. Hens were randomly selected from two rooms of CF (n = 20) and EC (n = 20) at 35 and 76 weeks of age. One hour following an oral gavage of fluorescein isothiocyanate dextran (FITC-D), hens were euthanized, and ileal contents and blood were collected. Serum FITC-D using a fluorescent spectrophotometer and corticosterone using a commercial competitive ELISA kit were analyzed. Following DNA isolation from the ileum contents, the V4 region of the 16S rRNA gene was sequenced. Sequence data were filtered in Mothur v1.43.0, followed by de novo operational taxonomic unit (OTU) clustering and classifying with the SILVA SSU v138 reference database. Serum FITC-D was altered by housing type, age of hens, and the interaction between housing type and age of hens (p < 0.001), with 76-week-old hens housed in EC having the highest FITC-D. Corticosterone increased with age (p = 0.023). Microbial community diversity measurements favored hens housed in the CF housing system as ileal contents tended to have increased species evenness (p = 0.008) and greater alpha diversity (p = 0.006). The majority of the over-representation of OTUs were associated with peak lay.

Published
2024
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20. Vanzacaftor–tezacaftor–deutivacaftor versus elexacaftor–tezacaftor–ivacaftor in individuals with cystic fibrosis aged 12 years and older (SKYLINE Trials VX20-121-102 and VX20-121-103): results from two randomised, active-controlled, phase 3 trials

Author

Krick, Stefanie, McCoy, Karen, Goss, Christopher, Weiner, Daniel, Billings, Joanne, Ren, Clement, Atkinson, Jeffrey, Linnemann, Rachel, Trimble, Aaron, Frederick, Carla, Liou, Theodore, Escobar, Hugo, Wang, Janice, Deschamps, Ashley, Froh, Deborah, Chmiel, James, Wurth, Mark, Yonker, Lael, Jain, Manu, Millard, Susan, Tolle, James, Filbrun, Amy, Taylor-Cousar, Jennifer, Ruiz, Fadel, Brown, Cynthia, Quick, Bryon, Brown, Perry, Kennedy, John, Ruddy, Jennifer, Shkolnik, Boris, Pancham, Krishna, DiMango, Emily, Fiel, Stanley, Mueller, Gary, Livingston, Floyd, Braun, Andrew, O'Sullivan, Brian, Dubin, Patricia, Teneback, Charlotte, Kremer, Ted, Welter, John, Hoppe, Jordana, Jain, Raksha, Calimano, Francisco, Johannes, Jimmy, Daines, Cori, Green, Deanna, Keens, Thomas, Trillo-Alvarez, Cesar, Scher, Herschel, Mermis, Joel, Morrissey, Brian, Wallace, James, Barrios, Christopher, Callison, John, Holliday, Zachary, Klingsberg, Ross, Efrati, Ori, Cohen-Cymberknoh, Malena, Prais, Dario, Azevedo, Pilar, Quintana-Gallego, Esther, Wong, Janice, Mondejar-Lopez, Pedro, Lopez Neyra, Alejandro, Amorim, Adelina, Sivam, Sheila, Asensio de la Cruz, Oscar, Waller, Michael, Forseen, Caralee, Marion, Chad, Dittrich, Anna-Maria, Lysinger, Jerimiah, Casserly, Brian, Luna, Carmen, Brown, Sarah, Singer, Jonathan, Schellhase, Dennis, Roberts, Jon, King, Christopher, Byrnes, Catherine, Ringshausen, Felix, Hebestreit, Helge, Lorenz, Michael, Ruppel, Renate, Drevinek, Pavel, Dillenhofer, Stefanie, Stahl, Mirjam, Gleiber, Wolfgang, Homola, Lukas, Naehrig, Susanne, Stehling, Florian, Sutharsan, Sivagurunathan, Fischer, Rainald, Poplawska, Krystyna, Haworth, Charles, Simmonds, Nicholas, Urquhart, Don, Duckers, Jamie, Carroll, Mary, Barry, Peter, Doe, Simon, Hilliard, Tom, MacGregor, Gordon, Patel, Neil, Withers, Nicholas, Peckham, Daniel, Ledson, Martin, Koser, Usma, Gartner, Silvia, Blitz Castro, Enrique, Sole Jover, Amparo, Cox, Desmond, McKone, Edward, Plant, Barry, Williamson, Michael, Elnazir, Basil, Barbosa, Telma, Cols Roig, Maria, Burr, Lucy, Henderson, Daniel, Middleton, Peter, Kotsimbos, Tom, De Monestrol, Isabelle, Gilljam, Marita, Lindberg, Ulrika, Schwarz, Carsten, Bode, Sebastian, Epton, Michael, Halasz, Adrien, Laki, Istvan, O'Carroll, Mark, Fortner, Christopher, Bilodeau, Lara, Lapey, Allen, Moffett, Kathryn, Jia, Shijing, Uluer, Ahmet, Sears, Edmund, Keating, Claire, Omlor, Gregory, Mehdi, Nighat, Tupayachi Ortiz, Maria Gabriela, Messore, Barbara, Lambert, Allison, Mukadam, Zubin, Egan, Marie, Wilhelm, Andrew, Reyes, Santiago, Schaeffer, David, Szepfalusi, Zsolt, Epaud, Ralph, Leroy, Sylvie, Fischer Biner, Reta, Moeller, Alexander, Salvatore, Donatello, Johnson, Larry, Gohy, Sophie, Gramegna, Andrea, Olesen, Hanne, Tissot, Adrien, Marguet, Christophe, Desai, Maya, Owens, Louisa, Kherani, Tamizan, Sands, Dorota, Bakkeheim, Egil, Tullis, Elizabeth, Chilvers, Mark, Quon, Bradley, Lands, Larry, Parkins, Michael, Tremblay, Francois, Maitra, Anirban, Staples, Heather, Elidemir, Okan, Chatziagorou, Elpis, Tirakitsoontorn, Pornchai, Grenet, Dominique, Linnane, Barry, Nicholson, John Michael, Manika, Katerina, Tatopoulos, Aurelie, Pressler, Tacjana, Bagheri-Potthoff, Azadeh, Legg, Julian, Horsley, Alexander, Lee, Timothy, Houdouin, Veronique, Ramel, Sophie, Fajac, Isabelle, Sermet-Gaudelus, Isabelle, Kamphuis, Lieke, van der Ent, Kors, Vermeulen, Francois, Chiron, Raphael, Reix, Philippe, Van Braeckel, Eva, Knoop, Christiane, Vanderhelst, Eef, Durieu, Isabelle, Bui, Stephanie, Weiss, Laurence, Le Rouzic, Olivier, van der Meer, Renske, Roukema, Jolt, Altenburg, Josje, Robinson, Philip, Wainwright, Claire, Mulrennan, Siobhain, Clements, Barry, Taccetti, Giovanni, Cipolli, Marco, Dacco, Valeria, Fabrizzi, Benedetta, Castellani, Carlo, Eber, Ernst, Appelt, Dorothea, Studnicka, Michael, Sapiejka, Ewa, Mazurek, Henryk, Engan, Mette, Sauty, Alain, Mankikian, Julie, Yonker, Lael M, Vermeulen, François, Linnemann, Rachel W, Braun, Andrew T, Ramsey, Bonnie, Mall, Marcus A, Taylor-Cousar, Jennifer L, McKone, Edward F, Floreth, Tim, Michelson, Peter, Sosnay, Patrick R, Nair, Nitin, Zahigian, Rachel, Martin, Hannah, Ahluwalia, Neil, and Lam, Anna

Published
2025
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27. Localization and tracking of beluga whales in aerial video using deep learning

Author

Mostapha Alsaidi, Mohammed G. Al-Jassani, Chiron Bang, Gregory O’Corry-Crowe, Cortney Watt, Maha Ghazal, and Hanqi Zhuang

Subjects
marine mammals, beluga whale, localization, tracking, deep learning, aerial video footage, Science, General. Including nature conservation, geographical distribution, QH1-199.5
Abstract

Aerial images are increasingly adopted and widely used in various research areas. In marine mammal studies, these imagery surveys serve multiple purposes: determining population size, mapping migration routes, and gaining behavioral insights. A single aerial scan using a drone yields a wealth of data, but processing it requires significant human effort. Our research demonstrates that deep learning models can significantly reduce human effort. They are not only able to detect marine mammals but also track their behavior using continuous aerial (video) footage. By distinguishing between different age classes, these algorithms can inform studies on population biology, ontogeny, and adult-calf relationships. To detect beluga whales from imagery footage, we trained the YOLOv7 model on a proprietary dataset of aerial footage of beluga whales. The deep learning model achieved impressive results with the following precision and recall scores: beluga adult = 92%—92%, beluga calf = 94%—89%. To track the detected beluga whales, we implemented the deep Simple Online and Realtime Tracking (SORT) algorithm. Unfortunately, the performance of the deep SORT algorithm was disappointing, with Multiple Object Tracking Accuracy (MOTA) scores ranging from 27% to 48%. An analysis revealed that the low tracking accuracy resulted from identity switching; that is, an identical beluga whale was given two IDs in two different frames. To overcome the problem of identity switching, a new post-processing algorithm was implemented, significantly improving MOTA to approximately 70%. The main contribution of this research is providing a system that accurately detects and tracks features of beluga whales, both adults and calves, from aerial footage. Additionally, this system can be customized to identify and analyze other marine mammal species by fine-tuning the model with annotated data.

Published
2024
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28. A structural, genetic and clinical comparison of CAR-T cells and CAR-NK cells: companions or competitors?

Author

Alain E. Andrea, Andrada Chiron, Guillaume Sarrabayrouse, Stéphanie Bessoles, and Salima Hacein-Bey-Abina

Subjects
CAR-T cells, CAR-NK cells, Cytokines, Hematological malignancies, Off-the-shelf Immunotherapies, Immunologic diseases. Allergy, RC581-607
Abstract

In recent years, following the groundbreaking achievements of chimeric antigen receptor (CAR) T cell therapy in hematological cancers, and advancements in cell engineering technologies, the exploration of other immune cells has garnered significant attention. CAR-Therapy extended beyond T cells to include CAR natural killer (NK) cells and CAR-macrophages, which are firmly established in the clinical trial landscape. Less conventional immune cells are also making their way into the scene, such as CAR mucosal-associated invariant T (MAIT) cells. This progress is advancing precision medicine and facilitating the development of ready-to-use biological treatments. However, in view of the unique features of natural killer cells, adoptive NK cell immunotherapy has emerged as a universal, allogenic, “off-the shelf” therapeutic strategy. CAR-NK cytotoxic cells present targeted tumor specificity but seem to be devoid of the side effects associated with CAR-T cells. CAR-NK cells appear to be potentially promising candidates for cancer immunotherapy. However, their application is hindered by significant challenges, particularly the limited persistence of CAR-NK cells in the body, which poses a hurdle to their sustained effectiveness in treating cancer. Based upon the foregoing, this review discusses the current status and applications of both CAR-T cells and CAR-NK cells in hematological cancers, and provides a comparative analysis of the structure, genetics, and clinical outcomes between these two types of genetically modified immune cells.

Published
2024
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29. Combined inhibition of CTPS1 and ATR is a metabolic vulnerability in p53‐deficient myeloma cells

Author

Romane Durand, Céline Bellanger, Géraldine Descamps, Christelle Dousset, Sophie Maïga, Jennifer Derrien, Laura Thirouard, Louise Bouard, Hélène Asnagli, Philip Beer, Andrew Parker, Patricia Gomez‐Bougie, Marie‐Claire Devilder, Philippe Moreau, Cyrille Touzeau, Agnès Moreau‐Aubry, David Chiron, and Catherine Pellat‐Deceunynck

Subjects
Diseases of the blood and blood-forming organs, RC633-647.5
Abstract

Abstract In multiple myeloma, as in B‐cell malignancies, mono‐ and especially bi‐allelic TP53 gene inactivation is a high‐risk factor for treatment resistance, and there are currently no therapies specifically targeting p53 deficiency. In this study, we evaluated if the loss of cell cycle control in p53‐deficient myeloma cells would confer a metabolically actionable vulnerability. We show that CTP synthase 1 (CTPS1), which encodes a CTP synthesis rate‐limiting enzyme essential for DNA and RNA synthesis in lymphoid cells, is overexpressed in samples from myeloma patients displaying a high proliferation rate (high MKI67 expression) or a low p53 score (synonymous with TP53 deletion and/or mutation). This overexpression of CTPS1 was associated with reduced survival in two cohorts. Using scRNA‐seq analysis in 24 patient samples, we further demonstrate that myeloma cells in the S or G2/M phase display high CTPS1 expression. Pharmacological inhibition of CTPS1 by STP‐B induced cell cycle arrest in early S phase in isogenic NCI‐H929 or XG7 TP53+/+, TP53−/−, and TP53R175H/R175H cells and in a TP53−/R123STOP patient sample. The functional annotation of transcriptional changes in 10 STP‐B‐treated myeloma cell lines revealed a decrease in protein translation and confirmed the blockade of cells into the S phase. The pharmacological inhibition of ATR, which governs the intrinsic S/G2 checkpoint, in STP‐B‐induced S‐phase arrested cells synergistically induced cell death in TP53+/+, TP53−/−, and TP53R175H/R175H isogenic cell lines (Bliss score >15). This combination induced replicative stress and caspase‐mediated cell death and was highly effective in resistant/refractory patient samples with TP53 deletion and/or mutation and in TP53−/− NCI‐H929 xenografted NOD‐scid IL2Rgamma mice. Our in vitro, ex vivo, and in vivo data provide the rationale for combined CTPS1 and ATR inhibition for the treatment of p53‐deficient patients.

Published
2024
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30. Cross-scale Attention Guided Multi-instance Learning for Crohn's Disease Diagnosis with Pathological Images

Author

Deng, Ruining, Cui, Can, Remedios, Lucas W., Bao, Shunxing, Womick, R. Michael, Chiron, Sophie, Li, Jia, Roland, Joseph T., Lau, Ken S., Liu, Qi, Wilson, Keith T., Wang, Yaohong, Coburn, Lori A., Landman, Bennett A., and Huo, Yuankai

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

Multi-instance learning (MIL) is widely used in the computer-aided interpretation of pathological Whole Slide Images (WSIs) to solve the lack of pixel-wise or patch-wise annotations. Often, this approach directly applies "natural image driven" MIL algorithms which overlook the multi-scale (i.e. pyramidal) nature of WSIs. Off-the-shelf MIL algorithms are typically deployed on a single-scale of WSIs (e.g., 20x magnification), while human pathologists usually aggregate the global and local patterns in a multi-scale manner (e.g., by zooming in and out between different magnifications). In this study, we propose a novel cross-scale attention mechanism to explicitly aggregate inter-scale interactions into a single MIL network for Crohn's Disease (CD), which is a form of inflammatory bowel disease. The contribution of this paper is two-fold: (1) a cross-scale attention mechanism is proposed to aggregate features from different resolutions with multi-scale interaction; and (2) differential multi-scale attention visualizations are generated to localize explainable lesion patterns. By training ~250,000 H&E-stained Ascending Colon (AC) patches from 20 CD patient and 30 healthy control samples at different scales, our approach achieved a superior Area under the Curve (AUC) score of 0.8924 compared with baseline models. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Published
2022

33. Comparative efficacy and safety of stiripentol, cannabidiol and fenfluramine as first‐line add‐on therapies for seizures in Dravet syndrome: A network meta‐analysis

Author

Renzo Guerrini, Catherine Chiron, Delphine Vandame, Warren Linley, and Toby Toward

Subjects
cannabidiol, Dravet syndrome, fenfluramine, network meta‐analysis, stiripentol, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objectives Stiripentol, fenfluramine, and cannabidiol are licensed add‐on therapies to treat seizures in Dravet Syndrome (DS). There are no direct or indirect comparisons assessing their full licensed dose regimens, across different jurisdictions, as first‐line add‐on therapies in DS. Methods We conducted a systematic review and frequentist network meta‐analysis (NMA) of randomized controlled trial (RCT) data for licensed add‐on DS therapies. We compared the proportions of patients experiencing: reductions from baseline in monthly convulsive seizure frequency (MCSF) of ≥50% (clinically meaningful), ≥75% (profound), and 100% (seizure‐free); serious adverse events (SAEs); discontinuations due to AEs. Results We identified relevant data from two placebo‐controlled RCTs for each drug. Stiripentol 50 mg/kg/day and fenfluramine 0.7 mg/kg/day had similar efficacy in achieving ≥50% (clinically meaningful) and ≥75% (profound) reductions from baseline in MCSF (absolute risk difference [RD] for stiripentol versus fenfluramine 1% [95% confidence interval: −20% to 22%; p = 0.93] and 6% [−15% to 27%; p = 0.59], respectively), and both were statistically superior (p

Published
2024
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37. The expanded French compassionate programme for elexacaftor–tezacaftor–ivacaftor use in people with cystic fibrosis without a F508del CFTR variant: a real-world study

Author

Kanaan, Reem, Carlier, Nicolas, Honoré, Isabelle, Chedevergne, Frédérique, Dreano, Elise, Hatton, Aurélie, Hinzpeter, Alexandre, Pranke, Iwona, Le Clainche-Viala, Laurence, Mayer, Sophie, Corvol, Harriet, Thouvenin, Guillaume, de Miranda, Sandra, Remus, Natascha, Douvry, Benoit, Duthoit, Louise, Perez, Thierry, Le Rouzic, Olivier, Wizla, Nathalie, Bon, Claire, Bui, Stéphanie, Poey, Nora, Stremler, Nathalie, Coltey, Bérengère, Dufeu, Nadine, Lebihan, Jean, Gabsi, Asma, Pouradier, Delphine, Andrejak, Claire, Rames, Cinthia, Dupuy-Grasset, Magali, Languepin, Jeanne, Marguet, Christophe, Pramil, Stéphanie, Arnouat, Baptiste, Fanton, Annlyse, Abely, Michel, Ravoninjatovo, Bruno, Blondé, Aurore, Guillaumot, Anne, Kieffer, Sebastien, Tatopoulos, Aurélie, Nove-Josserand, Raphaële, Ohlmann, Camille, Perrin, Thomas, Reynaud, Quitterie, Llerena, Catherine, Quétant, Sébastien, Valois, Sophie, Dalphin, Marie-Laure, Richaud-Thiriez, Bénédicte, Deneuville, Eric, Chiron, Raphael, Socchi, Floriane, Bihouée, Tiphaine, Mankikian, Julie, Flament, Thomas, Coolen-Allou, Nathalie, Gachelin, Elsa, Périsson, Caroline, Vuillard, Constance, Dupuis, Marion, Alkoussa, Wael, Marchal, Sarah, Leroy, Sylvie, Scalbert, Manuela, Campbell, Karine, Laurans, Muriel, Labbé, Guillaume, Montcouquiol, Sylvie, Priou, Pascaline, de Carli, Paola, Lemonnier, Lydie, Dehillotte, Clémence, Nouvel, Thierry, Burgel, Pierre-Régis, Sermet-Gaudelus, Isabelle, Girodon, Emmanuelle, Durieu, Isabelle, Houdouin, Véronique, Audousset, Camille, Macey, Julie, Grenet, Dominique, Porzio, Michele, Murris-Espin, Marlène, Reix, Philippe, Baravalle, Mélisande, Belleguic, Chantal, Mely, Laurent, Verhille, Juliette, Weiss, Laurence, Reynaud-Gaubert, Martine, Mittaine, Marie, Hamidfar, Rebecca, Ramel, Sophie, Cosson, Laure, Danner-Boucher, Isabelle, Foucaud, Pierre, Roy, Charlotte, Burnet, Espérie, Raynal, Caroline, Audrezet, Marie-Pierre, Da Silva, Jennifer, and Martin, Clémence

Published
2024
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39. L’élevage de poulets biologiques : les verrous à lever et les opportunités offertes par cette production

Author

Karine GERMAIN, Claire BONNEFOUS, Ludovic CALANDREAU, Geoffrey CHIRON, Brieuc DESAINT, Elisabeth LE BIHAN-DUVAL, Vitor FERREIRA, Vanessa GUESDON, Laurence A. GUILLOTEAU, Sarah LOMBARD, Bertrand MÉDA, Anne SILVESTRE, and Anne COLLIN

Subjects
Animal culture, SF1-1100, Aquaculture. Fisheries. Angling, SH1-691
Abstract

La France est actuellement au premier rang des productions européennes de poulets biologiques. Cette production ne représente toutefois encore que quelques pourcents de la production nationale. L’élevage avicole biologique est souvent considéré comme respectueux du bien-être animal et de l’environnement. Cependant, au-delà de ces images positives que l’élevage avicole biologique et les produits biologiques véhiculent, certaines contraintes déjà identifiées limitent leur développement. Le contact avec la faune sauvage présentant un risque sanitaire, la gestion du parcours, les tensions sur l’environnement dues à une excrétion des déjections sur le parcours difficilement maîtrisables et la sensibilité au contexte économique et aux comportements de consommation sont autant d’obstacles. L’élevage biologique offre néanmoins des opportunités 1) pour l’animal : le parcours facilite l’expression de nombreux comportements et permet la consommation d’insectes et de plantes possédant diverses propriétés (nutritionnelle, médicinales…) ; 2) pour l’éleveur : une amélioration des conditions de travail ainsi qu’une plus grande satisfaction sont mises en avant ; 3) pour l’environnement : le parcours constitue une source de biodiversité. Après une présentation des conditions d’élevage des poulets biologiques en France et en Europe, nous proposons d’identifier les barrières à l’extension de l’élevage biologique et les leviers/opportunités pour les dépasser.

Published
2024
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40. Pitfalls of using video‐EEG for a trial endpoint in children aged

Author

Ali Bozorg, Cynthia Beller, Lori Jensen, Alexis Arzimanoglou, Catherine Chiron, Dennis Dlugos, John Gaitanis, James W. Wheless, and Carrie McClung

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Objective Double‐blind, randomized, and placebo‐controlled trial SP0967 (NCT02477839/2013‐000717‐20) did not demonstrate superior efficacy of lacosamide versus placebo in patients aged ≥1 month to

Published
2024
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41. Impact of COVID-19 infection on lung function and nutritional status amongst individuals with cystic fibrosis: A global cohort study

Author

Kasmi, Irena, Drali, Ouardia, Burghart, Sabine, Lakatos-Krepcik, Andrea, Eder, Johannes, Jaksch, Peter, Kainz, Katharina, Kallinger, Margit, Leitner, Alexander, Mozdzen, Marta, Pfleger, Andreas, Renner, Sabine, Stadlinger, Martin, Thir, Christina, Nuriyev, Emil, Boboli, Hedwige, De Wachter, Elke, Dupont, Lieven, Gohy, Sophie, Hanssens, Laurence, Knoop, Christiane, Lammertyn, Elise, Nowé, Vicky, Pirson, Jessica, Thimmesch, Matthieu, Van Braeckel, Eva, Van Hoorenbeeck, Kim, Vanderhelst, Eef, Filho, Eduardo Piacentini, Athanazio, Rodrigo Abensur, Martins, Valéria de Carvalho, Duarte, Marta Cristina, Monte, Luciana de Freitas Velloso, de Fuccio, Marcelo Bicalho, Knabben, Adriana de Siqueira Carvalho, Melloti, Roberta, Meneses, Daniela Gois, Petrova, Guergana, Tješić-Drinković, Duška, Dugac, Andrea Vukić, Bambir, Ivan, Yiallouros, Panayiotis, Bilkova, Alena, Drevinek, Pavel, Macek, Milan, Jr, Olesen, Hanne Vebert, Pressler, Tania, Fouda, Eman Mahmoud, Nasr, Samya, Weldetsadik, Abate Yeshidinber, Al-iede, Montaha, Abdrakhmanov, Olzhas, Corvol, Harriet, Lemonnier-Videau, Lydie, Abely, Michel, Piccini, Carole Bailly, Belleguic, Chantal, Bihouee, Tiphaine, Billon, Yves, Bui, Stéphanie, Camara, Boubou, Cheraud, Marie-Christine, Chiron, Raphael, Duet, Emmanuelle Coirier, Cosson, Laure, Dalphin, Marie-Laure, Boucher, Isabelle Danner, De Miranda, Sandra, Deneuville, Eric, Dubus, Jean-Christophe, Durieu, Isabelle, Epaud, Ralph, Gerardin, Michèle, Grenet, Dominique, Houdouin, Véronique, Huet, Frédéric, Reem, Kanaan, Kessler, Romain, Languepin, Jeanne, Laurans, Muriel, Leroy, Sylvie, Llerena, Cathie, Macey, Julie, Mankikian, Julie, Marguet, Christophe, Martin, Clémence, Mely, Laurent, Mittaine, Marie, Murris-Espin, Marlène, Perisson, Caroline, Prevotat, Anne, Ramel, Sophie, Rames, Cinthia, Reix, Philippe, Revillon, Marine, Reynaud-Gaubert, Martine, Richaud-Thiriez, Bénédicte, Rittie, Jean-Luc, Scalbert-Dujardin, Manuëla, Sermet-Gaudelus, Isabelle, Storni, Véronique, Tatopoulos, Aurélie, Thouvenin, Guillaume, Troussier, Françoise, Weiss, Laurence, Wizla, Nathalie, Behl, Eva-Susanne, Brinkmann, Folke, Claßen, Martin, Graepler-Mainka, Ute, Griese, Matthias, Grübl, Armin, Hammermann, Jutta, Hebestreit, Helge, Heinzmann, Andrea, Herz, Alexander, Kiefer, Alexander, Kinder, Birte, Köster, Holger, Kuhnert, Stefan, Mainz, Jochen, Mayer, Angelika, Naehrig, Susanne, Niehues, Tim, Nüßlein, Thomas, Poplawska, Krystyna, Ringshausen, Felix, Rose, Markus, Rosenecker, Josef, Ruppel, Renate, Scharschinger, Anette, Schropp, Christian, Schwarz, Carsten, Smaczny, Christina, Sommerburg, Olaf, Sutharsan, Sivagurunathan, Stolz, Simone, Thomas, Wolfgang, Wege, Sabine, Welzenbach, Britta, Wollschläger, Bettina, Diamantea, Filia, Hatziagorou, Elpis, Manika, Katerina, Cox, Des, Elnazir, Basil, Fletcher, Godfrey, Gunaratnam, Cedric, McKone, Edward F., Plant, Barry J., Cohen-Cymberknoh, Malena, Gur, Michal, Livnat, Galit, Mei-Zahav, Meir, Amato, Annalisa, Ferrari, Gianluca, Badolato, Raffaele, Poli, Piercarlo, Battistini, Fiorella, Donati, Valentina, Bignamini, Elisabetta, Folino, Anna, Carnovale, Vincenzo, Castellani, Carlo, Casciaro, Rosaria, Cimino, Giuseppe, Cipolli, Marco, Lucca, Francesca, Collura, Mirella, Ficili, Francesca, Daccò, Valeria, Gagliano, Vanessa, Pizzamiglio, Giovanna, Mencarini, Valeria, Palladino, Nicola, Leonardi, Salvatore, Rotolo, Novella, Lucanto, Maria Cristina, Quattromano, Ester, Lucidi, Vincenzina, Majo, Fabio, Alghisi, Federico, Ciciriello, Fabiana, Manca, Antonio, Leonetti, Giuseppina, Maschio, Massimo, Messore, Barbara, Pantano, Stefano, Pisi, Giovanna, Spaggiari, Cinzia, Raia, Valeria, Laezza, Caterina, Ros, Mirco, Salvatore, Donatello, Taccetti, Giovanni, Francalanci, Michela, Vitullo, Pamela, Zolin, Anna, Aleksejeva, Elina, Malakauskas, Kestutis, Misevičiene, Valdone, Charatsi, Anna-Maria, la Barrière, Hélène De, Altenburg, Josje, Bannier, Michiel, Heijerman, Harry, Janssens, Hettie, Koppelman, Gerard, van der Meer, Renske, Merkus, Peter, Nuijsink, Marianne, Terheggen, Suzanne, van der Vaart, Hester, Wesseling, Geert-Jan, de Winter, Karin, Danevska, Ivana Arnaudova, Maretti, Tatjana Jakovska, Fustik, Stojka, Dziecichowicz-Latała, Daria, Wojsyk-Banaszak, Irena, Wozniacki, Lukasz, Amorim, Adelina, Santos, Ana Sofia Araújo, Castanhinha, Susana, Gamboa, Fernanda, Silva, Teresa Reis, Gonçalves, Fabienne, Pereira, Luísa, Ciuca, Ioana, Silva, Sónia, Csilla-Enikö, Szabo, Stan, Iustina, Amelina, Elena, Boitсova, Evgeniya, Chernyavskaya, Anastasia, Gorinova, Yuliya, Krasovskiy, Stanislav, Mukhina, Maria, Sherman, Victoria, Simonova, Olga, Kondratyeva, Elena, Bérešová, Eva, Bližnáková, Nina, Kayserová, Hana, Salobir, Barbara, Šelb, Julij, Krivec, Uroš, Fernandez, Antonio José Aguilar, Fernàndez, Antonio Alvarez, García, Félix Baranda, Aparicio, Marina Blanco, Corullón, Silvia Castillo, Cortell-Aznar, Isidoro, Pérez, Inés, Colomer, Jordi Costa i, Roig, María Cols, Pecellín, Isabel Delgado, Cáceres, Layla Diab, Paredes, Carmen Luna, Gartner, Silvia, Martínez, José Ramón Gutiérrez, Labarga, Inés Herrero, Girón-Moreno, Rosa Maria, Nogueira, Esperanza Jiménez, Ferreiro, Adelaida Lamas, Neyra, Alejandro López, Castro, Enrique Blitz, Galarraga, Laura Moreno, de Vincente, Carlos Martin, Navarro, Silvia Merlos, Nieto-Royo, Rosa, Fuster, Casilda Olveira, Pastor, Maria Dolores, Pérez-Ruiz, Estela, Prados-Sánchez, Concepción, Cancelo, Isabel Ramos, de Valbuena, Marta Ruiz, Asensi, José R. Villa, Santiago, Veronica Sanz, García, Patricia Fernández, Tawfeeq, Reem Mustafa, Banki, Adrienn, Gilljam, Marita, Krantz, Christina, Lindberg, Ulrika, Lindblad, Anders, Clarenbach, Christian, Steinack, Carolin, Hage, René, Schuurmans, Macé, Fischer, Reta, Kusche, Rachel, Rochat, Isabelle, Walter, Anna-Lena, Kamalaporn, Harutai, Hamouda, Samia, Tural, Dilber Ademhan, Ozcelik, Ugur, Asfuroğlu, Pelin, Eyüboğlu, Tuğba Şişmanlar, Aslan, Ayse Tana, Bingöl, Ayşen, Çobanoğlu, Nazan, Ozcan, Gizem, Dogru, Deniz, Gökdemir, Yasemin, KÖSE, Mehmet, Pekcan, Sevgi, Cosgriff, Rebecca, Semenchuk, Julie, Naito, Yumi, Charman, Susan C., Carr, Siobhán B, Cheng, Stephanie Y., Marshall, Bruce C., Faro, Albert, Elbert, Alexander, Gutierrez, Hector H., Goss, Christopher H., Karadag, Bulent, Burgel, Pierre-Régis, Colombo, Carla, Salvatore, Marco, Padoan, Rita, Daneau, Géraldine, Harutyunyan, Satenik, Kashirskaya, Nataliya, Kirwan, Laura, Middleton, Peter G, Ruseckaite, Rasa, de Monestrol, Isabelle, Naehrlich, Lutz, Mondejar-Lopez, Pedro, Jung, Andreas, van Rens, Jacqui, Bakkeheim, Egil, Orenti, Annalisa, Zomer-van Ommen, Domenique, da Silva-Filho, Luiz Vicente RF, Fernandes, Flavia Fonseca, Zampoli, Marco, and Stephenson, Anne L.

Published
2024
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44. Random Multi-Channel Image Synthesis for Multiplexed Immunofluorescence Imaging

Author

Bao, Shunxing, Tang, Yucheng, Lee, Ho Hin, Gao, Riqiang, Chiron, Sophie, Lyu, Ilwoo, Coburn, Lori A., Wilson, Keith T., Roland, Joseph T., Landman, Bennett A., and Huo, Yuankai

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

Multiplex immunofluorescence (MxIF) is an emerging imaging technique that produces the high sensitivity and specificity of single-cell mapping. With a tenet of 'seeing is believing', MxIF enables iterative staining and imaging extensive antibodies, which provides comprehensive biomarkers to segment and group different cells on a single tissue section. However, considerable depletion of the scarce tissue is inevitable from extensive rounds of staining and bleaching ('missing tissue'). Moreover, the immunofluorescence (IF) imaging can globally fail for particular rounds ('missing stain''). In this work, we focus on the 'missing stain' issue. It would be appealing to develop digital image synthesis approaches to restore missing stain images without losing more tissue physically. Herein, we aim to develop image synthesis approaches for eleven MxIF structural molecular markers (i.e., epithelial and stromal) on real samples. We propose a novel multi-channel high-resolution image synthesis approach, called pixN2N-HD, to tackle possible missing stain scenarios via a high-resolution generative adversarial network (GAN). Our contribution is three-fold: (1) a single deep network framework is proposed to tackle missing stain in MxIF; (2) the proposed 'N-to-N' strategy reduces theoretical four years of computational time to 20 hours when covering all possible missing stains scenarios, with up to five missing stains (e.g., '(N-1)-to-1', '(N-2)-to-2'); and (3) this work is the first comprehensive experimental study of investigating cross-stain synthesis in MxIF. Our results elucidate a promising direction of advancing MxIF imaging with deep image synthesis., Comment: Accepted at the third MICCAI workshop on Computational Pathology (COMPAY 2021)

Published
2021

45. A uniqueness result for the two vortex travelling wave in the Nonlinear Schrodinger equation

Author

Chiron, David and Pacherie, Eliot

Subjects
Mathematics - Analysis of PDEs, 35A02, 35A15, 35B35, 35C07, 35Q56
Abstract

For the Nonlinear Schrodinger equation in dimension 2, the existence of a global minimizer of the energy at fixed momentum has been established by Bethuel-Gravejat-Saut. This minimizer is a travelling wave for the Nonlinear Schrodinger equation. For large momentums, the propagation speed is small and the minimizer behaves like two well separated vortices. In that limit, we show the uniqueness of this minimizer, up to the invariances of the problem, hence proving the orbital stability of this travelling wave. This work is a follow up to two previous papers, where we constructed and studied a particular travelling wave of the equation. We show a uniqueness result on this travelling wave in a class of functions that contains in particular all possible minimizers of the energy.

Published
2021
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