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2,499 results on '"Janda, P."'

1. A General-Purpose Multimodal Foundation Model for Dermatology

Author

Yan, Siyuan, Yu, Zhen, Primiero, Clare, Vico-Alonso, Cristina, Wang, Zhonghua, Yang, Litao, Tschandl, Philipp, Hu, Ming, Tan, Gin, Tang, Vincent, Ng, Aik Beng, Powell, David, Bonnington, Paul, See, Simon, Janda, Monika, Mar, Victoria, Kittler, Harald, Soyer, H. Peter, and Ge, Zongyuan

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

Diagnosing and treating skin diseases require advanced visual skills across multiple domains and the ability to synthesize information from various imaging modalities. Current deep learning models, while effective at specific tasks such as diagnosing skin cancer from dermoscopic images, fall short in addressing the complex, multimodal demands of clinical practice. Here, we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on a dataset of over 2 million real-world images of skin diseases, sourced from 11 clinical institutions across 4 imaging modalities. We evaluated PanDerm on 28 diverse datasets covering a range of clinical tasks, including skin cancer screening, phenotype assessment and risk stratification, diagnosis of neoplastic and inflammatory skin diseases, skin lesion segmentation, change monitoring, and metastasis prediction and prognosis. PanDerm achieved state-of-the-art performance across all evaluated tasks, often outperforming existing models even when using only 5-10% of labeled data. PanDerm's clinical utility was demonstrated through reader studies in real-world clinical settings across multiple imaging modalities. It outperformed clinicians by 10.2% in early-stage melanoma detection accuracy and enhanced clinicians' multiclass skin cancer diagnostic accuracy by 11% in a collaborative human-AI setting. Additionally, PanDerm demonstrated robust performance across diverse demographic factors, including different body locations, age groups, genders, and skin tones. The strong results in benchmark evaluations and real-world clinical scenarios suggest that PanDerm could enhance the management of skin diseases and serve as a model for developing multimodal foundation models in other medical specialties, potentially accelerating the integration of AI support in healthcare., Comment: 56 pages; Technical report

Published
2024

2. Gromov--Witten Invariants of Non-Convex Complete Intersections in Weighted Projective Stacks

Author

Janda, Felix, Sultani, Nawaz, and Zhou, Yang

Subjects
Mathematics - Algebraic Geometry
Abstract

In this paper we compute genus 0 orbifold Gromov--Witten invariants of Calabi--Yau threefold complete intersections in weighted projective stacks, regardless of convexity conditions. The traditional quantumn Lefschetz principle may fail even for invariants with ambient insertions. Using quasimap wall-crossing, we are able to compute invariants with insertions from a specific subring of the Chen--Ruan cohomology, which contains all the ambient cohomology classes. Quasimap wall-crossing gives a mirror theorem expressing the I-function in terms of the J-function via a mirror map. The key of this paper is to find a suitable GIT presentation of the target space, so that the mirror map is invertible. An explicit formula for the I-function is given for all those target spaces and many examples with explicit computations of invariants are provided., Comment: 38 pgs; comments are welcome!

Published
2024

8. Structure results for torus fixed loci

Author

Alper, Jarod and Janda, Felix

Subjects
Mathematics - Algebraic Geometry, 14D23 (Primary) 14L30 (Secondary)
Abstract

Motivated by localization theorems on moduli spaces, we prove a structural classification of Deligne-Mumford stacks with an action of a torus where the induced action on the coarse moduli space is trivial. We also establish a general local structure theorem for morphisms of algebraic stacks., Comment: 16 pages; comments welcome!

Published
2024

9. Prompt-driven Latent Domain Generalization for Medical Image Classification

Author

Yan, Siyuan, Liu, Chi, Yu, Zhen, Ju, Lie, Mahapatra, Dwarikanath, Betz-Stablein, Brigid, Mar, Victoria, Janda, Monika, Soyer, Peter, and Ge, Zongyuan

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

Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifacts bias, camera variations, differences in the imaging station, etc., leading to unreliable diagnoses in real-world clinical settings. Domain generalization (DG) methods, which aim to train models on multiple domains to perform well on unseen domains, offer a promising direction to solve the problem. However, existing DG methods assume domain labels of each image are available and accurate, which is typically feasible for only a limited number of medical datasets. To address these challenges, we propose a novel DG framework for medical image classification without relying on domain labels, called Prompt-driven Latent Domain Generalization (PLDG). PLDG consists of unsupervised domain discovery and prompt learning. This framework first discovers pseudo domain labels by clustering the bias-associated style features, then leverages collaborative domain prompts to guide a Vision Transformer to learn knowledge from discovered diverse domains. To facilitate cross-domain knowledge learning between different prompts, we introduce a domain prompt generator that enables knowledge sharing between domain prompts and a shared prompt. A domain mixup strategy is additionally employed for more flexible decision margins and mitigates the risk of incorrect domain assignments. Extensive experiments on three medical image classification tasks and one debiasing task demonstrate that our method can achieve comparable or even superior performance than conventional DG algorithms without relying on domain labels. Our code will be publicly available upon the paper is accepted., Comment: 10 pages

Published
2024

10. Universal equations for higher genus Gromov-Witten invariants from Hodge integrals

Author

Janda, Felix and Wang, Xin

Subjects
Mathematics - Algebraic Geometry, 14N35 (Primary) 1410 (Secondary)
Abstract

We establish new universal equations for higher genus Gromov-Witten invariants of target manifolds, by studying both the Chern character and Chern classes of the Hodge bundle on the moduli space of curves. As a consequence, we find new push-forward relations on the moduli space of stable curves., Comment: 22 pages. Agrees with published version

Published
2023
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11. Management and outcome of cutaneous diphtheria in adolescent refugees in Germany, June 2022 – October 2023

Author

Zink, Alicia, Hofer, Juliana, Schneider, Christian, Kessler, Franziska, Klenze, Hannes, Klauwer, Dietrich, Maleki, Klaudia, Müller, Andreas, Goretzki, Sarah, Wang, Shubei, Kobbe, Robin, Ramirez, Andrea Vanegas, Bode, Sebastian, Janda, Ales, Fressle, Roland, Remppis, Jonathan, Henneke, Philipp, Rieg, Siegbert, Berger, Anja, Sing, Andreas, Hufnagel, Markus, and Spielberger, Benedikt D.

Published
2024
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16. Gromov--Witten invariants with naive tangency conditions

Author

Janda, Felix and Yu, Tony Yue

Subjects
Mathematics - Algebraic Geometry, Mathematics - Symplectic Geometry, Primary 14N35, Secondary 14N10, 14J33
Abstract

We introduce Gromov-Witten invariants with naive tangency conditions at the marked points of the source curve. We then establish an explicit formula which expresses Gromov-Witten invariants with naive tangency conditions in terms of descendent Gromov-Witten invariants. Several examples of genus zero Gromov-Witten invariants with naive tangencies are computed in the case of curves and surfaces. In particular, the counts of rational curves naively tangent to an anticanonical divisor on a del Pezzo surface are studied, and via mirror symmetry, we obtain a relation to the local Gromov-Witten invariants.

Published
2023

17. Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review

Author

Zegair, Fatima Al, Naranpanawa, Nathasha, Betz-Stablein, Brigid, Janda, Monika, Soyer, H. Peter, and Chandra, Shekhar S.

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

Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features that sets it apart from other lesions in the vicinity. As lesions within the same individual typically share similarities and follow a predictable pattern, an ugly duckling naevus stands out as unusual and may indicate the presence of a cancerous melanoma. Computer-aided diagnosis (CAD) has become a significant player in the research and development field, as it combines machine learning techniques with a variety of patient analysis methods. Its aim is to increase accuracy and simplify decision-making, all while responding to the shortage of specialized professionals. These automated systems are especially important in skin cancer diagnosis where specialist availability is limited. As a result, their use could lead to life-saving benefits and cost reductions within healthcare. Given the drastic change in survival when comparing early stage to late-stage melanoma, early detection is vital for effective treatment and patient outcomes. Machine learning (ML) and deep learning (DL) techniques have gained popularity in skin cancer classification, effectively addressing challenges, and providing results equivalent to that of specialists. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi. It begins with general information on skin cancer and different types of naevi, then introduces AI, ML, DL, and CAD. The article then discusses the successful applications of various ML techniques like convolutional neural networks (CNN) for melanoma detection compared to dermatologists' performance. Lastly, it examines ML methods for UD naevus detection and identifying suspicious naevi.

Published
2023

32. Living in a Material World: Learning Material Properties from Full-Waveform Flash Lidar Data for Semantic Segmentation

Author

Janda, Andrej, Merriaux, Pierre, Olivier, Pierre, and Kelly, Jonathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Advances in lidar technology have made the collection of 3D point clouds fast and easy. While most lidar sensors return per-point intensity (or reflectance) values along with range measurements, flash lidar sensors are able to provide information about the shape of the return pulse. The shape of the return waveform is affected by many factors, including the distance that the light pulse travels and the angle of incidence with a surface. Importantly, the shape of the return waveform also depends on the material properties of the reflecting surface. In this paper, we investigate whether the material type or class can be determined from the full-waveform response. First, as a proof of concept, we demonstrate that the extra information about material class, if known accurately, can improve performance on scene understanding tasks such as semantic segmentation. Next, we learn two different full-waveform material classifiers: a random forest classifier and a temporal convolutional neural network (TCN) classifier. We find that, in some cases, material types can be distinguished, and that the TCN generally performs better across a wider range of materials. However, factors such as angle of incidence, material colour, and material similarity may hinder overall performance., Comment: In Proceedings of the Conference on Robots and Vision (CRV'23), Montreal, Canada, Jun. 6-8, 2023

Published
2023
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33. EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition

Author

Yan, Siyuan, Liu, Chi, Yu, Zhen, Ju, Lie, Mahapatrainst, Dwarikanath, Mar, Victoria, Janda, Monika, Soyer, Peter, and Ge, Zongyuan

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

Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent research has revealed that deep neural networks for skin lesion recognition may overly depend on disease-irrelevant image artifacts (i.e., dark corners, dense hairs), leading to poor generalization in unseen environments. To address this issue, we propose a novel domain generalization method called EPVT, which involves embedding prompts into the vision transformer to collaboratively learn knowledge from diverse domains. Concretely, EPVT leverages a set of domain prompts, each of which plays as a domain expert, to capture domain-specific knowledge; and a shared prompt for general knowledge over the entire dataset. To facilitate knowledge sharing and the interaction of different prompts, we introduce a domain prompt generator that enables low-rank multiplicative updates between domain prompts and the shared prompt. A domain mixup strategy is additionally devised to reduce the co-occurring artifacts in each domain, which allows for more flexible decision margins and mitigates the issue of incorrectly assigned domain labels. Experiments on four out-of-distribution datasets and six different biased ISIC datasets demonstrate the superior generalization ability of EPVT in skin lesion recognition across various environments. Code is avaliable at https://github.com/SiyuanYan1/EPVT., Comment: Accepted by MICCAI 2023

Published
2023

34. Towards Trustable Skin Cancer Diagnosis via Rewriting Model's Decision

Author

Yan, Siyuan, Yu, Zhen, Zhang, Xuelin, Mahapatra, Dwarikanath, Chandra, Shekhar S., Janda, Monika, Soyer, Peter, and Ge, Zongyuan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep neural networks have demonstrated promising performance on image recognition tasks. However, they may heavily rely on confounding factors, using irrelevant artifacts or bias within the dataset as the cue to improve performance. When a model performs decision-making based on these spurious correlations, it can become untrustable and lead to catastrophic outcomes when deployed in the real-world scene. In this paper, we explore and try to solve this problem in the context of skin cancer diagnosis. We introduce a human-in-the-loop framework in the model training process such that users can observe and correct the model's decision logic when confounding behaviors happen. Specifically, our method can automatically discover confounding factors by analyzing the co-occurrence behavior of the samples. It is capable of learning confounding concepts using easily obtained concept exemplars. By mapping the black-box model's feature representation onto an explainable concept space, human users can interpret the concept and intervene via first order-logic instruction. We systematically evaluate our method on our newly crafted, well-controlled skin lesion dataset and several public skin lesion datasets. Experiments show that our method can effectively detect and remove confounding factors from datasets without any prior knowledge about the category distribution and does not require fully annotated concept labels. We also show that our method enables the model to focus on clinical-related concepts, improving the model's performance and trustworthiness during model inference., Comment: Accepted by CVPR 2023

Published
2023

35. Feasibility of using real-world data to emulate substance use disorder clinical trials: a cross-sectional study

Author

Guneet S. Janda, Molly Moore Jeffery, Reshma Ramachandran, Joseph S. Ross, and Joshua D. Wallach

Subjects
Real-world evidence, Trial emulation, Substance use disorders, Medicine (General), R5-920
Abstract

Abstract Introduction Real-world evidence is receiving considerable attention as a way to evaluate the efficacy and safety of medical products for substance use disorders (SUDs). However, the feasibility of using real-world data (RWD) to emulate clinical trials evaluating treatments for SUDs is uncertain. The aim of this study is to identify the number of clinical trials evaluating treatments for SUDs with reported results that could be feasibly emulated using observational data from contemporary insurance claims and/or electronic health record (EHR) data. Methods In this cross-sectional study, all phase 2–4 trials evaluating treatments for SUDs registered on ClinicalTrials.gov with reported results were identified. Each trial was evaluated to determine if the indications, interventions, at least 80% of eligibility criteria, comparators, and primary end points could be ascertained using contemporarily available administrative claims and/or structured EHR data. Results There were 272 SUD trials on ClinicalTrials.gov with reported results. Of these, when examining feasibility using contemporarily available administrative claims and/or structured EHR data, 262 (96.3%) had indications that were ascertainable; 194 (71.3%) had interventions that were ascertainable; 21 (7.7%) had at least 80% of eligibility criteria that were ascertainable; 17 (6.3%) had active comparators that were ascertainable; and 61 (22.4%) had primary end points that were ascertainable. In total, there were no trials for which all 5 characteristics were ascertainable using contemporarily available administrative claims and/or structured EHR data. When considering placebo comparators as ascertainable, there were 6 (2.2%) trials that had all 5 key characteristics classified as ascertainable from contemporarily available administrative claims and/or structured EHR data. Conclusions No trials evaluating treatments for SUDs could be feasibly emulated using contemporarily available RWD, demonstrating a need for an increase in the resolution of data capture within a public health system to facilitate trial emulation.

Published
2024
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36. A comprehensive overview of liquid biopsy applications in pediatric solid tumors

Author

Ferdinand W. Janssen, Nathalie S. M. Lak, Claudia Y. Janda, Lennart A. Kester, Michael T. Meister, Johannes H. M. Merks, Marry M. van den Heuvel-Eibrink, Max M. van Noesel, Jozsef Zsiros, Godelieve A. M. Tytgat, and Leendert H. J. Looijenga

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Liquid biopsies are emerging as an alternative source for pediatric cancer biomarkers with potential applications during all stages of patient care, from diagnosis to long-term follow-up. While developments within this field are reported, these mainly focus on dedicated items such as a specific liquid biopsy matrix, analyte, and/or single tumor type. To the best of our knowledge, a comprehensive overview is lacking. Here, we review the current state of liquid biopsy research for the most common non-central nervous system pediatric solid tumors. These include neuroblastoma, renal tumors, germ cell tumors, osteosarcoma, Ewing sarcoma, rhabdomyosarcoma and other soft tissue sarcomas, and liver tumors. Within this selection, we discuss the most important or recent studies involving liquid biopsy-based biomarkers, anticipated clinical applications, and the current challenges for success. Furthermore, we provide an overview of liquid biopsy-based biomarker publication output for each tumor type based on a comprehensive literature search between 1989 and 2023. Per study identified, we list the relevant liquid biopsy-based biomarkers, matrices (e.g., peripheral blood, bone marrow, or cerebrospinal fluid), analytes (e.g., circulating cell-free and tumor DNA, microRNAs, and circulating tumor cells), methods (e.g., digital droplet PCR and next-generation sequencing), the involved pediatric patient cohort, and proposed applications. As such, we identified 344 unique publications. Taken together, while the liquid biopsy field in pediatric oncology is still behind adult oncology, potentially relevant publications have increased over the last decade. Importantly, steps towards clinical implementation are rapidly gaining ground, notably through validation of liquid biopsy-based biomarkers in pediatric clinical trials.

Published
2024
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37. Search for central exclusive production of top quark pairs in proton-proton collisions at s $$ \sqrt{s} $$ = 13 TeV with tagged protons

Author

The CMS collaboration, A. Tumasyan, W. Adam, J. W. Andrejkovic, T. Bergauer, S. Chatterjee, K. Damanakis, M. Dragicevic, A. Escalante Del Valle, P. S. Hussain, M. Jeitler, N. Krammer, L. Lechner, D. Liko, I. Mikulec, P. Paulitsch, F. M. Pitters, J. Schieck, R. Schöfbeck, D. Schwarz, S. Templ, W. Waltenberger, C.-E. Wulz, M. R. Darwish, T. Janssen, T. Kello, H. Rejeb Sfar, P. Van Mechelen, E. S. Bols, J. D’Hondt, A. De Moor, M. Delcourt, H. El Faham, S. Lowette, S. Moortgat, A. Morton, D. Müller, A. R. Sahasransu, S. Tavernier, W. Van Doninck, D. Vannerom, B. Clerbaux, G. De Lentdecker, L. Favart, D. Hohov, J. Jaramillo, K. Lee, M. Mahdavikhorrami, I. Makarenko, A. Malara, S. Paredes, L. Pétré, N. Postiau, E. Starling, L. Thomas, M. Vanden Bemden, C. Vander Velde, P. Vanlaer, D. Dobur, J. Knolle, L. Lambrecht, G. Mestdach, M. Niedziela, C. Rendón, C. Roskas, A. Samalan, K. Skovpen, M. Tytgat, N. Van Den Bossche, B. Vermassen, L. Wezenbeek, A. Benecke, G. Bruno, F. Bury, C. Caputo, P. David, C. Delaere, I. S. Donertas, A. Giammanco, K. Jaffel, Sa. Jain, V. Lemaitre, K. Mondal, J. Prisciandaro, A. Taliercio, T. T. Tran, P. Vischia, S. Wertz, G. A. Alves, E. Coelho, C. Hensel, A. Moraes, P. Rebello Teles, W. L. Aldá Júnior, M. Alves Gallo Pereira, M. Barroso Ferreira Filho, H. Brandao Malbouisson, W. Carvalho, J. Chinellato, E. M. Da Costa, G. G. Da Silveira, D. De Jesus Damiao, V. Dos Santos Sousa, S. Fonseca De Souza, J. Martins, C. Mora Herrera, K. Mota Amarilo, L. Mundim, H. Nogima, A. Santoro, S. M. Silva Do Amaral, A. Sznajder, M. Thiel, F. Torres Da Silva De Araujo, A. Vilela Pereira, C. A. Bernardes, L. Calligaris, T. R. Fernandez Perez Tomei, E. M. Gregores, P. G. Mercadante, S. F. Novaes, Sandra S. Padula, A. Aleksandrov, R. Hadjiiska, P. Iaydjiev, M. Misheva, M. Rodozov, M. Shopova, G. Sultanov, A. Dimitrov, T. Ivanov, L. Litov, B. Pavlov, P. Petkov, A. Petrov, E. Shumka, T. Cheng, T. Javaid, M. Mittal, L. Yuan, M. Ahmad, G. Bauer, Z. Hu, S. Lezki, K. Yi, G. M. Chen, H. S. Chen, M. Chen, F. Iemmi, C. H. Jiang, A. Kapoor, H. Liao, Z.-A. Liu, V. Milosevic, F. Monti, R. Sharma, J. Tao, J. Thomas-Wilsker, J. Wang, H. Zhang, J. Zhao, A. Agapitos, Y. An, Y. Ban, C. Chen, A. Levin, C. Li, Q. Li, X. Lyu, Y. Mao, S. J. Qian, X. Sun, D. Wang, J. Xiao, H. Yang, J. Li, M. Lu, Z. You, X. Gao, D. Leggat, H. Okawa, Y. Zhang, Z. Lin, C. Lu, M. Xiao, C. Avila, D. A. Barbosa Trujillo, A. Cabrera, C. Florez, J. Fraga, J. Mejia Guisao, F. Ramirez, M. Rodriguez, J. D. Ruiz Alvarez, D. Giljanovic, N. Godinovic, D. Lelas, I. Puljak, Z. Antunovic, M. Kovac, T. Sculac, V. Brigljevic, B. K. Chitroda, D. Ferencek, D. Majumder, M. Roguljic, A. Starodumov, T. Susa, A. Attikis, K. Christoforou, G. Kole, M. Kolosova, S. Konstantinou, J. Mousa, C. Nicolaou, F. Ptochos, P. A. Razis, H. Rykaczewski, H. Saka, M. Finger, A. Kveton, E. Ayala, E. Carrera Jarrin, S. Elgammal, A. Ellithi Kamel, M. A. Mahmoud, Y. Mohammed, S. Bhowmik, R. K. Dewanjee, K. Ehataht, M. Kadastik, T. Lange, S. Nandan, C. Nielsen, J. Pata, M. Raidal, L. Tani, C. Veelken, P. Eerola, H. Kirschenmann, M. Voutilainen, S. Bharthuar, E. Brücken, J. Havukainen, M. S. Kim, R. Kinnunen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, M. Lotti, L. Martikainen, M. Myllymäki, J. Ott, M. m. Rantanen, H. Siikonen, E. Tuominen, J. Tuominiemi, P. Luukka, H. Petrow, T. Tuuva, C. Amendola, M. Besancon, F. Couderc, M. Dejardin, D. Denegri, J. L. Faure, F. Ferri, S. Ganjour, P. Gras, G. Hamel de Monchenault, P. Jarry, V. Lohezic, J. Malcles, J. Rander, A. Rosowsky, M. Ö. Sahin, A. Savoy-Navarro, P. Simkina, M. Titov, F. Beaudette, A. Buchot Perraguin, P. Busson, A. Cappati, C. Charlot, F. Damas, O. Davignon, B. Diab, G. Falmagne, B. A. Fontana Santos Alves, S. Ghosh, R. Granier de Cassagnac, A. Hakimi, B. Harikrishnan, G. Liu, J. Motta, M. Nguyen, C. Ochando, L. Portales, J. Rembser, R. Salerno, U. Sarkar, J. B. Sauvan, Y. Sirois, A. Tarabini, E. Vernazza, A. Zabi, A. Zghiche, J.-L. Agram, J. Andrea, D. Apparu, D. Bloch, G. Bourgatte, J.-M. Brom, E. C. Chabert, C. Collard, D. Darej, U. Goerlach, C. Grimault, A.-C. Le Bihan, P. Van Hove, S. Beauceron, C. Bernet, B. Blancon, G. Boudoul, A. Carle, N. Chanon, J. Choi, D. Contardo, P. Depasse, C. Dozen, H. El Mamouni, J. Fay, S. Gascon, M. Gouzevitch, G. Grenier, B. Ille, I. B. Laktineh, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, L. Torterotot, M. Vander Donckt, P. Verdier, S. Viret, G. Adamov, I. Lomidze, Z. Tsamalaidze, V. Botta, L. Feld, K. Klein, M. Lipinski, D. Meuser, A. Pauls, N. Röwert, M. Teroerde, S. Diekmann, A. Dodonova, N. Eich, D. Eliseev, M. Erdmann, P. Fackeldey, D. Fasanella, B. Fischer, T. Hebbeker, K. Hoepfner, F. Ivone, M. y. Lee, L. Mastrolorenzo, M. Merschmeyer, A. Meyer, S. Mondal, S. Mukherjee, D. Noll, A. Novak, F. Nowotny, A. Pozdnyakov, Y. Rath, W. Redjeb, H. Reithler, A. Schmidt, S. C. Schuler, A. Sharma, L. Vigilante, S. Wiedenbeck, S. Zaleski, C. Dziwok, G. Flügge, W. Haj Ahmad, O. Hlushchenko, T. Kress, A. Nowack, O. Pooth, A. Stahl, T. Ziemons, A. Zotz, H. Aarup Petersen, M. Aldaya Martin, P. Asmuss, S. Baxter, M. Bayatmakou, O. Behnke, A. Bermúdez Martínez, S. Bhattacharya, A. A. Bin Anuar, F. Blekman, K. Borras, D. Brunner, A. Campbell, A. Cardini, C. Cheng, F. Colombina, S. Consuegra Rodríguez, G. Correia Silva, M. De Silva, L. Didukh, G. Eckerlin, D. Eckstein, L. I. Estevez Banos, O. Filatov, E. Gallo, A. Geiser, A. Giraldi, G. Greau, A. Grohsjean, V. Guglielmi, M. Guthoff, A. Jafari, N. Z. Jomhari, B. Kaech, A. Kasem, M. Kasemann, H. Kaveh, C. Kleinwort, R. Kogler, M. Komm, D. Krücker, W. Lange, D. Leyva Pernia, K. Lipka, W. Lohmann, R. Mankel, I.-A. Melzer-Pellmann, M. Mendizabal Morentin, J. Metwally, A. B. Meyer, G. Milella, M. Mormile, A. Mussgiller, A. Nürnberg, Y. Otarid, D. Pérez Adán, A. Raspereza, B. Ribeiro Lopes, J. Rübenach, A. Saggio, A. Saibel, M. Savitskyi, M. Scham, V. Scheurer, S. Schnake, P. Schütze, C. Schwanenberger, M. Shchedrolosiev, R. E. Sosa Ricardo, D. Stafford, N. Tonon, M. Van De Klundert, F. Vazzoler, A. Ventura Barroso, R. Walsh, D. Walter, Q. Wang, Y. Wen, K. Wichmann, L. Wiens, C. Wissing, S. Wuchterl, Y. Yang, A. Zimermmane Castro Santos, R. Aggleton, A. Albrecht, S. Albrecht, M. Antonello, S. Bein, L. Benato, M. Bonanomi, P. Connor, K. De Leo, M. Eich, K. El Morabit, F. Feindt, A. Fröhlich, C. Garbers, E. Garutti, M. Hajheidari, J. Haller, A. Hinzmann, H. R. Jabusch, G. Kasieczka, R. Klanner, W. Korcari, T. Kramer, V. Kutzner, J. Lange, A. Lobanov, C. Matthies, A. Mehta, L. Moureaux, M. Mrowietz, A. Nigamova, Y. Nissan, A. Paasch, K. J. Pena Rodriguez, M. Rieger, O. Rieger, P. Schleper, M. Schröder, J. Schwandt, H. Stadie, G. Steinbrück, A. Tews, M. Wolf, J. Bechtel, S. Brommer, M. Burkart, E. Butz, R. Caspart, T. Chwalek, A. Dierlamm, A. Droll, N. Faltermann, M. Giffels, J. O. Gosewisch, A. Gottmann, F. Hartmann, M. Horzela, U. Husemann, P. Keicher, M. Klute, R. Koppenhöfer, S. 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Ogul, Y. Onel, A. Penzo, C. Snyder, E. Tiras, O. Amram, B. Blumenfeld, L. Corcodilos, J. Davis, A. V. Gritsan, L. Kang, S. Kyriacou, P. Maksimovic, J. Roskes, S. Sekhar, M. Swartz, T. Á. Vámi, A. Abreu, L. F. Alcerro Alcerro, J. Anguiano, P. Baringer, A. Bean, Z. Flowers, S. Khalil, J. King, G. Krintiras, M. Lazarovits, C. Le Mahieu, J. Marquez, M. Murray, M. Nickel, C. Rogan, R. Salvatico, S. Sanders, E. Schmitz, C. Smith, G. Wilson, B. Allmond, S. Duric, R. Gujju Gurunadha, A. Ivanov, K. Kaadze, Y. Maravin, T. Mitchell, A. Modak, K. Nam, J. Natoli, D. Roy, F. Rebassoo, D. Wright, E. Adams, A. Baden, O. Baron, A. Belloni, A. Bethani, S. C. Eno, N. J. Hadley, S. Jabeen, R. G. Kellogg, T. Koeth, Y. Lai, S. Lascio, A. C. Mignerey, S. Nabili, C. Palmer, C. Papageorgakis, M. Seidel, L. Wang, K. Wong, D. Abercrombie, R. Bi, W. Busza, I. A. Cali, M. D’Alfonso, J. Eysermans, C. Freer, G. Gomez-Ceballos, M. Goncharov, P. Harris, M. Hu, D. Kovalskyi, J. Krupa, Y.-J. Lee, K. Long, C. Mironov, C. Paus, D. Rankin, C. Roland, G. Roland, Z. Shi, G. S. F. Stephans, Z. Wang, B. Wyslouch, R. M. Chatterjee, B. Crossman, A. Evans, J. Hiltbrand, Sh. Jain, B. M. Joshi, C. Kapsiak, M. Krohn, Y. Kubota, J. Mans, M. Revering, R. Rusack, R. Saradhy, N. Schroeder, N. Strobbe, M. A. Wadud, L. M. Cremaldi, K. Bloom, M. Bryson, D. R. Claes, C. Fangmeier, L. Finco, F. Golf, C. Joo, I. Kravchenko, I. Reed, J. E. Siado, G. R. Snow, W. Tabb, A. Wightman, F. Yan, A. G. Zecchinelli, G. Agarwal, H. Bandyopadhyay, L. Hay, I. Iashvili, A. Kharchilava, C. McLean, M. Morris, D. Nguyen, J. Pekkanen, S. Rappoccio, A. Williams, G. Alverson, E. Barberis, Y. Haddad, Y. Han, A. Krishna, J. Lidrych, G. Madigan, B. Marzocchi, D. M. Morse, V. Nguyen, T. Orimoto, A. Parker, L. Skinnari, A. Tishelman-Charny, T. Wamorkar, B. Wang, A. Wisecarver, D. Wood, J. Bueghly, Z. Chen, A. Gilbert, T. Gunter, K. A. Hahn, Y. Liu, N. Odell, M. H. Schmitt, M. Velasco, R. Band, R. Bucci, S. Castells, M. Cremonesi, A. Das, R. Goldouzian, M. Hildreth, K. Hurtado Anampa, C. Jessop, K. Lannon, J. Lawrence, N. Loukas, L. Lutton, J. Mariano, N. Marinelli, I. Mcalister, T. McCauley, C. Mcgrady, K. Mohrman, C. Moore, Y. Musienko, H. Nelson, R. Ruchti, A. Townsend, M. Wayne, H. Yockey, M. Zarucki, L. Zygala, B. Bylsma, M. Carrigan, L. S. Durkin, B. Francis, C. Hill, A. Lesauvage, M. Nunez Ornelas, K. Wei, B. L. Winer, B. R. Yates, F. M. Addesa, B. Bonham, P. Das, G. Dezoort, P. Elmer, A. Frankenthal, B. Greenberg, N. Haubrich, S. Higginbotham, A. Kalogeropoulos, G. Kopp, S. Kwan, D. Lange, D. Marlow, K. Mei, I. Ojalvo, J. Olsen, D. Stickland, C. Tully, S. Norberg, A. S. Bakshi, V. E. Barnes, R. Chawla, S. Das, L. Gutay, M. Jones, A. W. Jung, D. Kondratyev, A. M. Koshy, M. Liu, G. Negro, N. Neumeister, G. Paspalaki, S. Piperov, A. Purohit, J. F. Schulte, M. Stojanovic, J. Thieman, F. Wang, R. Xiao, W. Xie, J. Dolen, N. Parashar, D. Acosta, A. Baty, T. Carnahan, M. Decaro, S. Dildick, K. M. Ecklund, P. J. Fernández Manteca, S. Freed, P. Gardner, F. J. M. Geurts, W. Li, B. P. Padley, R. Redjimi, J. Rotter, W. Shi, E. Yigitbasi, L. Zhang, X. Zuo, A. Bodek, P. de Barbaro, R. Demina, J. L. Dulemba, C. Fallon, T. Ferbel, M. Galanti, A. Garcia-Bellido, O. Hindrichs, A. Khukhunaishvili, E. Ranken, R. Taus, G. P. Van Onsem, K. Goulianos, B. Chiarito, J. P. Chou, Y. Gershtein, E. Halkiadakis, A. Hart, M. Heindl, D. Jaroslawski, O. Karacheban, I. Laflotte, A. Lath, R. Montalvo, K. Nash, M. Osherson, S. Salur, S. Schnetzer, S. Somalwar, R. Stone, S. A. Thayil, S. Thomas, H. Wang, H. Acharya, A. G. Delannoy, S. Fiorendi, T. Holmes, E. Nibigira, S. Spanier, O. Bouhali, M. Dalchenko, A. Delgado, R. Eusebi, J. Gilmore, T. Huang, T. Kamon, S. Luo, S. Malhotra, R. Mueller, D. Overton, D. Rathjens, A. Safonov, N. Akchurin, J. Damgov, V. Hegde, K. Lamichhane, T. Mengke, S. Muthumuni, T. Peltola, I. Volobouev, A. Whitbeck, E. Appelt, S. Greene, A. Gurrola, W. Johns, A. Melo, F. Romeo, P. Sheldon, S. Tuo, J. Velkovska, J. Viinikainen, B. Cardwell, B. Cox, G. Cummings, J. Hakala, R. Hirosky, M. Joyce, A. Ledovskoy, A. Li, C. Neu, C. E. Perez Lara, B. Tannenwald, P. E. Karchin, N. Poudyal, K. Black, T. Bose, S. Dasu, I. De Bruyn, P. Everaerts, C. Galloni, H. He, M. Herndon, A. Herve, C. K. Koraka, A. Lanaro, A. Loeliger, R. Loveless, J. Madhusudanan Sreekala, A. Mallampalli, A. Mohammadi, G. Parida, D. Pinna, A. Savin, V. Shang, W. H. Smith, D. Teague, H. F. Tsoi, W. Vetens, S. Afanasiev, V. Andreev, Yu. Andreev, T. Aushev, M. Azarkin, A. Babaev, V. Blinov, E. Boos, D. Budkouski, V. Bunichev, O. Bychkova, M. Chadeeva, V. Chekhovsky, A. Dermenev, T. Dimova, I. Dremin, M. Dubinin, L. Dudko, V. Epshteyn, G. Gavrilov, V. Gavrilov, S. Gninenko, V. Golovtcov, N. Golubev, I. Golutvin, I. Gorbunov, A. Gribushin, Y. Ivanov, V. Kachanov, L. Kardapoltsev, V. Karjavine, A. Karneyeu, V. Kim, M. Kirakosyan, D. Kirpichnikov, M. Kirsanov, V. Klyukhin, D. Konstantinov, V. Korenkov, A. Kozyrev, N. Krasnikov, E. Kuznetsova, A. Lanev, P. Levchenko, A. Litomin, O. Lukina, N. Lychkovskaya, V. Makarenko, A. Malakhov, V. Matveev, V. Murzin, A. Nikitenko, S. Obraztsov, V. Okhotnikov, I. Ovtin, V. Palichik, P. Parygin, V. Perelygin, M. Perfilov, G. Pivovarov, V. Popov, E. Popova, O. Radchenko, V. Rusinov, M. Savina, V. Savrin, D. Selivanova, V. Shalaev, S. Shmatov, S. Shulha, Y. Skovpen, S. Slabospitskii, V. Smirnov, D. Sosnov, A. Stepennov, V. Sulimov, A. Terkulov, O. Teryaev, I. Tlisova, M. Toms, A. Toropin, L. Uvarov, A. Uzunian, E. Vlasov, P. Volkov, A. Vorobyev, N. Voytishin, B. S. Yuldashev, A. Zarubin, I. Zhizhin, A. Zhokin, The TOTEM collaboration, G. Antchev, P. Aspell, I. Atanassov, V. Avati, J. Baechler, C. Baldenegro Barrera, V. Berardi, M. Berretti, V. Borshch, E. Bossini, U. Bottigli, M. Bozzo, H. Burkhardt, F. S. Cafagna, M. G. Catanesi, M. Deile, F. De Leonardis, M. Doubek, D. Druzhkin, K. Eggert, V. Eremin, A. Fiergolski, F. Garcia, V. Georgiev, S. Giani, L. Grzanka, J. Hammerbauer, T. Isidori, V. Ivanchenko, M. Janda, A. Karev, J. Kašpar, B. Kaynak, J. Kopal, V. Kundrát, S. Lami, R. Linhart, C. Lindsey, M. V. Lokajíček, L. Losurdo, F. Lucas Rodríguez, M. Macrí, M. Malawski, N. Minafra, S. Minutoli, K. Misan, T. Naaranoja, F. Nemes, H. Niewiadomski, E. Oliveri, F. Oljemark, M. Oriunno, K. Österberg, S. Ozkorucuklu, P. Palazzi, V. Passaro, Z. Peroutka, O. Potok, J. Procházka, M. Quinto, E. Radermacher, E. Radicioni, F. Ravotti, C. Royon, G. Ruggiero, H. Saarikko, V. D. Samoylenko, A. Scribano, J. Široký, J. Smajek, W. Snoeys, R. Stefanovitch, C. Taylor, E. Tcherniaev, N. Turini, O. Urban, V. Vacek, O. Vavroch, J. Welti, J. Williams, and J. Zich

Subjects
Hadron-Hadron Scattering, Top Physics, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

Abstract A search for the central exclusive production of top quark-antiquark pairs ( t t ¯ $$ \textrm{t}\overline{\textrm{t}} $$ ) is performed for the first time using proton-tagged events in proton-proton collisions at the LHC at a centre-of-mass energy of 13 TeV. The data correspond to an integrated luminosity of 29.4 fb −1. The t t ¯ $$ \textrm{t}\overline{\textrm{t}} $$ decay products are reconstructed using the central CMS detector, while forward protons are measured in the CMS-TOTEM precision proton spectrometer. An observed (expected) upper bound on the production cross section of 0.59 (1.14) pb is set at 95% confidence level, for collisions of protons with fractional momentum losses between 2 and 20%.

Published
2024
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38. Movement behaviour typologies and their associations with adiposity indicators in children and adolescents: a latent profile analysis of 24-h compositional data

Author

David Janda, Aleš Gába, Karel Hron, Lauren Arundell, and Ana Maria Contardo Ayala

Subjects
Clusters, Obesity, Sedentary behaviour, Physical activity, Sleep, Profiles, Public aspects of medicine, RA1-1270
Abstract

Abstract Objectives Growing evidence supports the important role of 24-hour movement behaviours (MB) in preventing childhood obesity. However, research to understand the heterogeneity and variability of MB among individuals and what kind of typologies of individuals are at risk of developing obesity is lacking. To bridge this gap, this study identified typologies of 24-hour MB in children and adolescents and investigated their associations with adiposity indicators. Methods In this cross-sectional study, 374 children and 317 adolescents from the Czech Republic wore wrist-worn accelerometers for seven consecutive days. Time spent in moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), sedentary behaviour (SB), and sleep was quantified using raw accelerometery data. Adiposity indicators included body mass index (BMI) z-score, fat mass percentage (FM%), fat mass index (FMI), and visceral adipose tissue (VAT). Bias-adjusted latent profile analysis was used on the 24-hour MB data to identify MB typologies and their associations with adiposity indicators. The models were adjusted for potential confounders. The identified typologies were labelled to reflect the behavioural profiles of bees to aid interpretability for the general public. Results Two typologies were identified in children: highly active Workers characterised by high levels of MVPA and LPA, and inactive Queens characterised by low levels of MVPA and LPA, high levels of SB and longer sleep duration compared to Workers. In adolescents, an additional typology labelled as Drones was characterised by median levels of MVPA, LPA, SB and longest sleep duration. After controlling for covariates, we found that children labelled as Queens were associated with 1.38 times higher FM%, 1.43 times higher FMI, and 1.67 times higher VAT than Workers. In adolescents, Drones had 1.14 times higher FM% and Queens had 1.36 higher VAT in comparison with Workers, respectively. Conclusion Our study highlights the importance of promoting active lifestyles in children and adolescents to potentially reduce adiposity. These findings can provide insights for interventions aimed at promoting healthy MB and preventing childhood obesity. Graphical Abstract

Published
2024
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39. Search for central exclusive production of top quark pairs in proton-proton collisions at s = 13 TeV with tagged protons

Author

Tumasyan, A., Adam, W., Andrejkovic, J. W., Bergauer, T., Chatterjee, S., Damanakis, K., Dragicevic, M., Escalante Del Valle, A., Hussain, P. S., Jeitler, M., Krammer, N., Lechner, L., Liko, D., Mikulec, I., Paulitsch, P., Pitters, F. M., Schieck, J., Schöfbeck, R., Schwarz, D., Templ, S., Waltenberger, W., Wulz, C.-E., Darwish, M. R., Janssen, T., Kello, T., Rejeb Sfar, H., Van Mechelen, P., Bols, E. S., D’Hondt, J., De Moor, A., Delcourt, M., El Faham, H., Lowette, S., Moortgat, S., Morton, A., Müller, D., Sahasransu, A. R., Tavernier, S., Van Doninck, W., Vannerom, D., Clerbaux, B., De Lentdecker, G., Favart, L., Hohov, D., Jaramillo, J., Lee, K., Mahdavikhorrami, M., Makarenko, I., Malara, A., Paredes, S., Pétré, L., Postiau, N., Starling, E., Thomas, L., Vanden Bemden, M., Vander Velde, C., Vanlaer, P., Dobur, D., Knolle, J., Lambrecht, L., Mestdach, G., Niedziela, M., Rendón, C., Roskas, C., Samalan, A., Skovpen, K., Tytgat, M., Van Den Bossche, N., Vermassen, B., Wezenbeek, L., Benecke, A., Bruno, G., Bury, F., Caputo, C., David, P., Delaere, C., Donertas, I. S., Giammanco, A., Jaffel, K., Jain, Sa., Lemaitre, V., Mondal, K., Prisciandaro, J., Taliercio, A., Tran, T. T., Vischia, P., Wertz, S., Alves, G. A., Coelho, E., Hensel, C., Moraes, A., Rebello Teles, P., Aldá Júnior, W. L., Alves Gallo Pereira, M., Barroso Ferreira Filho, M., Brandao Malbouisson, H., Carvalho, W., Chinellato, J., Da Costa, E. M., Da Silveira, G. G., De Jesus Damiao, D., Dos Santos Sousa, V., Fonseca De Souza, S., Martins, J., Mora Herrera, C., Mota Amarilo, K., Mundim, L., Nogima, H., Santoro, A., Silva Do Amaral, S. M., Sznajder, A., Thiel, M., Torres Da Silva De Araujo, F., Vilela Pereira, A., Bernardes, C. A., Calligaris, L., Fernandez Perez Tomei, T. R., Gregores, E. M., Mercadante, P. G., Novaes, S. F., Padula, Sandra S., Aleksandrov, A., Hadjiiska, R., Iaydjiev, P., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Ivanov, T., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Shumka, E., Cheng, T., Javaid, T., Mittal, M., Yuan, L., Ahmad, M., Bauer, G., Hu, Z., Lezki, S., Yi, K., Chen, G. M., Chen, H. S., Chen, M., Iemmi, F., Jiang, C. H., Kapoor, A., Liao, H., Liu, Z.-A., Milosevic, V., Monti, F., Sharma, R., Tao, J., Thomas-Wilsker, J., Wang, J., Zhang, H., Zhao, J., Agapitos, A., An, Y., Ban, Y., Chen, C., Levin, A., Li, C., Li, Q., Lyu, X., Mao, Y., Qian, S. J., Sun, X., Wang, D., Xiao, J., Yang, H., Li, J., Lu, M., You, Z., Gao, X., Leggat, D., Okawa, H., Zhang, Y., Lin, Z., Lu, C., Xiao, M., Avila, C., Barbosa Trujillo, D. A., Cabrera, A., Florez, C., Fraga, J., Mejia Guisao, J., Ramirez, F., Rodriguez, M., Ruiz Alvarez, J. D., Giljanovic, D., Godinovic, N., Lelas, D., Puljak, I., Antunovic, Z., Kovac, M., Sculac, T., Brigljevic, V., Chitroda, B. K., Ferencek, D., Majumder, D., Roguljic, M., Starodumov, A., Susa, T., Attikis, A., Christoforou, K., Kole, G., Kolosova, M., Konstantinou, S., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Saka, H., Finger, M., Finger, Jr., M., Kveton, A., Ayala, E., Carrera Jarrin, E., Elgammal, S., Ellithi Kamel, A., Mahmoud, M. A., Mohammed, Y., Bhowmik, S., Dewanjee, R. K., Ehataht, K., Kadastik, M., Lange, T., Nandan, S., Nielsen, C., Pata, J., Raidal, M., Tani, L., Veelken, C., Eerola, P., Kirschenmann, H., Voutilainen, M., Bharthuar, S., Brücken, E., Havukainen, J., Kim, M. S., Kinnunen, R., Lampén, T., Lassila-Perini, K., Lehti, S., Lindén, T., Lotti, M., Martikainen, L., Myllymäki, M., Ott, J., Rantanen, M. m., Siikonen, H., Tuominen, E., Tuominiemi, J., Luukka, P., Petrow, H., Tuuva, T., Amendola, C., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J. L., Ferri, F., Ganjour, S., Gras, P., Hamel de Monchenault, G., Jarry, P., Lohezic, V., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. Ö., Savoy-Navarro, A., Simkina, P., Titov, M., Beaudette, F., Buchot Perraguin, A., Busson, P., Cappati, A., Charlot, C., Damas, F., Davignon, O., Diab, B., Falmagne, G., Fontana Santos Alves, B. A., Ghosh, S., Granier de Cassagnac, R., Hakimi, A., Harikrishnan, B., Liu, G., Motta, J., Nguyen, M., Ochando, C., Portales, L., Rembser, J., Salerno, R., Sarkar, U., Sauvan, J. B., Sirois, Y., Tarabini, A., Vernazza, E., Zabi, A., Zghiche, A., Agram, J.-L., Andrea, J., Apparu, D., Bloch, D., Bourgatte, G., Brom, J.-M., Chabert, E. 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S., Dolek, F., Dumanoglu, I., Eskut, E., Guler, Y., Gurpinar Guler, E., Isik, C., Kara, O., Kayis Topaksu, A., Kiminsu, U., Onengut, G., Ozdemir, K., Polatoz, A., Simsek, A. E., Tali, B., Tok, U. G., Turkcapar, S., Uslan, E., Zorbakir, I. S., Karapinar, G., Ocalan, K., Yalvac, M., Akgun, B., Atakisi, I. O., Gülmez, E., Kaya, M., Kaya, O., Özçelik, Ö., Tekten, S., Cakir, A., Cankocak, K., Komurcu, Y., Sen, S., Aydilek, O., Cerci, S., Hacisahinoglu, B., Hos, I., Isildak, B., Simsek, C., Sunar Cerci, D., Grynyov, B., Levchuk, L., Anthony, D., Bhal, E., Brooke, J. J., Bundock, A., Clement, E., Cussans, D., Flacher, H., Glowacki, M., Goldstein, J., Heath, G. P., Heath, H. F., Kreczko, L., Krikler, B., Paramesvaran, S., Seif El Nasr-Storey, S., Smith, V. J., Stylianou, N., Walkingshaw Pass, K., White, R., Ball, A. H., Bell, K. W., Belyaev, A., Brew, C., Brown, R. M., Cockerill, D. J. A., Cooke, C., Ellis, K. 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V., Kang, L., Kyriacou, S., Maksimovic, P., Roskes, J., Sekhar, S., Swartz, M., Vámi, T. Á., Abreu, A., Alcerro Alcerro, L. F., Anguiano, J., Baringer, P., Bean, A., Flowers, Z., Khalil, S., King, J., Krintiras, G., Lazarovits, M., Le Mahieu, C., Marquez, J., Murray, M., Nickel, M., Rogan, C., Salvatico, R., Sanders, S., Schmitz, E., Smith, C., Wang, Q., Wilson, G., Allmond, B., Duric, S., Gujju Gurunadha, R., Ivanov, A., Kaadze, K., Kim, D., Maravin, Y., Mitchell, T., Modak, A., Nam, K., Natoli, J., Roy, D., Rebassoo, F., Wright, D., Adams, E., Baden, A., Baron, O., Belloni, A., Bethani, A., Eno, S. C., Hadley, N. J., Jabeen, S., Kellogg, R. G., Koeth, T., Lai, Y., Lascio, S., Mignerey, A. C., Nabili, S., Palmer, C., Papageorgakis, C., Seidel, M., Wang, L., Wong, K., Abercrombie, D., Bi, R., Busza, W., Cali, I. A., Chen, Y., D’Alfonso, M., Eysermans, J., Freer, C., Gomez-Ceballos, G., Goncharov, M., Harris, P., Hu, M., Kovalskyi, D., Krupa, J., Lee, Y.-J., Long, K., Mironov, C., Paus, C., Rankin, D., Roland, C., Roland, G., Shi, Z., Stephans, G. S. F., Wang, J., Wang, Z., Wyslouch, B., Chatterjee, R. M., Crossman, B., Evans, A., Hiltbrand, J., Jain, Sh., Joshi, B. M., Kapsiak, C., Krohn, M., Kubota, Y., Mans, J., Revering, M., Rusack, R., Saradhy, R., Schroeder, N., Strobbe, N., Wadud, M. A., Cremaldi, L. M., Bloom, K., Bryson, M., Claes, D. R., Fangmeier, C., Finco, L., Golf, F., Joo, C., Kravchenko, I., Reed, I., Siado, J. E., Snow, G. R., Tabb, W., Wightman, A., Yan, F., Zecchinelli, A. G., Agarwal, G., Bandyopadhyay, H., Hay, L., Iashvili, I., Kharchilava, A., McLean, C., Morris, M., Nguyen, D., Pekkanen, J., Rappoccio, S., Williams, A., Alverson, G., Barberis, E., Haddad, Y., Han, Y., Krishna, A., Li, J., Lidrych, J., Madigan, G., Marzocchi, B., Morse, D. M., Nguyen, V., Orimoto, T., Parker, A., Skinnari, L., Tishelman-Charny, A., Wamorkar, T., Wang, B., Wisecarver, A., Wood, D., Bhattacharya, S., Bueghly, J., Chen, Z., Gilbert, A., Gunter, T., Hahn, K. A., Liu, Y., Odell, N., Schmitt, M. H., Velasco, M., Band, R., Bucci, R., Castells, S., Cremonesi, M., Das, A., Goldouzian, R., Hildreth, M., Hurtado Anampa, K., Jessop, C., Lannon, K., Lawrence, J., Loukas, N., Lutton, L., Mariano, J., Marinelli, N., Mcalister, I., McCauley, T., Mcgrady, C., Mohrman, K., Moore, C., Musienko, Y., Nelson, H., Ruchti, R., Townsend, A., Wayne, M., Yockey, H., Zarucki, M., Zygala, L., Bylsma, B., Carrigan, M., Durkin, L. S., Francis, B., Hill, C., Lesauvage, A., Nunez Ornelas, M., Wei, K., Winer, B. L., Yates, B. R., Addesa, F. M., Bonham, B., Das, P., Dezoort, G., Elmer, P., Frankenthal, A., Greenberg, B., Haubrich, N., Higginbotham, S., Kalogeropoulos, A., Kopp, G., Kwan, S., Lange, D., Marlow, D., Mei, K., Ojalvo, I., Olsen, J., Stickland, D., Tully, C., Malik, S., Norberg, S., Bakshi, A. S., Barnes, V. E., Chawla, R., Das, S., Gutay, L., Jones, M., Jung, A. W., Kondratyev, D., Koshy, A. M., Liu, M., Negro, G., Neumeister, N., Paspalaki, G., Piperov, S., Purohit, A., Schulte, J. F., Stojanovic, M., Thieman, J., Wang, F., Xiao, R., Xie, W., Dolen, J., Parashar, N., Acosta, D., Baty, A., Carnahan, T., Decaro, M., Dildick, S., Ecklund, K. M., Fernández Manteca, P. J., Freed, S., Gardner, P., Geurts, F. J. M., Kumar, A., Li, W., Padley, B. P., Redjimi, R., Rotter, J., Shi, W., Yang, S., Yigitbasi, E., Zhang, L., Zhang, Y., Zuo, X., Bodek, A., de Barbaro, P., Demina, R., Dulemba, J. L., Fallon, C., Ferbel, T., Galanti, M., Garcia-Bellido, A., Hindrichs, O., Khukhunaishvili, A., Ranken, E., Taus, R., Van Onsem, G. P., Goulianos, K., Chiarito, B., Chou, J. P., Gershtein, Y., Halkiadakis, E., Hart, A., Heindl, M., Jaroslawski, D., Karacheban, O., Laflotte, I., Lath, A., Montalvo, R., Nash, K., Osherson, M., Salur, S., Schnetzer, S., Somalwar, S., Stone, R., Thayil, S. A., Thomas, S., Wang, H., Acharya, H., Delannoy, A. G., Fiorendi, S., Holmes, T., Nibigira, E., Spanier, S., Bouhali, O., Dalchenko, M., Delgado, A., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Kim, H., Luo, S., Malhotra, S., Mueller, R., Overton, D., Rathjens, D., Safonov, A., Akchurin, N., Damgov, J., Hegde, V., Lamichhane, K., Lee, S. W., Mengke, T., Muthumuni, S., Peltola, T., Volobouev, I., Wang, Z., Whitbeck, A., Appelt, E., Greene, S., Gurrola, A., Johns, W., Melo, A., Romeo, F., Sheldon, P., Tuo, S., Velkovska, J., Viinikainen, J., Cardwell, B., Cox, B., Cummings, G., Hakala, J., Hirosky, R., Joyce, M., Ledovskoy, A., Li, A., Neu, C., Perez Lara, C. E., Tannenwald, B., Karchin, P. E., Poudyal, N., Banerjee, S., Black, K., Bose, T., Dasu, S., De Bruyn, I., Everaerts, P., Galloni, C., He, H., Herndon, M., Herve, A., Koraka, C. K., Lanaro, A., Loeliger, A., Loveless, R., Madhusudanan Sreekala, J., Mallampalli, A., Mohammadi, A., Mondal, S., Parida, G., Pinna, D., Savin, A., Shang, V., Sharma, V., Smith, W. H., Teague, D., Tsoi, H. F., Vetens, W., Afanasiev, S., Andreev, V., Andreev, Yu., Aushev, T., Azarkin, M., Babaev, A., Belyaev, A., Blinov, V., Boos, E., Budkouski, D., Bunichev, V., Bychkova, O., Chadeeva, M., Chekhovsky, V., Dermenev, A., Dimova, T., Dremin, I., Dubinin, M., Dudko, L., Epshteyn, V., Gavrilov, G., Gavrilov, V., Gninenko, S., Golovtcov, V., Golubev, N., Golutvin, I., Gorbunov, I., Gribushin, A., Ivanov, Y., Kachanov, V., Kardapoltsev, L., Karjavine, V., Karneyeu, A., Kim, V., Kirakosyan, M., Kirpichnikov, D., Kirsanov, M., Klyukhin, V., Konstantinov, D., Korenkov, V., Kozyrev, A., Krasnikov, N., Kuznetsova, E., Lanev, A., Levchenko, P., Litomin, A., Lukina, O., Lychkovskaya, N., Makarenko, V., Malakhov, A., Matveev, V., Murzin, V., Nikitenko, A., Obraztsov, S., Okhotnikov, V., Ovtin, I., Palichik, V., Parygin, P., Perelygin, V., Perfilov, M., Pivovarov, G., Popov, V., Popova, E., Radchenko, O., Rusinov, V., Savina, M., Savrin, V., Selivanova, D., Shalaev, V., Shmatov, S., Shulha, S., Skovpen, Y., Slabospitskii, S., Smirnov, V., Sosnov, D., Stepennov, A., Sulimov, V., Terkulov, A., Teryaev, O., Tlisova, I., Toms, M., Toropin, A., Uvarov, L., Uzunian, A., Vlasov, E., Volkov, P., Vorobyev, A., Voytishin, N., Yuldashev, B. S., Zarubin, A., Zhizhin, I., Zhokin, A., Antchev, G., Aspell, P., Atanassov, I., Avati, V., Baechler, J., Baldenegro Barrera, C., Berardi, V., Berretti, M., Borshch, V., Bossini, E., Bottigli, U., Bozzo, M., Burkhardt, H., Cafagna, F. S., Catanesi, M. G., Deile, M., De Leonardis, F., Doubek, M., Druzhkin, D., Eggert, K., Eremin, V., Fiergolski, A., Garcia, F., Georgiev, V., Giani, S., Grzanka, L., Hammerbauer, J., Isidori, T., Ivanchenko, V., Janda, M., Karev, A., Kašpar, J., Kaynak, B., Kopal, J., Kundrát, V., Lami, S., Linhart, R., Lindsey, C., Lokajíček, M. V., Losurdo, L., Lucas Rodríguez, F., Macrí, M., Malawski, M., Minafra, N., Minutoli, S., Misan, K., Naaranoja, T., Nemes, F., Niewiadomski, H., Oliveri, E., Oljemark, F., Oriunno, M., Österberg, K., Ozkorucuklu, S., Palazzi, P., Passaro, V., Peroutka, Z., Potok, O., Procházka, J., Quinto, M., Radermacher, E., Radicioni, E., Ravotti, F., Royon, C., Ruggiero, G., Saarikko, H., Samoylenko, V. D., Scribano, A., Široký, J., Smajek, J., Snoeys, W., Stefanovitch, R., Taylor, C., Tcherniaev, E., Turini, N., Urban, O., Vacek, V., Vavroch, O., Welti, J., Williams, J., and Zich, J.

Published
2024
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40. Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data

Author

Janda, Andrej, Wagstaff, Brandon, Ng, Edwin G., and Kelly, Jonathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is particularly important for semantic segmentation tasks involving 3D datasets, which are often significantly smaller and more challenging to annotate than their image-based counterparts. Self-supervised pre-training on unlabelled data is one way to reduce the amount of manual annotations needed. Previous work has focused on pre-training with point clouds exclusively. While useful, this approach often requires two or more registered views. In the present work, we combine image and point cloud modalities by first learning self-supervised image features and then using these features to train a 3D model. By incorporating image data, which is often included in many 3D datasets, our pre-training method only requires a single scan of a scene and can be applied to cases where localization information is unavailable. We demonstrate that our pre-training approach, despite using single scans, achieves comparable performance to other multi-scan, point cloud-only methods., Comment: In Proceedings of the Conference on Robots and Vision (CRV'23), Montreal, Canada, Jun. 6-8, 2023. arXiv admin note: substantial text overlap with arXiv:2211.11801

Published
2023
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41. Hunger and housing: Economic disparities in current and daily tobacco use among high school students in the United States in 2021

Author

Dale S. Mantey, Kathryn M. Janda-Thomte, Adam C. Alexander, Onyinye Omega-Njemnobi, and Steven H. Kelder

Subjects
Economic inequalities, Youth, Tobacco use, Daily use, Addiction, Homelessness, Medicine
Abstract

Introduction: Economic disparities in tobacco use and dependence are well-documented among adults but not adolescents. This study aims to examine economic disparities in patterns of tobacco use among a nationally representative sample of high school students in Spring 2021. Methods: We analyzed data from n = 6750 US high school student via the 2021 Adolescent Behaviors and Experiences Survey (ABES). We estimated the association between experiencing homelessness and food insecurity (analyzed independently) and current (past 30-day) and daily (all 30 days) use of four tobacco products (e-cigarettes; cigarettes; cigars; smokeless). Models controlled for sex, race/ethnicity, age, and sexual identity. Results: Overall, ∼1.8 % experienced homelessness and 23.7 % experienced food insecurity. Experiencing homeless was significantly associated with greater odds of current use for e-cigarette (aOR: 3.43), cigarettes (aOR: 5.58), cigars (aOR: 10.47), and smokeless tobacco (aOR: 4.41) as well as greater risk for daily use of e-cigarettes (aOR: 2.66), cigarettes (aOR: 10.94), and cigars (aOR: 5.23) but not smokeless tobacco (aOR: 2.48; 95 %CI: 0.51–12.16). Food insecurity was significantly associated with greater odds of current use of e-cigarettes (aOR: 2.00), cigarettes (aOR: 2.15), and cigars (aOR: 2.44) but not smokeless (aOR: 1.04; 95 % CI: 0.56–1.93). No association was observed between food insecurity and daily tobacco use. Conclusion: Substantial economic disparities in tobacco use were observed in a nationally representative sample of high school students. Interventions should consider prioritizing economic determinants of health during adolescence, including a focus on preventing youth tobacco use as well as addressing upstream determinants of homelessness and food insecurity.

Published
2024
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42. Systematic Review of Nevus Counting and Reporting Methodologies in Contemporary Studies of the General Population

Author

Dilki Jayasinghe, Nathasha Naranpanawa, Daniel Paul Ashley, Åsa Ingvar, Brigid Betz-Stablein, H. Peter Soyer, and Monika Janda

Subjects
melanoma risk, standardization, naevi, reliability, vailidity, inter- and intra-observer variation, Dermatology, RL1-803
Abstract

Introduction: Having many melanocytic nevi on the skin is a risk factor for melanoma. However, the reproducibility of nevus counts in previous studies is limited due to high inter- and intraobserver variation. Despite the introduction of a protocol for counting and reporting of nevi in 1990 by the International Agency for Research on Cancer (IARC), significant variations in nevus counting methods persist across studies. Objectives: We sought to review the variations in nevus counting and reporting methods, adherence and deviations from the IARC protocol, and the reproducibility of nevus counting studies. Methods: A systematic search of Embase, PubMed and Web of Science was conducted. The review was limited to nevus (>2 mm) counting studies of general population adults conducted between 2000 and 2022, and studies using skilled examiners. Results: Out of the 8 studies which were eligible for inclusion, none followed the IARC protocol. Three studies used a predefined criterion to count nevi. Five studies provided training for their observers. Three studies assessed the inter- or intraobserver variation using the correlation coefficient (>0.75), and 3 studies attempted to verify the validity and the reproducibility of the counts. There was little to no agreement in nevus counting and reporting procedures in the reviewed studies, and most studies did not report their procedures adequately. Conclusion: This review highlights the need for an easily accessible and feasible protocol for identification, counting and reporting of nevi, which also considers nevus counting from total-body imaging and automated nevus counts since these technologies are expected to become widely available for future studies.

Published
2024
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43. Participant Motivators and Expectations in the MEL-SELF Randomized Clinical Trial of Patient-Led Surveillance for Recurrent Melanoma: Content Analysis of Survey Responses

Author

Deonna Ackermann, Jolyn Hersch, Dana Jordan, Emily Clinton-Gray, Karen Bracken, Monika Janda, Robin Turner, and Katy Bell

Subjects
Dermatology, RL1-803
Abstract

BackgroundLimited data exist on the motivations and expectations of participants when enrolling in dermatology clinical trials, including melanoma early detection trials. Understanding participant motivators for research engagement has been identified as a prioritized area for trial methodology research. ObjectiveThe study aimed to determine motivators of participation and expectations from trial involvement among patients enrolled in the MEL-SELF randomized clinical trial of patient-led surveillance for new or recurrent melanoma. MethodsThe MEL-SELF trial is recruiting patients previously treated for localized melanoma, who own a smartphone, have a partner to assist with skin self-examination (SSE), and attend routinely scheduled follow-up at specialist and primary care skin clinics in Australia. We evaluated responses from the first 100 randomized participants to 2 open-ended questions about their motivations and expectations for participating in the trial, administered through the internet-based baseline questionnaire. A total of 3 coders independently coded the free-text responses and resolved discrepancies through consensus. Qualitative content analysis by an iterative process was used to group responses into themes. Responses from potential participants who were not randomized and the 404 participants randomized subsequently into the trial, were also checked for new themes. Coding and analysis were conducted in Microsoft Excel. ResultsOut of the 100 survey participants, 98 (98%) answered at least 1 of the 2 questions. Overall, responses across the motivation and expectation items indicated 3 broad themes: community benefit, perceived personal benefit, and trusting relationship with their health care provider. The most common motivators for participation were related to community benefit. These included progressing medical research, benefitting future melanoma patients who may have similar experiences, and broader altruistic sentiments such as “helping others” or “giving back.” The most common expectations from the trial related to personal benefit. These included perceived improved outcomes such as earlier diagnosis and treatment, access to additional care, and increased self-empowerment to take actions themselves that benefit their health. Patients expressed a desire to gain health-related knowledge and skills and were interested in the potential advantages of teledermatology. There were no new themes in responses from those who were not randomized or were randomized subsequent to the first 100. ConclusionsWe report a tailorable, patient-focused approach to identify drivers of research engagement in clinical research. Clinical trials offer an opportunity to collate a substantial evidence base on determinants of research participation and to identify context-specific factors. Results from the MEL-SELF trial emphasized notable altruism, self-empowerment, and perceived advantages of teledermatology as specific motivators. These findings informed consent processes, recruitment, retention, response to trial tasks, and intervention adherence for the MEL-SELF host trial. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12621000176864. https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=379527&

Published
2024
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44. Genome-wide identification of HKT gene family in wheat (Triticum aestivum L.): Insights from the expression of multiple genes (HKT, SOS, TVP and NHX) under salt stress

Author

Fatemeh Gholizadeh, Iman Mirmazloum, and Tibor Janda

Subjects
Genome-wide screening, HKT gene family, Salinity stress, Na+ and K+ transporters, Plant ecology, QK900-989
Abstract

Potassium (K+) transporters, particularly high-affinity K+ transporters (HKT), are crucial for plant growth and abiotic stress response. These transporters regulate Na+ and K+ balance in the cells to prevent sodium toxicity in plant tissues. Although HKT genes have been extensively studied in various crops, there is a deficiency in their characterization and understanding of their role in wheat, particularly regarding their involvement in adaptive response to environmental stresses like salinity. In this study, several bioinformatics tools including web-based platforms and software applications were employed to identify, characterize and localize HKTs genes within the wheat genome. A greenhouse trial was also conducted to investigate the mRNA expression levels of some of the significant salt-responsive genes in wheat seedlings grown under different concentrations of NaCl (0, 200, and 400 mM). Genome-wide analysis, resulted in identification of 35 TaHKTs that are unevenly distributed on ten chromosomes. The majority of these transporters are localized on plasma membrane. Analysis of promoter regions showed that most of the TaHKT genes have abundant cis-elements that involved in hormones and abiotic stress responses. Gene expression analysis indicated a significant upregulation of TaHKT6 and TaSOS1 genes were found in wheat seedlings treated with 400 mM NaCl. Expression levels of TaNHX1, TaNHX2 and TaTVP1 genes were also higher in 200 mM and 400 mM NaCl-treated plants when compared to non-stressed plants. Overall, the results provide further evidence for the role of the TaHKT in wheat's stress response to elevated salt levels and pave the way for further functional research in K+ transporters in plants.

Published
2024
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45. Self-Supervised Pre-training of 3D Point Cloud Networks with Image Data

Author

Janda, Andrej, Wagstaff, Brandon, Ng, Edwin G., and Kelly, Jonathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Reducing the quantity of annotations required for supervised training is vital when labels are scarce and costly. This reduction is especially important for semantic segmentation tasks involving 3D datasets that are often significantly smaller and more challenging to annotate than their image-based counterparts. Self-supervised pre-training on large unlabelled datasets is one way to reduce the amount of manual annotations needed. Previous work has focused on pre-training with point cloud data exclusively; this approach often requires two or more registered views. In the present work, we combine image and point cloud modalities, by first learning self-supervised image features and then using these features to train a 3D model. By incorporating image data, which is often included in many 3D datasets, our pre-training method only requires a single scan of a scene. We demonstrate that our pre-training approach, despite using single scans, achieves comparable performance to other multi-scan, point cloud-only methods., Comment: In Proceedings of the Conference on Robot Learning (CoRL'22) Workshop on Pre-Training Robot Learning, Auckland, New Zealand, December 15, 2022

Published
2022

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