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15,128 results on '"Bülow A"'

1. Machine learning methods to study disordered proteins

Author

von Bülow, Sören, Tesei, Giulio, and Lindorff-Larsen, Kresten

Subjects
Quantitative Biology - Biomolecules
Abstract

Recent years have seen tremendous developments in the use of machine learning models to link amino acid sequence, structure and function of folded proteins. These methods are, however, rarely applicable to the wide range of proteins and sequences that comprise intrinsically disordered regions. We here review developments in the study of disordered proteins that exploit or are used to train machine learning models. These include methods for generating conformational ensembles and designing new sequences, and for linking sequences to biophysical properties and biological functions. We highlight how these developments are built on a tight integration between experiment, theory and simulations, and account for evolutionary constraints, which operate on sequences of disordered regions differently than on those of folded domains., Comment: 11 pages, 2 figures

Published
2024

2. Machine Learning with Physics Knowledge for Prediction: A Survey

Author

Watson, Joe, Song, Chen, Weeger, Oliver, Gruner, Theo, Le, An T., Hansel, Kay, Hendawy, Ahmed, Arenz, Oleg, Trojak, Will, Cranmer, Miles, D'Eramo, Carlo, Bülow, Fabian, Goyal, Tanmay, Peters, Jan, and Hoffman, Martin W.

Subjects
Computer Science - Machine Learning, Mathematics - Numerical Analysis, Physics - Computational Physics
Abstract

This survey examines the broad suite of methods and models for combining machine learning with physics knowledge for prediction and forecast, with a focus on partial differential equations. These methods have attracted significant interest due to their potential impact on advancing scientific research and industrial practices by improving predictive models with small- or large-scale datasets and expressive predictive models with useful inductive biases. The survey has two parts. The first considers incorporating physics knowledge on an architectural level through objective functions, structured predictive models, and data augmentation. The second considers data as physics knowledge, which motivates looking at multi-task, meta, and contextual learning as an alternative approach to incorporating physics knowledge in a data-driven fashion. Finally, we also provide an industrial perspective on the application of these methods and a survey of the open-source ecosystem for physics-informed machine learning., Comment: 56 pages, 8 figures, 2 tables

Published
2024

3. Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Author

Yang, Zhijian, Wen, Junhao, Erus, Guray, Govindarajan, Sindhuja, Melhem, Randa, Mamourian, Elizabeth, Cui, Yuhan, Srinivasan, Dhivya, Abdulkadir, Ahmed, Parmpi, Paraskevi, Wittfeld, Katharina, Grabe, Hans, Bülow, Robin, Frenzel, Stefan, Tosun, Duygu, Bilgel, Murat, An, Yang, Yi, Dahyun, Marcus, Daniel, LaMontagne, Pamela, Benzinger, Tammie, Heckbert, Susan, Austin, Thomas, Waldstein, Shari, Evans, Michele, Zonderman, Alan, Launer, Lenore, Sotiras, Aristeidis, Espeland, Mark, Masters, Colin, Maruff, Paul, Fripp, Jurgen, Toga, Arthur, OBryant, Sid, Chakravarty, Mallar, Villeneuve, Sylvia, Johnson, Sterling, Morris, John, Albert, Marilyn, Yaffe, Kristine, Völzke, Henry, Ferrucci, Luigi, Nick Bryan, R, Shinohara, Russell, Fan, Yong, Habes, Mohamad, Lalousis, Paris, Koutsouleris, Nikolaos, Wolk, David, Resnick, Susan, Shou, Haochang, Nasrallah, Ilya, and Davatzikos, Christos

Subjects
Humans, Brain, Aging, Magnetic Resonance Imaging, Male, Female, Aged, Cohort Studies, Middle Aged, Atrophy, Life Style, Adult, Aged, 80 and over
Abstract

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Published
2024

4. TotalVibeSegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank

Author

Graf, Robert, Platzek, Paul-Sören, Riedel, Evamaria Olga, Ramschütz, Constanze, Starck, Sophie, Möller, Hendrik Kristian, Atad, Matan, Völzke, Henry, Bülow, Robin, Schmidt, Carsten Oliver, Rüdebusch, Julia, Jung, Matthias, Reisert, Marco, Weiss, Jakob, Löffler, Maximilian, Bamberg, Fabian, Wiestler, Bene, Paetzold, Johannes C., Rueckert, Daniel, and Kirschke, Jan Stefan

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

Objectives: To present a publicly available torso segmentation network for large epidemiology datasets on volumetric interpolated breath-hold examination (VIBE) images. Materials & Methods: We extracted preliminary segmentations from TotalSegmentator, spine, and body composition networks for VIBE images, then improved them iteratively and retrained a nnUNet network. Using subsets of NAKO (85 subjects) and UK Biobank (16 subjects), we evaluated with Dice-score on a holdout set (12 subjects) and existing organ segmentation approach (1000 subjects), generating 71 semantic segmentation types for VIBE images. We provide an additional network for the vertebra segments 22 individual vertebra types. Results: We achieved an average Dice score of 0.89 +- 0.07 overall 71 segmentation labels. We scored > 0.90 Dice-score on the abdominal organs except for the pancreas with a Dice of 0.70. Conclusion: Our work offers a detailed and refined publicly available full torso segmentation on VIBE images., Comment: https://github.com/robert-graf/TotalVibeSegmentator

Published
2024

7. Histopathological biomarkers for predicting the tumour accumulation of nanomedicines

Author

May, Jan-Niklas, Moss, Jennifer I., Mueller, Florian, Golombek, Susanne K., Biancacci, Ilaria, Rizzo, Larissa, Elshafei, Asmaa Said, Gremse, Felix, Pola, Robert, Pechar, Michal, Etrych, Tomáš, Becker, Svea, Trautwein, Christian, Bülow, Roman D., Boor, Peter, Knuechel, Ruth, von Stillfried, Saskia, Storm, Gert, Puri, Sanyogitta, Barry, Simon T., Schulz, Volkmar, Kiessling, Fabian, Ashford, Marianne B., and Lammers, Twan

Published
2024
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9. Genetic variants for head size share genes and pathways with cancer

Author

Knol, Maria J, Poot, Raymond A, Evans, Tavia E, Satizabal, Claudia L, Mishra, Aniket, Sargurupremraj, Muralidharan, van der Auwera, Sandra, Duperron, Marie-Gabrielle, Jian, Xueqiu, Hostettler, Isabel C, van Dam-Nolen, Dianne HK, Lamballais, Sander, Pawlak, Mikolaj A, Lewis, Cora E, Carrion-Castillo, Amaia, van Erp, Theo GM, Reinbold, Céline S, Shin, Jean, Scholz, Markus, Håberg, Asta K, Kämpe, Anders, Li, Gloria HY, Avinun, Reut, Atkins, Joshua R, Hsu, Fang-Chi, Amod, Alyssa R, Lam, Max, Tsuchida, Ami, Teunissen, Mariël WA, Aygün, Nil, Patel, Yash, Liang, Dan, Beiser, Alexa S, Beyer, Frauke, Bis, Joshua C, Bos, Daniel, Bryan, R Nick, Bülow, Robin, Caspers, Svenja, Catheline, Gwenaëlle, Cecil, Charlotte AM, Dalvie, Shareefa, Dartigues, Jean-François, DeCarli, Charles, Enlund-Cerullo, Maria, Ford, Judith M, Franke, Barbara, Freedman, Barry I, Friedrich, Nele, Green, Melissa J, Haworth, Simon, Helmer, Catherine, Hoffmann, Per, Homuth, Georg, Ikram, M Kamran, Jack, Clifford R, Jahanshad, Neda, Jockwitz, Christiane, Kamatani, Yoichiro, Knodt, Annchen R, Li, Shuo, Lim, Keane, Longstreth, WT, Macciardi, Fabio, Consortium, The Cohorts for Heart and Aging Research in Genomic Epidemiology, Amouyel, Philippe, Arfanakis, Konstantinos, Aribisala, Benjamin S, Bastin, Mark E, Chauhan, Ganesh, Chen, Christopher, Cheng, Ching-Yu, de Jager, Philip L, Deary, Ian J, Fleischman, Debra A, Gottesman, Rebecca F, Gudnason, Vilmundur, Hilal, Saima, Hofer, Edith, Janowitz, Deborah, Jukema, J Wouter, Liewald, David CM, Lopez, Lorna M, Lopez, Oscar, Luciano, Michelle, Martinez, Oliver, Niessen, Wiro J, Nyquist, Paul, Rotter, Jerome I, Rundek, Tatjana, Sacco, Ralph L, Schmidt, Helena, Tiemeier, Henning, Trompet, Stella, van der Grond, Jeroen, Völzke, Henry, Wardlaw, Joanna M, Yanek, Lisa, Yang, Jingyun, and Consortium, The Enhancing NeuroImaging Genetics through Meta-Analysis

Subjects
Biomedical and Clinical Sciences, Stem Cell Research, Human Genome, Cancer, Genetics, Neurosciences, Biotechnology, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Humans, Genome-Wide Association Study, Head, Neoplasms, Female, Male, Polymorphism, Single Nucleotide, Genetic Variation, Organ Size, Signal Transduction, Adult, Genetic Predisposition to Disease, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium, cancer, genetics, genome-wide association study, head circumference, head size, intracranial volume, meta-analysis, Biomedical and clinical sciences
Abstract

The size of the human head is highly heritable, but genetic drivers of its variation within the general population remain unmapped. We perform a genome-wide association study on head size (N = 80,890) and identify 67 genetic loci, of which 50 are novel. Neuroimaging studies show that 17 variants affect specific brain areas, but most have widespread effects. Gene set enrichment is observed for various cancers and the p53, Wnt, and ErbB signaling pathways. Genes harboring lead variants are enriched for macrocephaly syndrome genes (37-fold) and high-fidelity cancer genes (9-fold), which is not seen for human height variants. Head size variants are also near genes preferentially expressed in intermediate progenitor cells, neural cells linked to evolutionary brain expansion. Our results indicate that genes regulating early brain and cranial growth incline to neoplasia later in life, irrespective of height. This warrants investigation of clinical implications of the link between head size and cancer.

Published
2024

13. Generating synthetic high-resolution spinal STIR and T1w images from T2w FSE and low-resolution axial Dixon

Author

Graf, Robert, Platzek, Paul-Sören, Riedel, Evamaria Olga, Kim, Su Hwan, Lenhart, Nicolas, Ramschütz, Constanze, Paprottka, Karolin Johanna, Kertels, Olivia Ruriko, Möller, Hendrik Kristian, Atad, Matan, Bülow, Robin, Werner, Nicole, Völzke, Henry, Schmidt, Carsten Oliver, Wiestler, Benedikt, Paetzold, Johannes C., Rueckert, Daniel, and Kirschke, Jan Stefan

Published
2024
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15. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Zeng, Ling-Li, Ching, Christopher R. K., Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Brosch, Katharina, Bülow, Robin, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Cullen, Kathryn, Dannlowski, Udo, Davey, Christopher G., Dols, Annemiek, Ernsting, Jan, Evans, Jennifer W., Fisch, Lukas, Fuentes-Claramonte, Paola, Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke A., Grotegerd, Dominik, Hahn, Tim, Hamilton, J. Paul, Han, Laura K. M., Harrison, Ben J, Ho, Tiffany C., Jahanshad, Neda, Jamieson, Alec J., Karuk, Andriana, Kircher, Tilo, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Leenings, Ramona, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Meinert, Susanne, Melloni, Elisa, Mueller, Bryon A., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Okamoto, Yasumasa, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Radua, Joaquim, Rodríguez-Cano, Elena, Sacchet, Matthew D., Salvador, Raymond, Schrantee, Anouk, Sim, Kang, Soares, Jair C., Solanes, Aleix, Stein, Dan J., Stein, Frederike, Stolicyn, Aleks, Thomopoulos, Sophia I., Toenders, Yara J., Uyar-Demir, Aslihan, Vieta, Eduard, Vives-Gilabert, Yolanda, Völzke, Henry, Walter, Martin, Whalley, Heather C., Whittle, Sarah, Winter, Nils, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible.

Published
2023

19. Genetic risk factors underlying white matter hyperintensities and cortical atrophy

Author

Patel, Yash, Shin, Jean, Sliz, Eeva, Tang, Ariana, Mishra, Aniket, Xia, Rui, Hofer, Edith, Rajula, Hema Sekhar Reddy, Wang, Ruiqi, Beyer, Frauke, Horn, Katrin, Riedl, Max, Yu, Jing, Völzke, Henry, Bülow, Robin, Völker, Uwe, Frenzel, Stefan, Wittfeld, Katharina, Van der Auwera, Sandra, Mosley, Thomas H., Bouteloup, Vincent, Lambert, Jean-Charles, Chêne, Geneviève, Dufouil, Carole, Tzourio, Christophe, Mangin, Jean-François, Gottesman, Rebecca F., Fornage, Myriam, Schmidt, Reinhold, Yang, Qiong, Witte, Veronica, Scholz, Markus, Loeffler, Markus, Roshchupkin, Gennady V., Ikram, M. Arfan, Grabe, Hans J., Seshadri, Sudha, Debette, Stephanie, Paus, Tomas, and Pausova, Zdenka

Published
2024
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25. Prednisolone Versus Colchicine for Acute Gout in Primary Care: statistical analysis plan for the pragmatic, multicenter, randomized, and double-blinded COPAGO non-inferiority trial

Author

Richter, Adrian, Truthmann, Julia, Hummers, Eva, Pereira, Julia Freyer Martins, Gágyor, Ildikó, Schuster, Franziska, Witte, Amelie, Böhm, Susanne, Greser, Alexandra, Kamin, Petra, Stracke, Sylvia, Dörr, Marcus, Bülow, Robin, Engeli, Stefan, Chenot, Jean François, and Ittermann, Till

Published
2024
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27. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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29. Arts on Prescription’s influence on Sense of Coherence: A one-year follow up controlled study with people having mental health problems

Author

Paula Bergman, Inger Jansson, Pia H Bülow, David F Rusaw, Mikael Skillmark, and Olle Eriksson

Subjects
salutogenesis, well-being, culture and health, arts activities, financial hardship, Arts in general, NX1-820, Medicine (General), R5-920
Abstract

Purpose: To evaluate the long-term effects of an Arts on Prescription (AoP) programme on sense of coherence (SOC) in Sweden. Design: A controlled intervention study with a quasi-experimental prospective design, including questionnaires at baseline, with follow-ups at 6 and 12 months. Participants were on sick leave due to common mental disorders (CMD) and/or non-specific musculoskeletal pain, recruited from primary care in six regions. The intervention group also included participants from open psychiatric care. The study sample comprised 586 participants (335 in the intervention group and 251 in the control group). Results: Both groups showed a significantly stronger SOC at follow-up, with a medium effect size for the intervention group and a small effect size for the control group. No statistically significant difference in SOC change over time was observed between the groups. Participants from open psychiatric care in the intervention group showed a significant improvement in SOC compared to those from primary care. Concerns about finances had a significant main effect on SOC for the entire study population. Originality: Unlike most AoP studies, this controlled study with a relatively large sample provides insights into the long-term effects of AoP on SOC, with results reported using p-values and effect sizes.

Published
2024
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30. Transition from Academic Integrity to Research Integrity: The Use of Checklists in the Supervision of Master and Doctoral Students

Author

Veronika Krásnican, Inga Gaižauskaite, William Bülow, Dita Henek Dlabolova, and Sonja Bjelobaba

Abstract

Given the prevalence of misconduct in research and among students in higher education, there is a need to create solutions for how best to prevent such behaviour in academia. This paper proceeds on the assumption that one way forward is to prepare students in higher education at an early stage and to encourage a smoother transition from academic integrity to research integrity by incorporating academic integrity training as an ongoing part of the curriculum. To this end, this paper presents three checklists developed as part of the Erasmus+ Strategic Partnership project "Bridging Integrity in Higher Education, Business and Society" (BRIDGE, 2020-1-SE01-KA203-077973). The aim of the checklists is to help students and their supervisors to bridge academic integrity and research integrity in research training. The checklists target master students, doctoral students, and their supervisors. This paper presents the theoretical background of the checklists, how they were developed, their content, and how they may be used in supervising thesis/dissertation work to promote a transition from academic integrity to research integrity.

Published
2024
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31. Towards an MLOps Architecture for XAI in Industrial Applications

Author

Faubel, Leonhard, Woudsma, Thomas, Methnani, Leila, Ghezeljhemeidan, Amir Ghorbani, Buelow, Fabian, Schmid, Klaus, van Driel, Willem D., Kloepper, Benjamin, Theodorou, Andreas, Nosratinia, Mohsen, and Bång, Magnus

Subjects
Computer Science - Software Engineering, Computer Science - Artificial Intelligence
Abstract

Machine learning (ML) has become a popular tool in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to streamline this deployment and management process. One of the remaining MLOps challenges is the need for explanations. These explanations are essential for understanding how ML models reason, which is key to trust and acceptance. Better identification of errors and improved model accuracy are only two resulting advantages. An often neglected fact is that deployed models are bypassed in practice when accuracy and especially explainability do not meet user expectations. We developed a novel MLOps software architecture to address the challenge of integrating explanations and feedback capabilities into the ML development and deployment processes. In the project EXPLAIN, our architecture is implemented in a series of industrial use cases. The proposed MLOps software architecture has several advantages. It provides an efficient way to manage ML models in production environments. Further, it allows for integrating explanations into the development and deployment processes.

Published
2023

35. Unwrapping NPT simulations to calculate diffusion coefficients

Author

Bullerjahn, Jakob Tómas, von Bülow, Sören, Heidari, Maziar, Hénin, Jérôme, and Hummer, Gerhard

Subjects
Physics - Computational Physics, Condensed Matter - Statistical Mechanics, Physics - Biological Physics
Abstract

In molecular dynamics simulations in the NPT ensemble at constant pressure, the size and shape of the periodic simulation box fluctuate with time. For particle images far from the origin, the rescaling of the box by the barostat results in unbounded position displacements. Special care is thus required when a particle trajectory is unwrapped from a projection into the central box under periodic boundary conditions to a trajectory in full three-dimensional space, e.g., for the calculation of diffusion coefficients. Here, we review and compare different schemes in use for trajectory unwrapping. We also specify the corresponding rewrapping schemes to put an unwrapped trajectory back into the central box. On this basis, we then identify a scheme for the calculation of meaningful diffusion coefficients, which is a primary application of trajectory unwrapping. In this scheme, the wrapped and unwrapped trajectory are mutually consistent and their statistical properties are preserved. We conclude with advice on best practice for the consistent unwrapping of constant-pressure simulation trajectories and the calculation of accurate translational diffusion coefficients., Comment: 30 pages, 8 figures

Published
2023

41. Data

Author

von Bülow, Friedrich, Volkswagen Aktiengesellschaft, and von Bülow, Friedrich

Published
2024
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43. Related Work

Author

von Bülow, Friedrich, Volkswagen Aktiengesellschaft, and von Bülow, Friedrich

Published
2024
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45. Introduction

Author

von Bülow, Friedrich, Volkswagen Aktiengesellschaft, and von Bülow, Friedrich

Published
2024
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48. Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western populationResearch in context

Author

Matthias Jung, Vineet K. Raghu, Marco Reisert, Hanna Rieder, Susanne Rospleszcz, Tobias Pischon, Thoralf Niendorf, Hans-Ulrich Kauczor, Henry Völzke, Robin Bülow, Maximilian F. Russe, Christopher L. Schlett, Michael T. Lu, Fabian Bamberg, and Jakob Weiss

Subjects
Magnetic resonance imaging, Artificial intelligence, Deep learning, Body composition, Public health, Mortality, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population. Methods: The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body. Findings: In 36,317 UKBB participants (mean age 65.1 ± 7.8 years, age range 45–84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between VSM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81–0.91], p = 0.00023), VSMFF (aHR: 1.06, 95% CI [1.02–1.10], p = 0.0043), and VIMAT (aHR: 1.19, 95% CI [1.05–1.35], p = 0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R = 0.947), at L3 for VAT (R = 0.892), SM (R = 0.944), and at L4 for IMAT (R = 0.546) (all p

Published
2024
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