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How childhood adversities shape minds and lives: An analysis across the affective-to-psychotic spectrum

Psychiatry Research 2025年 350卷 116536页

作者： Greiner, Sophie-Kathrin Pons, María Dech Ablimit, Ayimnisagul Brauße, Elisa Adorjan, Kristina Budde, Monika Heilbronner, Maria Heilbronner, Urs Kalman, Janos L. Navarro-Flores, Alba Kohshour, Mojtaba Oraki Reich-Erkelenz, Daniela Schulte, Eva C. Vogl, Thomas Andlauer, Till Anghelescu, Ion-George Arolt, Volker Baune, Bernhardt T. Dannlowski, Udo Degenhardt, Franziska Dietrich, Detlef E. Fallgatter, Andreas J. Figge, Christian Forstner, Andreas Jäger, Markus Juckel, Georg Konrad, Carsten Nöthen, Markus M. Lang, Fabian U. Reimer, Jens Reinighaus, Eva Z. Rietschel, Marcella Schmauß, Max Schmitt, Andrea Senner, Simon Spitzer, Carsten Wiltfang, Jens Witt, Stephanie H. Zimmermann, Jörg Hasan, Alkomiet Falkai, Peter Schulze, Thomas G. Papiol, Sergi Senner, Fanny Department of Psychiatry Psychotherapy and Psychosomatics Faculty of Medicine University of Augsburg Augsburg Germany Department of Psychiatry and Psychotherapy LMU University Hospital LMU Munich Munich Germany Cognitive Systems Lab Mathematics and Computer Science University of Bremen Bremen Germany Institute of Psychiatric Phenomics and Genomics (IPPG) LMU University Hospital LMU Munich Munich Germany University Hospital of Psychiatry and Psychotherapy University of Bern Bern Switzerland International Max Planck Research School for Translational Psychiatry (IMPRS-TP) Munich Germany Department of Immunology Faculty of Medicine Ahvaz Jundishapur University of Medical Sciences Ahvaz Iran Laboratory of Neuroscience (LIM27) Institute of Psychiatry University of Sao Paulo SP São Paulo Brazil Institute of Human Genetics University Hospital Bonn Faculty of Medicine University of Bonn Bonn Germany Department of Neurology Klinikum rechts der Isar School of Medicine Technical University of Munich Munich Germany Department of Psychiatry and Psychotherapy Mental Health Institute Berlin Berlin Germany Institute for Translational Psychiatry University of Münster Münster Germany Department of Psychiatry University of Münster Münster Germany Department of Psychiatry Melbourne Medical School The University of Melbourne Melbourne Australia The Florey Institute of Neuroscience and Mental Health The University of Melbourne Parkville VIC Australia Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany Department of Child and Adolescent Psychiatry Psychosomatics and Psychotherapy University of Essen University of Duisburg-Essen Duisburg Germany AMEOS Clinical Center Hildesheim Hildesheim Germany Center for Systems Neuroscience (ZSN) Hannover Germany Department of Psychiatry Social Psychiatry and Psychotherapy Medical School of Hannover Hannover Germany Department of Psychiatry and Psychotherapy Tübingen Cen

Adverse childhood experiences (ACE) contribute significantly to mental disorders. While existing research has primarily focused on specific diagnostic categories, a comprehensive understanding of how childhood trauma interacts with biological factors, symptom severity and functioning requires a broader perspective. Therefore, this study adopted a cross-diagnostic approach to examine the impact of ACE on quality of life (QoL), psychosocial functioning, and symptom burden by analyzing data from the PsyCourse Study, a longitudinal, multicenter research project conducted in Germany and Austria. We used multivariate linear regression models and cluster analysis to evaluate data from 725 participants with affective and psychotic disorders and healthy controls who completed the self-assessed Childhood Trauma Screener (CTS) during the course of the study. The results showed that across diagnoses, QoL was significantly impacted by ACE, particularly emotional neglect. An ablation study revealed that 2.3 % to 6.2 % of the variability in QoL domains could be attributed to ACE. Across diagnoses, symptoms of depression were significantly associated with ACE, especially emotional abuse, but psychotic and manic symptoms were not. Polygenic risk scores (PRS) did not emerge as significant predictors for any examined outcomes. Cluster analysis revealed distinct symptom profiles: Averaged over time, patients with less trauma exposure were rather in the subclinical than in the clinically ill clusters. We conclude that the pervasive influence of ACE on disease severity should be considered when evaluating and treating patients with affective and psychotic disorders. © 2025 The Author(s)

关键词： Adverse childhood experiences Affective disorders Global functioning Psychotic disorders Quality of life

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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Foundation versus domain-specific models for left ventricular segmentation on cardiac ultrasound

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NPJ digital medicine 2025年第1期8卷 341页

作者： Chieh-Ju Chao Yunqi Richard Gu Wasan Kumar Tiange Xiang Lalith Appari Justin Wu Juan M Farina Rachael Wraith Jiwoon Jeong Reza Arsanjani Garvan C Kane Jae K Oh Curtis P Langlotz Imon Banerjee Li Fei-Fei Ehsan Adeli Department of Cardiovascular Medicine Mayo Clinic Rochester Rochester MN USA. Stanford Institute for Human-Centered Artificial Intelligence Stanford University Stanford CA USA. Department of Radiology Mayo Clinic Arizona Scottsdale AZ USA. School of Computing and Augmented Intelligence Arizona State University Phoenix AZ USA. Department of Cardiovascular Medicine Mayo Clinic Arizona Scottsdale AZ USA. Center of Artificial Intelligence in Medical Imaging Stanford University Stanford CA USA. Department of Radiology Stanford University Stanford CA USA. Stanford Institute for Human-Centered Artificial Intelligence Stanford University Stanford CA USA. feifeili@cs.stanford.edu. Department of Computer Science Stanford University Stanford CA USA. feifeili@cs.stanford.edu. Stanford Institute for Human-Centered Artificial Intelligence Stanford University Stanford CA USA. eadeli@stanford.edu. Department of Computer Science Stanford University Stanford CA USA. eadeli@stanford.edu. Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. eadeli@stanford.edu.

The Segment Anything Model (SAM) was fine-tuned on the EchoNet-Dynamic dataset and evaluated on external transthoracic echocardiography (TTE) and Point-of-Care Ultrasound (POCUS) datasets from CAMUS (University Hospital of St Etienne) and Mayo Clinic (99 patients: 58 TTE, 41 POCUS). Fine-tuned SAM was superior or comparable to MedSAM. The fine-tuned SAM also outperformed EchoNet and U-Net models, demonstrating strong generalization, especially on apical 2-chamber (A2C) images (fine-tuned SAM vs. EchoNet: CAMUS-A2C: DSC 0.891 ± 0.040 vs. 0.752 ± 0.196, p < 0.0001) and POCUS (DSC 0.857 ± 0.047 vs. 0.667 ± 0.279, p < 0.0001). Additionally, SAM-enhanced workflow reduced annotation time by 50% (11.6 ± 4.5 sec vs. 5.7 ± 1.7 sec, p < 0.0001) while maintaining segmentation quality. We demonstrated an effective strategy for fine-tuning a vision foundation model for enhancing clinical workflow efficiency and supporting human-AI collaboration.

关键词： Echocardiography

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Approaching Ocular Risks during Spaceflight with 3D Printing: Technical Strategies to Protect Astronaut Vision

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Life sciences in Space Research 2025年

作者： Saif Pasha Joshua Ong Yannie Guo Ryung Lee Ethan Waisberg Andrew G. Lee Prithul Sarker Alireza Tavakkoli Virginia Tech Carilion School of Medicine Roanoke Virginia USA Department of Ophthalmology and Visual Sciences University of Michigan Kellogg Eye Center Ann Arbor Michigan USA Baylor College of Medicine Houston Texas USA Touro College of Osteopathic Medicine New York NY USA Department of Clinical Neuroscience University of Cambridge Cambridge UK Center for Space Medicine Baylor College of Medicine Houston Texas USA Department of Ophthalmology Blanton Eye Institute Houston Methodist Hospital Houston Texas USA The Houston Methodist Research Institute Houston Methodist Hospital Houston Texas USA Departments of Ophthalmology Neurology and Neurosurgery Weill Cornell Medicine New York New York USA Department of Ophthalmology University of Texas Medical Branch Galveston Texas USA University of Texas MD Anderson Cancer Center Houston Texas USA Texas A&M School of Medicine Bryan Texas USA Department of Ophthalmology The University of Iowa Hospitals and Clinics Iowa City Iowa USA Human-Machine Perception Laboratory Department of Computer Science and Engineering University of Nevada Reno NV USA Department of Computer Science University of Nevada Reno Reno Nevada USA

Long-duration spaceflight poses significant risks to ocular health due to prolonged microgravity exposure and space environmental stressors, contributing to conditions such as Spaceflight-Associated Neuro-ocular Syndrome (SANS) and Spaceflight-Associated Dry Eye Syndrome (SADES). These conditions, along with radiation exposure and risk of ocular trauma in resource-limited environments, necessitate development of innovative countermeasures to safeguard astronauts' vision, particularly for future planetary missions such as Mars. Traditional ophthalmic care depends on specialized equipment and materials impractical for cargo limitations and transport in space, highlighting the need for adaptive solutions. Advances in 3D printing and bioprinting offer an innovative approach to space ophthalmology by enabling on-demand fabrication of customized eyewear, contact lenses, moisture chambers, radiation-shielding lenses, and surgical tools. Furthermore, emerging bioprinting capabilities may facilitate production of biocompatible tissues for ocular repair. The precision, adaptability, and mission-specific applicability of 3D printing provide a strategic advantage to address preventive and therapeutic ocular health needs. However, challenges include optimizing biocompatible materials, refining high-resolution printing techniques, and ensuring structural and functional viability of printed tissues in space conditions. Further research is required to improve material durability, integrate protective elements such as boron nitride nanotubes, and adapt 3D printing processes to the constraints of microgravity. Beyond space medicine, 3D printing applications in space can drive innovations for ophthalmic care on Earth, from customized intraocular lenses to regenerative therapies. This review highlights the critical role of 3D printing in space ophthalmology and need for continued development and deployment to ensure success of future deep-space missions.

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The micro-randomized trial for developing digital interventions: Data analysis methods

arXiv

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arXiv 2020年

作者： Qian, Tianchen Russell, Michael A. Collins, Linda M. Klasnja, Predrag Lanza, Stephanie T. Yoo, Hyesun Murphy, Susan A. Department of Statistics Harvard University Methodology Center Department of Biobehavioral Health Pennsylvania State University Methodology Center Department of Human Development & Family Studies Pennsylvania State University School of Information University of Michigan Kaiser Permanente Washington Health Research Institute Edna Bennett Pierce Prevention Research Center Department of Biobehavioral Health Pennsylvania State University Department of Statistics University of Michigan Departments of Statistics and Computer Science Harvard University

Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, data analysis methods for constructing and optimizing digital interventions lag behind. Here, we elucidate data analysis methods for primary and secondary analyses of micro-randomized trials (MRTs), an experimental design to optimize digital just-in-time adaptive interventions. We provide a definition of causal "excursion" effects suitable for use in digital intervention development. We introduce the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators for digital interventions from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as SAS (SAS Institute Inc., 2019) or R (R Core Team, 2019). Throughout we use HeartSteps, an MRT designed to increase physical activity among sedentary individuals, to illustrate potential primary and secondary analyses. Copyright © 2020, The Authors. All rights reserved.

关键词： Statistical tests

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Internal lower limb rotation increases patella cartilage pressure in individuals with patellofemoral instability

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Gait & Posture 2023年 106卷 S71-S72页

作者： Guggenberger, Bernhard Horsak, Brian Habersack, Andreas Smith, Colin Svehlik, Martin Kainz, Hans Department of Orthopaedics and Trauma Medical University of Graz Graz Austria Neuromechanics Research Group Department of Biomechanics- Kinesiology and Computer Science in Sport- Centre for Sport Science and University Sports Vienna Austria Institute of Physiotherapy JOANNEUM University of Applied Sciences Graz Austria Center of Digital Health and Social Innovation St. Pölten University of Applied Sciences St. Pölten Austria Institute of Human Movement Science Sport and Health- University of Graz Graz Austria Department of Biomedical Engineering Steadman Philippon Research Institute Vail- CO USA

IntroductionFemoral anteversion (FA) and tibial torsion (TT) are known factors to contribute to patellofemoral instability [1]. Studies showed increased knee joint loading in individuals with idiopathic rotational deformities [2]. Investigating the effect of FA and TT on patellofemoral joint loading and lower leg muscle forces could help to better understand their impact on patellofemoral instability. Research questionHow do FA and TT influence the knee joint loading and muscle force in individuals with patellofemoral instability? MethodsMusculoskeletal simulations were performed based on the retrospective data set of 11 individuals diagnosed with patellofemoral instability (13 affected knees). We measured FA and TT from magnetic resonance images. The mean FA and TT were 27±11° and 31±8°, respectively. To account for the interaction of FA and TT, we calculated Lower Limb Rotation (LLR=TT-FA) [3]. The mean LLR value was 4±12°. For each individual an OpenSim model [4] was scaled for height, weight and maximum isometric muscle force [5]. We performed simulations for each participant based on a generic-scaled model and a second model with personalized FA and TT [6]. Kinematics, kinetics, muscle forces and patella cartilage pressure were estimated using the COMAK routine [7]. For muscle forces and patella cartilage loading, we calculated differences between both models for each participant and correlated them to LLR, FA and TT using Pearson correlation (α=0.05). ResultsLLR correlated to patella cartilage loading during different phases of stance (Fig. 1). No significant correlations were found between patella cartilage loading, FA and TT in our preliminary results. A more internally rotated LLR correlated with higher rectus femoris (r=-0.79, p=0.001) and gluteus minimus force (r=-0.77, p=0.002) as well as lower gluteus maximus (r=0.90, p<0.001) and gluteus medius force (r=0.71, p=0.007). Fig. 1 - Correlation between LLR, FA, TA and patella cartilage pressure. Bold values

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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What motivates bladder cancer patients to be active? A qualitative study assessing attitudes towards physical activity and digital health technologies

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Urologic oncology 2025年

作者： Alex Zhu Jessica Sher Richard Li Otari Ioseliani Leah Cantor Elena G Brewer Karla Landis David Bridges Sean A Munson Hanna Hunter Cindy Lin Sarah P Psutka Department of Urology University of Washington Seattle WA. Department of Rehabilitation Medicine University of Washington Seattle WA. University of Washington Paul G. Allen School of Computer Science & Engineering Seattle WA. The Sports Institute at UW Medicine Seattle WA. Department of Human Centered Design & Engineering University of Washington Seattle WA. Department of Rehabilitation Medicine University of Washington Seattle WA Clinical Research Division Fred Hutchison Cancer Center Seattle WA. Department of Urology University of Washington Seattle WA Clinical Research Division Fred Hutchison Cancer Center Seattle WA. Electronic address: spsutka@uw.edu.

INTRODUCTION AND OBJECTIVE:Patients undergoing neoadjuvant chemotherapy (NAC) and radical cystectomy (RC) are often older, frail and deconditioned, leaving them with limited reserve to endure the demands of treatment. As a result, improving physical fitness in this population remains a critical need. Two emerging means to do so include the implementation of exercise programs pre- and postsurgery (i.e. (p)rehabilitation), and the use of digital health technologies (i.e. Fitbit®, Apple Watch™, exercise apps) to incentivize physical activity. However, little is known about the motivations of NAC/RC patients to be active or their perceptions towards digital health technologies. METHODS:We conducted semi-structured interviews with bladder cancer survivors who previously underwent NAC/RC. We asked participants to reflect upon their experience throughout the course of treatment as it pertained to (1) barriers of physical activity, (2) facilitators of physical activity, and (3) how digital health technologies can promote physical activity. These findings will inform the design of a Phase II exercise prehabilitation trial (GetMoving, NCT06040762). RESULTS:9 patients were interviewed and common themes emerged. Patients undergoing NAC/RC desired a structured exercise plan outlining the duration, intensity and frequency of exercise to perform. Encouragement to exercise and "check ins" from their provider were critical in maintaining their motivation to exercise. Objective measurements of their fitness would help with setting fitness goals and adhering to training plans. All patients would use digital health technologies to improve their health. CONCLUSIONS:We provide a novel study assessing the perceptions of bladder cancer survivors towards exercise motivations and digital health technologies.

关键词： Digital health technology Exercise Muscle invasive bladder cancer Neoadjuvant chemotherapy Physical activity Radical cystectomy

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Corrigendum to “Review of artificial neural networks-contribution methods integrated with structural equation modeling and multi-criteria decision analysis for selection customization” [Eng. Appl. Artif. Intell. 124 (2023) 106643]

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Engineering Applications of Artificial Intelligence 2024年 128卷

作者： A.A. Zaidan Alhamzah Alnoor O.S. Albahri R.T. Mohammed A.H. Alamoodi A.S. Albahri B.B. Zaidan Salem Garfan Hamsa Hameed Mohammed S. Al-Samarraay Ali Najm Jasim R.Q. Malik SP Jain School of Global Management Sydney Australia Management Technical College Southern Technical University Basrah Iraq Department of Computer Science and Information Technology La Trobe University Melbourne VIC Australia Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq Department of Computing Science Faculty of Science Komar University of Science and Technology (KUST) Sulaymaniyah Iraq Department of Computing and META Technology (FKMT) Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia MEU Research Unit Middle East University Amman Jordan Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan Faculty of Human Development Sultan Idris University of Education (UPSI) Tanjung Malim Malaysia Foundation of Alshuhda Baghdad Iraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq

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Perspectives on adaptive dynamical systems

arXiv

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arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

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