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arXiv 2022年

作者： Yang, Bowen Goh, Yin Min Sung, Suk Hyun Ye, Gaihua Biswas, Sananda Kaib, David A.S. Dhakal, Ramesh Yan, Shaohua Li, Chenghe Jiang, Shengwei Chen, Fangchu Lei, Hechang He, Rui Valentí, Roser Winter, Stephen M. Hovden, Robert Tsen, Adam W. Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics University of Michigan Ann ArborMI48109 United States Department of Materials Science and Engineering University of Michigan Ann ArborMI48109 United States Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Strasse 1 Frankfurt am Main60438 Germany Department of Physics Center for Functional Materials Wake Forest University Winston-SalemNC27109 United States Department of Physics Beijing Key Laboratory of Opto-electronic Functional Materials & Micro-nano Devices Renmin University of China Beijing100872 China Department of Physics Cornell University IthacaNY United States School of Physics and Astronomy Shanghai Jiao Tong University Shanghai China Applied Physics Program University of Michigan Ann ArborMI48109 United States Department of Chemistry University of Waterloo WaterlooONN2L 3G1 Canada

The Kitaev model is a celebrated spin-½ model on the two-dimensional (2D) honeycomb lattice with bond-dependent Ising interactions1, which features a highly entangled quantum spin liquid (QSL) ground state, fractionalized Majorana excitations, and a series of magnetic-field-induced quantum phase transitions1-7. The search for materials realizing the Kitaev model has been an ongoing challenge for over a decade and may potentially lead to applications in fault-tolerant topological quantum computing8. Yet, the unavoidable presence of non-Kitaev interactions (Heisenberg, off-diagonal, next-nearest neighbor, etc.) almost always drives the ground state away from the QSL phase, and a careful tuning of the exchange parameters is needed9-12. α-RuCl3 is a layered van der Waals material that is a particularly promising candidate to realize Kitaev physics13-15. Although the ground state is zigzag (ZZ) antiferromagnetic (AFM), this ordering can be suppressed with the application of an ~ 6-8T in-plane magnetic field. The presence of a half-integer thermal quantum Hall effect has been reported in this intermediate phase at low temperature16,17, while an unusual continuum of magnetic excitations can be seen even without magnetic field that persists far above the Néel temperature (TN ~ 7-8K)18,19. Both observations hint at α-RuCl3 being in proximity to a QSL, making it a current subject of intense scrutiny. Yet from a theoretical point of view, a QSL induced by in-plane field generally cannot be accounted for, as most calculations for α-RuCl3 show Kitaev phases more broadly emerging from an out-of-plane field6,20-23. Due to the strong "easy-plane" magnetic anisotropy of bulk crystals, however, prohibitively high fields above 30T are required to access such states24-26. The strong coupling between the spin, charge, and lattice degrees of freedom in α-RuCl3 presents an exciting opportunity to tune its magnetic interactions via external perturbations. Although pressure27-29, strain30,

关键词： Ground state

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A Reconfigurable DNA Accordion Rack

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Angewandte Chemie 2018年第11期130卷

作者： Yeongjae Choi Hansol Choi Amos C. Lee Hyunung Lee Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul 08826 Republic of Korea Interdisciplinary Program for Bioengineering Seoul National University 1 Gwanak-ro Gwanak-gu Seoul 08826 Republic of Korea Institute of Entrepreneurial Bio Convergence Seoul National University 1 Gwanak-ro Gwanak-gu Seoul 08826 Republic of Korea Seoul National University Hospital Biomedical Research Institute Seoul National University Hospital 101 Daehak-ro Jongno-gu Seoul 03080 Republic of Korea

DNA nanostructure‐based mechanical systems that control the distance between elements of interest have demonstrated great potential for various applications, including nanoplasmonic systems, molecular reactors, and other nanotechnology platforms. However, previously reported systems could not collectively manipulate a 2D or 3D nanoscale network of elements to various forms in multiple stages. A reconfigurable DNA accordion rack structure is introduced that is a DNA beam lattice that changes its conformation with a small amount of short‐length DNA locks as the controlling input. The lattice shape of the 2D DNA accordion rack and the diameter and the height of the 3D DNA nanotubular structure made of the DNA accordion rack could be controlled. Furthermore, by sequentially repeating the detachment and the attachment of the different DNA locks using strand displacement, the shape reconfiguration was repeatedly carried out.

关键词： DNA-Nanomaschinen DNA-Nanotechnologie DNA-Strukturen Nanoporen Selbstorganisation

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Generation of terahertz transients from Co2Fe0.4Mn0.6Si-Heusler-alloy/normal-metal nanobilayers excited by femtosecond optical pulses

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Physical Review Research 2021年第4期3卷 043025-043025页

作者： Sarah Heidtfeld Roman Adam Takahide Kubota Koki Takanashi Derang Cao Carolin Schmitz-Antoniak Daniel E. Bürgler Fangzhou Wang Christian Greb Genyu Chen Ivan Komissarov Hilde Hardtdegen Martin Mikulics Roman Sobolewski Shigemasa Suga Claus M. Schneider Research Centre Jülich Peter Grünberg Institute 52425 Jülich Germany Faculty of Physics University of Duisburg-Essen 47048 Duisburg Germany Institute for Materials Research Tohoku University Sendai 980-8577 Japan Center for Spintronics Research Network Tohoku University Sendai 980-8577 Japan Center for Science and Innovation in Spintronics (Core Research Cluster) Tohoku University Sendai 980-8577 Japan College of Physics National Demonstration Center for Experimental Applied Physics Education Qingdao University Qingdao 266071 China Materials Science Graduate Program University of Rochester Rochester New York 14627 USA Laboratory for Laser Energetics University of Rochester Rochester New York 14623 USA Department of Electrical and Computer Engineering University of Rochester Rochester New York 14627 USA Belarusian State University of Informatics and Radioelectronics Minsk 220013 Belarus Research Centre Jülich Ernst Ruska Centre for Microscopy and Spectroscopy with Electrons 52425 Jülich Germany Department of Physics and Astronomy University of Rochester Rochester New York 14627 USA The Institute of Scientific and Industrial Research Osaka University Ibaraki Osaka 567-0047 Japan Department of Physics University of California Davis Davis California 95616 USA

We generated pulses of electromagnetic radiation in the terahertz (THz) frequency range by optical excitation of Co2Fe0.4Mn0.6Si (CFMS)/normal-metal (NM) bilayer structures. The CFMS is a Heusler alloy showing a band gap in one spin channel and is therefore a half metal. We compared the THz emission efficiency in a systematic manner for four different CFMS/NM bilayers, where NM was either Pt, Ta, Cr, or Al. Our measurements show that the THz intensity is highest for a Pt capping. We also demonstrate the tunability of the THz amplitude by varying the magnetic field for all four bilayers. We attribute the THz generation to the inverse spin Hall effect. In order to investigate the role of the interface in THz generation, we measured the spin mixing conductance for each CFMS/NM bilayer using a ferromagnetic resonance method. We found that the spin-orbit coupling cannot completely describe the THz generation in the bilayers and that the spin transmission efficiency of the CFMS/NM interface and the spin diffusion length, as well as the oxidation of the NM layer, play crucial roles in the THz emission process.

关键词： Inverse spin Hall effect Magnetism Spin injection Spin pumping Spintronics Ferromagnetic resonance Terahertz time-domain spectroscopy

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Consensus Recommendations for Hyperpolarized [1‐13C]pyruvate MRI Multi-center Human Studies

arXiv

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arXiv 2025年

作者： Punwani, Shonit Larson, Peder E.Z. Laustsen, Christoffer VanderMeulen, Jan Ardenkjær-Larsen, Jan Henrik Autry, Adam W. Bankson, James A. Bernard, Jenna Bok, Robert Bertelsen, Lotte Bonde Che, Jenny Chen, Albert P. Chowdhury, Rafat Comment, Arnaud Cunningham, Charles H. Dang, Duy Gallagher, Ferdia A. Gaunt, Adam Gong, Yangcan Gordon, Jeremy W. Grimmer, Ashley Grist, James Hansen, Esben Søvsø Szocska Lerche, Mathilde Hauge Hesketh, Richard L. Hoevener, Jan-Bernd Hsieh, Ching-Yi Keshari, Kayvan R. Kozerke, Sebastian Lanz, Titus Mayer, Dirk McLean, Mary Park, Jae Mo Slater, Jim Tyler, Damian Vanderheyden, Jean-Luc von Morze, Cornelius Zaccagna, Fulvio Zaha, Vlad Xu, Duan Vigneron, Daniel Department of Radiology University College London Hospital NHS Foundation Trust London United Kingdom Centre for Medical Imaging University College London London United Kingdom Department of Radiology and Biomedical Imaging University of California San FranciscoCA94143 United States The MR Research Center Department of Clinical Medicine Aarhus University Aarhus Denmark Department of Health Services Research & Policy London School of Hygiene & Tropical Medicine London United Kingdom Department of Imaging Physics The University of Texas MD Anderson Cancer Center HoustonTX United States GE Healthcare TorontoON Canada GE HealthCare Pollards Wood Nightingales Lane Chalfont St Giles BuckinghamshireHP8 4SP United Kingdom Physical Sciences Sunnybrook Research Institute TorontoON Canada Department of Medical Biophysics University of Toronto TorontoON Canada Department of Radiology University of Cambridge Cambridge University Hospitals NHS Foundation Trust Cambridge United Kingdom GE Healthcare San FranciscoCA United States Oxford Centre for Clinical Magnetic Resonance Research University of Oxford Oxford United Kingdom Department of Health Technology Technical University of Denmark Kgs. Lyngby2800 Denmark Cancer Research UK Cambridge Institute University of Cambridge Cambridge United Kingdom Section Biomedical Imaging University Hospital Schleswig-Holstein Kiel University Campus Kiel Germany Research Center for Radiation Medicine Chang Gung University Taoyuan Taiwan Department of Medical Imaging and Intervention Chang Gung Memorial Hospital at Linkou Taoyuan Taiwan Department of Radiology Memorial Sloan Kettering Cancer Center New YorkNY United States Molecular Pharmacology Program Memorial Sloan Kettering Cancer Center New YorkNY United States Institute for Biomedical Engineering University and ETH Zurich Zurich Switzerland RAPID Biomedical GmbH Rimpar97222 Germany Department of Diagnostic Radiology and Nuclear Medicine University of Mary

Magnetic resonance imaging of hyperpolarized (HP) [1-13C]pyruvate allows in-vivo assessment of metabolism and has translated into human studies across diseases at 15 centers worldwide. Consensus on best practice for multi-center studies is required to develop clinical applications. This paper presents the results of a 2-round formal consensus building exercise carried out by experts with HP [1-13C]pyruvate human study experience. Twenty-nine participants from 13 sites brought together expertise in pharmacy methods, MR physics, translational imaging, and data-analysis;with the goal of providing recommendations and best practice statements on conduct of multi-center human studies of HP [1-13C]pyruvate MRI. Overall, the group reached consensus on approximately two-thirds of 246 statements in the questionnaire, covering ‘HP 13C-Pyruvate Preparation’, ‘MRI System Setup, Calibration, and Phantoms’, ‘Acquisition and Reconstruction’, and ‘Data Analysis and Quantification’. Consensus was present across categories, examples include that: (i) different HP pyruvate preparation methods could be used in human studies, but that the same release criteria have to be followed;(ii) site qualification and quality assurance must be performed with phantoms and that the same field strength must be used, but that the rest of the system setup and calibration methods could be determined by individual sites;(iii) the same pulse sequence and reconstruction methods were preferable, but the exact choice should be governed by the anatomical target;(iv) normalized metabolite area-under-curve (AUC) values and metabolite AUC were the preferred metabolism metrics. The work confirmed areas of consensus for multi-center study conduct and identified where further research is required to ascertain best practice. © 2025, CC BY.

关键词： Redundancy

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An Open-Source Knowledge Graph Ecosystem for the Life Sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Tissue engineering: Controlled Apoptosis of Stromal Cells to Engineer Human Microlivers (Adv. Funct. Mater. 48/2020)

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Advanced Functional Materials 2020年第48期30卷

作者： Amanda X. Chen Arnav Chhabra Hyun‐Ho Greco Song Heather E. Fleming Christopher S. Chen Sangeeta N. Bhatia Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA 02139 USA Harvard–MIT Program in Health Sciences and Technology Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139 USA Biological Design Center Department of Biomedical Engineering Boston University Boston MA 02215 USA Wyss Institute for Biologically Inspired Engineering Boston MA 02115 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Medicine Brigham and Women's Hospital and Harvard Medical School Boston MA 02115 USA Broad Institute of Massachusetts Institute of Technology and Harvard Cambridge MA 02139 USA Howard Hughes Medical Institute Chevy Chase MD 20815 USA

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scTenifoldXct: A semi-supervised method for predicting cell-cell interactions and mapping cellular communication graphs

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Cell systems 2023年第4期14卷 302-311.e4页

作者： Yongjian Yang Guanxun Li Yan Zhong Qian Xu Yu-Te Lin Cristhian Roman-Vicharra Robert S Chapkin James J Cai Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA. Department of Statistics Texas A&M University College Station TX 77843 USA. Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE School of Statistics East China Normal University 3663 North Zhongshan Road Shanghai 200062 China. Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA. Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan. Department of Nutrition and the Program in Integrative Nutrition & Complex Diseases Texas A&M University College Station TX 77843 USA. Electronic address: r-chapkin@tamu.edu. Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Department of Veterinary Integrative Biosciences Texas A&M University College Station TX 77843 USA Interdisciplinary Program of Genetics Texas A&M University College Station TX 77843 USA. Electronic address: jcai@tamu.edu.

We present scTenifoldXct, a semi-supervised computational tool for detecting ligand-receptor (LR)-mediated cell-cell interactions and mapping cellular communication graphs. Our method is based on manifold alignment, using LR pairs as inter-data correspondences to embed ligand and receptor genes expressed in interacting cells into a unified latent space. Neural networks are employed to minimize the distance between corresponding genes while preserving the structure of gene regression networks. We apply scTenifoldXct to real datasets for testing and demonstrate that our method detects interactions with high consistency compared with other methods. More importantly, scTenifoldXct uncovers weak but biologically relevant interactions overlooked by other methods. We also demonstrate how scTenifoldXct can be used to compare different samples, such as healthy vs. diseased and wild type vs. knockout, to identify differential interactions, thereby revealing functional implications associated with changes in cellular communication status.

关键词： cell-cell interaction cellular communication gene regression network machine learning manifold alignment neural networks scRNA-seq single-cell RNA sequencing

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Biomimetics: Biomimetic Microfingerprints for Anti‐Counterfeiting Strategies (Adv. Mater. 12/2015)

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Advanced Materials 2015年第12期27卷

作者： Hyung Jong Bae Sangwook Bae Cheolheon Park Sangkwon Han Junhoi Kim Lily Nari Kim Kibeom Kim Suk‐Heung Song Wook Park Sunghoon Kwon Department of Electrical and Computer Engineering Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Center for Nanoparticle Research Institute for Basic Science (IBS) Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Institute of Entrepreneurial Bio Convergence Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Interdisciplinary Program for Bioengineering Seoul National University Gwanak‐ro Gwanak‐gu Seoul 151‐744 South Korea Department of Electronics and Radio Engineering Institute for Laser Engineering Kyung Hee University Deongyeong‐daero Giheung‐gu Yongin‐si Gyeonggi‐do 446‐701 South Korea

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Charting brain growth in tandem with brain templates at school age

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Science Bulletin 2020年第22期65卷 1924-1934,M0004页

作者： Hao-Ming Dong F.Xavier Castellanos Ning Yang Zhe Zhang Quan Zhou Ye He Lei Zhang Ting Xu Avram J.Holmes B.T.Thomas Yeo Feiyan Chen Bin Wang Christian Beckmann Tonya White Olaf Sporns Jiang Qiu Tingyong Feng Antao Chen Xun Liu Xu Chen Xuchu Weng Michael P.Milham Xi-Nian Zuo Research Center for Lifespan Development of Brain and Mind Institute of PsychologyChinese Academy of Sciences(CAS)Beijing 100101China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Department of Child and Adolescent Psychiatry New York University School of MedicineNew YorkNY 10016USA Nathan Kline Institute for Psychiatric Research OrangeburgNew YorkNY 10962USA Department of Psychology University of CASBeijing 100049China Department of Psychological and Brain Sciences Indiana UniversityBloomingtonIN 47405USA Child Mind Institute New YorkNY 10022USA Department of Psychology Yale UniversityNew HavenCT 06511USA Department of Electrical and Computer Engineering Clinical Imaging Research CentreN.1 Institute for Health and Memory Networks ProgramNational University of SingaporeSingapore 117574Singapore Department of Physics Zhejiang UniversityHangzhou 310058China Binzhou Medical University Yantai 264100China Donders Institute Radboud University Medical CentreNijmegen 6525 ENNetherlands Department of Child and Adolescent Psychiatry Erasmus UniversityRotterdam 3000 CBNetherlands Department of Radiology Erasmus University Medical CentreRotterdam 3000 CANetherlands School of Psychology Southwest UniversityChongqing 400715China Institute for Brain Research and Rehabilitation South China Normal UniversityGuangzhou 510631China Institute of Children Health Changzhou Children’s HospitalChangzhou 213003China Key Laboratory of Brain and Education School of Education ScienceNanning Normal UniversityNanning 530001China CAS Key Laboratory of Behavioral Science Institute of PsychologyBeijing 100101China IDG/McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China

Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental *** present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging *** accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and *** magnetic resonance imaging(MRI)of typically developing school-age children(TDC)was obtained up to three times at nominal intervals of1.25 *** protocol generated and compared population-and age-specific brain templates and growth charts,respectively.A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American ***-and age-specific brain templates were used to quantify warp cost,the differences between individual brains and brain *** growth charts for labeled brain network areas were *** analyses of cost functions supported the necessity of age-specific and ethnicitymatched brain templates,which was confirmed by growth chart *** analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas,regions which are most variable across individuals in regard to their structure and ***-and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts,indicating the necessity of the brain charts and brain templates generated in *** templates and growth charts as well as related codes have been made freely available to the public for open neuroscience(https://***/zuoxinian/CCS/tree/master/H3/Growth Charts).

关键词： Growth charts Neuroimaging Ethnicity Brain templates Development

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OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

arXiv

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arXiv 2022年

作者： Babier, Aaron Mahmood, Rafid Zhang, Binghao Alves, Victor G.L. Barragán-Montero, Ana Maria Beaudry, Joel Cardenas, Carlos E. Chang, Yankui Chen, Zijie Chun, Jaehee Diaz, Kelly Eraso, Harold David Faustmann, Erik Gaj, Sibaji Gay, Skylar Gronberg, Mary Guo, Bingqi He, Junjun Heilemann, Gerd Hira, Sanchit Huang, Yuliang Ji, Fuxin Jiang, Dashan Giraldo, Jean Carlo Jimenez Lee, Hoyeon Lian, Jun Liu, Shuolin Liu, Keng-Chi Marrugo, José Miki, Kentaro Nakamura, Kunio Netherton, Tucker Nguyen, Dan Nourzadeh, Hamidreza Osman, Alexander F.I. Peng, Zhao Muñoz, José Darío Quinto Ramsl, Christian Rhee, Dong Joo Rodriguez, Juan David Shan, Hongming Siebers, Jeffrey V. Soomro, Mumtaz H. Sun, Kay Hoyos, Andrés Usuga Valderrama, Carlos Verbeek, Rob Wang, Enpei Willems, Siri Wu, Qi Xu, Xuanang Yang, Sen Yuan, Lulin Zhu, Simeng Zimmermann, Lukas Moore, Kevin L. Purdie, Thomas G. McNiven, Andrea L. Chan, Timothy C.Y. Department Of Mechanical And Industrial Engineering University Of Toronto TorontoON Canada Vector Institute TorontoON Canada Department Of Radiation Oncology University Of Virginia Health System CharlottesvilleVA United States Department Of Molecular Imaging Radiation Oncology UCLouvain Louvain-la-Neuve Belgium Department Of Radiation Oncology Memorial Sloan Kettering Cancer Center New YorkNY United States Department Of Radiation Oncology The University Of Alabama At Birmingham BirminghamAL United States Department Of Engineering And Applied Physics University Of Science And Technology Of China Hefei China Shenying Medical Technology Co. Ltd. Guangdong Shenzhen China Department Of Radiation Oncology Yonsei University Seoul Korea Republic of Department Of Physics National University Of Colombia Medellín Colombia Atominstitut Vienna University Of Technology Vienna Austria Department Of Biomedical Engineering Cleveland Clinic ClevelandOH United States Department Of Radiation Physics The University Of Texas MD Anderson Cancer Center HoustonTX United States Department Of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Department Of Radiation Oncology Medical University Of Vienna Vienna Austria Department Of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Department Of Radiation Oncology Peking University Cancer Hospital And Institute Beijing China Department Of Electrical Engineering And Automation Anhui University Hefei China Department Of Radiation Oncology Massachusetts General Hospital BostonMA United States Department Of Radiation Oncology University Of North Carolina At Chapel Hill Chapel HillNC United States Department Of Medical Imaging Taiwan AI Labs Taipei Taiwan Department Of Biomedical And Health Sciences Hiroshima University Hiroshima Japan Laboratory Department Of Radiation Oncology The University Of Texas Southwestern Medical Center DallasTX United States Department Of Radiatio

We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved;the deviation in dosevolume histogram (DVH) criterion;and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBPgenerated plans performed significantly better (P © 2022, CC BY.

关键词： Forecasting

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