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arXiv 2022年

作者： Bari, Sumra Vike, Nicole L. Stetsiv, Khrystyna Blood, Anne J. Nauman, Eric A. Talavage, Thomas M. Slobounov, Semyon Breiter, Hans C. Department of Computer Science University of Cincinnati CincinnatiOH United States Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences Feinberg School of Medicine Northwestern University ChicagoIL United States Mood and Motor Control Laboratory Departments of Neurology and Psychiatry Massachusetts General Hospital Harvard Medical School BostonMA02129 United States Laboratory of Neuroimaging and Genetics Department of Psychiatry Massachusetts General Hospital Harvard Medical School BostonMA02129 United States Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital Harvard Medical School BostonMA02129 United States Department of Biomedical Engineering University of Cincinnati CincinnatiOH United States Department of Kinesiology Pennsylvania State University University ParkPA United States The Pennsylvania State University 268P Recreation Building University ParkPA16802 United States Department of Computer Science College of Engineering and Applied Science University of Cincinnati 2901 Woodside Drive CincinnatiOH45219 United States

Abnormalities in motor-control behavior, which have been associated with concussion and head acceleration events (HAE), can be quantified using virtual reality (VR) technologies. Motor-control behavior has been consistently mapped to the brain’s somatomotor network (SM) using both structural (sMRI) and functional MRI (fMRI). However, no studies have integrated HAE, motor-control behavior, sMRI and fMRI measures. Here, brain networks important for motor-control were hypothesized to show changes in tractography-based diffusion weighted imaging [difference in fractional anisotropy (∆FA)] and resting-state fMRI (rs-fMRI) measures in collegiate American football players across the season, and that these measures would relate to VR-based motor-control. We further tested if nine inflammation-related miRNAs were associated with behavior-structure-function variables. Using permutation-based mediation/moderation methods, we found that across-season ∆FA from the SM structural connectome (SM-∆FA) mediated the relationship between across-season VR-based Sensory-motor Reactivity (∆SR) and rs-fMRI SM fingerprint similarity (psobelperm = 0.007 and Teff = 47%). The interaction between ∆SR and SM-∆FA also predicted (pFPerm= 0.036, PB₃perm = 0.058) across-season levels of ∆miRNA-30d through permutation-based moderation analysis. These results suggest (1) that motor-control is in a feedback relationship with brain structure and function, (2) behavior-structure-function can be connected to HAE, and (3) behavior-structure might predict molecular biology measures. © 2022, CC BY.

关键词： Molecular biology

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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PID2018 Benchmark Challenge: Multi-Objective Stochastic Optimization Algorithm

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IFAC-PapersOnLine 2018年第4期51卷 877-881页

作者： Ates, Abdullah Yuan, Jie Dehghan, Sina Zhao, Yang Yeroglu, Celaleddin Chen, YangQuan Inonu University Computer Engineering Department Malatya44280 Turkey School of Automation Southeast University Nanjing210096 China UC Merced Mechanical Engineering Departments MESA Lab MercedCA95301 United States School of Control Science and Engineering at Shandong University Jinan250061 China

This paper presents a multi-objective stochastic optimization method for tuning of the controller parameters of Refrigeration Systems based on Vapour Compression. Stochastic Multi Parameter Divergence Optimization (SMDO) algorithm is modified for minimization of the Multi Objective function for optimization process. System control performance is improved by tuning of the PI controller parameters according to discrete time model of the refrigeration system with multi objective function by adding conditional integral structure that is preferred to reduce the steady state error of the system. Simulations are compared with existing results via many graphical and numerical solutions. © 2018

关键词： Multiobjective optimization

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Perturbed Tumor Immunotherapy Domain of Attraction Estimation via the Arc-Length Function

Perturbed Tumor Immunotherapy Domain of Attraction Estimatio...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mojtaba Zarei Kimia Javadi Ahmad Kalhor Cyber-Physical Systems Lab (CPSL) Departments of Electrical and Computer Engineering Duke University NC USA Faculty of Computer and Electrical Engineering K.N.Toosi University of Technology Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

This paper aims at the estimation of the Domain of Attraction (DoA) of the free tumor equilibrium point of perturbed tumor immunotherapy model via the Arc-Length Function (ALF). The ALFs are categorized among the maximal Lyapunov functions which are able to provide a more accurate estimation of the DoA in comparison to their other counterparts such as Rational Lyapunov Functions (RLFs), Sum Of Square (SOS) polynomial Lyapunov functions, and Optimal Quadratic Lyapunov Functions (OQLFs). There is no analytical method to construct the ALFs, however, some numerical methods have been proposed in the literature. Based on the existing method, one can approximate the ALF with a certain degree of a polynomial function. That the system under study has a polynomial structure was the main basis of the previously proposed method to estimate the DoA via the ALFs. However, the intended model in this paper describing the tumor-immune system competition dynamics contains non-polynomial terms. To cope with the aforementioned problem, the Taylor expansion of the non-polynomial terms are considered and by solving an optimization problem, one can calculate the corresponding lower boundary of the level set with the approximated ALF as an estimation of the DoA. In order to represent the performance of the employed method, the obtained result is compared with the reported result in the literature.

关键词： Tumors Direction-of-arrival estimation Lyapunov methods Estimation Immune system Mathematical model Cancer

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Pid2018 benchmark challenge: Multi-objective stochastic optimization algorithm

arXiv

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arXiv 2018年

关键词： Stochastic systems

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Self-Induced Backaction Optical Pulling Force

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Physical Review Letters 2018年第12期120卷 123901-123901页

作者： Tongtong Zhu Yongyin Cao Lin Wang Zhongquan Nie Tun Cao Fangkui Sun Zehui Jiang Manuel Nieto-Vesperinas Yongmin Liu Cheng-Wei Qiu Weiqiang Ding Department of Physics Harbin Institute of Technology Harbin 150001 China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi Province College of Physics and Optoelectronics Taiyuan University of Technology Taiyuan 030024 China Department of Biomedical Engineering Dalian University of Technology Dalian 116024 China Instituto de Ciencia de Materiales de Madrid Consejo Superior de Investigaciones Científicas Campus de Cantoblanco Madrid 28049 Spain Departments of Mechanical and Industrial Engineering and Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA

We achieve long-range and continuous optical pulling in a periodic photonic crystal background, which supports a unique Bloch mode with the self-collimation effect. Most interestingly, the pulling force reported here is mainly contributed by the intensity gradient force originating from the self-induced backaction of the object to the self-collimation mode. This force is sharply distinguished from the widely held conception of optical tractor beams based on the scattering force. Also, this pulling force is insensitive to the angle of incidence and can pull multiple objects simultaneously.

关键词： Electromagnetism Mechanical effects of light on material media Photonic crystals Optical microcavities Optical tweezers

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Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission

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四川生理科学杂志 2022年第6期44卷 1118-1118页

作者： Smruthi Karthikeyan Department of Pediatrics University of California San Diego La Jolla CA USA Department of Biomedical Informatics University of California San Diego La Jolla California USA Department of Computer Science and Engineering University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Department of Immunology and Microbiology The Scripps Research Institute La Jolla CA USA Expedited COVID Identification Environment (EXCITE) Laboratory Department of Pediatrics University of California San Diego La Jolla CA USA Department of Obstetrics Gynecology and Reproductive Sciences University of California San Diego La Jolla CA USA COVID-19 Detection Investigation Surveillance Clinical and Outbreak Response California Department of Public Health Richmond CA USA Sanford Consortium of Regenerative Medicine University of California San Diego La Jolla CA USA Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Student Health and Well-Being University of California San Diego La Jolla CA USA Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA USA Institute for Genomic Medicine University of California San Diego La Jolla CA USA Operational Strategic Initiatives University of California San Diego La Jolla CA USA Return to Learn University of California San Diego La Jolla CA USA Student Affairs University of California San Diego La Jolla CA USA Academic Affairs University of California San Diego La Jolla CA USA Department of Pathology University of California San Diego La Jolla CA USA Scripps Health San Diego La Jolla CA USA Rady Children’s Institute for Genomic Medicine San Diego CA USA Sharp Healthcare San Diego CA USA Helix San Mateo CA USA County of San Diego Health and Human Services Agency San Diego CA USA Herbert Wertheim School of Public Health and Human Longevity Science Univers

As SARS-CoV-2 continues to spread and evolve,detecting emerging variants early is critical for public health *** lineage prevalence by clinical testing is infeasible at scale,especially in areas with limited resources,participation,or testing/sequencing capacity,which can also introduce ***-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,*** virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants.

关键词： wastewater Wastewater testing

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Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient

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Journal of Medical engineering and Technology 2016年第3期40卷 87-98页

作者： Moridani, Mohammad Karimi Setarehdan, Seyed Kamaledin Motie Nasrabadi, Ali Hajinasrollah, Esmaeil Department of Biomedical Engineering Science and Research Branch Islamic Azad University Tehran Iran Control and Intelligent Processing Centre of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Departments of Biomedical Engineering Shahed University Tehran Iran Loghman Medical Center Shahid Beheshti University of Medical Sciences Tehran Iran

Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patients ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patients death is very informative for diagnosing the patients condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics. © 2016 Taylor & Francis.

关键词： Forecasting

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Brain Perfusion Mediates the Relationship Between miRNA Levels and Postural control

Cerebral cortex communications

引用

Cerebral cortex communications 2020年第1期1卷 tgaa078页

作者： Yufen Chen Amy A Herrold Zoran Martinovich Sumra Bari Nicole L Vike Anne J Blood Alexa E Walter Jaroslaw Harezlak Peter H Seidenberg Manish Bhomia Barbara Knollmann-Ritschel Khrystyna Stetsiv James L Reilly Eric A Nauman Thomas M Talavage Linda Papa Semyon Slobounov Hans C Breiter Center for Translational Imaging Department of Radiology Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. Edward Hines Jr. VA Hospital Research Service Hines IL 60141 USA. Mental Health Services and Policy Program Department of Psychiatry and Behavioral Sciences Northwestern University Feinberg School of Medicine Chicago IL 60611 USA. Warren Wright Adolescent Center Department of Psychiatry and Behavioral Sciences Feinberg School of Medicine Northwestern University Chicago IL 60611 USA. Mood and Motor Control Laboratory Departments of Neurology and Psychiatry Massachusetts General Hospital and Harvard Medical School Boston MA 02129 USA. Department of Kinesiology Pennsylvania State University University Park PA 16802 USA. Department of Epidemiology and Biostatistics Indiana University Bloomington IN 47405 USA. Departments of Orthopaedics & Rehabilitation and Family & Community Medicine Penn State College of Medicine Hershey PA 17033 USA. Department of Pathology Uniformed Services University of the Health Sciences Bethesda MD USA. Weldon School of Biomedical Engineering Purdue University West Lafayette IN 47907 USA. School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907 USA. Department of Emergency Medicine Orlando Regional Medical Center Orlando FL USA.

Transcriptomics, regional cerebral blood flow (rCBF), and a virtual reality-based spatial motor task were integrated using mediation analysis in a novel demonstration of "imaging omics." Data collected in National Collegiate Athletic Association (NCAA) Division I football athletes cleared for play before in-season training showed significant relationships in 1) elevated levels of miR-30d and miR-92a to elevated putamen rCBF, 2) elevated putamen rCBF to compromised Balance scores, and 3) compromised Balance scores to elevated microRNA (miRNA) levels. rCBF acted as a consistent mediator variable (Sobel's test < 0.05) between abnormal miRNA levels and compromised Balance scores. Given the involvement of these miRNAs in inflammation and immune function and that vascular perfusion is a component of the inflammatory response, these findings support a chronic inflammatory model in these athletes with 11 years of average football exposure. rCBF, a systems biology measure, was necessary for miRNA to affect behavior.

关键词： football athletes head acceleration events microRNAs postural control regional cerebral blood flow

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