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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Separable Nonlinear Model Predictive control via Sequential Quadratic programming for Large-scale systems *

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IFAC-PapersOnLine 2015年第23期48卷 495-500页

作者： Liang Lu Department of Chemical and Biomolecular Engineering (BK21 Plus Program) Korea Advanced Institute of Science & Technology Daejeon Korea. Laboratory for Automatic Control and System Dynamics Technische Universität Chemnitz Chemnitz Germany.

In this paper, a sequential quadratic programming method is presented for largescale nonlinear and possibly non-convex model predictive control (MPC) optimization problem which is often set up with a separable objective function. By introducing the so-call consensus constraints to separate the couplings among the subsystems. The resulting QP subproblem is formulated in a separable form, which makes it possible to use the existing alternating direction methods, like ADMM, to efficiently compute Newton steps for the overall system in a distributed way. In order to enforce the convergence rate of the distributed computation, a distributed line search with local merit functions is also proposed.

关键词： Sequential quadratic programming separable model predictive control alternating direction method of multipliers nonlinear systems non-convex optimization large-scale optimization distributed line search

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Simulation of a PV system using multilevel current source inverter

Simulation of a PV system using multilevel current source in...

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IEEE International Symposium on Environment Friendly Energies and Applications (EFEA)

作者： R. Guedouani B. Fiala M.S. Boucherit Laboratory of Industrial and Electrical System (LSEI) University of Technology and sciences Houari boumediene Bab Ezzouar El Aliaa Algeria Automatic Department High School of Polytechnic Laboratory of Processus Control Algiers Algeria

This paper presents a new current source converter topology that is mainly intended for photovoltaic (PV) applications. The proposed system composed of a PV generator, an impedance adapter, DC-DC converter, a storage battery, multilevel current source Inverter CSI and a vector controlled induction machine. Our objectives here are focused on modeling, control, and steady-state and transient performances of a PV system based on multilevel three phase CSL The PV generator operates at its maximum power point by using an appropriate search algorithm. The MPPT control allows the extraction of the maximum output power delivered by the PV generator. And the inverter insures a PWM control with a sine wave form of output signals destined to feed an induction machine. The Simulation results show the effectiveness and great features of the proposed system which could apply in different applications, such as water pumping systems and electric vehicle ect.

关键词： Inverters Harmonic analysis Integrated circuit modeling Induction motors Topology Modulation Generators

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Three-dimensional passive micromixer fabricated by two-photon polymerization for microfluidic mixing

Three-dimensional passive micromixer fabricated by two-photo...

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作者： Liu, Yi-Jui Chen, Po-Yu Yang, Jiun-Yi Tsou, Chingfu Lee, Yi-Hsiung Baldeck, Patrice Lin, Chih-Lang Institute of Biomedical Engineering and Materials Science Central Taiwan University of Science and Technology Taichung 406 Taiwan Department of Automatic Control Engineering Feng Chia University Taichung 407 Taiwan Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung 407 Taiwan Program of Electrical and Communications Engineering Feng Chia University Taichung 407 Taiwan University Grenoble 1 CNRS LIPhy UMR 5588 Grenoble F-38041 France

Flow mixing is an important process for many microfluidic applications. Efficient mixing is difficult in microscale channels owing to laminar flow. Disturbing the flow stream by modifying channel geometries or embedding barriers improves the mixing rate. In this study, a three-dimensional (3-D) passive micromixer with propeller blades fabricated by two-photon polymerization (TPP) technology is embedded in the channel for fluid mixing. The propeller blades are designed to disturb the laminar flow in three dimensions to improve the mixing rate. Screw-shaped and flat blades are compared. The experimental results indicate that the screw-shaped propeller provides additional streamlines in the Z direction, which enhances the mixing efficiency. Finally, this phenomenon was verified through simulation.

关键词： Laminar flow

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New PWM control strategy for multilevel three — Phase current source inverter

New PWM control strategy for multilevel three — Phase curre...

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International Conference on Power Engineering, Energy and electrical Drives, POWERENG

作者： R. Guedouani B. Fiala M. S. Boucherit Laboratory of Industrial and Electrical System (LSEI) University of Technology and sciences Algiers Algeria Laboratory of Processus Control Automatic Department High School of Polytechnic Algiers Algeria

In this paper, a multilevel current source inverter (CSI) fed induction motor drive with multilevel PWM control strategy is proposed for high-power applications. The multilevel CSI is configured with two classical CSI connected in parallel. This inverter with proposed PWM control strategy is firstly used for feeding constant RL. This strategy used for multilevel CSI permit to have lower THD of load current. For verifying of achieved results, proposed strategy is used for multilevel current source inverter fed induction motor drive. Compared with classical current source inverter, simulation results show that motor torque ripple with the proposed inverter reduces considerably then we have better performance.

关键词： Inverters Harmonic analysis Induction motors Pulse width modulation Topology Integrated circuit modeling

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Sensor Fault Detection and Isolation in the Quadrotor Vehicle Using Nonlinear Identity Observer Approach

Sensor Fault Detection and Isolation in the Quadrotor Vehicl...

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Conference on control and Fault-Tolerant systems

作者： Younes Al Younes Hassan Noura Abdelhamid Rabhi Ahmed El Hajjaji Nedaa Al Hussien Mechanical Engineering Faculty at Higher Colleges of Technology Chairman of Electrical Engineering Department at United Arab Emirates University Information & System Lab - Control & Vehicle Group- at University of Picardie Electrical Engineering Program at United Arab Emirates University

ISBN: (纸本)9781479928569

The Fault Detection and Isolation (FDI) of a quadrotor vehicle, which is described by a nonlinear model, are tackled in this paper. The nonlinear identity observer is utilized to detect the various types of sensors faults, and the Generalized Observer Scheme (GOS) is used to isolate the faulty sensor. The robustness of the observer and its scheme is investigated through several simulation results to build the Decision- Making Unit (DMU) for the residuals. Various simulation results have validated the FDI method and shown the system performance towards different fault scenarios.

关键词： Quadrotor Vehicle Nonlinear Observer Observer Scheme Structured Residuals

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Optimal allocation of distributed generators in a distribution network using adaptive multi-objective particle swarm optimization

Optimal allocation of distributed generators in a distributi...

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2011 International Conference on electrical, Information Engineering and Mechatronics, EIEM 2011

作者： Cheng, Shan Chen, Min-You Fleming, Peter J. Li, Xia State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical Engineering Chongqing University Chongqing China Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom

ISBN: (纸本)9781447124665

This study presents the optimal allocation of distributed generators (DGs) in distribution network based on an adaptive multi-objective particle swarm optimization (AMPSO). In order to enable the DGs to make the best contribution to the distribution network, a multi-objective optimization model addressing power loss and a voltage stability index (VSI) is constructed, in which the capacity factor of DGs is taken into consideration. In the AMPSO, some adaptive techniques including the use of dynamically changing inertia weight, time-varying acceleration coefficients, mutation operation and circular crowding sorting have been integrated to improve the convergence and the diversity performance of the algorithm. The proposed algorithm has been used to determine the optimal allocation of DGs in a IEEE 33-bus distribution network. Simulation results show that the proposed method can identify the optimal solutions with a good tradeoff between the power loss and the VSI. © 2012 Springer-Verlag London Limited.

关键词： Multiobjective optimization

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Proceedings of the 8th Annual Conference on the Science of Dissemination and Implementation : Washington, DC, USA. 14-15 December 2015

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Implementation science : IS 2016年第2期11 Suppl 2卷 100页

作者： Chambers, David Simpson, Lisa Hill-Briggs, Felicia Neta, Gila Vinson, Cynthia Beidas, Rinad Marcus, Steven Aarons, Gregory Hoagwood, Kimberly Schoenwald, Sonja Evans, Arthur Hurford, Matthew Rubin, Ronnie Hadley, Trevor Barg, Frances Walsh, Lucia Adams, Danielle Mandell, David Martin, Lindsey Mignogna, Joseph Mott, Juliette Hundt, Natalie Kauth, Michael Kunik, Mark Naik, Aanand Cully, Jeffrey McGuire, Alan White, Dominique Bartholomew, Tom McGrew, John Luther, Lauren Rollins, Angie Salyers, Michelle Cooper, Brittany Funaiole, Angie Richards, Julie Lee, Amy Lapham, Gwen Caldeiro, Ryan Lozano, Paula Gildred, Tory Achtmeyer, Carol Ludman, Evette Addis, Megan Marx, Larry Bradley, Katharine VanDeinse, Tonya Wilson, Amy Blank Stacey, Burgin Powell, Byron Bunger, Alicia Cuddeback, Gary Barnett, Miya Stadnick, Nicole Brookman-Frazee, Lauren Lau, Anna Dorsey, Shannon Pullmann, Michael Mitchell, Shannon Schwartz, Robert Kirk, Arethusa Dusek, Kristi Oros, Marla Hosler, Colleen Gryczynski, Jan Barbosa, Carolina Dunlap, Laura Lounsbury, David O’Grady, Kevin Brown, Barry Damschroder, Laura Waltz, Thomas Ritchie, Mona Atkins, David Imel, Zac E. Xiao, Bo Can, Doğan Georgiou, Panayiotis Narayanan, Shrikanth Berkel, Cady Gallo, Carlos Sandler, Irwin Brown, C. Hendricks Wolchik, Sharlene Mauricio, Anne Marie Mehrotra, Sanjay Chandurkar, Dharmendra Bora, Siddhartha Das, Arup Tripathi, Anand Saggurti, Niranjan Raj, Anita Hughes, Eric Jacobs, Brian Kirkendall, Eric Loeb, Danielle Trinkley, Katy Yang, Michael Sprowell, Andrew Nease, Donald Lyon, Aaron Lewis, Cara Boyd, Meredith Melvin, Abigail Nicodimos, Semret Liu, Freda Jungbluth, Nathanial Flynn, Allen Landis-Lewis, Zach Sales, Anne Baloh, Jure Ward, Marcia Zhu, Xi Bennett, Ian Unutzer, Jurgen Mao, Johnny Proctor, Enola Vredevoogd, Mindy Chan, Ya-Fen Williams, Nathaniel Green, Phillip Bernstein, Steven Rosner, June-Marie DeWitt, Michelle Tetrault, Jeanette Dziura, James Hsiao, Allen Sussman, Scott O’Connor, Patrick Toll, Benjamin Jones, Michael Gassaway, Julie Tobin, Jonathan Zatzic Division of Cancer Control and Population Sciences National Cancer Institute National Institutes of Health Rockville USA AcademyHealth Washington USA Department of Medicine and Welch Center for Prevention Epidemiology and Clinical Research Johns Hopkins Medical Institutions Baltimore USA Psychiatry University of Pennsylvania Philadelphia USA Family Medicine and Community Health University of Pennsylvania Philadelphia USA School of Social Policy and Practice University of Pennsylvania Philadelphia USA Psychiatry UC San Diego La Jolla USA Child & Adolescent Psychiatry The Child Study Center at NYU Langone Medical Center New York NY New York USA Psychiatry and Behavioral Sciences MUSC Charleston USA DBHIDS City of Philadelphia Philadelphia USA CBH City of Philadelphia Philadelphia USA Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia USA Health Services Research & Development Department of Veterans Affairs & Baylor College of Medicine Houston USA Treatment Core Department of Veterans Affairs Waco USA National Center for PTSD Executive Division Department of Veterans Affairs White River Junction USA HSR&D HSR&D Center for Health Information and Communication Roudebush VA Medical Center Indianapolis USA Psychology Indiana University Purdue University Indianapolis Indianapolis USA Department of Psychiatric Rehabilitation and Counseling Rutgers University Newark USA Health Services Research & Development Richard L. Roudebush VAMC Indianapolis USA IUPUI Department of Psychology ACT Center of Indiana Indianapolis USA Human Development Washington State University Pullman USA Prevention Science Washington State University Pullman USA Group Health Research Institute Group Health Seattle USA Behavioral Health Services Group Health Seattle USA Preventive Care Group Health Seattle USA Health Services Research & Development Primary and Specialty Medical Care Service VA Puget Sound Seattle USA School of Social Work Un

A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing

关键词： Veteran Affair Motivational Interview Qualitative Comparative Analysis Community That Care Supplemental Nutrition Assistance program

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