NRT-HDR: Transdisciplinary Graduate Training Program in Data-Driven Adaptive Systems of Brain-Body Interactions

NRT-HDR:数据驱动的脑体交互自适应系统跨学科研究生培训计划

基本信息

  • 批准号:
    1922598
  • 负责人:
  • 金额:
    $ 299.99万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-09-01 至 2025-08-31
  • 项目状态:
    未结题

项目摘要

Nearly one in five Americans currently live with a disability that affects performance of their daily activities. Individuals living with complex disabilities as a result of substance use disorders, autism spectrum disorders, post-traumatic stress disorder and traumatic brain injury often have multiple negative health outcomes involving physical and psychosocial factors. These disabilities have pervasive societal, public health and economic consequences that impact individuals, families, support networks, and sometimes whole communities. Addressing this challenge requires an in-depth understanding of the physical and physiological underpinnings of the disabling condition, the environmental factors and social-ecological context that limits an individual's participation in daily activities, and the development of technological solutions to help overcome these barriers. The next generation STEM workforce charged with addressing this challenge must work together across traditional academic disciplinary boundaries to integrate engineering, data science and social science knowledge, while meaningfully engage with stakeholder communities in a mutually beneficial manner. This National Science Foundation Research Traineeship (NRT) award to George Mason University will address the need to train a versatile, interdisciplinary community-engaged STEM workforce with the skills to harness the data revolution to create fundamental research and translational innovations aimed at improving the quality of life for individuals with disabilities. The project anticipates training one hundred (100) PhD students, including nineteen (19) funded trainees, from electrical and bioengineering, data science, computer science, neuroscience, and the social sciences. The NRT project will include students with disabilities in the trainee cohort.Trainees will develop and use both data-collection devices and data-analysis techniques to promote data-driven solutions associated with complex disabilities that have major social, public health and economic consequences. For example, students may utilize their training in data analytics, wearable sensors and imaging to design new technology-assisted methods for personalized service delivery, to develop new machine learning algorithms to support the next generation of brain computer interfaces and improved prosthetic control systems, or to advance research on virtual reality based multisensory training. This NRT program will cross-train engineers, data scientists and social scientists to use state-of-the-art data analytic methods and wearable computing technologies based on a novel transdisciplinary CAP (competencies, applications and practice) curriculum and a community-engaged design project. The CAP curriculum will provide immersive, individualized training in data analytics, wearable sensors and imaging, contextualized in terms of applications (cognitive and behavioral neuroscience, human movement and function and neuroengineering) and scaffolded by hands-on practice in research and professional skills development. The design project will provide experience in convergent research, communication across disciplinary boundaries, and communication to a broad audience. Within George Mason University, the project will involve 14 core faculty from the Center for Adaptive Systems of Brain-Body Interactions, and an additional 15 faculty from 14 departments and 5 schools/colleges. By aligning with strategic institutional priorities and engaging diverse faculty mentors, this NRT project is well-positioned to sustain the traineeship model beyond the funded period.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
目前,近五分之一的美国人患有影响日常活动的残疾。因药物使用障碍、自闭症谱系障碍、创伤后应激障碍和创伤性脑损伤而患有复杂残疾的人往往会有多种负面健康后果,涉及身体和心理社会因素。这些残疾具有普遍的社会、公共卫生和经济后果,影响到个人、家庭、支助网络,有时甚至影响到整个社区。应对这一挑战需要深入了解残疾状况的物理和生理基础,限制个人参与日常活动的环境因素和社会生态背景,以及开发技术解决方案以帮助克服这些障碍。负责应对这一挑战的下一代STEM劳动力必须跨越传统的学术学科界限,整合工程、数据科学和社会科学知识,同时以互利的方式与利益相关者社区进行有意义的互动。这个国家科学基金会研究培训(NRT)奖给乔治梅森大学将解决需要培养一个多才多艺的,跨学科的社区参与干劳动力的技能,利用数据革命创造基础研究和转化创新,旨在提高生活质量的个人残疾人。该项目预计将培训一百(100)名博士生,其中包括十九(19)名受资助的学员,来自电气和生物工程,数据科学,计算机科学,神经科学和社会科学。 NRT项目将把残疾学生纳入实习生队列。实习生将开发和使用数据收集设备和数据分析技术,以推广与具有重大社会、公共卫生和经济后果的复杂残疾相关的数据驱动解决方案。 例如,学生可以利用他们在数据分析,可穿戴传感器和成像方面的培训来设计个性化服务提供的新技术辅助方法,开发新的机器学习算法以支持下一代脑机接口和改进的假肢控制系统,或者推进基于虚拟现实的多感官训练研究。 该NRT计划将交叉培训工程师,数据科学家和社会科学家,以使用最先进的数据分析方法和基于新的跨学科CAP(能力,应用和实践)课程和社区参与设计项目的可穿戴计算技术。CAP课程将提供数据分析,可穿戴传感器和成像方面的沉浸式个性化培训,在应用(认知和行为神经科学,人体运动和功能以及神经工程)方面进行情境化,并通过研究和专业技能发展的实践进行支架。该设计项目将提供融合研究,跨学科界限的沟通和沟通,以广泛的受众的经验。在乔治梅森大学内,该项目将涉及来自脑-体相互作用适应系统中心的14名核心教师,以及来自14个系和5所学校/学院的另外15名教师。通过与战略机构的优先事项,并聘请不同的教师导师,这个NRT项目是很好的定位,以维持培训模式超出资助期间。NSF研究培训(NRT)计划的目的是鼓励开发和实施大胆的,新的潜在的变革性模型STEM研究生教育培训。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求相一致的综合培训模式,在高优先级的跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了NSF的法定使命,并通过使用基金会的智力价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(12)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Mixture Modeling of Nonsuicidal Self-Injury and Binge Eating: Behaviors and Motives
  • DOI:
    10.1016/j.beth.2021.02.005
  • 发表时间:
    2021-09-01
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Pak, K. Naomi;Nelson, Jillian;Fischer, Sarah
  • 通讯作者:
    Fischer, Sarah
Eating Disorders and Posttraumatic Stress Disorder: A Network Analysis of the Comorbidity
饮食失调和创伤后应激障碍:共病的网络分析
  • DOI:
    10.1016/j.beth.2021.09.006
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Nelson, Jillian D.;Cuellar, Alison E.;Cheskin, Lawrence J.;Fischer, Sarah
  • 通讯作者:
    Fischer, Sarah
Negative Urgency Linked to Craving and Substance Use Among Adults on Buprenorphine or Methadone
  • DOI:
    10.1007/s11414-023-09845-4
  • 发表时间:
    2023-07-06
  • 期刊:
  • 影响因子:
    1.9
  • 作者:
    Goncalves,Stefanie F.;Izquierdo,Alyssa M.;Sikdar,Siddhartha
  • 通讯作者:
    Sikdar,Siddhartha
High-Frequency Heart Rate Variability and Emotion-Driven Impulse Control Difficulties During Adolescence: Examining Experienced and Expressed Negative Emotion as Moderators.
  • DOI:
    10.1177/0272431620983453
  • 发表时间:
    2021-10
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Gonçalves SF;Chaplin TM;López R Jr;Regalario IM;Niehaus CE;McKnight PE;Stults-Kolehmainen M;Sinha R;Ansell EB
  • 通讯作者:
    Ansell EB
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Siddhartha Sikdar其他文献

Relationship Between Interhemispheric Cerebral Perfusion Delay and Carotid Artery Stenosis
  • DOI:
    10.1016/j.jvs.2019.06.047
  • 发表时间:
    2019-08-01
  • 期刊:
  • 影响因子:
  • 作者:
    Brajesh K. Lal;Amir A. Khan;Jigar Patel;Matthew Chrencik;Anthony Laila;John Y. Yokemick;John D. Sorkin;Siddhartha Sikdar
  • 通讯作者:
    Siddhartha Sikdar
Ultrasonic interrogation of tissue vibrations in arterial and organ injuries: Preliminary <em>in vivo</em> results
  • DOI:
    10.1016/j.ultrasmedbio.2006.05.002
  • 发表时间:
    2006-08-01
  • 期刊:
  • 影响因子:
  • 作者:
    Siddhartha Sikdar;Kirk W. Beach;Marla Paun;Shahram Vaezy;Yongmin Kim
  • 通讯作者:
    Yongmin Kim
Ultrasound–Based Muscle Activity Sensing for Intuitive Proportional Control in Upper Extremity Amputees
  • DOI:
    10.1016/j.apmr.2018.07.297
  • 发表时间:
    2018-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Biswarup Mukherjee;Ananya S. Dhawan;Shriniwas Patwardhan;Joseph Majdi;Rahsaan J. Holley;Wilsaan M. Joiner;Michelle Harris-Love;Siddhartha Sikdar
  • 通讯作者:
    Siddhartha Sikdar
Computed tomography angiographic biomarkers help identify vulnerable carotid artery plaque
  • DOI:
    10.1016/j.jvs.2021.10.056
  • 发表时间:
    2022-04-01
  • 期刊:
  • 影响因子:
  • 作者:
    Brajesh K. Lal;Amir A. Khan;Vikram S. Kashyap;Matthew T. Chrencik;Ajay Gupta;Alexander H. King;Jigar B. Patel;Janice Martinez-Delcid;Domingo Uceda;Sarasi Desikan;Siddhartha Sikdar;John D. Sorkin;Andrew Buckler
  • 通讯作者:
    Andrew Buckler
Poster 147: Novel Use of Ultrasound Imaging to Investigate Myofascial Trigger Points and the Effects of Dry Needling: A Case Series
  • DOI:
    10.1016/j.pmrj.2009.08.167
  • 发表时间:
    2009-09-01
  • 期刊:
  • 影响因子:
  • 作者:
    Ru-Huey Yen;Jerome Danoff;Tadesse M. Gebreab;Naomi Lynn H. Gerber;Jay P. Shah;Siddhartha Sikdar
  • 通讯作者:
    Siddhartha Sikdar

Siddhartha Sikdar的其他文献

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{{ truncateString('Siddhartha Sikdar', 18)}}的其他基金

I-Corps: Translation Potential of Simultaneous Musculoskeletal Assessment with Real-Time Ultrasound
I-Corps:实时超声同步肌肉骨骼评估的转化潜力
  • 批准号:
    2413735
  • 财政年份:
    2024
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant
EAGER: An Open Data Sharing Platform for Substance Use Disorders
EAGER:药物使用障碍的开放数据共享平台
  • 批准号:
    1945764
  • 财政年份:
    2019
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant
Planning Grant: Engineering Research Center for Technology-Empowered Communities of Recovery (TECOR)
规划补助金:技术赋能康复社区工程研究中心(TECOR)
  • 批准号:
    1840399
  • 财政年份:
    2018
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant
CPS: Synergy: Collaborative Research: Closed-loop Hybrid Exoskeleton utilizing Wearable Ultrasound Imaging Sensors for Measuring Fatigue
CPS:协同:协作研究:利用可穿戴超声成像传感器测量疲劳的闭环混合外骨骼
  • 批准号:
    1646204
  • 财政年份:
    2017
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant
CPS: Synergy: A Novel Biomechatronic Interface Based on Wearable Dynamic Imaging Sensors
CPS:Synergy:基于可穿戴动态成像传感器的新型生物机电接口
  • 批准号:
    1329829
  • 财政年份:
    2014
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant
CAREER: An Integrated Systems Approach to Understanding Complex Muscle Disorders
职业:理解复杂肌肉疾病的综合系统方法
  • 批准号:
    0953652
  • 财政年份:
    2010
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant

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  • 批准号:
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CRISPR/Cas9介导HDR-SSA两步法猪IGF2基因“无缝编辑”新技术研究
  • 批准号:
    31702099
  • 批准年份:
    2017
  • 资助金额:
    25.0 万元
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单曝光HDR成像关键技术研究
  • 批准号:
    61401072
  • 批准年份:
    2014
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    27.0 万元
  • 项目类别:
    青年科学基金项目

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EAGER: A Genome Wide HDR Enhancement Screen in Maize
EAGER:玉米全基因组 HDR 增强屏幕
  • 批准号:
    2409037
  • 财政年份:
    2024
  • 资助金额:
    $ 299.99万
  • 项目类别:
    Standard Grant
NRT-HDR: Integrative Training in Data Science-Enabled Sensing of the Environment for Climate Adaptation (DataSENSE)
NRT-HDR:数据科学支持的气候适应环境感知综合培训 (DataSENSE)
  • 批准号:
    2244403
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Evaluation of genetic variants affecting platelet function with CRISPR HDR in human megakaryocytes
利用 CRISPR HDR 评估影响人类巨核细胞血小板功能的遗传变异
  • 批准号:
    10737494
  • 财政年份:
    2023
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NRT-FW-HTF-HDR: PLATFORMS FOR EXCHANGE AND ALLOCATION OF RESOURCES (PEAR)
NRT-FW-HTF-HDR:资源交换和分配平台(PEAR)
  • 批准号:
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NRT-HDR: Interdisciplinary Studies in Entomology, Computer Science and Technology NETwork (INSECT NET)
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HDR DSC: Collaborative Research: Creating and Integrating Data Science Corps to Improve the Quality of Life in Urban Areas
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Cas9を用いたゲノム編集におけるHDR効率向上に重要な要素の探索
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