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A Biomathematical Learning Enhancement Network for Diversity (BLEND)

A Biomathematical Learning Enhancement Network for Diversity (BLEND)
多样性生物数学学习增强网络(BLEND)
批准号:
0634598
负责人:
Gregory Goins
金额:
$24.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2006
资助国家:
美国
项目状态:
已结题
起止时间:
2006-09-15 至 2009-08-31

项目摘要

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中文摘要
翻译
尽管历史上少数民族大学(hmu)的生物学和数学专业一直培养出大量的STEM学士学位,但需要更多的创新项目来增加继续获得博士学位的本科生数量。挑战在于将跨学科研究合作和伙伴关系聚集在一起,以解决数学和生物学上代表性不足的少数民族成就差距的基本根源。北卡罗来纳农工州立大学的生物数学学习多样性增强网络(BLEND)项目是由一个跨部门的教师联盟提出的,这些教师是早期接受转型变革的人,这些变革要求学生为生物学和数学的研究生学习做好准备。BLEND项目将提供一个物理和虚拟的智力环境,在这里学生可以找到一种认同感、归属感、责任感,最重要的是,成就,为他们在生物数学研究事业中的领导和服务角色做好准备。BLEND项目的总体目标是培养为生物数学研究的跨学科性质做好准备的本科生。为了实现这一目标,BLEND项目将(1)将研究和课堂经验联系起来,(2)在数学和生物学的界面上提供联合指导,(3)扩大对生物数学培训和研究的吸引力。我们的核心基因组学研究集群将为学生提供一个自然的机会,从数学和计算的角度探索,学习和参与生物学问题,同时深入了解基因组学研究。研究项目的中心主题是基因组测序和蛋白质结构预测中的高通量数据收集方法。研究活动将集中在非生物植物的逆境抗性策略和逆境相关基因和途径的鉴定。通过这些研究活动,学生将对分子、生化和生理机制之间的联系有一个连贯的认识,这些机制使高等植物能够应对干旱、盐度和极端温度等非生物胁迫条件。这些研究活动是情境驱动的,旨在提供一种创新的学习文化,将本科生与生物学和数学之间的深刻互动联系起来。该项目将推进一种前瞻性的思维模式,以激励未被充分代表的少数民族在数学和生物学的界面上追求专业。
英文摘要
Although biology and mathematics programs at Historically Minority Universities (HMUs) have consistently produced high numbers of STEM baccalaureate degrees, more innovative programs are needed to increase the number of undergraduates who go on to earn the Ph.D. The challenge is to bring together a critical mass of interdisciplinary research collaborations and partnerships that address basic root origins of achievement gaps for underrepresented minorities in mathematics and biology. The Biomathematics Learning Enhancement Network for Diversity (BLEND) project at North Carolina A&T State University was conceptualized by an interdepartmental alliance of faculty who are early adopters of transformational change required to prepare students for graduate study at the interface of biology and mathematics. The BLEND project will supply both a physical and virtual intellectual setting where students may find a sense of identification, belonging, responsibility, and most importantly, achievement that prepares them for roles of leadership and service in biomathematical research careers. The overall goal of the BLEND project is to produce undergraduate students outstandingly prepared for the interdisciplinary nature of biomathematical research. To accomplish this goal, the BLEND project will (1) link research and classroom experiences, (2) provide joint-mentoring at the interface of mathematics and biology, and (3) broaden the appeal to biomathematics training and research. Our Core Genomics Research Cluster will provide a natural opportunity for students to explore, learn, and engage problems in biology from a mathematical and computational perspective while gaining deeper insight into genomics research. The central theme of the research projects is high-throughput data collection methods in genome sequencing and protein structure prediction. Research activities will focus on abiotic plant stress tolerance strategies and identification of stress-relevant genes and pathways. Students will emerge from these research activities with a coherent picture of links between molecular, biochemical, and physiological mechanisms that enable higher plants to cope with abiotic stress conditions such as drought, salinity, and temperature extremes. These research activities are context-driven and purposed to provide an innovative learning culture that connects undergraduates with insightful interactions between biology and mathematics. This project will advance a forward-thinking model for motivating underrepresented minorities to pursue professions at the interface of mathematics and biology.
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NSF Engines Development Award: Creating climate-resilient opportunities for plant systems (NC)
NSF INCLUDES: Building Diverse and Integrative STEM Continua Using Socio-environmental Systems In and Out of Neighborhoods (DISCUSSION)
UBM Group - An Integrative Biomathematical Learning and Empowerment Network for Diversity (iBLEND)
Improving and Expanding Student Experiences in Introductory Biology Courses with Online Learning Modules
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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