The Mathematical Sciences in Obesity Research

项目摘要/摘要 数学科学包括工程学、统计学、计算机科学、物理学、计量经济学和 作为数学的数学正越来越多地被用来促进我们对原因的理解， 后果和减轻肥胖症。这些应用程序超越了易于实现的常规方法 在现有的商业软件中。相反，它们越来越多地涉及计算要求高的任务， 开发新的分析方法和软件，新的派生，以及特殊程度的 两种或两种以上现有技术的交错。此外，这些方法和应用继续 进步；今天的技术和问题与五年前的不同，并继续 更新课程是必不可少的。数学科学与肥胖研究的交界点进展 需要对这两个学科的调查人员进行双边培训。然而，据我们所知，我们现有的成熟课程是， 由科学家提供此类培训的唯一持续资源。我们成功的五天短期课程特色 一些世界上最优秀的科学家致力于这一接口，通过提供多个主题- 驱动模块，旨在搭建学科之间的桥梁。我们开设的课程的需求和成功 在过去的五年里，每年都有超过100人报名参加我们的课程，这就是明证 超过1300名用户访问了我们的课程视频档案，这导致了十几次合作 在成功的拨款申请或同行评议的出版物，我们的课程参与者和教职员工。第一 模块作为调查人员接近界面的共同方向，主要从 量化或肥胖镜头，后面是8个模块，主题包括使用能量模拟体重变化 平衡，模拟人群效应，肥胖的基因组分析，模拟肥胖的行为反应， 传感器和工程模型，以及比例定律和肥胖。讲座被录制下来，并发布到我们的 课程网站供免费浏览，从而扩大了我们课程的覆盖范围。因为个人在以下方面学习最好 复杂的任务时，他们可以互动的材料，我们包括一些互动课程设计 让参与者参与主动学习。这些会议包括小组讨论、与观众的辩论 参与、问答时间和基于发现的学习活动。这些已由以下人员改进 我们认为，在前一个供资周期中，最能提供服务且最受参与方高度赞赏的是那些。 资深教员提供讲座，并领导小组和个人与参与者就以下主题进行磋商 作为格兰特收购和跨学科职业生涯的领航者。肥胖研究中的新数学科学 卓越研究(更多)奖将吸引课程以外的参与者来评选优秀的 关于定量肥胖研究的出版物将在接下来的几年中获得荣誉。美国国立卫生研究院和 科学界普遍表示，需要在数学科学的界面上进行更多的培训 和关键的生物医学领域，我们要求有机会继续成为解决方案的一部分。

Project Summary/Abstract The mathematical sciences including engineering, statistics, computer science, physics, econometrics, and mathematics qua mathematics are increasingly being applied to advance our understanding of the causes, consequences, and alleviation of obesity. These applications go beyond routine approaches easily implemented in available commercial software. Rather, they increasingly involve computationally demanding tasks, development of novel analytic methods and software, new derivations, and an exceptional degree of interdigitation of two or more existing techniques. Moreover, these methods and applications continue to advance; the techniques and questions today are not identical to those from five years ago and continuing to refresh curricula is essential. Advances at the interface of the mathematical sciences and obesity research require bilateral training for investigators in both disciplines. Yet, our existing proven course is, to our knowledge, the only ongoing resource to provide such training by scientists. Our successful five day short course features some of the world’s finest scientists working at this interface to fill the unmet need by providing multiple, topic- driven modules designed to bridge the disciplines. The demand for and success of the course we offered annually for the last five years is evidenced by the facts that over 100 people have enrolled in our course, that over 1300 users have accessed our course video archives, and that over a dozen collaborations have resulted in successful grant applications or peer-reviewed publications from our course participants and faculty. The first module serves as a common orientation for investigators approaching the interface predominantly from a quantitative or obesity lens, followed by 8 modules with topics such as modeling weight change using energy balance, modeling effects in populations, genomic analysis in obesity, modeling behavioral responses to obesity, sensor and engineering models, and scaling laws and obesity. Lectures are video-recorded and posted to our course website for free viewing, thereby extending the reach of our course. Because individuals learn best in complex tasks when they can interact with the material, we include a number of interactive sessions designed to engage the participants in active learning. These sessions include panel discussion, debates with audience participation, question and answer periods, and discovery-based learning activities. These have been refined by us over the prior funding cycle to be those that best serve and are most highly appreciated by our participants. Senior faculty offer lectures and lead small group and individual consultations with participants on topics such as grant acquisition and navigating an interdisciplinary career. The new Mathematical sciences in Obesity Research Excellence (MORE) Prize will engage participants beyond the course to identify an outstanding publication on quantitative obesity research to be honored at the following years’ course. The NIH and the scientific community at large have voiced the need for more training at the interface of the mathematical sciences and key biomedical domains, and we request the opportunity to continue to be part of the solution.