IntBIO COLLABORATIVE RESEARCH: Integrating fossils, genomics, and machine learning to reveal drivers of Cretaceous innovations in flowering plants
IntBIO 协作研究:整合化石、基因组学和机器学习,揭示白垩纪开花植物创新的驱动因素
基本信息
- 批准号:2217117
- 负责人:
- 金额:$ 33.19万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-01 至 2025-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The Tree of Life is marked by short periods of rapid innovation where groups emerge with dramatically altered forms and diversify quickly. This has happened multiple times throughout geologic history including with the rise of birds, mammals, the transition of plants from water to land, and the origination of flowering plants. The rapid emergence and diversification of flowering plants in particular represents one of the most remarkable episodes in the history of life on earth. However, while fundamental to understanding the ecology and evolution of modern ecosystems, this episode remains unexplained and leads to one of the grand challenges in the biological sciences – determining what processes may be responsible for such rapid changes in form and function across the Tree of Life. A major impediment to addressing this question is the availability of data and methods for analyzing those data. The goal of this study is to use and develop new machine learning approaches to gathering data for both fossil and living plant species and to use these data to help develop new techniques. These techniques will help identify what contributed to the rapid change in plants that resulted in their dominance in the environment today. This project will train undergraduates, graduate students, and postdoctoral fellows in machine learning methods, evolutionary biology, and techniques for working with both fossil and living specimens. The project will also include resource development and training for middle schoolers, high schoolers, undergraduates, and the broader research community.This project aims to evaluate the evolutionary processes underlying the emergence of innovation, using flowering plants as a case study. Specifically, the project will examine Cretaceous radiations of flowering plants characterized by rapid evolution, a rich fossil record, and the origin of innovations and lineages of great ecological significance. The central goals of the proposed research are to (a) generate a large morphological dataset for flowering plants using novel machine learning methods, (b) develop new statistical methods for modeling evolution, and (c) use these advances in data collection and methods to identify the processes that led to the episodic and rapid emergence of novelty across the Tree of Life. Collectively, these new developments in machine learning techniques, morphological data collection, and analytical techniques for addressing evolutionary processes will be potentially transformative to several fields including the biological sciences, computational biology, and machine learning. The large scope and scale of this project, together with its highly integrative nature, creates the potential to address one of the most important standing questions in evolutionary biology.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.
生命之树的标志是快速创新的短期,在这些创新中,团体以动态变化的形式出现并迅速多样化。在整个地质历史中,这已经发生了多次,包括随着鸟类的兴起,哺乳动物的兴起,植物从水到土地的过渡以及开花植物的起源。尤其是开花植物的快速出现和多样化是地球生命史上最杰出的事件之一。但是,尽管对于理解现代生态系统的生态学和演变的基础,但这一集仍然未知并导致生物科学中的巨大挑战之一 - 确定哪些过程可能导致生命之树的形式和功能的这种快速变化。解决此问题的主要障碍是数据和分析这些数据的方法的可用性。这项研究的目的是使用和开发新的机器学习方法来收集化石和生物植物物种的数据,并使用这些数据来帮助开发新技术。这些技术将有助于确定导致其在当今环境中占主导地位的植物迅速变化的原因。该项目将在机器学习方法,进化生物学以及与化石和生活标本一起工作的机器学习方法,进化生物学和技术中培训本科生,研究生和博士后研究员。 The project will also include resource development and training for middle schoolers, high schoolers, undergraduates, and the broader research community.This project aims to evaluate the evolutionary processes underlying the emergence of innovation, using flowering plants as a case study.Specifically, the project will examine Cretaceous radiations of flowering plants characterized by rapid evolution, a rich fossil record, and the origin of innovations and lineages of great ecological significance.拟议研究的核心目标是(a)使用新型机器学习方法生成一个大型的形态数据集,用于开花植物,(b)开发用于建模进化的新统计方法,(c)在数据收集和方法中使用这些进步来识别导致生命之树的情节和快速出现的过程。总的来说,这些新的发展技术方面的新发展,形态数据收集以及解决进化过程的分析技术将有可能转化为包括生物制剂,计算生物学和机器学习在内的几个领域。该项目的巨大范围和规模及其高度综合性的性质,可以解决进化生物学中最重要的常规问题之一。该奖项反映了NSF的法定任务,并被认为是通过基金会的智力优点和更广泛的影响来通过评估来获得的支持。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Sorting of persistent morphological polymorphisms links paleobiological pattern to population process
持久形态多态性的排序将古生物学模式与种群过程联系起来
- DOI:10.1017/pab.2023.27
- 发表时间:2023
- 期刊:
- 影响因子:2.7
- 作者:Parins-Fukuchi, C. Tomomi
- 通讯作者:Parins-Fukuchi, C. Tomomi
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James Pease其他文献
Regulation of CCR4 expression after segmental bronchial allergen challenge in atopic asthmatics
- DOI:
10.1016/s0091-6749(02)81208-8 - 发表时间:
2002-01-01 - 期刊:
- 影响因子:
- 作者:
Stephen Durham;Kayhan T Nouri-Aria;Duncan R Wilson;Mikila R Jacobson;Eva M Varga;Tim Williams;James Pease;Clare Lloyd;Ian Sabroe - 通讯作者:
Ian Sabroe
9α,11β-PGF<sub>2</sub> and its stereoisomer PGF<sub>2α</sub> are novel agonists of the chemoattractant receptor, CRTH2
- DOI:
10.1016/j.febslet.2005.11.052 - 发表时间:
2006-01-23 - 期刊:
- 影响因子:
- 作者:
Hilary Sandig;David Andrew;Ashley A. Barnes;Ian Sabroe;James Pease - 通讯作者:
James Pease
James Pease的其他文献
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{{ truncateString('James Pease', 18)}}的其他基金
Collaborative Research: BEE: Bridging the ecology and evolution of East African Acacias across time and space: genomics, ecosystem, and diversification.
合作研究:BEE:跨越时间和空间连接东非金合欢的生态和进化:基因组学、生态系统和多样化。
- 批准号:
2105917 - 财政年份:2021
- 资助金额:
$ 33.19万 - 项目类别:
Standard Grant
BBSRC Industrial CASE Partnership Grant
BBSRC 工业案例合作伙伴资助
- 批准号:
BB/I53247X/1 - 财政年份:2010
- 资助金额:
$ 33.19万 - 项目类别:
Training Grant
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