| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000432941 |
| 005 | | 20200225105336 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088374269 |
| 035 | |
▼a (MiAaPQ)AAI22589862 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Qiao, Pengfei. |
| 245 | 10 |
▼a Pattern Recognition in the Developing Maize Leaf Epidermis: Gene Network Analyses and Machine Learning Approaches. |
| 260 | |
▼a [S.l.]:
▼b Cornell University.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 209 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Scanlon, Michael J. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a Biological systems contain data of high dimensions and magnitudes, including biochemistry, cellular patterning, transcriptomics, and genomics. Here I combined network analyses and machine learning, to identify developmental patterns that may be amenable to the improvement of drought tolerance. Cuticles comprise the hydrophobic lipid layer covering the aboveground plant body, and have long been a research focus into water conservation in plants. However, no prior studies have examined cuticle development across a temporal and spatial gradient in a crop plant. I used gene network analyses to correlate the biochemical/developmental gradient of cuticle components with the underlying transcriptomic transitions to identify the role of PHYTOCHROME B-mediated light signaling in cuticle development. Subsequent statistical and biochemical analysis revealed LIPID-TRANSFER PROTEINs as evolutionary novelties contributing to the emergence of cuticles in land plants. Additionally, combining the power of genome- and transcriptome-wide association studies (GWAS and TWAS), vesicular trafficking was implicated in the regulation of cuticular evaporation rate. Water loss through the leaf surface is also moderated by specialized cell types (bulliform cells) in maize. Bulliform cell ontogeny was investigated in the developing maize leaf, and a machine learning approach (convolutional neural networks) was employed to conduct high-throughput phenotyping of microscopic bulliform cell traits in 60,780 leaf epidermal glue-impression images. A subsequent GWAS analysis on bulliform cell column number and column width identified a set of gene candidates implicated to function in cell division and DNA methylation. Overall this dissertation demonstrates a multidisciplinary approach combining developmental biology, transcriptomics, quantitative genetics, machine learning, and statistical data analysis, toward a more holistic understanding of the mechanisms of water conservation in maize. |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Plant sciences. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Bioinformatics. |
| 690 | |
▼a 0479 |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0715 |
| 710 | 20 |
▼a Cornell University.
▼b Plant Biology. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0058 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493187
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |