@@ -268,12 +268,20 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\noindent\textbf{Miao C}, \textbf{Hoban TP}$^\ddagger$, \textbf{Pages A}$^\ddagger$, Xu Z, Rodene E, Ubbens J, Stavness I, Yang J, \textbf{Schnable JC}$^\S$ Simulated plant images improve maize leaf counting accuracy. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/706994}{10.1101/706994}\\
\noindent Gaillard M$^*$, \textbf{Miao C}$^*$, \textbf{Schnable JC}, Benes B$^\S$ Voxel carving based 3D reconstruction of sorghum identifies genetic determinants of radiation interception efficiency. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.04.06.028605}{10.1101/2020.04.06.028605v1}\\
\noindent Weissmann S, Huang P, Furoyama K, Wiechert M, Taniguchi M, \textbf{Schnable JC},$^\S$ Brutnell TP, Mockler TC$^\S$ DCT4 - a new member of the dicarboxylate transporter family in C\textsubscript{4} grasses. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/762724}{10.1101/762724}\\
%\subsection*{Other Manuscripts in Review}
%\noindent Sankaran S, Marzougui A, \textbf{Hurst JP}, Zhang C, \textbf{Schnable JC}, Shi Y. Can high resolution satellite imagery be used in high-throughput field phenotyping?. \textit{(In Review)}\\
%\noindent Zhu Y, Chen Y, Ali Md. A, Dong L, Wang X, Archontoulis SV, Schnable JC, Castellano MJ. Continuous in situ soil nitrate sensors: a comparison with conventional measurements and the value of high temporal resolution measurements. \textit{(In Review)}\\
%\noindent Busta L, Schmitz E, Kosma D, Schnable JC, Cahoon EB. A co-opted steroid synthesis gene, maintained in sorghum but not maize, seals leaves against water loss. \textit{(In Review)}\\
%\noindent Rogers AR, Dunne JC, Romay C ... \textbf{Schnable JC} (24th of 39 authors) ... Kaeppler S, De Leon N, Holland JB. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. \textit{(In Review)}\\
%\noindent Jarquin D, de Leon N, Romay C ... \textbf{Schnable JC} (24th of 32 authors) ... Wisser RJ, Xu W, Lorenz A. Utility of climatic information via combining ability models to improve genomic prediction for yield within the Genomes to Fields maize project. \textit{(In Review)}\\
%\noindent Wang M, Shilo S, Levy AA, Zelkowski M, Olson MA, Jiang J, \textbf{Schnable JC}, Sun Q, Pillardy J, Kianian PMA, Kianian SF, Chen C, Pawlowski WP$^\S$ Elucidating features and evolution of recombination sites in plants using machine learning. \textit{(In Review)}
...
...
@@ -282,18 +290,20 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\begin{etaremune}
\subsection*{Faculty Publications}
\item\textbf{Raju SKK}, Atkins M, \textbf{Enerson A}$^\ddagger$, \textbf{Carvalho DS}, Studer AJ, Ganapathysubramanian B, Schnable PS, \textbf{Schnable JC}$^\S$ (2020) Leaf Angle eXtractor - A high throughput image processing framework for leaf angle measurement in maize and sorghum. \textsc{Applications in Plant Sciences}\textit{(Accepted)}
\item Wang R, Qiu Y,$^\S$ Zhou Y, \textbf{Liang Z}, \textbf{Schnable JC} (2020) A high-throughput phenotyping pipeline for image processing and functional growth curve analysis. \textsc{Plant Phenomics}\textit{(Accepted)}
\item Gaillard M$^*$, \textbf{Miao C}$^*$, \textbf{Schnable JC}, Benes B$^\S$ (2020) Voxel carving based 3D reconstruction of sorghum identifies genetic determinants of radiation interception efficiency. \textsc{Plant Direct}\textit{(Accepted)}\textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.04.06.028605}{10.1101/2020.04.06.028605v1}
\item\textbf{Lai X}, Bendix C, \textbf{Yan L}, \textbf{Zhang Y}, \textbf{Schnable JC}, Harmon F$^\S$ (2020) Interspecific analysis of diurnal gene regulation in panicoid grasses identifies known and novel regulatory motifs. \textsc{BMC Genomics} doi: \href{https://doi.org/10.1186/s12864-020-06824-3}{10.1186/s12864-020-06824-3}
\item Han J, Wang P, Wang Q, Lin Q, Yu G, \textbf{Miao C}, Dao Y, Wu R, \textbf{Schnable JC}, Tang H, Wang K$^\S$ (2020) Genome-wide characterization of DNase I-hypersensitive sites and cold response regulatory landscapes in grasses. \textsc{The Plant Cell} doi: \href{https://doi.org/10.1105/tpc.19.00716}{10.1105/tpc.19.00716}
\item Wang R, Qiu Y,$^\S$ Zhou Y, \textbf{Schnable JC} (2020) A high-throughput phenotyping pipeline for image processing and functional growth curve analysis. \textsc{Plant Phenomics}\textit{(Accepted)}
\item Moisseyev G, Park K, Cui X, Freitas D, Rajagopa D, Konda A, Martin-Olenski M, Mcham M, Liu K, Du Q, \textbf{Schnable JC}, Moriyama E, Cahoon E, Chi Z$^\S$ (2020) RGPDB: Database of root-associated genes and promoters in maize, soybean, and sorghum. \textsc{Database} doi: \href{https://doi.org/10.1093/database/baaa038}{10.1093/database/baaa038}
\item\textbf{Miao C}, Xu Y, Liu S, Schnable PS, \textbf{Schnable JC}$^\S$ (2020) Increased power and accuracy of causal locus identification in time-series genome-wide association in sorghum. \textsc{Plant Physiology} doi: \href{https://doi.org/10.1104/pp.20.00277}{10.1104/pp.20.00277}\textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.02.16.951467}{10.1101/2020.02.16.951467}
\item\textbf{Raju SKK}, Atkins M, \textbf{Enerson A}$^\ddagger$, \textbf{Carvalho DS}, Studer AJ, Ganapathysubramanian B, Schnable PS, \textbf{Schnable JC}$^\S$ (2020) Leaf Angle eXtractor - A high throughput image processing framework for leaf angle measurement in maize and sorghum. \textsc{Applications in Plant Sciences}\textit{(Accepted)}
\item\textbf{Dai X}, Xu Z, \textbf{Liang Z}, Tu X, Zhong S, \textbf{Schnable JC}$^\S$, Li P$^\S$ (2020) Non-homology based prediction of gene functions. \textsc{The Plant Genome} doi: \href{https://doi.org/10.1002/tpg2.20015}{10.1002/tpg2.20015}\textsc{bioRxiv} doi: \href{https://doi.org/10.1101/730473}{10.1101/730473}
\item Peng B, Guan K$^\S$, Ainsworth EA, Asseng S, Bernacchi CJ, Cooper M, Delucia EH, Elliot JW, Ewert F, Grant RF, Gustafson DI, Hammer GL, Jin Z, Jones JW, Kimm H, Lawrence DM, Li Y, Lombardozzi DL, Marshall-Colon A, Messina CD, Ort DR, \textbf{Schnable JC}, Tang J, Vallejos CE, Wu A, Yin X, Zhou W (2020) Advancing multi-scale crop modeling for agricultural climate change adaptation assessment. \textsc{Nature Plants} doi: \href{https://t.co/rl4ywzzDhy?amp=1}{10.1038/s41477-020-0625-3}
...
...
@@ -475,6 +485,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\subsection*{Peer Reviewed Conference Papers}
\begin{etaremune}
\item
\item Sankaran S, Zhang C, \textbf{Hurst JP}, Marzougui A, Sivakumar ANV, Li J, \textbf{Schnable JC}, Shi Y (2020) Investigating the potential of satellite imagery for high-throughput field phenotyping applications. \textsc{SPIE Defense + Commercial Sensing} California, USA doi: \href{https://doi.org/10.1117/12.2558729}{10.1117/12.2558729}
\item Al-Zadjali A, Shi Y, Scott S, Deogun JS, and \textbf{Schnable JC} (2020) Faster-R-CNN based deep learning for locating corn tassels in UAV imagery. \textsc{SPIE Defense + Commercial Sensing} California, USA doi: \href{https://doi.org/10.1117/12.2560596}{10.1117/12.2560596}
\item\textbf{Miao C}, \textbf{Pages A},$^\ddagger$ Xu Z, \textbf{Schnable JC} (2019) Sorghum organ classification in hyperspectral images using supervised machine learning classification methods. \textsc{Second International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2019)} Ames, IA, USA
