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Commit 5a55d95a authored by James Schnable's avatar James Schnable
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......@@ -268,13 +268,13 @@ Srinidhi Bashyam (co-advised, MS, Computer Science \& Engineering)
2 UCARE (Undergraduate Creative Activities and Research Experience) students;
and 9 undergraduate students supported by regular research funding.
\item \textbf{High School Researchers:}
1 student supported through the Young Nebraska Scientist program;
2 students supported through the Young Nebraska Scientist program;
1 supported by regular research funding.
\end{itemize}
\section*{Publications}
\begin{center}
\textbf{H-Index:} \textbf{\href{https://scholar.google.com/citations?user=cik4JVYAAAAJ}{32}} \\
\textbf{H-Index:} \textbf{\href{https://scholar.google.com/citations?user=cik4JVYAAAAJ}{33}} \\
Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$undergraduate author, $^\S$corresponding author
\end{center}
......@@ -283,32 +283,40 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\addtolength{\leftskip}{9mm}
\subsection*{Preprints}
\noindent Rodene E, Xu G, Delen SP, \textbf{Smith C}, Ge Y, \textbf{Schnable JC}, Yang J$^\S$ A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2021.05.24.445447}{10.1101/2021.05.24.445447} \\
\noindent \textbf{Sun G}$^\S$, \textbf{Mural RV}, \textbf{Turkus JD}, \textbf{Schnable JC} Quantitative resistance loci to southern rust mapped in a temperate maize diversity panel. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2021.04.02.438220}{10.1101/2021.04.02.438220} \\
\noindent \textbf{Miao C}, \textbf{Guo A}$^\ddagger$, Yang J, Ge Y, \textbf{Schnable JC}$^\S$ Automation of leaf counting in maize and sorghum using deep learning. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.10.27.355495}{10.1101/2020.12.19.423626}\\
\noindent \textbf{Mural RV}, \textbf{Grzybowski M}, \textbf{Miao C}, \textbf{Damke A}$^\ddagger$, Sapkota S, Boyles RE, Salas Fernandez MG, Schnable PS, \textbf{Sigmon B}, Kresovich S, \textbf{Schnable JC}$^\S$ Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.10.27.355495}{10.1101/2020.10.27.355495}\\
\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 Zhang Z$^\S$, Chen C, Rutkoski J, \textbf{Schnable JC}, Murray S, Wang L, Jin X, Stich B, Crossa J, Hayes B. Harnessing Agronomics Through Genomics and Phenomics in Plant Breeding: A Review. \textsc{preprints.org} doi: \href{https://www.preprints.org/manuscript/202103.0519/v1}{10.20944/preprints202103.0519.v1}
\subsection*{Other Manuscripts 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)} \\
%\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)} \\
\noindent Kusmec A, Yeh CT, AlKhalifa N ... \textbf{Schnable JC} (26th of 38 authors) ... Willis DM, Wisser RJ, Schnable PS$^\S$ Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates: a case study involving grain yield of hybrid maize. \textit{(In Review)}\\
\noindent Kusmec A, Yeh CT, AlKhalifa N ... \textbf{Schnable JC} (26th of 38 authors) ... Willis DM, Wisser RJ, Schnable PS$^\S$ Data-driven identification of environmental variables influencing phenotypic plasticity to facilitate breeding for future climates: a case study involving grain yield of hybrid maize. \textit{(In Review)}
\noindent Serba DD, \textbf{Meng X}, \textbf{Schnable JC}, Bashir E, Michaud JP, Prasad PVV, Perumal R$^\S$ Comparative transcriptome analysis reveals genetic mechanisms of sugarcane aphid resistance in grain sorghum. \textit{(In Review)} \\
\noindent \textbf{Sun G}, Wase N, Shu S, Jenkins J, Zhou B, Chen C, Sandor L, Plott C, Yoshinga Y, Daum C, Qi P, Barry K, Lipzen A, Berry L, Gottilla T, \textbf{Foltz A}, Yu H, O'Malley R, Zhang C, Devos KM, \textbf{Sigmon B}, Yu B, Obata T, Schmutz J$^\S$, \textbf{Schnable JC}$^\S$ Genome sequence of \textit{Paspalum vaginatum} indicates trehalose may act as a conserved trigger for increased nitrogen use efficiency in grasses. \textit{In Review} \\
\noindent Hurst JP, \textbf{Schnable JC}, Holding DR. Tandem duplicate expression patterns areconserved between maize haplotypes of the α-zeingene family. \textit{In Review}
\begin{etaremune}
\subsection*{Faculty Publications}
\item \textbf{Grzybowski M}, Wijewardane NK, Atefi A, Ge Y, \textbf{Schnable JC}$^S$ (2021) The potential of hyperspectral reflectance as a tool for quantitative genetics in crops. \textsc{Plant Communications} \textit{(Accepted)}
\item \textbf{Mural RV}, \textbf{Grzybowski M}, \textbf{Miao C}, \textbf{Damke A}$^\ddagger$, Sapkota S, Boyles RE, Salas Fernandez MG, Schnable PS, \textbf{Sigmon B}, Kresovich S, \textbf{Schnable JC}$^\S$ (2021) Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum. \textsc{Genetics} doi: \href{https://doi.org/10.1093/genetics/iyab087}{10.1093/genetics/iyab087} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.10.27.355495}{10.1101/2020.10.27.355495}
\item \textbf{Grzybowski M}, Wijewardane NK, Atefi A, Ge Y, \textbf{Schnable JC}$^\S$ (2021) The potential of hyperspectral reflectance as a tool for quantitative genetics in crops. \textsc{Plant Communications} doi: \href{https://doi.org/10.1016/j.xplc.2021.100209}{10.1016/j.xplc.2021.100209}
\item Zhou Y, Kusmec A, Mirnezami SV, Srinivasan L, Jubery TZ, \textbf{Schnable JC}, Salas-Fernandez MG, Nettleton D, Ganapathysubramanian B, Schnable PS$^\S$ (2021) Identification and exploitation of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping. \textsc{The Plant Cell} \textit{(Accepted)}
\item Zhou Y, Kusmec A, Mirnezami SV, Srinivasan L, Jubery TZ, \textbf{Schnable JC}, Salas-Fernandez MG, Nettleton D, Ganapathysubramanian B, Schnable PS$^\S$ (2021) Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping. \textsc{The Plant Cell} doi: \href{https://doi.org/10.1093/plcell/koab134}{10.1093/plcell/koab134}
\item Atefi A, Ge Y$^\S$, Pitla S, \textbf{Schnable JC} (2021) Robotic Technologies for High-Throughput Plant Phenotyping: Reviews and Perspectives. \textsc{Frontiers in Plant Science} \textit{(Accepted)}
\item Atefi A, Ge Y$^\S$, Pitla S, \textbf{Schnable JC} (2021) Robotic Technologies for High-Throughput Plant Phenotyping: Reviews and Perspectives. \textsc{Frontiers in Plant Science} doi: \href{https://www.frontiersin.org/articles/10.3389/fpls.2021.611940/}{10.3389/fpls.2021.611940} \textit{(Final Version In Press)}
\item Alzadjali A, Veeranampalayam-Sivakumar A, Alali MH, Deogun JS, Scott S, \textbf{Schnable JC}, Shi Y$^\S$ (2021) Maize tassel detection from UAV imagery using deep learning. \textsc{Frontiers in Robotics and AI} \textit{(Accepted)}
\item Alzadjali A, Veeranampalayam-Sivakumar A, Alali MH, Deogun JS, Scott S, \textbf{Schnable JC}, Shi Y$^\S$ (2021) Maize tassel detection from UAV imagery using deep learning. \textsc{Frontiers in Robotics and AI} \href{https://www.frontiersin.org/articles/10.3389/frobt.2021.600410/}{10.3389/frobt.2021.600410} \textit{(Final Version In Press)}
\item Meier MA, Lopenz-Guerrero MG, Guo M, Schmer MR, Herr JR, \textbf{Schnable JC}, Alfano JR, Yang J$^\S$ (2021) Rhizosphere microbiomes in a historical maize/soybean rotation system respond to host species and nitrogen fertilization at genus and sub-genus levels. \textsc{Applied and Environmental Microbiology} doi: \href{https://doi.org/10.1128/AEM.03132-20}{10.1128/AEM.03132-20} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.10.244384}{10.1101/2020.08.10.244384}
......@@ -538,7 +546,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\subsection*{Peer Reviewed Conference Papers}
\begin{etaremune}
\item Khan SH, Tope S, Dalpati R, Kim KH, Noh M, Bulbul A, \textbf{Mural RV}, Banerjee A, \textbf{Schnable JC}, Ji M, Mastrango C, Zang L, Kim H. (2020) Development of a gas sensor for green leaf volatile detection. \textsc{Transducers 2021} \textit{(Accepted)}
\item Khan SH, Tope S, Dalpati R, Kim KH, Noh M, Bulbul A, \textbf{Mural RV}, Banerjee A, \textbf{Schnable JC}, Ji M, Mastrango C, Zang L, Kim H. (2021) Development of a gas sensor for green leaf volatile detection. \textsc{Transducers 2021} \textit{(Accepted)}
\item Gaillard M, \textbf{Miao C}, \textbf{Schnable JC}, Benes B (2020) Sorghum Segmentation by Skeleton Extraction. \textsc{Computer Vision Problems in Plant Phenotyping (CVPPP 2020)} Glasgow, UK
\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}
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