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Commit c0ea98f3 authored by James Schnable's avatar James Schnable
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h-index increased by one. yes I am that vane.

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...@@ -153,17 +153,15 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013 ...@@ -153,17 +153,15 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013
\end{itemize} \end{itemize}
\section*{Research Support} \section*{Research Support}
\begin{center}
\$25.6M in total federal funding as PI/co-PI 2015-Present\\
\textit{(Excludes \$20M CRRI award)}
\end{center}
\subsection*{Federal (Current)} \subsection*{Federal (Current)}
%\begin{center}
%\$36.8M in total federal funding\\
%\$3.6M as PI and \$12.9M as co-PI\\
%\$3.9M in active federal funding to own research group.\\
%\end{center}
\begin{itemize} \begin{itemize}
\item DOE ``\href{https://news.unl.edu/newsrooms/today/article/nebraska-team-merges-machine-learning-plant-genetics-to-maximize-sorghum/}{TGCM: (T)rait, (G)ene, and (C)rop Growth (M)odel directed targeted gene characterization in sorghum}.'' (PI) 2019-2022. \$2.7M \item DOE ``\href{https://news.unl.edu/newsrooms/today/article/nebraska-team-merges-machine-learning-plant-genetics-to-maximize-sorghum/}{TGCM: (T)rait, (G)ene, and (C)rop Growth (M)odel directed targeted gene characterization in sorghum}.'' (PI) 2019-2022. \$2.7M
\item NSF ``\href{https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838307}{RoL: FELS: EAGER: Genetic constraints on the increase of organismal complexity over time.}'' (PI) 2018-2021. \$300k \item NSF ``\href{https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838307}{RoL: FELS: EAGER: Genetic constraints on the increase of organismal complexity over time.}'' (PI) 2018-2021. \$300k
\item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1008702-identifying-mechanisms-conferring-low-temperature-tolerance-in-maize-sorghum-and-frost-tolerant-relatives.html}{Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives.}'' (PI) 2015-2020. \$455k
\item NSF ``\href{https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844707}{BTT EAGER: A wearable plant sensor for real-time monitoring of sap flow and stem diameter to accelerate breeding for water use efficiency.}'' (PI) 2019-2021. \$300k \item NSF ``\href{https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844707}{BTT EAGER: A wearable plant sensor for real-time monitoring of sap flow and stem diameter to accelerate breeding for water use efficiency.}'' (PI) 2019-2021. \$300k
\item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1022298-high-intensity-phenotyping-sites.html}{High Intensity Phenotyping Sites: Transitioning To A Nationwide Plant Phenotyping Network.}'' (co-PI) 2020-2023. \$3M \item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1022298-high-intensity-phenotyping-sites.html}{High Intensity Phenotyping Sites: Transitioning To A Nationwide Plant Phenotyping Network.}'' (co-PI) 2020-2023. \$3M
\item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1022368-high-intensity-phenotyping-sitesa-multi-scale-multi-modal-sensing-and-sense-making-cyber-ecosystem-for-genomes-to-fields.html}{High Intensity Phenotyping Sites: A multi-scale, multi-modal sensing and sense-making cyber-ecosystem for Genomes to Fields.}'' (co-PI) 2020-2023. \$2.7M \item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1022368-high-intensity-phenotyping-sitesa-multi-scale-multi-modal-sensing-and-sense-making-cyber-ecosystem-for-genomes-to-fields.html}{High Intensity Phenotyping Sites: A multi-scale, multi-modal sensing and sense-making cyber-ecosystem for Genomes to Fields.}'' (co-PI) 2020-2023. \$2.7M
...@@ -178,22 +176,23 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013 ...@@ -178,22 +176,23 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013
\end{itemize} \end{itemize}
\subsection*{Non-Federal (Current)} \subsection*{Non-Federal (Current)}
\begin{itemize} \begin{itemize}
\item ICRISAT ``Identifying Novel Loci Controlling Priority Traits in Pearl Millet and Sorghum using Supervised Classification Algorithms.'' (PI) 2020-2021 \item ICRISAT ``Identifying Novel Loci Controlling Priority Traits in Pearl Millet and Sorghum using Supervised Classification Algorithms.'' (PI) 2020-2021 \$50k
\item Nebraska Corn Board ``Genomes to Fields (G2F) - Predicting Final Yield Performance in Variable Environments.'' (PI) 2016-2020. %\$200k \textit{(to date)} \item Nebraska Corn Board ``Genomes to Fields (G2F) - Predicting Final Yield Performance in Variable Environments.'' (PI) 2016-2021. \$250k \textit{(to date)}
\item Daugherty Water for Food Global Institute ``Optimizing the Water Use Efficiency of C4 Grain Crops Using Comparative Phenomics and Crop Models to Guide Breeding Targets.'' (PI) 2017-2019. %\$27k \item Wheat Innovation Foundation ``A Low-Cost, High-Throughput Cold Stress Perception Assay for Sorghum Breeding.'' (co-PI) 2019-2021. \$205k
\item Wheat Innovation Foundation ``A Low-Cost, High-Throughput Cold Stress Perception Assay for Sorghum Breeding.'' (co-PI) 2019-2021. %\$205k
\item North Central Sun Grants ``High through put phenotyping to accelerate biomass sorghum improvement.'' (co-PI) 2017-2019. %\$193k
\end{itemize} \end{itemize}
\subsection*{Completed Projects} \subsection*{Completed Projects}
\begin{itemize} \begin{itemize}
\item \item ARPA-E ``\href{https://arpa-e.energy.gov/?q=slick-sheet-project/soil-sensors-nitrogen-use-efficiency}{In-plant and in-soil microsensors enabled high-throughput phenotyping of root nitrogen uptake and nitrogen use efficiency.}'' (co-PI) 2017-2019. %\$1.1M \item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1008702-identifying-mechanisms-conferring-low-temperature-tolerance-in-maize-sorghum-and-frost-tolerant-relatives.html}{Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives.}'' (PI) 2015-2020. \$455k
\item Agricultural Research Division ``A High Throughput Phenotyping Reference Dataset for GWAS in Sorghum'' (PI) 2016-2018. %\$100k \item ARPA-E ``\href{https://arpa-e.energy.gov/?q=slick-sheet-project/soil-sensors-nitrogen-use-efficiency}{In-plant and in-soil microsensors enabled high-throughput phenotyping of root nitrogen uptake and nitrogen use efficiency.}'' (co-PI) 2017-2019. \$1.1M
\item ICRISAT ``Application of tGBS And Genomic Selection to a Hybrid Pearl Millet Breeding Program.'' 2015-2017. %\$45k \item USDA/NSF Joint Program ``PAPM EAGER: Transitioning to the next generation plant phenotyping robots.'' (co-PI) 2016-2018. \$285k
\item ConAgra ``Marker Discovery \& Genetic Diversity.'' (replacement PI) 2014-2017. %\$162k \item North Central Sun Grants ``High through put phenotyping to accelerate biomass sorghum improvement.'' (co-PI) 2017-2019. \$193k
\item Iowa Corn Board ``Field Deployable Cameras to Quantify Dynamic Whole Plant Phenotypes in the Field.'' (PI) 2014-2016. %\$43k \item Daugherty Water for Food Global Institute ``Optimizing the Water Use Efficiency of C4 Grain Crops Using Comparative Phenomics and Crop Models to Guide Breeding Targets.'' (PI) 2017-2019. \$27k
\item Midwest Big Data Hub ``Automatic feature extraction pipeline development for high-throughput plant phenotyping'' (co-PI) 2017-2018. %\$5k \item Agricultural Research Division ``A High Throughput Phenotyping Reference Dataset for GWAS in Sorghum'' (PI) 2016-2018. \$100k
\item USDA/NSF Joint Program ``PAPM EAGER: Transitioning to the next generation plant phenotyping robots.'' (co-PI) 2016-2018. %\$285k \item ICRISAT ``Application of tGBS And Genomic Selection to a Hybrid Pearl Millet Breeding Program.'' 2015-2017. \$45k
\item Layman Award ``Developing genomic tools in proso millet and comparing water use efficiency among panicoid grass crops (proso millet, corn, sorghum, foxtail millet)'' (co-PI) 2014-2015. %\$10k \item ConAgra ``Marker Discovery \& Genetic Diversity.'' (replacement PI) 2014-2017. \$162k
\item Iowa Corn Board ``Field Deployable Cameras to Quantify Dynamic Whole Plant Phenotypes in the Field.'' (PI) 2014-2016. \$43k
\item Midwest Big Data Hub ``Automatic feature extraction pipeline development for high-throughput plant phenotyping'' (co-PI) 2017-2018. \$5k
\item Layman Award ``Developing genomic tools in proso millet and comparing water use efficiency among panicoid grass crops (proso millet, corn, sorghum, foxtail millet)'' (co-PI) 2014-2015. \$10k
\end{itemize} \end{itemize}
%\section*{Pending Support} %\section*{Pending Support}
%\begin{itemize} %\begin{itemize}
...@@ -213,8 +212,8 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013 ...@@ -213,8 +212,8 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013
\subsection*{Entrepreneurship-Related Funding} \subsection*{Entrepreneurship-Related Funding}
\begin{itemize} \begin{itemize}
\item NSF (to EnGeniousAg) ``SBIR Phase I: Low-cost in-planta nitrate sensor'' 2019-2020 %\$225k \item NSF (to EnGeniousAg) ``SBIR Phase I: Low-cost in-planta nitrate sensor'' 2019-2020 \$225k
\item USDA (to EnGeniousAg) ``SBIR Phase I: Low-cost field-deployable sensors to monitor nitrate in soil and water.'' 2019-2020 %\$100k \item USDA (to EnGeniousAg) ``SBIR Phase I: Low-cost field-deployable sensors to monitor nitrate in soil and water.'' 2019-2020 \$100k
\end{itemize} \end{itemize}
\subsection*{Industry Cooperation} \subsection*{Industry Cooperation}
...@@ -265,8 +264,8 @@ Xiuru Dai (PhD, Shandong Agriculture University), ...@@ -265,8 +264,8 @@ Xiuru Dai (PhD, Shandong Agriculture University),
Bhushit Agarwal (co-advised, MS, Computer Science \& Engineering), Bhushit Agarwal (co-advised, MS, Computer Science \& Engineering),
Srinidhi Bashyam (co-advised, MS, Computer Science \& Engineering) Srinidhi Bashyam (co-advised, MS, Computer Science \& Engineering)
\item \textbf{Undergraduate Researchers:} \item \textbf{Undergraduate Researchers:}
6 NSF supported REU (Research Experience for Undergraduates) students; 2 6 NSF supported REU (Research Experience for Undergraduates) students;
UCARE (Undergraduate Creative Activities and Research Experience) students; 2 UCARE (Undergraduate Creative Activities and Research Experience) students;
and 9 undergraduate students supported by regular research funding. and 9 undergraduate students supported by regular research funding.
\item \textbf{High School Researchers:} \item \textbf{High School Researchers:}
1 student supported through the Young Nebraska Scientist program; 1 student supported through the Young Nebraska Scientist program;
...@@ -275,7 +274,7 @@ Srinidhi Bashyam (co-advised, MS, Computer Science \& Engineering) ...@@ -275,7 +274,7 @@ Srinidhi Bashyam (co-advised, MS, Computer Science \& Engineering)
\section*{Publications} \section*{Publications}
\begin{center} \begin{center}
\textbf{H-Index:} \textbf{\href{https://scholar.google.com/citations?user=cik4JVYAAAAJ}{31}} \\ \textbf{H-Index:} \textbf{\href{https://scholar.google.com/citations?user=cik4JVYAAAAJ}{32}} \\
Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$undergraduate author, $^\S$corresponding author Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$undergraduate author, $^\S$corresponding author
\end{center} \end{center}
...@@ -290,11 +289,12 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg ...@@ -290,11 +289,12 @@ 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 \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 Meier MA, Lopenz-Guerrero MG, Guo M, Schmer MR, Herr JR, \textbf{Schnable JC}, Alfano JR, Yang J$^\S$. Rhizosphere microbiomes in a historical maize/soybean rotation system respond to host species and nitrogen fertilization at genus and sub-genus levels. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.10.244384}{10.1101/2020.08.10.244384}\\ \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} \subsection*{Other Manuscripts in Review}
\noindent \textbf{Grzybowski M}, Wijewardane NK, Atefi A, Ge Y, \textbf{Schnable JC}$^S$ The potential of hyperspectral reflectance as a tool for quantitative genetics in crops. \textit{(In Review)}\\
\noindent Zhou Y, Kusmec A, Mirnezami SV, Srinivasan L, Jubery TZ, \textbf{Schnable JC}, Salas-Fernandez MG, Nettleton D, Ganapathysubramanian B, Schnable PS$^\S$ Identification and exploitation of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping. \textit{(In Review)}\\ \noindent Zhou Y, Kusmec A, Mirnezami SV, Srinivasan L, Jubery TZ, \textbf{Schnable JC}, Salas-Fernandez MG, Nettleton D, Ganapathysubramanian B, Schnable PS$^\S$ Identification and exploitation of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping. \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 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)} \\
...@@ -306,6 +306,8 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg ...@@ -306,6 +306,8 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\begin{etaremune} \begin{etaremune}
\subsection*{Faculty Publications} \subsection*{Faculty Publications}
\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} \texit{(Accepted)} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.10.244384}{10.1101/2020.08.10.244384}
\item Busta L, Schmitz E, Kosma D, \textbf{Schnable JC}, Cahoon EB$^\S$ (2021) A co-opted steroid synthesis gene, maintained in sorghum but not maize, is associated with a divergence in leaf wax chemistry. \textsc{Proceedings of the National Academy of Sciences of the United States of America} doi: \href{https://doi.org/10.1073/pnas.2022982118}{10.1073/pnas.2022982118} \item Busta L, Schmitz E, Kosma D, \textbf{Schnable JC}, Cahoon EB$^\S$ (2021) A co-opted steroid synthesis gene, maintained in sorghum but not maize, is associated with a divergence in leaf wax chemistry. \textsc{Proceedings of the National Academy of Sciences of the United States of America} doi: \href{https://doi.org/10.1073/pnas.2022982118}{10.1073/pnas.2022982118}
\item \textbf{Meng X}, \textbf{Liang Z}, \textbf{Dai X}, \textbf{Zhang Y}, Mahboub S, \textbf{Ngu DW}$^\ddagger$, Roston RL, \textbf{Schnable JC}$^\S$ (2021) Predicting transcriptional responses to cold stress across plant species. \textsc{Proceedings of the National Academy of Sciences of the United States of America}. doi: \href{https://doi.org/10.1073/pnas.2026330118}{10.1073/pnas.2026330118} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.25.266635}{10.1101/2020.08.25.266635} \item \textbf{Meng X}, \textbf{Liang Z}, \textbf{Dai X}, \textbf{Zhang Y}, Mahboub S, \textbf{Ngu DW}$^\ddagger$, Roston RL, \textbf{Schnable JC}$^\S$ (2021) Predicting transcriptional responses to cold stress across plant species. \textsc{Proceedings of the National Academy of Sciences of the United States of America}. doi: \href{https://doi.org/10.1073/pnas.2026330118}{10.1073/pnas.2026330118} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.25.266635}{10.1101/2020.08.25.266635}
...@@ -669,6 +671,8 @@ Science ...@@ -669,6 +671,8 @@ Science
\emph{Invited presentations only. Excludes presentations selected based on abstracts or applications.} \emph{Invited presentations only. Excludes presentations selected based on abstracts or applications.}
\end{center} \end{center}
\begin{itemize} \begin{itemize}
\item Soybean Breeders Workshop \hfill 2021 \textit{(Remote, COVID)}
\item NAPPN 2021\hfill 2021 \textit{(Remote, COVID)}
\item DIGICROP 2020 International Conference on Digital Technologies for Sustainable Crop Production\hfill 2020 \textit{(Remote, COVID)} \item DIGICROP 2020 International Conference on Digital Technologies for Sustainable Crop Production\hfill 2020 \textit{(Remote, COVID)}
\item National Association of Plant Breeders Annual Meeting, Lincoln, NE, USA\hfill 2020 \textit{(Remote, COVID)} \item National Association of Plant Breeders Annual Meeting, Lincoln, NE, USA\hfill 2020 \textit{(Remote, COVID)}
\item iGenomX Session, Plant and Animal Genome, San Diego, CA, USA\hfill2020 \item iGenomX Session, Plant and Animal Genome, San Diego, CA, USA\hfill2020
......
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