Skip to content
Snippets Groups Projects
Commit 646fc853 authored by James Schnable's avatar James Schnable
Browse files

updates

parent 5a8ed209
No related branches found
No related tags found
No related merge requests found
Show whitespace changes
Inline Side-by-side
Showing
with 14 additions and 12 deletions
JSchnable.tex
+ 14
− 12
View file @ 646fc853
...@@ -283,7 +283,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg ...@@ -283,7 +283,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\addtolength{\leftskip}{9mm} \addtolength{\leftskip}{9mm}
\subsection*{Preprints} \subsection*{Preprints}
\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{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{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}\\
...@@ -291,24 +291,26 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg ...@@ -291,24 +291,26 @@ 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 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}\\ \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 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 Atefi A, Ge Y$^\S$, Pitla S, \textbf{Schnable JC}. Robotic Technologies for High-Throughput Plant Phenotyping: Reviews and Perspectives. \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)} \\
\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 \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 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 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 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 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}
\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}
...@@ -536,7 +538,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg ...@@ -536,7 +538,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\subsection*{Peer Reviewed Conference Papers} \subsection*{Peer Reviewed Conference Papers}
\begin{etaremune} \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) Detections of Plant Signature Gases Utilizing a Nano-Gap Conductivity Sensor. \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 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 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 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 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}
...@@ -675,7 +677,7 @@ Science ...@@ -675,7 +677,7 @@ Science
\begin{itemize} \begin{itemize}
\item Soybean Breeders Workshop \hfill 2021 \textit{(Remote, COVID)} \item Soybean Breeders Workshop \hfill 2021 \textit{(Remote, COVID)}
\item NAPPN 2021\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\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
\item Systems Biology and Ontologies Session, Plant and Animal Genome, San Diego, CA, USA\hfill2020 \item Systems Biology and Ontologies Session, Plant and Animal Genome, San Diego, CA, USA\hfill2020
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment