From 646fc853d12decc022e4f3f489abee8f502fa79b Mon Sep 17 00:00:00 2001
From: James Schnable <james.schnable@gmail.com>
Date: Fri, 14 May 2021 21:50:23 -0500
Subject: [PATCH] updates
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JSchnable.tex | 26 ++++++++++++++------------
1 file changed, 14 insertions(+), 12 deletions(-)
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@@ -283,7 +283,7 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
\addtolength{\leftskip}{9mm}
\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}\\
@@ -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 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}
-\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 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}
\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}
@@ -536,7 +538,7 @@ 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) 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 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}
@@ -675,7 +677,7 @@ Science
\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\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 Systems Biology and Ontologies Session, Plant and Animal Genome, San Diego, CA, USA\hfill2020
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