diff --git a/JSchnable.tex b/JSchnable.tex
index 55dc5b8aaedb0f10fd896308db06f30ba7f1b9c1..725cc3563503db7e83e0b82be89c7f036d5e3a0d 100644
--- a/JSchnable.tex
+++ b/JSchnable.tex
@@ -270,9 +270,14 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
 
 \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}\\
 
+\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}\\
+
+
 %\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 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 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)}\\
 
@@ -286,25 +291,28 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
 
 %\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)}
 
-
 \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 \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} doi: \href{https://doi.org/10.1002/aps3.11385}{10.1002/aps3.11385}
 
 \item Gaillard M$^*$, \textbf{Miao C}$^*$, \textbf{Schnable JC}$^\S$, 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 Raju SKK$^\S$, Thompson AM, \textbf{Schnable JC} (2020) Advances in plant phenomics: From data and algorithms to biological insights. \textsc{Applications in Plant Sciences} doi: \href{https://doi.org/10.1002/aps3.11386}{10.1002/aps3.11386}
+
 \item Atefi A, Ge Y$^\S$, Pitla S, \textbf{Schnable JC} (2020) Robotic detection and grasp of maize and sorghum: stem measurement with contact. \textsc{Robotics} doi: \href{https://doi.org/10.3390/robotics9030058}{10.3390/robotics9030058}
 
 \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} doi: \href{https://doi.org/10.34133/2020/7481687}{10.34133/2020/7481687}
 
 \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 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}\\
+\textbf{\textit{ "In Brief" highlighting this article by SKK Raju}} doi: \href{https://doi.org/10.1105/tpc.20.00471}{10.1105/tpc.20.00471}
 
 \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{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}\\
+\textbf{\textit{ "News and Views" highlighting this article by Y Yu}} doi: \href{https://doi.org/10.1104/pp.20.00797}{10.1104/pp.20.00797}
 
 \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}
 
@@ -487,7 +495,7 @@ 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
@@ -582,9 +590,11 @@ Science
 %  \item University of Massachusetts Amherst, Oxford, OH, USA\textit{\hfill(Sept. 2019)}
 %\end{itemize}
 \begin{itemize}
-%Bayer Crop Science, St. Louis, MO, USA\hfill2020
-%\item University of Bonn, Bonn, Germany\hfill2020
-%\item King Abdullah University of Science and Technology, Jeddeh, Saudi Arabia \hfill 2020
+%\item University of Missouri, Columbia, MO, USA\hfill2020 \textit{(Remote, COVID)}
+%\item Rutgers University, New Brunswick, NJ, USA\hfill2020 \textit{(Remote, COVID)}
+\item Bayer Crop Science, St. Louis, MO, USA\hfill2020 \textit{(Remote, COVID)}
+\item University of Bonn, Bonn, Germany\hfill2020 \textit{(Remote, COVID)}
+\item King Abdullah University of Science and Technology, Jeddeh, Saudi Arabia \hfill 2020 \textit{(Remote, COVID)}
 \item University of Hawaii, Manoa, HI, USA \textit{(Brewbaker Lecture)} \hfill 2019
 \item Miami University, Oxford, OH, USA \hfill 2019
 \item University of Massachusetts Amherst, Oxford, OH, USA \hfill 2019
@@ -621,7 +631,7 @@ Science
 \emph{Invited presentations only. Excludes presentations selected based on abstracts or applications.}
 \end{center}
 \begin{itemize}
-%\item Maximizing Genetic Gain in Plant Breeding, ASA-CSSA-SSSA International Annual Meeting, San Antonio, TX, USA\hfill2019
+\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
 \item Guelph Plant Sciences Symposium (Student Organized), Guelph, Ontario, Canada\hfill2019
@@ -649,6 +659,7 @@ Science
 \end{itemize}
 \subsection*{Internal}
 \begin{itemize}
+%\item Agronomy \& Horticulture Departmental Seminar Series, UNL\hfill2020 \textit{(Remote, COVID)}
 \item Nebraska Plant Science Symposium (Student Organized)\hfill2019
 \item UNL Plant Phenomics Symposium\hfill2018
 \item NeDA 2017: 2nd Nebraska Data Analytics Workshop, UNL\hfill2017