diff --git a/JSchnable.tex b/JSchnable.tex
index 706c5fdc39c58c7b4b832b1f2c9d0fb54d803e1e..1f5d2a97e5f4a928247013d19b61b73dfa78d24a 100644
--- a/JSchnable.tex
+++ b/JSchnable.tex
@@ -162,7 +162,7 @@ NSF PGRP Fellowship Supported Visiting Scholar \hfill 2013-2014
 
 \section*{Research Support}
 \begin{center}
-  \$25.6M in total federal funding as PI/co-PI 2015-Present\\
+  \$29.6M in total federal funding as PI/co-PI 2015-Present\\
   \textit{(Excludes \$20M NSF Center for Root and Rhizobiome Innovation award (2016) and \$20M NSF AI Institute for Resilient Agriculture award (2021).)}
 \end{center}
 \subsection*{Federal (Current)}
@@ -225,14 +225,16 @@ NSF PGRP Fellowship Supported Visiting Scholar \hfill 2013-2014
 
 \subsection*{Entrepreneurship-Related Funding}
 \begin{itemize}
-%\item NSF (to EnGeniousAg) ``SBIR Phase II: Low-cost in-planta nitrate sensor'' 2023-2025 \$1M
+\item NSF (to EnGeniousAg) ``SBIR Phase II: Low-cost in-planta nitrate sensor'' 2023-2025 \$1M
 \item NSF (to EnGeniousAg) ``SBIR Phase I: Low-cost in-planta nitrate sensor'' 2019-2022 \$225k
 \item USDA (to EnGeniousAg) ``SBIR Phase I: Low-cost field-deployable sensors to monitor nitrate in soil and water.'' 2019-2021 \$100k
+\item Raised more than \$7M in private sector equity funding.
 \end{itemize}
 
 \subsection*{Industry Cooperation}
 \begin{itemize}
 \item Scientific Advisory Council, GeneSeek, Inc\hfill2017-Present
+\item Advisor, DeepCropVision \textit{(UNL student lead-startup)}\hfill2022-Present
 \item Advisory Board, Afflo Sensors\hfill2023-Present
 \item External Advisor to the Scientific Advisory Board, Indigo Agriculture\hfill2017
 \item External Advisor to the Scientific Advisory Board, Syngenta AG\hfill2016
@@ -300,14 +302,14 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
 
 %Figure out how to emphasis italics stuff more.
 
-\addtolength{\leftskip}{9mm}
-\subsection*{Preprints}
+%\addtolength{\leftskip}{9mm}
+%\subsection*{Preprints}
 
-\noindent Kick D, Wallace J, \textbf{Schnable JC}, Kolkman JM, Alaca B, Beissinger TM, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta D, Singh MP, Weldekidan T, Washburn JD$^\S$ Yield prediction through integration of genetic, environment, and management data through deep learning. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2022.07.29.502051}{10.1101/2022.07.29.502051}\\
+%\noindent Kick D, Wallace J, \textbf{Schnable JC}, Kolkman JM, Alaca B, Beissinger TM, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta D, Singh MP, Weldekidan T, Washburn JD$^\S$ Yield prediction through integration of genetic, environment, and management data through deep learning. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2022.07.29.502051}{10.1101/2022.07.29.502051}\\
 
-\noindent Xu G, Lyu J, Obata T, Liu S, Ge Y, \textbf{Schnable JC}, Yang J$^\S$ A historically balanced locus under recent directional selection in responding to changed nitrogen conditions during modern maize breeding. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2022.02.09.479784}{10.1101/2022.02.09.479784}\\
+%\noindent Xu G, Lyu J, Obata T, Liu S, Ge Y, \textbf{Schnable JC}, Yang J$^\S$ A historically balanced locus under recent directional selection in responding to changed nitrogen conditions during modern maize breeding. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2022.02.09.479784}{10.1101/2022.02.09.479784}\\
 
-\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}
 
@@ -326,6 +328,14 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
 \begin{etaremune}
 \subsection*{Faculty Publications}
 
+\item Barnes AC, Myers JL, Surber SM, \textbf{Liang Z}, Mower JP, \textbf{Schnable JC}, Roston RL (2023) Oligogalactolipid production during cold challenge is conserved in early diverging lineages. \textsc{Journal of Experimental Botany} doi: \href{https://doi.org/10.1093/jxb/erad241}{10.1093/jxb/erad241}
+
+\item Chen J, Wang Z, Tan K, Huang W, Shi J, Li T, Hu J, Wang K, Xin B, Zhao H, Song W, Hufford MB, \textbf{Schnable JC}, Ware DH, Jin W, Lai J$^\S$ (2023) A complete telomere-to-telomere assembly of the maize genome. \textsc{Nature Genetics} doi: \href{https://doi.org/10.1038/s41588-023-01419-6}{10.1038/s41588-023-01419-6}
+
+\item Kick D, Wallace J, \textbf{Schnable JC}, Kolkman JM, Alaca B, Beissinger TM, Ertl D, Flint-Garcia S, Gage JL, Hirsch CN, Knoll JE, de Leon N, Lima DC, Moreta D, Singh MP, Weldekidan T, Washburn JD$^\S$ Yield prediction through integration of genetic, environment, and management data through deep learning. \textsc{G3} doi: \href{https://doi.org/10.1093/g3journal/jkad006}{10.1093/g3journal/jkad006} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2022.07.29.502051}{10.1101/2022.07.29.502051}
+
+\item Lima DC$^\S$, Aviles AC, Alphers RT ... \textbf{Schnable JC} (26th of 37 authors) ... Wisser RJ, Xu W, de Leon N (2023) 2018–2019 field seasons of the Maize Genomes to Fields (G2F) G x E project. \textsc{BMC Genomic Data} doi: \href{https://doi.org/10.1186/s12863-023-01129-2}{10.1186/s12863-023-01129-2}
+
 \item Sahay S$^*$, \textbf{Grzybowski M}$^*$, \textbf{Schnable JC}, Glowacka K$^\S$ (2023) Genetic control of photoprotection and photosystem II operating efficiency in plants. \textsc{New Phytologist} doi: \href{https://doi.org/10.1111/nph.18980}{10.1111/nph.18980}
 
 \item Wijewardane NK, Zhang H, Yang J, \textbf{Schnable JC}, Schachtman DP, Ge Y$^\S$ (2023) A leaf-level spectral library to support high throughput plant phenotyping: Predictive accuracy and model transfer. \textsc{Journal of Experimental Botany} doi: \href{https://doi.org/10.1093/jxb/erad129}{10.1093/jxb/erad129}
@@ -765,7 +775,8 @@ Science Advances
 \emph{Invited presentations only. Excludes presentations selected based on abstracts or applications.}
 \end{center}
 \begin{itemize}
-\item Corteva Symposium Series, North of Rio de Janeiro State University (Student Organized), Campos dos Goytacazes, Brazil\hfill2023
+\item Sorghum in the 21st Century, Montpellier, France\hfill2023
+\item Corteva Symposium Series, North of Rio de Janeiro State University (Student Organized), Campos dos Goytacazes, Brazil\hfill2023\textit{(Remote)}
 \item Iowa Biotech Showcase, Ankeny, IA\hfill2023
 \item SFBV (French Society of Plant Biology), Montpellier, France\hfill 2022
 \item Plant Response to Stresses and Environmental Signals, Beijing, China\hfill 2022 \textit{(Remote)}