diff --git a/JSchnable.tex b/JSchnable.tex
index 1201a9d5902293c458008568bb2df9228c843bf3..f0783108ff0f24d5edca3c606043538acb1948d3 100644
--- a/JSchnable.tex
+++ b/JSchnable.tex
@@ -161,18 +161,19 @@ NSF PGRP Fellowship Supported Postdoctoral Researcher \hfill 2013
 %\$3.9M in active federal funding to own research group.\\
 %\end{center}
 \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 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) 2018-2020. %\$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) 2015-2019. %\$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 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.
-\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.
-\item USDA-NIFA ``\href{https://portal.nifa.usda.gov/web/crisprojectpages/1022122-cps-medium-collaborative-research-field-scale-single-plant-resolution-agricultural-management-using-coupled-molecular-and-macro-sensing-and-multi-scale-data-fusion-and-modeling.html}{CPS: Medium: Field-scale, single plant-resolution agricultural management using coupled molecular and macro sensing and multi-scale data fusion and modeling}'' (co-PI) (2020-2023)
-\item ARPA-E ``\href{https://arpa-e.energy.gov/?q=news-item/arpa-e-announces-165-million-technologies-supporting-biofuels-supply-chain}{Soil Organic Carbon Networked Measurement System (SOCNET)}'' (co-PI) 2020-2023
-\item NSF ``\href{https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826781}{RII Track-2 FEC: Functional analysis of nitrogen responsive networks in Sorghum.}'' (co-PI) 2018-2022. %\$4M
-\item ARPA-E ``\href{https://unews.utah.edu/protecting-the-field-of-dreams/}{Low cost wireless chemical sensor networks.}'' (co-PI) 2019-2022. %\$2.2M
-\item FFAR ``\href{http://www.ncsa.illinois.edu/news/story/crops_in_silico_project_awarded_5_million}{Crops in silico: Increasing crop production by connecting models from the microscale to the macroscale.}'' (co-PI) 2019-2023. %\$5M
-\item NSF ``\href{https://nsf.gov/awardsearch/showAward?AWD_ID=1557417}{Center for Root and Rhizobiome Innovation.}'' (Investigator \& Management Team Member) 2016-2021. %\$20M
+\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 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 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/1022122-cps-medium-collaborative-research-field-scale-single-plant-resolution-agricultural-management-using-coupled-molecular-and-macro-sensing-and-multi-scale-data-fusion-and-modeling.html}{CPS: Medium: Field-scale, single plant-resolution agricultural management using coupled molecular and macro sensing and multi-scale data fusion and modeling}'' (co-PI) (2020-2023) \$1.05M
+\item ARPA-E ``\href{https://arpa-e.energy.gov/?q=news-item/arpa-e-announces-165-million-technologies-supporting-biofuels-supply-chain}{Soil Organic Carbon Networked Measurement System (SOCNET)}'' (co-PI) 2020-2023 \$1.9M
+\item ARPA-E ``CORN- Crop Optimization Realized through Neuralnets'' (co-PI) 2020-2022 \$620k
+\item NSF ``\href{https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826781}{RII Track-2 FEC: Functional analysis of nitrogen responsive networks in Sorghum.}'' (co-PI) 2018-2022. \$4M
+\item ARPA-E ``\href{https://unews.utah.edu/protecting-the-field-of-dreams/}{Low cost wireless chemical sensor networks.}'' (co-PI) 2019-2022. \$2.2M
+\item FFAR ``\href{http://www.ncsa.illinois.edu/news/story/crops_in_silico_project_awarded_5_million}{Crops in silico: Increasing crop production by connecting models from the microscale to the macroscale.}'' (co-PI) 2019-2023. \$5M
+\item NSF ``\href{https://nsf.gov/awardsearch/showAward?AWD_ID=1557417}{Center for Root and Rhizobiome Innovation.}'' (Investigator \& Management Team Member) 2016-2021. \$20M
 %\item DOE-JGI Community Sequencing Program ``Expanding grass genome comparators.''
 \end{itemize}
 \subsection*{Non-Federal (Current)}
@@ -274,7 +275,7 @@ Srinidhi Bashyam (co-advised, MS, Computer Science \& Engineering)
 
 \section*{Publications}
 \begin{center}
-\textbf{H-Index:} \textbf{\href{https://scholar.google.com/citations?user=cik4JVYAAAAJ}{30}} \\
+\textbf{H-Index:} \textbf{\href{https://scholar.google.com/citations?user=cik4JVYAAAAJ}{31}} \\
 Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$undergraduate author, $^\S$corresponding author
 \end{center}
 
@@ -287,37 +288,35 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
 
 \noindent \textbf{Mural RV}, \textbf{Grzybowski M}, \textbf{Miao C}, \textbf{Damke A}$^\ddagger$, Sapkota S, Boyles RE, Salas Fernandez MG, Schnable PS, \textbf{Sigmon B}, Kresovich S, \textbf{Schnable JC}$^\S$ Meta-analysis identifies pleiotropic loci controlling phenotypic trade-offs in sorghum. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.10.27.355495}{10.1101/2020.10.27.355495}\\
 
-\noindent \textbf{Meng X}, \textbf{Liang Z}, \textbf{Dai X}, \textbf{Zhang Y}, Mahboub S, \textbf{Ngu DW}$^\ddagger$, Roston RL, \textbf{Schnable JC}$^\S$ Predicting transcriptional responses to cold stress across plant species. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.25.266635}{10.1101/2020.08.25.266635}\\
-
 \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}\\
 
 
-%\subsection*{Other Manuscripts in Review}
+\subsection*{Other Manuscripts 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 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 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 Busta L, Schmitz E, Kosma D, Schnable JC, Cahoon EB. A co-opted steroid synthesis gene, maintained in sorghum but not maize, seals leaves against water loss. \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 Rogers AR, Dunne JC, Romay C ... \textbf{Schnable JC} (24th of 39 authors) ... Kaeppler S, De Leon N, Holland JB. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. \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 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}
 
-%\noindent Atefi A, Ge Y$^\S$, Pitla S, Schnable JC. Robotic Technologies for High-Throughput Plant Phenotyping: Reviews and Perspectives. \textit{(In Review)}
+\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}. \textit{(Accepted)} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/2020.08.25.266635}{10.1101/2020.08.25.266635}
 
-%\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)}
+\item Busta L, Schmitz E, Kosma D, \textbf{Schnable JC}, Cahoon EB. A co-opted steroid synthesis gene, maintained in sorghum but not maize, seals leaves against water loss. \textsc{Proceedings of the National Academy of Sciences of the United States of America}. \textit{(Accepted)}
 
-%\noindent Lai X, Bendix C, Zhang Y, \textbf{Schnable JC}, Harmon FG$^\S$ 72-hour diurnal RNA-seq analysis of fully expanded third leaves from maize, sorghum, and foxtail millet at 3-hour resolution. \textit{(In Review)}
+\item Sankaran S$^\S$, Marzougui A, \textbf{Hurst JP}, Zhang C, \textbf{Schnable JC}, Shi Y (2021) Can high resolution satellite imagery be used in high-throughput field phenotyping? \textsc{Transactions of the ASABE} \textit{(Accepted)}
 
-\begin{etaremune}
-\subsection*{Faculty Publications}
+\item Zhu Y, Chen Y, Ali Md. A, Dong L, Wang X, Archontoulis SV, \textbf{Schnable JC}, Castellano MJ$^\S$ (2021) Continuous in situ soil nitrate sensors: a comparison with conventional measurements and the value of high temporal resolution measurements. \textsc{Soil Science Society of America Journal} doi: \href{https://doi.org/10.1002/saj2.20226}{10.1002/saj2.20226}
 
-\item Zhu Y, Chen Y, Ali Md. A, Dong L, Wang X, Archontoulis SV, \textbf{Schnable JC}, Castellano MJ$^\S$ (2021) Continuous in situ soil nitrate sensors: a comparison with conventional measurements and the value of high temporal resolution measurements. \textsc{Soil Science Society of America Journal} \textit{(Accepted)}
+\item \textbf{Lai X}, Bendix C, \textbf{Zhang Y}, \textbf{Schnable JC}, Harmon FG$^\S$ (2021) 72-hour diurnal RNA-seq analysis of fully expanded third leaves from maize, sorghum, and foxtail millet at 3-hour resolution. \textsc{BMC Research Notes} doi: \href{https://doi.org/10.1186/s13104-020-05431-5}{10.1186/s13104-020-05431-5}
 
-\item Weissmann S, Huang P, Furoyama K, Wiechert M, Taniguchi M, \textbf{Schnable JC},$^\S$ Brutnell TP, Mockler TC$^\S$ (2021) DCT4 - a new member of the dicarboxylate transporter family in C\textsubscript{4} grasses. \textsc{Genome Biology and Evolution} \textit{(Accepted)} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/762724}{10.1101/762724}
+\item Rogers AR, Dunne JC, Romay C ... \textbf{Schnable JC} (24th of 39 authors) ... Kaeppler S, De Leon N, Holland JB$^\S$ (2021) The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. \textsc{G3:Genes|Genomes|Genetics} doi: \href{https://doi.org/10.1093/g3journal/jkaa050}{10.1093/g3journal/jkaa050}
 
 \item Jarquin D, de Leon N, Romay C ... \textbf{Schnable JC} (24th of 33 authors) ... Wisser RJ, Xu W, Lorenz A (2021) Utility of climatic information via combining ability models to improve genomic prediction for yield within the Genomes to Fields maize project. \textsc{Frontiers in Genetics} doi: \href{https://doi.org/10.3389/fgene.2020.592769}{10.3389/fgene.2020.592769}
 
@@ -463,6 +462,8 @@ Lab members in \textbf{bold}, $^*$authors contributed equally, $^\ddagger$underg
 
 \subsection*{Postdoctoral Publications}
 
+\item Weissmann S, Huang P, Wiechert M, Furoyama K, Brutnell TP, Taniguchi M, \textbf{Schnable JC},$^\S$ Mockler TC$^\S$ (2021) DCT4 - a new member of the dicarboxylate transporter family in C\textsubscript{4} grasses. \textsc{Genome Biology and Evolution} doi: \href{https://doi.org/10.1093/gbe/evaa251}{10.1093/gbe/evaa251} \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/762724}{10.1101/762724}
+
 \item Nani TF, \textbf{Schnable JC}, Washburn JD, Albert P, Pereira WA, Sobrinho FS, Birchler JA, Techia VH$^\S$ (2018). Location of low copy genes in chromosomes of \textit{Brachiaria} spp. \textsc{Molecular Biology Reports} doi: \href{https://doi.org/10.1007/s11033-018-4144-5}{10.1007/s11033-018-4144-5}
 
 \item Studer AJ$^*$, {\bf Schnable JC}$^*$, Weissmann S, Kolbe AR, McKain MR, Shao Y, Cousins AB, Kellogg EA, Brutnell TP$^\S$ (2016) The draft genome of \textit {Dichanthelium oligosanthes}: A C3 panicoid grass species. \textsc{Genome Biology} doi: \href{http://dx.doi.org/10.1186/s13059-016-1080-3}{10.1186/s13059-016-1080-3}