\$16.5M in federal funding as PI or co-PI (excludes CRRI).
\end{center}
\subsection*{Federal (Current)}
\subsection*{Federal (Current)}
\begin{itemize}
\begin{itemize}
\item NSF ``RoL: FELS: EAGER: Genetic Constraints on the Increase of Organismal Complexity Over Time'' (PI)
\item DOE ``TGCM: (T)rait, (G)ene, and (C)rop Growth (M)odel directed targeted gene characterization in sorghum.'' \$2.7M 2019-2022 (PI)
\item USDA-NIFA ``Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives.'' (PI)
\item NSF ``RoL: FELS: EAGER: Genetic constraints on the increase of organismal complexity over time.'' \$300k 2018-2020 (PI)
\item NSF ``BTT EAGER: A wearable plant sensor for real-time monitoring of sap flow and stem diameter to accelerate breeding for water use efficiency.'' (PI)
\item USDA-NIFA ``Identifying mechanisms conferring low temperature tolerance in maize, sorghum, and frost tolerant relatives.'' \$455k 2015-2019 (PI)
\item NSF ``RII Track-2 FEC: Functional analysis of nitrogen responsive networks in Sorghum.'' (co-PI)
\item NSF ``BTT EAGER: A wearable plant sensor for real-time monitoring of sap flow and stem diameter to accelerate breeding for water use efficiency.'' \$300k 2019-2021 (PI)
\item ARPA-E ``In-plant and in-soil microsensors enabled high-throughput phenotyping of root nitrogen uptake and nitrogen use efficiency.'' (co-PI)
\item NSF ``RII Track-2 FEC: Functional analysis of nitrogen responsive networks in Sorghum.'' \$4M 2018-2022 (co-PI)
\item ARPA-E ``Low cost wireless chemical sensor networks.'' (co-PI)
\item ARPA-E ``In-plant and in-soil microsensors enabled high-throughput phenotyping of root nitrogen uptake and nitrogen use efficiency.'' \$1.1M 2017-2019 (co-PI)
\item FFAR ``Crops in silico: Increasing crop production by connecting models from the microscale to the macroscale.'' (co-PI)
\item NSF ``Center for Root and Rhizobiome Innovation.'' (Investigator \& Management Team Member)
\item FFAR ``Crops in silico: Increasing crop production by connecting models from the microscale to the macroscale.'' \$5M 2019-2023 (co-PI)
\item NSF ``Center for Root and Rhizobiome Innovation.'' \$20M 2016-2021 (Investigator \& Management Team Member)
%\item DOE-JGI Community Sequencing Program ``Expanding grass genome comparators.''
%\item DOE-JGI Community Sequencing Program ``Expanding grass genome comparators.''
\end{itemize}
\end{itemize}
\subsection*{Non-Federal (Current)}
\subsection*{Non-Federal (Current)}
\begin{itemize}
\begin{itemize}
\item North Central Sun Grants ``High through put phenotyping to accelerate biomass sorghum improvement.'' (co-PI)
\item North Central Sun Grants ``High through put phenotyping to accelerate biomass sorghum improvement.'' \$193k 2017-2019 (co-PI)
\item Nebraska Corn Board ``Genomes to Fields (G2F) - Predicting Final Yield Performance in Variable Environments.'' (PI)
\item Nebraska Corn Board ``Genomes to Fields (G2F) - Predicting Final Yield Performance in Variable Environments.'' \$200k \textit{(to date)} 2016-2020 (PI)
\item Wheat Innovation Foundation ``A Low-Cost, High-Throughput Cold Stress Perception Assay for Sorghum Breeding.'' (co-PI)
\item Wheat Innovation Foundation ``A Low-Cost, High-Throughput Cold Stress Perception Assay for Sorghum Breeding.'' \$205k 2019-2021 (co-PI)
\item Daugherty Water for Food Global Institute ``Optimizing the Water Use Efficiency of C4 Grain Crops Using Comparative Phenomics and Crop Models to Guide Breeding Targets.'' (PI)
\item Daugherty Water for Food Global Institute ``Optimizing the Water Use Efficiency of C4 Grain Crops Using Comparative Phenomics and Crop Models to Guide Breeding Targets.'' \$27k 2017-2019 (PI)
\item Layman Award ``Developing genomic tools in proso millet and comparing water use efficiency among panicoid grass crops (proso millet, corn, sorghum, foxtail millet)'' (co-PI)
%\item Layman Award ``Developing genomic tools in proso millet and comparing water use efficiency among panicoid grass crops (proso millet, corn, sorghum, foxtail millet)'' (co-PI)
\item USDA/NSF Joint Program ``PAPM EAGER: Transitioning to the next generation plant phenotyping robots.'' (co-PI)
\item USDA/NSF Joint Program ``PAPM EAGER: Transitioning to the next generation plant phenotyping robots.'' \$285k 2016-2018 (co-PI)
\item Midwest Big Data Hub ``Automatic feature extraction pipeline development for high-throughput plant phenotyping'' (co-PI)
\item Midwest Big Data Hub ``Automatic feature extraction pipeline development for high-throughput plant phenotyping'' \$5k 2017-2018 (co-PI)
\item Agricultural Research Division ``A High Throughput Phenotyping Reference Dataset for GWAS in Sorghum'' (PI)
\item Agricultural Research Division ``A High Throughput Phenotyping Reference Dataset for GWAS in Sorghum'' \$100k 2016-2018 (PI)
\textit{Designs, manufactures, and deploys low-cost, instant readout, high-performance, field-based nutrient sensors for crops, soil, and water, improving agronomic management practices, increasing grower profitability and reducing the environmental footprint of agriculture.}
\textit{Designs, manufactures, and deploys low-cost, instant readout, high-performance, field-based nutrient sensors for crops, soil, and water, improving agronomic management practices, increasing grower profitability and reducing the environmental footprint of agriculture.}
...
@@ -191,6 +193,16 @@
...
@@ -191,6 +193,16 @@
\textit{Using high throughput quantitative genetics and field phenotyping techologies to develop and commericialize higher yielding cultivars of crops already naturally adapted to using little water and growing arid regions where conventional agriculture fails in the absence of irrigation.}
\textit{Using high throughput quantitative genetics and field phenotyping techologies to develop and commericialize higher yielding cultivars of crops already naturally adapted to using little water and growing arid regions where conventional agriculture fails in the absence of irrigation.}
\textit{Providing patented tGBS genotyping and genomic selection services to public and private sector plant and animal breeders in the USA and China.}
\textit{Providing patented tGBS genotyping and genomic selection services to public and private sector plant and animal breeders in the USA and China.}
\end{itemize}
\subsection*{Economic Development Related Funding}
\noindent\textbf{Carvalho DS}, \textbf{Schnable JC}.$^\S$ IsoSeq transcriptome assembly of C3 panicoid grasses provides tools to study evolutionary change in the Panicoideae. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/689356}{10.1101/689356}\\
\noindent\textbf{Carvalho DS}, \textbf{Schnable JC}.$^\S$ IsoSeq transcriptome assembly of C3 panicoid grasses provides tools to study evolutionary change in the Panicoideae. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/689356}{10.1101/689356}\\
\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: \ref{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\textbf{Dai X}, Xu Z, \textbf{Liang Z}, Tu X, Zhong S, \textbf{Schnable JC},$^\S$ Li P.$^\S$ Non-homology-based prediction of gene functions. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/730473}{10.1101/730473}\\
\noindent\textbf{Dai X}, Xu Z, \textbf{Liang Z}, Tu X, Zhong S, \textbf{Schnable JC},$^\S$ Li P.$^\S$ Non-homology-based prediction of gene functions. \textsc{bioRxiv} doi: \href{https://doi.org/10.1101/730473}{10.1101/730473}
\item Qi P, Eudy D, \textbf{Schnable JC}, Schmutz J, Raymer P, Devos KM.$^\S$ (2019) High density genetic maps of seashore paspalum using genotyping-by-sequencing and their relationship to the \textit{Sorghum bicolor} genome. \textsc{Scientific Reports}\textit{(Accepted)}
\item Qi P, Eudy D, \textbf{Schnable JC}, Schmutz J, Raymer P, Devos KM.$^\S$ (2019) High density genetic maps of seashore paspalum using genotyping-by-sequencing and their relationship to the \textit{Sorghum bicolor} genome. \textsc{Scientific Reports}\textit{(Accepted)}
\item Ali MA, Wang X, Chen Y, Jiao Y, Mahal NK, Satyanarayana M, Castellano MJ, \textbf{Schnable JC}, Schnable PS, Dong L$^\S$ (2019) Continuous Monitoring of Nitrate Variation Using Miniature Soil Sensor with Poly(3-octyl-thiophene) and Molybdenum Disulfide Nanocomposite. \textsc{ACS Applied Materials & Interfaces} doi: \href{https://doi.org/10.1021/acsami.9b07120}{10.1021/acsami.9b07120}
\item\textbf{Schnable JC}$^\S$ (2019) Genes and gene models, an important distinction. \textsc{New Phytologist} doi: \href{https://doi.org/10.1111/nph.16011}{10.1111/nph.16011}\\
\item\textbf{Schnable JC}$^\S$ (2019) Genes and gene models, an important distinction. \textsc{New Phytologist} doi: \href{https://doi.org/10.1111/nph.16011}{10.1111/nph.16011}
\itemGe Y$^\S$, Atefi A, Zhang H, \textbf{Miao C}, Ramamurthy RK, \textbf{Sigmon B}, Yang J, \textbf{Schnable JC} (2019) High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: A case study with a maize diversity panel. \textsc{Plant Methods} doi: \href{https://doi.org/10.1186/s13007-019-0450-8}{10.1186/s13007-019-0450-8}
\itemLi Y, \textbf{Li D}, Jiao Y, \textbf{Schnable JC}, Li Y, Li H, Chen H, Hong H, Zhang T, Liu B, Liu Z, You Q, Tian Y, Gou Y, Guan R, Zhang L, Chang R, Zhang Z, Reif J, Zhou X, Schnable PS, Qiu L.$^\S$ (2019)Identification of Loci Controlling Adaptation in Chinese Soybean Landraces via a Combination of Conventional and Bioclimatic GWAS. \textsc{Plant Biotechnology Journal} doi: \href{https://doi.org/10.1111/pbi.13206}{10.1111/pbi.13206}
\itemAli MA, Wang X, Chen Y, Jiao Y, Mahal NK, Satyanarayana M, Castellano MJ, \textbf{Schnable JC}, Schnable PS, Dong L$^\S$ (2019) Continuous Monitoring of Nitrate Variation Using Miniature Soil Sensor with Poly(3-octyl-thiophene) and Molybdenum Disulfide Nanocomposite. \textsc{ACS Applied Materials \& Interfaces} doi: \href{https://doi.org/10.1021/acsami.9b07120}{10.1021/acsami.9b07120}
\itemGe Y$^\S$, Atefi A, Zhang H, \textbf{Miao C}, Ramamurthy RK, \textbf{Sigmon B}, Yang J, \textbf{Schnable JC} (2019) High-throughput analysis of leaf physiological and chemical traits with VIS-NIR-SWIR spectroscopy: A case study with a maize diversity panel. \textsc{Plant Methods} doi: \href{https://doi.org/10.1186/s13007-019-0450-8}{10.1186/s13007-019-0450-8}
\itemLi Y, \textbf{Li D}, Jiao Y, \textbf{Schnable JC}, Li Y, Li H, Chen H, Hong H, Zhang T, Liu B, Liu Z, You Q, Tian Y, Gou Y, Guan R, Zhang L, Chang R, Zhang Z, Reif J, Zhou X, Schnable PS, Qiu L.$^\S$ (2019)Identification of Loci Controlling Adaptation in Chinese Soybean Landraces via a Combination of Conventional and Bioclimatic GWAS. \textsc{Plant Biotechnology Journal} doi: \href{https://doi.org/10.1111/pbi.13206}{10.1111/pbi.13206}
\item Atefi A, Ge Y,$^\S$ Pitla S, \textbf{Schnable JC} (2019) \textit{In vivo} human-like robotic phenotyping of leaf traits in maize and sorghum. \textsc{Computers and Electronics in Agriculture} doi: \href{https://doi.org/10.1016/j.compag.2019.104854}{10.1016/j.compag.2019.104854}
\item Atefi A, Ge Y,$^\S$ Pitla S, \textbf{Schnable JC} (2019) \textit{In vivo} human-like robotic phenotyping of leaf traits in maize and sorghum. \textsc{Computers and Electronics in Agriculture} doi: \href{https://doi.org/10.1016/j.compag.2019.104854}{10.1016/j.compag.2019.104854}
\item Tang H, Lyons E, Pedersen B, {\bf Schnable JC}, Paterson AH, Freeling M. (2011) Screening synteny blocks in pairwise genome comparisons through integer programming. \textsc{BMC Bioinformatics} doi: \href{http://dx.doi.org/10.1186/1471-2105-12-102}{10.1186/1471-2105-12-102}
\item Tang H, Lyons E, Pedersen B, {\bf Schnable JC}, Paterson AH, Freeling M. (2011) Screening synteny blocks in pairwise genome comparisons through integer programming. \textsc{BMC Bioinformatics} doi: \href{http://dx.doi.org/10.1186/1471-2105-12-102}{10.1186/1471-2105-12-102}
\item{\bf Schnable JC}, Pedersen BS, Subramaniam S, Freeling M$^\S$ (2011) Dose-sensitivity, conserved noncoding sequences and duplicate gene retention through multiple tetraploidies in the grasses. \textsc{Frontiers in Plant Science} doi: \href{http://dx.doi.org/10.3389/fpls.2011.00002}{10.3389/fpls.2011.00002}\\
\item{\bf Schnable JC}, Pedersen BS, Subramaniam S, Freeling M$^\S$ (2011) Dose-sensitivity, conserved noncoding sequences and duplicate gene retention through multiple tetraploidies in the grasses. \textsc{Frontiers in Plant Science} doi: \href{http://dx.doi.org/10.3389/fpls.2011.00002}{10.3389/fpls.2011.00002}\\
{\it Commentary by Birchlier and Veitia also published in Frontiers in Plant Science doi: \href{http://dx.doi.org/10.3389/fpls.2011.00064}{10.3389/fpls.2011.00064}}
{\it Commentary by Birchlier and Veitia also published in Frontiers in Plant Science} doi: \href{http://dx.doi.org/10.3389/fpls.2011.00064}{10.3389/fpls.2011.00064}
%<br>{\textsc (<a href="http://www.frontiersin.org/plant%20genetics%20and%20genomics/10.3389/fpls.2011.00064/full">Commentary on this article</a> by Birchler and Veitia, also published in Frontiers in Plant Science)}
%<br>{\textsc (<a href="http://www.frontiersin.org/plant%20genetics%20and%20genomics/10.3389/fpls.2011.00064/full">Commentary on this article</a> by Birchler and Veitia, also published in Frontiers in Plant Science)}
\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
\item\textbf{Askey B},$^\ddagger$ Yang Q, Benson AK, \textbf{Schnable JC} (2019) Computer vision phenotyping of 371 Sorghum bicolor BTx623 x ISC3620C recombinant inbred lines for QTL detection. \textsc{Second International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2019)} Ames, IA, USA
\item Jiao Y, Wang X, Chen Y, Castellano MJ, \textbf{Schnable JC}, Schnable PS, Dong L (2019) In-planta nitrate detection using insertable plant microsensor. \textsc{20th International Conference on Solid-State Sensors, Actuators and Microsystems} Berlin, Germany
\item Jiao Y, Wang X, Chen Y, Castellano MJ, \textbf{Schnable JC}, Schnable PS, Dong L (2019) In-planta nitrate detection using insertable plant microsensor. \textsc{20th International Conference on Solid-State Sensors, Actuators and Microsystems} Berlin, Germany
\item Ali MA, Wang X, Chen Y, Jiao Y, Castellano MJ, \textbf{Schnable JC}, Schnable PS, Dong L (2019) Novel all-solid-state soil nutrient sensor using nanocomposite of poly(3-octyl-thiophene) and molybdenum sulfate. \textsc{20th International Conference on Solid-State Sensors, Actuators and Microsystems} Berlin, Germany
\item Ali MA, Wang X, Chen Y, Jiao Y, Castellano MJ, \textbf{Schnable JC}, Schnable PS, Dong L (2019) Novel all-solid-state soil nutrient sensor using nanocomposite of poly(3-octyl-thiophene) and molybdenum sulfate. \textsc{20th International Conference on Solid-State Sensors, Actuators and Microsystems} Berlin, Germany
\item Behera S, Deogun JS, \textbf{Lai X}, \textbf{Schnable JC} (2017) B529 DiCE: Discovery of Conserved Noncoding Sequences Efficiently. \textsc{IEEE BIBM 2017} Kansas City, MO, USA doi: \href{http://doi.org/10.1109/BIBM.2017.8217628}{10.1109/BIBM.2017.8217628}
\item Behera S, Deogun JS, \textbf{Lai X}, \textbf{Schnable JC} (2017) B529 DiCE: Discovery of Conserved Noncoding Sequences Efficiently. \textsc{IEEE BIBM 2017} Kansas City, MO, USA doi: \href{http://doi.org/10.1109/BIBM.2017.8217628}{10.1109/BIBM.2017.8217628}
