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      Setaria viridis as a Model System to Advance Millet Genetics and Genomics

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          Abstract

          Millet is a common name for a group of polyphyletic, small-seeded cereal crops that include pearl, finger and foxtail millet. Millet species are an important source of calories for many societies, often in developing countries. Compared to major cereal crops such as rice and maize, millets are generally better adapted to dry and hot environments. Despite their food security value, the genetic architecture of agronomically important traits in millets, including both morphological traits and climate resilience remains poorly studied. These complex traits have been challenging to dissect in large part because of the lack of sufficient genetic tools and resources. In this article, we review the phylogenetic relationship among various millet species and discuss the value of a genetic model system for millet research. We propose that a broader adoption of green foxtail ( Setaria viridis) as a model system for millets could greatly accelerate the pace of gene discovery in the millets, and summarize available and emerging resources in S. viridis and its domesticated relative S. italica. These resources have value in forward genetics, reverse genetics and high throughput phenotyping. We describe methods and strategies to best utilize these resources to facilitate the genetic dissection of complex traits. We envision that coupling cutting-edge technologies and the use of S. viridis for gene discovery will accelerate genetic research in millets in general. This will enable strategies and provide opportunities to increase productivity, especially in the semi-arid tropics of Asia and Africa where millets are staple food crops.

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          NIH Image to ImageJ: 25 years of image analysis.

          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Machine Learning for High-Throughput Stress Phenotyping in Plants.

            Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.
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              The Chromosome Counts Database (CCDB) - a community resource of plant chromosome numbers.

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                Author and article information

                Contributors
                Journal
                Front Plant Sci
                Front Plant Sci
                Front. Plant Sci.
                Frontiers in Plant Science
                Frontiers Media S.A.
                1664-462X
                28 November 2016
                2016
                : 7
                : 1781
                Affiliations
                [1]Donald Danforth Plant Science Center, St Louis MO, USA
                Author notes

                Edited by: Manoj Prasad, National Institute of Plant Genome Research, India

                Reviewed by: Kevin Murphy, Washington State University, USA; Chandra Bhan Yadav, University of Milan, Italy

                *Correspondence: Pu Huang, phuang@ 123456danforthcenter.org

                This article was submitted to Plant Genetics and Genomics, a section of the journal Frontiers in Plant Science

                Article
                10.3389/fpls.2016.01781
                5124564
                27965689
                d54587b6-3e24-4428-871f-c0cffd43d352
                Copyright © 2016 Huang, Shyu, Coelho, Cao and Brutnell.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 September 2016
                : 11 November 2016
                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 94, Pages: 9, Words: 0
                Funding
                Funded by: U.S. Department of Energy 10.13039/100000015
                Award ID: DE-SC0008769
                Funded by: National Science Foundation 10.13039/100000001
                Award ID: 1546882
                Funded by: U.S. Department of Agriculture 10.13039/100000199
                Award ID: 2014-67012-22269
                Categories
                Plant Science
                Mini Review

                Plant science & Botany
                setaria viridis,foxtail millet,bulked segregant analysis,stress tolerance,high-throughput phenotyping,model grass,c4 photosynthesis

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