@article{12349,
  abstract     = {Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.},
  author       = {Gupta, Divyansh and Mlynarski, Wiktor F and Sumser, Anton L and Symonova, Olga and Svaton, Jan and Jösch, Maximilian A},
  issn         = {1546-1726},
  journal      = {Nature Neuroscience},
  pages        = {606--614},
  publisher    = {Springer Nature},
  title        = {{Panoramic visual statistics shape retina-wide organization of receptive fields}},
  doi          = {10.1038/s41593-023-01280-0},
  volume       = {26},
  year         = {2023},
}

@article{1793,
  abstract     = {We present a software platform for reconstructing and analyzing the growth of a plant root system from a time-series of 3D voxelized shapes. It aligns the shapes with each other, constructs a geometric graph representation together with the function that records the time of growth, and organizes the branches into a hierarchy that reflects the order of creation. The software includes the automatic computation of structural and dynamic traits for each root in the system enabling the quantification of growth on fine-scale. These are important advances in plant phenotyping with applications to the study of genetic and environmental influences on growth.},
  author       = {Symonova, Olga and Topp, Christopher and Edelsbrunner, Herbert},
  journal      = {PLoS One},
  number       = {6},
  publisher    = {Public Library of Science},
  title        = {{DynamicRoots: A software platform for the reconstruction and analysis of growing plant roots}},
  doi          = {10.1371/journal.pone.0127657},
  volume       = {10},
  year         = {2015},
}

@misc{9737,
  author       = {Symonova, Olga and Topp, Christopher and Edelsbrunner, Herbert},
  publisher    = {Public Library of Science},
  title        = {{Root traits computed by DynamicRoots for the maize root shown in fig 2}},
  doi          = {10.1371/journal.pone.0127657.s001},
  year         = {2015},
}

@article{2822,
  abstract     = {Identification of genes that control root system architecture in crop plants requires innovations that enable high-throughput and accurate measurements of root system architecture through time. We demonstrate the ability of a semiautomated 3D in vivo imaging and digital phenotyping pipeline to interrogate the quantitative genetic basis of root system growth in a rice biparental mapping population, Bala x Azucena. We phenotyped >1,400 3D root models and >57,000 2D images for a suite of 25 traits that quantified the distribution, shape, extent of exploration, and the intrinsic size of root networks at days 12, 14, and 16 of growth in a gellan gum medium. From these data we identified 89 quantitative trait loci, some of which correspond to those found previously in soil-grown plants, and provide evidence for genetic tradeoffs in root growth allocations, such as between the extent and thoroughness of exploration. We also developed a multivariate method for generating and mapping central root architecture phenotypes and used it to identify five major quantitative trait loci (r2 = 24-37%), two of which were not identified by our univariate analysis. Our imaging and analytical platform provides a means to identify genes with high potential for improving root traits and agronomic qualities of crops.},
  author       = {Topp, Christopher and Iyer Pascuzzi, Anjali and Anderson, Jill and Lee, Cheng and Zurek, Paul and Symonova, Olga and Zheng, Ying and Bucksch, Alexander and Mileyko, Yuriy and Galkovskyi, Taras and Moore, Brad and Harer, John and Edelsbrunner, Herbert and Mitchell Olds, Thomas and Weitz, Joshua and Benfey, Philip},
  journal      = {PNAS},
  number       = {18},
  pages        = {E1695 -- E1704},
  publisher    = {National Academy of Sciences},
  title        = {{3D phenotyping and quantitative trait locus mapping identify core regions of the rice genome controlling root architecture}},
  doi          = {10.1073/pnas.1304354110},
  volume       = {110},
  year         = {2013},
}

@inproceedings{2903,
  abstract     = {In order to enjoy a digital version of the Jordan Curve Theorem, it is common to use the closed topology for the foreground and the open topology for the background of a 2-dimensional binary image. In this paper, we introduce a single topology that enjoys this theorem for all thresholds decomposing a real-valued image into foreground and background. This topology is easy to construct and it generalizes to n-dimensional images.},
  author       = {Edelsbrunner, Herbert and Symonova, Olga},
  location     = {New Brunswick, NJ, USA },
  pages        = {41 -- 48},
  publisher    = {IEEE},
  title        = {{The adaptive topology of a digital image}},
  doi          = {10.1109/ISVD.2012.11},
  year         = {2012},
}

@article{492,
  abstract     = {Background: Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.Results: We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.Conclusions: We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.},
  author       = {Galkovskyi, Taras and Mileyko, Yuriy and Bucksch, Alexander and Moore, Brad and Symonova, Olga and Price, Charles and Topp, Chrostopher and Iyer Pascuzzi, Anjali and Zurek, Paul and Fang, Suqin and Harer, John and Benfey, Philip and Weitz, Joshua},
  journal      = {BMC Plant Biology},
  publisher    = {BioMed Central},
  title        = {{GiA Roots: Software for the high throughput analysis of plant root system architecture}},
  doi          = {10.1186/1471-2229-12-116},
  volume       = {12},
  year         = {2012},
}