---
_id: '12349'
abstract:
- lang: eng
text: 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.
acknowledged_ssus:
- _id: ScienComp
- _id: PreCl
- _id: LifeSc
- _id: Bio
acknowledgement: We thank Hiroki Asari for sharing the dataset of naturalistic images,
Anton Sumser for sharing visual stimulus code, Yoav Ben Simon for initial explorative
work with the generation of AAVs, and Tomas Vega-Zuñiga for help with immunostainings.
We also thank Gasper Tkacik and members of the Neuroethology group for their comments
on the manuscript. This research was supported by the Scientific Service Units of
IST Austria through resources provided by Scientific Computing, the Preclinical
Facility, the Lab Support Facility, and the Imaging and Optics Facility. This work
was supported by European Union Horizon 2020 Marie Skłodowska-Curie grant 665385
(DG), Austrian Science Fund (FWF) stand-alone grant P 34015 (WM), Human Frontiers
Science Program LT000256/2018-L (AS), EMBO ALTF 1098-2017 (AS) and the European
Research Council Starting Grant 756502 (MJ).
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Divyansh
full_name: Gupta, Divyansh
id: 2A485EBE-F248-11E8-B48F-1D18A9856A87
last_name: Gupta
orcid: 0000-0001-7400-6665
- first_name: Wiktor F
full_name: Mlynarski, Wiktor F
id: 358A453A-F248-11E8-B48F-1D18A9856A87
last_name: Mlynarski
- first_name: Anton L
full_name: Sumser, Anton L
id: 3320A096-F248-11E8-B48F-1D18A9856A87
last_name: Sumser
orcid: 0000-0002-4792-1881
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
orcid: 0000-0003-2012-9947
- first_name: Jan
full_name: Svaton, Jan
id: f7f724c3-9d6f-11ed-9f44-e5c5f3a5bee2
last_name: Svaton
orcid: 0000-0002-6198-2939
- first_name: Maximilian A
full_name: Jösch, Maximilian A
id: 2BD278E6-F248-11E8-B48F-1D18A9856A87
last_name: Jösch
orcid: 0000-0002-3937-1330
citation:
ama: Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. Panoramic
visual statistics shape retina-wide organization of receptive fields. Nature
Neuroscience. 2023;26:606-614. doi:10.1038/s41593-023-01280-0
apa: Gupta, D., Mlynarski, W. F., Sumser, A. L., Symonova, O., Svaton, J., &
Jösch, M. A. (2023). Panoramic visual statistics shape retina-wide organization
of receptive fields. Nature Neuroscience. Springer Nature. https://doi.org/10.1038/s41593-023-01280-0
chicago: Gupta, Divyansh, Wiktor F Mlynarski, Anton L Sumser, Olga Symonova, Jan
Svaton, and Maximilian A Jösch. “Panoramic Visual Statistics Shape Retina-Wide
Organization of Receptive Fields.” Nature Neuroscience. Springer Nature,
2023. https://doi.org/10.1038/s41593-023-01280-0.
ieee: D. Gupta, W. F. Mlynarski, A. L. Sumser, O. Symonova, J. Svaton, and M. A.
Jösch, “Panoramic visual statistics shape retina-wide organization of receptive
fields,” Nature Neuroscience, vol. 26. Springer Nature, pp. 606–614, 2023.
ista: Gupta D, Mlynarski WF, Sumser AL, Symonova O, Svaton J, Jösch MA. 2023. Panoramic
visual statistics shape retina-wide organization of receptive fields. Nature Neuroscience.
26, 606–614.
mla: Gupta, Divyansh, et al. “Panoramic Visual Statistics Shape Retina-Wide Organization
of Receptive Fields.” Nature Neuroscience, vol. 26, Springer Nature, 2023,
pp. 606–14, doi:10.1038/s41593-023-01280-0.
short: D. Gupta, W.F. Mlynarski, A.L. Sumser, O. Symonova, J. Svaton, M.A. Jösch,
Nature Neuroscience 26 (2023) 606–614.
date_created: 2023-01-23T14:14:19Z
date_published: 2023-04-01T00:00:00Z
date_updated: 2023-10-04T11:41:05Z
day: '01'
ddc:
- '570'
department:
- _id: GradSch
- _id: MaJö
doi: 10.1038/s41593-023-01280-0
ec_funded: 1
external_id:
isi:
- '000955258300002'
pmid:
- '36959418'
file:
- access_level: open_access
checksum: a33d91e398e548f34003170e10988368
content_type: application/pdf
creator: dernst
date_created: 2023-10-04T11:40:51Z
date_updated: 2023-10-04T11:40:51Z
file_id: '14395'
file_name: 2023_NatureNeuroscience_Gupta.pdf
file_size: 6144866
relation: main_file
success: 1
file_date_updated: 2023-10-04T11:40:51Z
has_accepted_license: '1'
intvolume: ' 26'
isi: 1
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
page: 606-614
pmid: 1
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
- _id: 626c45b5-2b32-11ec-9570-e509828c1ba6
grant_number: P34015
name: Efficient coding with biophysical realism
- _id: 2634E9D2-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '756502'
name: Circuits of Visual Attention
- _id: 266D407A-B435-11E9-9278-68D0E5697425
grant_number: LT000256
name: Neuronal networks of salience and spatial detection in the murine superior
colliculus
- _id: 264FEA02-B435-11E9-9278-68D0E5697425
grant_number: ALTF 1098-2017
name: Connecting sensory with motor processing in the superior colliculus
publication: Nature Neuroscience
publication_identifier:
eissn:
- 1546-1726
issn:
- 1097-6256
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '12370'
relation: research_data
status: public
scopus_import: '1'
status: public
title: Panoramic visual statistics shape retina-wide organization of receptive fields
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 26
year: '2023'
...
---
_id: '1793'
abstract:
- lang: eng
text: 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.
article_number: e0127657
author:
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
- first_name: Christopher
full_name: Topp, Christopher
last_name: Topp
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
citation:
ama: 'Symonova O, Topp C, Edelsbrunner H. DynamicRoots: A software platform for
the reconstruction and analysis of growing plant roots. PLoS One. 2015;10(6).
doi:10.1371/journal.pone.0127657'
apa: 'Symonova, O., Topp, C., & Edelsbrunner, H. (2015). DynamicRoots: A software
platform for the reconstruction and analysis of growing plant roots. PLoS One.
Public Library of Science. https://doi.org/10.1371/journal.pone.0127657'
chicago: 'Symonova, Olga, Christopher Topp, and Herbert Edelsbrunner. “DynamicRoots:
A Software Platform for the Reconstruction and Analysis of Growing Plant Roots.”
PLoS One. Public Library of Science, 2015. https://doi.org/10.1371/journal.pone.0127657.'
ieee: 'O. Symonova, C. Topp, and H. Edelsbrunner, “DynamicRoots: A software platform
for the reconstruction and analysis of growing plant roots,” PLoS One,
vol. 10, no. 6. Public Library of Science, 2015.'
ista: 'Symonova O, Topp C, Edelsbrunner H. 2015. DynamicRoots: A software platform
for the reconstruction and analysis of growing plant roots. PLoS One. 10(6), e0127657.'
mla: 'Symonova, Olga, et al. “DynamicRoots: A Software Platform for the Reconstruction
and Analysis of Growing Plant Roots.” PLoS One, vol. 10, no. 6, e0127657,
Public Library of Science, 2015, doi:10.1371/journal.pone.0127657.'
short: O. Symonova, C. Topp, H. Edelsbrunner, PLoS One 10 (2015).
date_created: 2018-12-11T11:54:02Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2023-02-23T14:06:33Z
day: '01'
ddc:
- '000'
department:
- _id: MaJö
- _id: HeEd
doi: 10.1371/journal.pone.0127657
file:
- access_level: open_access
checksum: d20f26461ca575276ad3ed9ce4bfc787
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:15:30Z
date_updated: 2020-07-14T12:45:16Z
file_id: '5150'
file_name: IST-2016-454-v1+1_journal.pone.0127657.pdf
file_size: 1850825
relation: main_file
file_date_updated: 2020-07-14T12:45:16Z
has_accepted_license: '1'
intvolume: ' 10'
issue: '6'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
publication: PLoS One
publication_status: published
publisher: Public Library of Science
publist_id: '5318'
pubrep_id: '454'
quality_controlled: '1'
related_material:
record:
- id: '9737'
relation: research_data
status: public
scopus_import: 1
status: public
title: 'DynamicRoots: A software platform for the reconstruction and analysis of growing
plant roots'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 10
year: '2015'
...
---
_id: '9737'
article_processing_charge: No
author:
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
- first_name: Christopher
full_name: Topp, Christopher
last_name: Topp
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
citation:
ama: Symonova O, Topp C, Edelsbrunner H. Root traits computed by DynamicRoots for
the maize root shown in fig 2. 2015. doi:10.1371/journal.pone.0127657.s001
apa: Symonova, O., Topp, C., & Edelsbrunner, H. (2015). Root traits computed
by DynamicRoots for the maize root shown in fig 2. Public Library of Science.
https://doi.org/10.1371/journal.pone.0127657.s001
chicago: Symonova, Olga, Christopher Topp, and Herbert Edelsbrunner. “Root Traits
Computed by DynamicRoots for the Maize Root Shown in Fig 2.” Public Library of
Science, 2015. https://doi.org/10.1371/journal.pone.0127657.s001.
ieee: O. Symonova, C. Topp, and H. Edelsbrunner, “Root traits computed by DynamicRoots
for the maize root shown in fig 2.” Public Library of Science, 2015.
ista: Symonova O, Topp C, Edelsbrunner H. 2015. Root traits computed by DynamicRoots
for the maize root shown in fig 2, Public Library of Science, 10.1371/journal.pone.0127657.s001.
mla: Symonova, Olga, et al. Root Traits Computed by DynamicRoots for the Maize
Root Shown in Fig 2. Public Library of Science, 2015, doi:10.1371/journal.pone.0127657.s001.
short: O. Symonova, C. Topp, H. Edelsbrunner, (2015).
date_created: 2021-07-28T06:20:13Z
date_published: 2015-06-01T00:00:00Z
date_updated: 2023-02-23T10:14:42Z
day: '01'
department:
- _id: MaJö
- _id: HeEd
doi: 10.1371/journal.pone.0127657.s001
month: '06'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '1793'
relation: used_in_publication
status: public
status: public
title: Root traits computed by DynamicRoots for the maize root shown in fig 2
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2015'
...
---
_id: '2822'
abstract:
- lang: eng
text: 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:
- first_name: Christopher
full_name: Topp, Christopher
last_name: Topp
- first_name: Anjali
full_name: Iyer Pascuzzi, Anjali
last_name: Iyer Pascuzzi
- first_name: Jill
full_name: Anderson, Jill
last_name: Anderson
- first_name: Cheng
full_name: Lee, Cheng
last_name: Lee
- first_name: Paul
full_name: Zurek, Paul
last_name: Zurek
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
- first_name: Ying
full_name: Zheng, Ying
last_name: Zheng
- first_name: Alexander
full_name: Bucksch, Alexander
last_name: Bucksch
- first_name: Yuriy
full_name: Mileyko, Yuriy
last_name: Mileyko
- first_name: Taras
full_name: Galkovskyi, Taras
last_name: Galkovskyi
- first_name: Brad
full_name: Moore, Brad
last_name: Moore
- first_name: John
full_name: Harer, John
last_name: Harer
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Thomas
full_name: Mitchell Olds, Thomas
last_name: Mitchell Olds
- first_name: Joshua
full_name: Weitz, Joshua
last_name: Weitz
- first_name: Philip
full_name: Benfey, Philip
last_name: Benfey
citation:
ama: Topp C, Iyer Pascuzzi A, Anderson J, et al. 3D phenotyping and quantitative
trait locus mapping identify core regions of the rice genome controlling root
architecture. PNAS. 2013;110(18):E1695-E1704. doi:10.1073/pnas.1304354110
apa: Topp, C., Iyer Pascuzzi, A., Anderson, J., Lee, C., Zurek, P., Symonova, O.,
… Benfey, P. (2013). 3D phenotyping and quantitative trait locus mapping identify
core regions of the rice genome controlling root architecture. PNAS. National
Academy of Sciences. https://doi.org/10.1073/pnas.1304354110
chicago: Topp, Christopher, Anjali Iyer Pascuzzi, Jill Anderson, Cheng Lee, Paul
Zurek, Olga Symonova, Ying Zheng, et al. “3D Phenotyping and Quantitative Trait
Locus Mapping Identify Core Regions of the Rice Genome Controlling Root Architecture.”
PNAS. National Academy of Sciences, 2013. https://doi.org/10.1073/pnas.1304354110.
ieee: C. Topp et al., “3D phenotyping and quantitative trait locus mapping
identify core regions of the rice genome controlling root architecture,” PNAS,
vol. 110, no. 18. National Academy of Sciences, pp. E1695–E1704, 2013.
ista: Topp C, Iyer Pascuzzi A, Anderson J, Lee C, Zurek P, Symonova O, Zheng Y,
Bucksch A, Mileyko Y, Galkovskyi T, Moore B, Harer J, Edelsbrunner H, Mitchell
Olds T, Weitz J, Benfey P. 2013. 3D phenotyping and quantitative trait locus mapping
identify core regions of the rice genome controlling root architecture. PNAS.
110(18), E1695–E1704.
mla: Topp, Christopher, et al. “3D Phenotyping and Quantitative Trait Locus Mapping
Identify Core Regions of the Rice Genome Controlling Root Architecture.” PNAS,
vol. 110, no. 18, National Academy of Sciences, 2013, pp. E1695–704, doi:10.1073/pnas.1304354110.
short: C. Topp, A. Iyer Pascuzzi, J. Anderson, C. Lee, P. Zurek, O. Symonova, Y.
Zheng, A. Bucksch, Y. Mileyko, T. Galkovskyi, B. Moore, J. Harer, H. Edelsbrunner,
T. Mitchell Olds, J. Weitz, P. Benfey, PNAS 110 (2013) E1695–E1704.
date_created: 2018-12-11T11:59:47Z
date_published: 2013-04-30T00:00:00Z
date_updated: 2021-01-12T06:59:58Z
day: '30'
department:
- _id: MaJö
- _id: HeEd
doi: 10.1073/pnas.1304354110
external_id:
pmid:
- '25673779'
intvolume: ' 110'
issue: '18'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4378147/
month: '04'
oa: 1
oa_version: Submitted Version
page: E1695 - E1704
pmid: 1
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '3979'
quality_controlled: '1'
scopus_import: 1
status: public
title: 3D phenotyping and quantitative trait locus mapping identify core regions of
the rice genome controlling root architecture
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 110
year: '2013'
...
---
_id: '2903'
abstract:
- lang: eng
text: 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:
- first_name: Herbert
full_name: Edelsbrunner, Herbert
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
citation:
ama: 'Edelsbrunner H, Symonova O. The adaptive topology of a digital image. In:
IEEE; 2012:41-48. doi:10.1109/ISVD.2012.11'
apa: 'Edelsbrunner, H., & Symonova, O. (2012). The adaptive topology of a digital
image (pp. 41–48). Presented at the ISVD: International Symposium on Voronoi Diagrams
in Science and Engineering, New Brunswick, NJ, USA : IEEE. https://doi.org/10.1109/ISVD.2012.11'
chicago: Edelsbrunner, Herbert, and Olga Symonova. “The Adaptive Topology of a Digital
Image,” 41–48. IEEE, 2012. https://doi.org/10.1109/ISVD.2012.11.
ieee: 'H. Edelsbrunner and O. Symonova, “The adaptive topology of a digital image,”
presented at the ISVD: International Symposium on Voronoi Diagrams in Science
and Engineering, New Brunswick, NJ, USA , 2012, pp. 41–48.'
ista: 'Edelsbrunner H, Symonova O. 2012. The adaptive topology of a digital image.
ISVD: International Symposium on Voronoi Diagrams in Science and Engineering,
41–48.'
mla: Edelsbrunner, Herbert, and Olga Symonova. The Adaptive Topology of a Digital
Image. IEEE, 2012, pp. 41–48, doi:10.1109/ISVD.2012.11.
short: H. Edelsbrunner, O. Symonova, in:, IEEE, 2012, pp. 41–48.
conference:
end_date: 2012-06-29
location: 'New Brunswick, NJ, USA '
name: 'ISVD: International Symposium on Voronoi Diagrams in Science and Engineering'
start_date: 2012-06-27
date_created: 2018-12-11T12:00:15Z
date_published: 2012-08-06T00:00:00Z
date_updated: 2021-01-12T07:00:35Z
day: '06'
ddc:
- '000'
department:
- _id: HeEd
- _id: MaJö
doi: 10.1109/ISVD.2012.11
file:
- access_level: open_access
checksum: 444869a4e8abf07834f88b6e5cb5e9c3
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:09:41Z
date_updated: 2020-07-14T12:45:52Z
file_id: '4765'
file_name: IST-2016-545-v1+1_2012-P-10-AdaptiveTopology.pdf
file_size: 760548
relation: main_file
file_date_updated: 2020-07-14T12:45:52Z
has_accepted_license: '1'
language:
- iso: eng
month: '08'
oa: 1
oa_version: Submitted Version
page: 41 - 48
publication_status: published
publisher: IEEE
publist_id: '3844'
pubrep_id: '545'
quality_controlled: '1'
scopus_import: 1
status: public
title: The adaptive topology of a digital image
type: conference
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
year: '2012'
...
---
_id: '492'
abstract:
- lang: eng
text: '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.'
article_number: '116'
article_processing_charge: No
author:
- first_name: Taras
full_name: Galkovskyi, Taras
last_name: Galkovskyi
- first_name: Yuriy
full_name: Mileyko, Yuriy
last_name: Mileyko
- first_name: Alexander
full_name: Bucksch, Alexander
last_name: Bucksch
- first_name: Brad
full_name: Moore, Brad
last_name: Moore
- first_name: Olga
full_name: Symonova, Olga
id: 3C0C7BC6-F248-11E8-B48F-1D18A9856A87
last_name: Symonova
- first_name: Charles
full_name: Price, Charles
last_name: Price
- first_name: Chrostopher
full_name: Topp, Chrostopher
last_name: Topp
- first_name: Anjali
full_name: Iyer Pascuzzi, Anjali
last_name: Iyer Pascuzzi
- first_name: Paul
full_name: Zurek, Paul
last_name: Zurek
- first_name: Suqin
full_name: Fang, Suqin
last_name: Fang
- first_name: John
full_name: Harer, John
last_name: Harer
- first_name: Philip
full_name: Benfey, Philip
last_name: Benfey
- first_name: Joshua
full_name: Weitz, Joshua
last_name: Weitz
citation:
ama: 'Galkovskyi T, Mileyko Y, Bucksch A, et al. GiA Roots: Software for the high
throughput analysis of plant root system architecture. BMC Plant Biology.
2012;12. doi:10.1186/1471-2229-12-116'
apa: 'Galkovskyi, T., Mileyko, Y., Bucksch, A., Moore, B., Symonova, O., Price,
C., … Weitz, J. (2012). GiA Roots: Software for the high throughput analysis of
plant root system architecture. BMC Plant Biology. BioMed Central. https://doi.org/10.1186/1471-2229-12-116'
chicago: 'Galkovskyi, Taras, Yuriy Mileyko, Alexander Bucksch, Brad Moore, Olga
Symonova, Charles Price, Chrostopher Topp, et al. “GiA Roots: Software for the
High Throughput Analysis of Plant Root System Architecture.” BMC Plant Biology.
BioMed Central, 2012. https://doi.org/10.1186/1471-2229-12-116.'
ieee: 'T. Galkovskyi et al., “GiA Roots: Software for the high throughput
analysis of plant root system architecture,” BMC Plant Biology, vol. 12.
BioMed Central, 2012.'
ista: 'Galkovskyi T, Mileyko Y, Bucksch A, Moore B, Symonova O, Price C, Topp C,
Iyer Pascuzzi A, Zurek P, Fang S, Harer J, Benfey P, Weitz J. 2012. GiA Roots:
Software for the high throughput analysis of plant root system architecture. BMC
Plant Biology. 12, 116.'
mla: 'Galkovskyi, Taras, et al. “GiA Roots: Software for the High Throughput Analysis
of Plant Root System Architecture.” BMC Plant Biology, vol. 12, 116, BioMed
Central, 2012, doi:10.1186/1471-2229-12-116.'
short: T. Galkovskyi, Y. Mileyko, A. Bucksch, B. Moore, O. Symonova, C. Price, C.
Topp, A. Iyer Pascuzzi, P. Zurek, S. Fang, J. Harer, P. Benfey, J. Weitz, BMC
Plant Biology 12 (2012).
date_created: 2018-12-11T11:46:46Z
date_published: 2012-07-26T00:00:00Z
date_updated: 2022-08-25T14:59:17Z
day: '26'
ddc:
- '005'
- '514'
- '516'
doi: 10.1186/1471-2229-12-116
extern: '1'
file:
- access_level: open_access
checksum: 0c629e36acd5f2878ff7dd088d67d494
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:12:35Z
date_updated: 2020-07-14T12:46:35Z
file_id: '4953'
file_name: IST-2018-946-v1+1_2012_Symonova_GiA_Roots.pdf
file_size: 1691436
relation: main_file
file_date_updated: 2020-07-14T12:46:35Z
has_accepted_license: '1'
intvolume: ' 12'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
publication: BMC Plant Biology
publication_status: published
publisher: BioMed Central
publist_id: '7328'
pubrep_id: '946'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'GiA Roots: Software for the high throughput analysis of plant root system
architecture'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 12
year: '2012'
...