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Items: 23

1.

Hydrogel-based transparent soils for root phenotyping in vivo.

Ma L, Shi Y, Siemianowski O, Yuan B, Egner TK, Mirnezami SV, Lind KR, Ganapathysubramanian B, Venditti V, Cademartiri L.

Proc Natl Acad Sci U S A. 2019 May 28;116(22):11063-11068. doi: 10.1073/pnas.1820334116. Epub 2019 May 14.

PMID:
31088969
2.

Shaped 3D microcarriers for adherent cell culture and analysis.

Wu CY, Stoecklein D, Kommajosula A, Lin J, Owsley K, Ganapathysubramanian B, Di Carlo D.

Microsyst Nanoeng. 2018 Aug 13;4:21. doi: 10.1038/s41378-018-0020-7. eCollection 2018.

3.

Defining Cell Cluster Size by Dielectrophoretic Capture at an Array of Wireless Electrodes of Several Distinct Lengths.

Banovetz JT, Li M, Pagariya D, Kim S, Ganapathysubramanian B, Anand RK.

Micromachines (Basel). 2019 Apr 23;10(4). pii: E271. doi: 10.3390/mi10040271.

4.

Semiautomated Feature Extraction from RGB Images for Sorghum Panicle Architecture GWAS.

Zhou Y, Srinivasan S, Mirnezami SV, Kusmec A, Fu Q, Attigala L, Salas Fernandez MG, Ganapathysubramanian B, Schnable PS.

Plant Physiol. 2019 Jan;179(1):24-37. doi: 10.1104/pp.18.00974. Epub 2018 Nov 2.

5.

Hyperspectral band selection using genetic algorithm and support vector machines for early identification of charcoal rot disease in soybean stems.

Nagasubramanian K, Jones S, Sarkar S, Singh AK, Singh A, Ganapathysubramanian B.

Plant Methods. 2018 Oct 3;14:86. doi: 10.1186/s13007-018-0349-9. eCollection 2018.

6.

Deep Learning for Plant Stress Phenotyping: Trends and Future Perspectives.

Singh AK, Ganapathysubramanian B, Sarkar S, Singh A.

Trends Plant Sci. 2018 Oct;23(10):883-898. doi: 10.1016/j.tplants.2018.07.004. Epub 2018 Aug 10. Review.

7.

Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning.

Zhou N, Siegel ZD, Zarecor S, Lee N, Campbell DA, Andorf CM, Nettleton D, Lawrence-Dill CJ, Ganapathysubramanian B, Kelly JW, Friedberg I.

PLoS Comput Biol. 2018 Jul 30;14(7):e1006337. doi: 10.1371/journal.pcbi.1006337. eCollection 2018 Jul.

8.

A deep learning framework to discern and count microscopic nematode eggs.

Akintayo A, Tylka GL, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S.

Sci Rep. 2018 Jun 14;8(1):9145. doi: 10.1038/s41598-018-27272-w.

9.

An explainable deep machine vision framework for plant stress phenotyping.

Ghosal S, Blystone D, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S.

Proc Natl Acad Sci U S A. 2018 May 1;115(18):4613-4618. doi: 10.1073/pnas.1716999115. Epub 2018 Apr 16.

10.

From Petri Dishes to Model Ecosystems.

Siemianowski O, Lind KR, Tian X, Cain M, Xu S, Ganapathysubramanian B, Cademartiri L.

Trends Plant Sci. 2018 May;23(5):378-381. doi: 10.1016/j.tplants.2018.03.006. Epub 2018 Apr 2. Review.

PMID:
29622395
11.

HOMEs for plants and microbes - a phenotyping approach with quantitative control of signaling between organisms and their individual environments.

Siemianowski O, Lind KR, Tian X, Cain M, Xu S, Ganapathysubramanian B, Cademartiri L.

Lab Chip. 2018 Feb 13;18(4):620-626. doi: 10.1039/c7lc01186e.

PMID:
29337318
12.

A deep learning framework for causal shape transformation.

Lore KG, Stoecklein D, Davies M, Ganapathysubramanian B, Sarkar S.

Neural Netw. 2018 Feb;98:305-317. doi: 10.1016/j.neunet.2017.12.003. Epub 2017 Dec 18.

PMID:
29301111
13.

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean.

Naik HS, Zhang J, Lofquist A, Assefa T, Sarkar S, Ackerman D, Singh A, Singh AK, Ganapathysubramanian B.

Plant Methods. 2017 Apr 8;13:23. doi: 10.1186/s13007-017-0173-7. eCollection 2017.

14.

Deep Learning for Flow Sculpting: Insights into Efficient Learning using Scientific Simulation Data.

Stoecklein D, Lore KG, Davies M, Sarkar S, Ganapathysubramanian B.

Sci Rep. 2017 Apr 12;7:46368. doi: 10.1038/srep46368.

15.

A farm-level precision land management framework based on integer programming.

Li Q, Hu G, Jubery TZ, Ganapathysubramanian B.

PLoS One. 2017 Mar 27;12(3):e0174680. doi: 10.1371/journal.pone.0174680. eCollection 2017.

16.

Computer vision and machine learning for robust phenotyping in genome-wide studies.

Zhang J, Naik HS, Assefa T, Sarkar S, Reddy RV, Singh A, Ganapathysubramanian B, Singh AK.

Sci Rep. 2017 Mar 8;7:44048. doi: 10.1038/srep44048.

17.

Deploying Fourier Coefficients to Unravel Soybean Canopy Diversity.

Jubery TZ, Shook J, Parmley K, Zhang J, Naik HS, Higgins R, Sarkar S, Singh A, Singh AK, Ganapathysubramanian B.

Front Plant Sci. 2017 Jan 19;7:2066. doi: 10.3389/fpls.2016.02066. eCollection 2016.

18.

Machine Learning for High-Throughput Stress Phenotyping in Plants.

Singh A, Ganapathysubramanian B, Singh AK, Sarkar S.

Trends Plant Sci. 2016 Feb;21(2):110-124. doi: 10.1016/j.tplants.2015.10.015. Epub 2015 Dec 1. Review.

19.

Genome-wide association analysis of seedling root development in maize (Zea mays L.).

Pace J, Gardner C, Romay C, Ganapathysubramanian B, Lübberstedt T.

BMC Genomics. 2015 Feb 5;16:47. doi: 10.1186/s12864-015-1226-9.

20.

Electrode materials, thermal annealing sequences, and lateral/vertical phase separation of polymer solar cells from multiscale molecular simulations.

Lee CK, Wodo O, Ganapathysubramanian B, Pao CW.

ACS Appl Mater Interfaces. 2014 Dec 10;6(23):20612-24. doi: 10.1021/am506015r. Epub 2014 Nov 19.

PMID:
25373018
21.

Micropillar sequence designs for fundamental inertial flow transformations.

Stoecklein D, Wu CY, Owsley K, Xie Y, Di Carlo D, Ganapathysubramanian B.

Lab Chip. 2014 Nov 7;14(21):4197-204. doi: 10.1039/c4lc00653d.

PMID:
25268387
22.

Analysis of maize (Zea mays L.) seedling roots with the high-throughput image analysis tool ARIA (Automatic Root Image Analysis).

Pace J, Lee N, Naik HS, Ganapathysubramanian B, Lübberstedt T.

PLoS One. 2014 Sep 24;9(9):e108255. doi: 10.1371/journal.pone.0108255. eCollection 2014.

23.

Engineering fluid flow using sequenced microstructures.

Amini H, Sollier E, Masaeli M, Xie Y, Ganapathysubramanian B, Stone HA, Di Carlo D.

Nat Commun. 2013;4:1826. doi: 10.1038/ncomms2841.

PMID:
23652014

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