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| 1 | Soybean Root System Architecture Trait Study through Genotypic,Phenotypic,and Shape-Based Clusters显示文摘We report a root system architecture(RSA)traits examination of a larger scale soybean accession set to study trait genetic diversity.Suffering from the limitation of scale,scope,and susceptibility to measurement variation,RSA traits are tedious to phenotype.Combining 35,448 SNPs with an imaging phenotyping platform,292 accessions(replications=14)were studied for RSA traits to decipher the genetic diversity.Based on literature search for root shape and morphology parameters,we used an ideotypebased approach to develop informative root(iRoot)categories using root traits.The RSA traits displayed genetic variability for root shape,length,number,mass,and angle.Soybean accessions clustered into eight genotype-and phenotype-based clusters and displayed similarity.Genotype-based clusters correlated with geographical origins.SNP profiles indicated that much of US origin genotypes lack genetic diversity for RSA traits,while diverse accession could infuse useful genetic variation for these traits.Shape-based clusters were created by integrating convolution neural net and Fourier transformation methods,enabling trait cataloging for breeding and research applications.The combination of genetic and phenotypic analyses in conjunction with machine learning and mathematical models provides opportunities for targeted root trait breeding efforts to maximize the beneficial genetic diversity for future genetic gains. | Kevin G.Falk Talukder Zaki Jubery Jamie A.O’Rourke Arti Singh Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2020 | Plant Phenomics2020，2，1： | 3 |
| 2 | Dielectro- phoretic separation of bioparticles in microdevices: A re- view显示文摘 | JUBERY T Z SRIVASTAVA S K DUTrA P | 2014 | Electrophoresis2014，35，5： | 1 |
| 3 | Dielectrophoretic separation of bioparticles in microdevices: A review显示文摘 | Talukder Z. Jubery Soumya K. Srivastava Prashanta Dutta | 2014 | ELECTROPHORESIS2014，，5： | 1 |
| 4 | Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline(SNAP)显示文摘Nodules form on plant roots through the symbiotic relationship between soybean(Glycine max L.Merr.)roots and bacteria(Bradyrhizobium japonicum)and are an important structure where atmospheric nitrogen(N2)is fixed into bioavailable ammonia(NH3)for plant growth and development.Nodule quantification on soybean roots is a laborious and tedious task;therefore,assessment is frequently done on a numerical scale that allows for rapid phenotyping,but is less informative and suffers from subjectivity.We report the Soybean Nodule Acquisition Pipeline(SNAP)for nodule quantification that combines RetinaNet and UNet deep learning architectures for object(i.e.,nodule)detection and segmentation.SNAP was built using data from 691 unique roots from diverse soybean genotypes,vegetative growth stages,and field locations and has a good model fit(R2=0:99).SNAP reduces the human labor and inconsistencies of counting nodules,while acquiring quantifiable traits related to nodule growth,location,and distribution on roots.The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors,and their interactions on nodulation from an early development stage.The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars,as well as enhanced insight into the plant-Bradyrhizobium relationship. | Talukder Zaki Jubery Clayton N.Carley Arti Singh Soumik Sarkar Baskar Ganapathysubramanian Asheesh K.Singh | 2021 | Plant Phenomics2021，3，1： | 1 |
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