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您的检索式：作者名="Tyler Wagner"
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| 1 | Maize Carbohydrate Partitioning Defective33 Encodes an MCTP Protein and Functions in Sucrose Export from Leaves显示文摘To sustain plant growth,development,and crop yield,sucrose must be transported from leaves to distant parts of the plant,such as seeds and roots.To identify genes that regulate sucrose accumulation and transport in maize(Zea mays),we isolated carbo/iydrafe part/f/ofi/ngf defecf/Ve33(cpd33),a recessive mutant that accumulated excess starch and soluble sugars in mature leaves.The cpd33 mutants also exhibited chlorosis in the leaf blades,greatly diminished plant growth,and reduced fertility.Cpd33 encodes a protein containing multiple C2 domains and transmembrane regions.Subcellular localization experiments showed the CPD33 protein localized to plasmodesmata(PD),the plasma membrane,and the endoplasmic reticulum.We also found that a loss-of-function mutant of the CPD33 homolog in Arabidopsis,QUIRKY,had a similar carbohydrate hyperaccumulation phenotype.Radioactively labeled sucrose transport assays showed that sucrose export was significantly lower in cpd33 mutant leaves relative to wild-type leaves.However,PD transport in the adaxial-abaxial direction was unaffected in cpd33 mutant leaves.Intriguingly,transmission electron microscopy revealed fewer PD at the companion cell-sieve element interface in mutant phloem tissue,providing a possible explanation for the reduced sucrose export in mutant leaves.Collectively,our results suggest that CPD33 functions to promote symplastic transport into sieve elements. | Thu M.Tran Tyler J.McCubbin Saadia Bihmidine Benjamin T.Julius R.Frank Baker Martin Schauflinger Clifford Weil Nathan Springer Paul Chomet Ruth Wagner Jeff Woessner Karen Grote Jeanette Peevers Thomas L.Slewinski David M.Braun | 2019 | Molecular Plant2019，12，9： | 1 |
| 2 | Approaching the upper boundary of driver-response relationships:identifying factors using a novel framework integrating quantile regression with interpretable machine learning显示文摘The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships,and can help inform ecosystem management,but has rarely been explored.In this study,we propose a novel framework integrating quantile regression with interpretable machine learning.In the first stage of the framework,we estimate the upper boundary of a driver-response relationship using quantile regression.Next,we calculate“potentials”of the response variable depending on the driver,which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot.Finally,we identify key factors impacting the potential using a machine learning model.We illustrate the necessary steps to implement the framework using the total phosphorus(TP)-Chlorophyll a(CHL)relationship in lakes across the continental US.We found that the nitrogen to phosphorus ratio(N:P),annual average precipitation,total nitrogen(TN),and summer average air temperature were key factors impacting the potential of CHL depending on TP.We further revealed important implications of our findings for lake eutrophication management.The important role of N:P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria.Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management.The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships. | Zhongyao Liang Yaoyang Xu Gang Zhao Wentao Lu Zhenghui Fu Shuhang Wang Tyler Wagner | 2023 | Frontiers of Environmental Science & Engineering2023，17，6： | 0 |
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