6篇
您的检索式：作者名="Kate Evans"
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| 1 | Modeling of genetic gain for single traits from marker-assisted seedling selection in clonally propagated crops显示文摘Seedling selection identifies superior seedlings as candidate cultivars based on predicted genetic potential for traits of interest.Traditionally,genetic potential is determined by phenotypic evaluation.With the availability of DNA tests for some agronomically important traits,breeders have the opportunity to include DNA information in their seedling selection operations—known as marker-assisted seedling selection.A major challenge in deploying marker-assisted seedling selection in clonally propagated crops is a lack of knowledge in genetic gain achievable from alternative strategies.Existing models based on additive effects considering seed-propagated crops are not directly relevant for seedling selection of clonally propagated crops,as clonal propagation captures all genetic effects,not just additive.This study modeled genetic gain from traditional and various marker-based seedling selection strategies on a single trait basis through analytical derivation and stochastic simulation,based on a generalized seedling selection scheme of clonally propagated crops.Various trait-test scenarios with a range of broad-sense heritability and proportion of genotypic variance explained by DNA markers were simulated for two populations with different segregation patterns.Both derived and simulated results indicated that marker-based strategies tended to achieve higher genetic gain than phenotypic seedling selection for a trait where the proportion of genotypic variance explained by marker information was greater than the broad-sense heritability.Results from this study provides guidance in optimizing genetic gain from seedling selection for single traits where DNA tests providing marker information are available. | Sushan Ru Craig Hardner Patrick A Carter Kate Evans Dorrie Main Cameron Peace | 2016 | Horticulture Research2016，3，1： | 1 |
| 2 | A Phase I Study of FOLFIRINOX Plus IPI-926, a Hedgehog Pathway Inhibitor, for Advanced Pancreatic Adenocarcinoma显示文摘 | Andrew H. Ko Noelle LoConte Margaret A. Tempero Evan J. Walker R. Kate Kelley Stephanie Lewis Wei-Chou Chang Emily Kantoff Michael W. Vannier Daniel V. Catenacci Alan P. Venook Hedy L. Kindler | 2016 | Pancreas2016，，3： | 1 |
| 3 | Forest carbon balance under elevated CO2显示文摘 | Jason G. Hamilton Evan H. DeLucia Kate George Shawna L. Naidu Adrien C. Finzi William H. Schlesinger | 2002 | Oecologia2002，，2： | 1 |
| 4 | Epidemiology of distal radius fractures显示文摘 | Kate W Nellans Evan Kowalski Kevin C Chung | 2012 | Hand Clin2012，28，2： | 1 |
| 5 | Cost and accuracy of advanced breeding trial designs in apple显示文摘Trialing advanced candidates in tree fruit crops is expensive due to the long-term nature of the planting and labor-intensive evaluations required to make selection decisions.How closely the trait evaluations approximate the true trait value needs balancing with the cost of the program.Designs of field trials of advanced apple candidates in which reduced number of locations,the number of years and the number of harvests per year were modeled to investigate the effect on the cost and accuracy in an operational breeding program.The aim was to find designs that would allow evaluation of the most additional candidates while sacrificing the least accuracy.Critical percentage difference,response to selection,and correlated response were used to examine changes in accuracy of trait evaluations.For the quality traits evaluated,accuracy and response to selection were not substantially reduced for most trial designs.Risk management influences the decision to change trial design,and some designs had greater risk associated with them.Balancing cost and accuracy with risk yields valuable insight into advanced breeding trial design.The methods outlined in this analysis would be well suited to other horticultural crop breeding programs. | Julia M Harshman Kate M Evans Craig M Hardner | 2016 | Horticulture Research2016，3，1： | 0 |
| 6 | A database of experimentally measured lithium solid electrolyte conductivities evaluated with machine learning显示文摘The application of machine learning models to predict material properties is determined by the availability of high-quality data.We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c.impedance spectroscopy.This dataset has 820 entries collected from 214 sources;entries contain a chemical composition,an expert-assigned structural label,and ionic conductivity at a specific temperature(from 5 to 873°C).There are 403 unique chemical compositions with an associated ionic conductivity near room temperature(15–35°C).The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance.This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity.This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors. | Cameron J.Hargreaves Michael W.Gaultois Luke M.Daniels Emma J.Watts Vitaliy A.Kurlin Michael Moran Yun Dang Rhun Morris Alexandra Morscher Kate Thompson Matthew A.Wright Beluvalli-Eshwarappa Prasad Frédéric Blanc Chris M.Collins Catriona A.Crawford Benjamin B.Duff Jae Evans Jacinthe Gamon Guopeng Han Bernhard T.Leube Hongjun Niu Arnaud J.Perez Aris Robinson Oliver Rogan Paul M.Sharp Elvis Shoko Manel Sonni William J.Thomas Andrij Vasylenko Lu Wang Matthew J.Rosseinsky Matthew S.Dyer | 2023 | npj Computational Materials2023，，1： | 0 |
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