A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution o...A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution of the different root types in the soil. The ability to image,track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system,while allowing for aeration,solution replenishment and the imposition of nutrient treatments,as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modi fications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity（detection of fine roots and other root details）,higher ef ficiency,and a broad array of growing conditions for plants that more closely mimic those found under field conditions.展开更多
This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-gra...This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning(ML)with optimization algorithms.In the bottom-up approach,bonded interactions of the CG model are optimized using deep neural networks(DNN),where atomistic bonded distributions are matched.In the top-down approach,optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density.We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches.The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics,the limiting behavior of the glass transition temperature,diffusion,and stress relaxation,where none were included in the parametrization process.The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.展开更多
Correction to:npj Computational Materials https://doi.org/10.1038/s41524-022-00914-4,published online 04 November 2022 The original version of this Article omitted some contributions in the Author Contributions sectio...Correction to:npj Computational Materials https://doi.org/10.1038/s41524-022-00914-4,published online 04 November 2022 The original version of this Article omitted some contributions in the Author Contributions section and incorrectly read[H.W.:Formal analysis,data visualization,review and editing.]The correct Author Contributions should read:[H.W.:Conceptua-lisation,formal analysis,data visualisation,writing,review and editing;Z.S.and H.W.contributed equally to the conduct of the research and preparation of the manuscript.]This has now been corrected in both the PDF and HTML versions of the Article.展开更多
基金the support of the Biotechnology and Biological Sciences Research Council and Engineering and Physical Sciences Research Council funding to the Centre for Plant Integrative Biologyfunding in the form of a Biotechnology and Biological Sciences Research Council Professorial Research Fellowship+1 种基金European Research Council Advanced Investigator Grant funding(FUTUREROOTS)the Distinguished Scientist Fellowship Program(DSFP)at King Saud University
文摘A plant＇s ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture（RSA）,the three-dimensional distribution of the different root types in the soil. The ability to image,track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system,while allowing for aeration,solution replenishment and the imposition of nutrient treatments,as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modi fications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity（detection of fine roots and other root details）,higher ef ficiency,and a broad array of growing conditions for plants that more closely mimic those found under field conditions.
基金This research is supported by the Commonwealth of Australia as represented by the Defence Science and Technology Group of the Department of Defence.
文摘This work presents a framework governing the development of an efficient,accurate,and transferable coarse-grained(CG)model of a polyether material.The framework combines bottom-up and top-down approaches of coarse-grained model parameters by integrating machine learning(ML)with optimization algorithms.In the bottom-up approach,bonded interactions of the CG model are optimized using deep neural networks(DNN),where atomistic bonded distributions are matched.In the top-down approach,optimization of nonbonded parameters is accomplished by reproducing the temperature-dependent experimental density.We demonstrate that developed framework addresses the thermodynamic consistency and transferability issues associated with the classical coarse-graining approaches.The efficiency and transferability of the CG model is demonstrated through accurate predictions of chain statistics,the limiting behavior of the glass transition temperature,diffusion,and stress relaxation,where none were included in the parametrization process.The accuracy of the predicted properties are evaluated in context of molecular theories and available experimental data.
文摘Correction to:npj Computational Materials https://doi.org/10.1038/s41524-022-00914-4,published online 04 November 2022 The original version of this Article omitted some contributions in the Author Contributions section and incorrectly read[H.W.:Formal analysis,data visualization,review and editing.]The correct Author Contributions should read:[H.W.:Conceptua-lisation,formal analysis,data visualisation,writing,review and editing;Z.S.and H.W.contributed equally to the conduct of the research and preparation of the manuscript.]This has now been corrected in both the PDF and HTML versions of the Article.
