With marker and phenotype information from observed populations, genomic selection （GS） can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effect...With marker and phenotype information from observed populations, genomic selection （GS） can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in hese methods for crop improvement are reviewed in this paper.展开更多
The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potentia...The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.展开更多
Objective To determine whether learning deficits could be seen in transgenic mice expressing human amyloid precursor protein 770 (APP 770 ) Methods Female heterozygous transgenic and nontransgenic mice aged 3,...Objective To determine whether learning deficits could be seen in transgenic mice expressing human amyloid precursor protein 770 (APP 770 ) Methods Female heterozygous transgenic and nontransgenic mice aged 3, 6 and 9 months at the start of testing were used, with eight mice in each age group All mice were subjected to various behavioral tasks including the Y maze task and the Morris water maze After behavioral testing, the mice were sacrificed, and their brain tissues were used for measuring the choline acetyltransferase (ChAT) activity Results Nine month old transgenic mice exhibited spatial learning deficits in the Morris water maze and in spontaneous alternation in the Y maze, compared with those of the age matched non transgenic mice The behavioral changes accompanied a reduction of ChAT activity in the cortical and hippocampal regions of transgenic mice On the other hand, these behavioral deficits were not observed in transgenic mice either at 3 or at 6 months of age, in which ChAT activity remained unchanged Conclusions The present results show that the learning impairment observed in 9 month old APP 770 transgenic mice are accompanied by a decrease in cortical and hippocampal ChAT activities This suggests that cholinergic deficits may be involved in the learning impairment observed in these APP 770 mice This model will be a useful tool in advancing our understanding of the relationship between the cholinergic system and the cognitive deficits observed in Alzheimer's disease (AD)展开更多
Life may have begun in an RNA world,which is supported by increasing evidence of the vital role that RNAs perform in biological systems.In the human genome,most genes actually do not encode proteins;they are noncoding...Life may have begun in an RNA world,which is supported by increasing evidence of the vital role that RNAs perform in biological systems.In the human genome,most genes actually do not encode proteins;they are noncoding RNA genes.The largest class of noncoding genes is known as long noncoding RNAs(lncRNAs),which are transcripts greater in length than 200 nucleotides,but with no protein-coding capacity.While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization,most lncRNAs are still uncharacterized.We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies.Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.展开更多
基金拉里奥哈国际大学研究和技术部(UNIR Research)(http://research.unir.net)教育创新和技术研究院(Research Institute for Innovation&Technology in Education+1 种基金简称iTEDhttp://research.unir.net/ited)的部分资助
基金supported by grants from the National High Technology Research and Development Program of China(2014AA10A601-5)the National Key Research and Development Program of China(2016YFD0100303)+5 种基金the National Natural Science Foundation of China(91535103)the Natural Science Foundations of Jiangsu Province(BK20150010)the Natural Science Foundation of the Jiangsu Higher Education Institutions(14KJA210005)the Open Research Fund of State Key Laboratory of Hybrid Rice(Wuhan University)(KF201701)the Science and Technology Innovation Fund Project in Yangzhou University(2016CXJ021)the Priority Academic Program Development of Jiangsu Higher Education Institutions and the Innovative Research Team of Universities in Jiangsu Province
文摘With marker and phenotype information from observed populations, genomic selection （GS） can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations. To overcome this issue and improve prediction accuracy, many models and algorithms, including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS. The principles and characteristics of current popular GS methods and research progress in hese methods for crop improvement are reviewed in this paper.
基金Project(61171133)supported by the National Natural Science Foundation of ChinaProject(11JJ1010)supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProject(61101182)supported by National Natural Science Foundation for Young Scientists of China
文摘The sparse recovery algorithms formulate synthetic aperture radar （SAR） imaging problem in terms of sparse representation （SR） of a small number of strong scatters＇ positions among a much large number of potential scatters＇ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning （SBL）, fast Bayesian matching pursuit （FBMP）, smoothed 10 norm method （SL0）, sparse reconstruction by separable approximation （SpaRSA）, fast iterative shrinkage-thresholding algorithm （FISTA）, and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.
文摘Objective To determine whether learning deficits could be seen in transgenic mice expressing human amyloid precursor protein 770 (APP 770 ) Methods Female heterozygous transgenic and nontransgenic mice aged 3, 6 and 9 months at the start of testing were used, with eight mice in each age group All mice were subjected to various behavioral tasks including the Y maze task and the Morris water maze After behavioral testing, the mice were sacrificed, and their brain tissues were used for measuring the choline acetyltransferase (ChAT) activity Results Nine month old transgenic mice exhibited spatial learning deficits in the Morris water maze and in spontaneous alternation in the Y maze, compared with those of the age matched non transgenic mice The behavioral changes accompanied a reduction of ChAT activity in the cortical and hippocampal regions of transgenic mice On the other hand, these behavioral deficits were not observed in transgenic mice either at 3 or at 6 months of age, in which ChAT activity remained unchanged Conclusions The present results show that the learning impairment observed in 9 month old APP 770 transgenic mice are accompanied by a decrease in cortical and hippocampal ChAT activities This suggests that cholinergic deficits may be involved in the learning impairment observed in these APP 770 mice This model will be a useful tool in advancing our understanding of the relationship between the cholinergic system and the cognitive deficits observed in Alzheimer's disease (AD)
基金supported by the Self Regional Healthcare Foundation,USA
文摘Life may have begun in an RNA world,which is supported by increasing evidence of the vital role that RNAs perform in biological systems.In the human genome,most genes actually do not encode proteins;they are noncoding RNA genes.The largest class of noncoding genes is known as long noncoding RNAs(lncRNAs),which are transcripts greater in length than 200 nucleotides,but with no protein-coding capacity.While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization,most lncRNAs are still uncharacterized.We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies.Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.
