|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Expression of miR-15/107 Family MicroRNAs in Human Tissues and Cultured Rat Brain Cells显示文摘The miR-15/107 family comprises a group of 10 paralogous microRNAs(miRNAs),sharing a 50AGCAGC sequence.These miRNAs have overlapping targets.In order to characterize the expression of miR-15/107 family miRNAs,we employed customized TaqMan Low-Density micro-fluid PCR-array to investigate the expression of miR-15/107 family members,and other selected miRNAs,in 11 human tissues obtained at autopsy including the cerebral cortex,frontal cortex,primary visual cortex,thalamus,heart,lung,liver,kidney,spleen,stomach and skeletal muscle.miR-103,miR-195 and miR-497 were expressed at similar levels across various tissues,whereas miR-107 is enriched in brain samples.We also examined the expression patterns of evolutionarily conserved miR-15/107 miRNAs in three distinct primary rat brain cell preparations(enriched for cortical neurons,astrocytes and microglia,respectively).In primary cultures of rat brain cells,several members of the miR-15/107 family are enriched in neurons compared to other cell types in the central nervous system(CNS).In addition to mature miRNAs,we also examined the expression of precursors(pri-miRNAs).Our data suggested a generally poor correlation between the expression of mature miRNAs and their precursors.In summary,we provide a detailed study of the tissue and cell type-specific expression profile of this highly expressed and phylogenetically conserved family of miRNA genes. | Wang-Xia Wang Robert J.Danaher Craig S.Miller Joseph R.Berger Vega G.Nubia Bernard S.Wilfred Janna H.Neltner Christopher M.Norris Peter T.Nelson | 2014 | Genomics, Proteomics & Bioinformatics2014，12，1： | 9 |
| 2 | Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data显示文摘Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions. | Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang Rama K.Vasudevan Eugene Eliseev Anna N.Morozovska Ichiro Takeuchi Sergei V.Kalinin | 2020 | npj Computational Materials2020，，1： | 2 |
| 3 | Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding显示文摘Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations. | Christopher T.Nelson Ayana Ghosh Mark Oxley Xiaohang Zhang Maxim Ziatdinov Ichiro Takeuchi Sergei V.Kalinin | 2021 | npj Computational Materials2021，，1： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员