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您的检索式：作者名="Peiting Li"
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| 1 | Estimation of Net Surface Shortwave Radiation from Land Surface Temperature in Regional Scale显示文摘The method to estimate NSSR (net surface shortwave radiation) from LST (land surface temperature) in regional scale is discussed. First, an elliptical model between the time series of normalized LST and NSSR was developed using the daily evolution of LST and NSSR. Second, time series of LST and NSSR were simulated by common land model (CoLM) and were proved to be of high accuracy. On the basis of these, a non-linear least square ellipse fitting using the genetic algorithm method was used to fit the normalized LST and NSSR. Finally, LST was inverted using MODIS (moderate resolution imaging spectroradiometer) data with the split-window algorithm, and the regional NSSR was then estimated with LST and an elliptical model. The validation result shows that the derived average NSSR of 50×50 pixels of MODIS data was quite close to the observed data, and the distribution was reasonable, which indicates that the proposed method was capable of estimating NSSR on a regional scale. | LENG Peit SONG Xiaoning LI Zhaoliang | 2011 | Wuhan University Journal of Natural Sciences2011，16，4： | 1 |
| 2 | Connectivity-based Cortical Parcellation via Contrastive Learning on Spatial-Graph Convolution显示文摘Objective.Objective of this work is the development and evaluation of a cortical parcellation framework based on tractography-derived brain structural connectivity.Impact Statement.The proposed framework utilizes novel spatial-graph representation learning methods for solving the task of cortical parcellation,an important medical image analysis and neuroscientific problem.Introduction.The concept of“connectional fingerprint”has motivated many investigations on the connectivity-based cortical parcellation,especially with the technical advancement of diffusion imaging.Previous studies on multiple brain regions have been conducted with promising results.However,performance and applicability of these models are limited by the relatively simple computational scheme and the lack of effective representation of brain imaging data.Methods.We propose the Spatial-graph Convolution Parcellation(SGCP)framework,a two-stage deep learning-based modeling for the graph representation brain imaging.In the first stage,SGCP learns an effective embedding of the input data through a self-supervised contrastive learning scheme with the backbone encoder of a spatial-graph convolution network.In the second stage,SGCP learns a supervised classifier to perform voxel-wise classification for parcellating the desired brain region.Results.SGCP is evaluated on the parcellation task for 5 brain regions in a 15-subject DWI dataset.Performance comparisons between SGCP,traditional parcellation methods,and other deep learning-based methods show that SGCP can achieve superior performance in all the cases.Conclusion.Consistent good performance of the proposed SGCP framework indicates its potential to be used as a general solution for investigating the regional/subregional composition of human brain based on one or more connectivity measurements. | Peiting You Xiang Li Fan Zhang Quanzheng Li | 2022 | Biomedical Engineering Frontiers2022，3，1： | 0 |
| 3 | Sequence analysis of Erianthus arundinaceus chromosome 1 isolated by flow sorting after genomic in situ hybridization in suspension显示文摘Erianthus arundinaceus is a wild relative of sugarcane(Saccharum officinarum L.)with many desirable agronomic traits for sugarcane genetic improvement.However,limited knowledge of the complex genome of hexaploid E.arundinaceus has impeded the development of required molecular tools.Dissecting complex genomes into single chromosomes can simplify analyses.The flow-cytometric sorting of a single chromosome of E.arundinaceus in a Saccharum-Erianthus introgression line is reported.A novel approach called genomic in situ hybridization in suspension was used to discriminate the alien chromosome from sugarcane chromosomes at the same size.A total of 218,000 E.arundinaceus chromosome 1(EaC1)were sorted to>97%purity and amplified DNA was sequenced using Illumina and Pac Bio technologies.The resulting assembly had a 70.93 Mb contig sequence with an N50 of 19.62 kb.A total of 56.69 Mb repeat sequences were predicted,accounting for 79.1%of the chromosome and 2646 genes having a total length of 1.84 Mb that represented 2.59%of the chromosome.Of these genes,1877(70.9%)genes were functionally annotated.The phylogenetic relationship of E.arundinaceus with other species using the chromosome1 sequence revealed that E.arundinaceus was distantly related to Oryza sativa and Zea mays,followed by Sorghum bicolor,and was closely related to S.spontaneum and Saccharum spp.hybrids.This study provides the first insights into the characteristics of EaC1,and the results will provide tools to support molecular improvement and alien introgression breeding of sugarcane. | Shan Yang Petr Cápal Jaroslav Dolezel Xueting Li Wang Qian Zhiqiang Wang Kai Zeng Peiting Li Hongkai Zhou Rui Xia Muqing Zhang Zuhu Deng | 2022 | The Crop Journal2022，10，6： | 0 |
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