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| 1 | Construction of a genetic linkage map for cotton based on SRAP显示文摘A genetic linkage map of cotton was constructed with a newly developed molecular marker-SRAP (sequence-related amplified polymorphism) using a population consisting of 129 F2 individuals derived from the interspecific cross of Handan208 Pima90. A total of 136 primer pairs were used to detect polymorphisms between the two parents and 76 primer pairs with better polymorphisms were picked out to analyze the F2 population. 285 polymorphic bands were generated in total with an average of 3.75 polymorphic bands per pair of primers. The primer pair showing most polymorphic bands was the combination of me3 and em2, which produced 13 polymorphic bands. The 285 loci were used to construct linkage map with MAPMAKER/EXP3.0 and 237 loci were mapped at a LOD≥3.0 on 39 linkage groups. The total length of the map is 3030.7 cM, covering 65.4% of the whole cotton genome, and the average distance between adjacent markers is 12.79 cM. All the markers are distributed evenly among the linkage groups without clustering of loci. This is the first linkage map of cotton comprised of SRAP markers. | LIN Zhongxu, ZHANG Xianlong, NIE Yichun, HE Daohua & WU Maoqing National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China | 2003 | Chinese Science Bulletin2003，48，19： | 111 |
| 2 | Exp2 polymorphisms associated with variation for fiber quality properties in cotton(Gossypium spp.)显示文摘Plant expansins are a group of extracellular proteins thought to affect the quality of cotton fibers. Previous expression profile analysis revealed that six Expansin A genes are present in cotton, of which two(GhExp1 and GhExp2) produce transcripts that are specific to the developing cotton fiber. To identify the phenotypic function of Exp2, and to determine whether nucleotide variation among alleles of Exp2 affects fiber quality, candidate gene association mapping was conducted. Gene-specific primers were designed to amplify the Exp2 gene. By amplicon sequencing, the nucleotide diversity of Exp2 was investigated across92 accessions(including 7 Gossypium arboreum, 74 Gossypium hirsutum, and 11 Gossypium barbadense accessions) with different fiber qualities. Twenty-six SNPs and seven InDels including 14 from the coding region of Exp2 were detected, forming twelve distinct haplotypes in the cotton collection. Among the 14 SNPs in the coding region, five were missense mutations and nine were synonymous nucleotide changes. The average SNP/InDel per nucleotide ratio was 2.61%(one SNP per 39 bp), with 1.81 and 3.87% occurring in coding and non-coding regions, respectively. Nucleotide and haplotype diversity across the entire Exp2 region was 0.00603(π) and 0.844, respectively, and diversity in non-coding regions was higher than that in coding regions. For linkage disequilibrium(LD), the mean r2 value for all polymorphism loci pairs was 0.48, and LD did not decay over 748 bp. Based on132 simple sequence repeat(SSR) loci evenly covering 26 chromosomes, the population structure was estimated, and the accessions were divided into seven groups that agreed well with their genomic origin and evolutionary history. A general linear model was used to calculate the Exp2-wide diversity–trait associations of 5 fiber quality traits, considering population structure(Q). Four SNPs in Exp2 were associated with at least one of the fiber quality traits, but not with fiber elongation. The highest positive effect on UHML and STR was observed for haplotype Hap_6 of Exp2. There was a significant association of Exp2 with fiber quality traits. There were many haplotypes in the Exp2 region, of which the most favorable was Hap_6. The association between nucleotide diversity and these fiber traitssheds light on the gene's potential contribution to the improvement of fiber quality, and should be useful to facilitate MAS programs in cotton. | Daohua He Zhongping Lei Hongyi Xing Baoshan Tang Junxing Zhao Bixia Lu | 2014 | The Crop Journal2014，2，5： | 1 |
| 3 | QTL mapping for economic traits based on a dense genetic map of cotton with PCR-based markers using the interspecific cross of Gossypium hirsutum x Gossypium barbadense显示文摘 | He Daohua Zhang Xulin Zhang Xianlong | 2007 | Euphytica2007，153，12： | 1 |
| 4 | QTL mapping for economic traits based on a dense genetic map of cotton with PCR-based markers using the interspecific cross of Gossypium hirsutum x Gossypium barbadense 显示文摘 | HE Daohua LIN Zhongxu ZHANG Xianlong | 2007 | Euphytica2007，153，： | 1 |
| 5 | Isolation,purificationand characterization of superoxide dismutase form garlic 显示文摘 | He Ning Li Qingbiao Sun Daohua ei al | 2008 | Biochemical Engineering Journal2008，38，1： | 1 |
| 6 | Rapid Preparation Process of Silver Nanoparticles by Bioreduction and Their Characterizations 1 1 Supported by the National Natural Science Foundation of China (No.20376076).显示文摘 | Mouxing FU Qingbiao LI Daohua SUN Yinghua LU Ning HE Xu DENG Huixuan WANG Jiale HUANG | 2006 | Chinese Journal of Chemical Engineering2006，，1： | 1 |
| 7 | Insight into the dynamic adsorption behavior of graphene oxide multichannel architecture toward contaminants显示文摘Graphene oxide(GO) channels exhibit unique mass transport behaviors due to their flexibility, controllable thinness and extraordinary physicochemical properties, enabling them to be widely used for adsorption and membrane separation. Nevertheless, the adsorption behavior of nanosized contaminants within the channels of GO membrane has not been fully discussed. In this study, we fabricated a GO membrane(PGn, where n represents the deposition cycles of GO) with multi channels via the crosslinking of GO and multibranched poly(ethyleneimine)(PEI). Phenol was used as molecular probe to determine the correlations between dynamic adsorption behavior and structural parameters of the multilevel GO/PEI membrane. PG8 shows higher adsorption capacities and affinity, which is predominantly attributed to the multichannel structure providing a large specific surface for phenol adsorption, enhancing the accessibility of active sites for phenol molecules and the transport of phenol. Density functional theory calculations demonstrate that the adsorption mechanism of phenol within GO channel is energetically oriented by hydrogen bonds, which is dominated by oxygen-containing groups compared to amino groups. Particularly, the interfaces which facilitate strong π-π interaction and hydrogen bonds maybe the most active regions. Moreover, the as-prepared PG8 membrane showed outstanding performance for other contaminants such as methyl orange and Cr(VI). It is anticipated that this study will have implications for design of GO-related environmental materials with enhanced efficiency. | Jian Tian Gen Li Wang He Kok Bing Tan Daohua Sun Junfu Wei Qingbiao Li | 2023 | Chinese Journal of Chemical Engineering2023，53，1： | 0 |
| 8 | RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection显示文摘Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts.They are not aimed at the original network data,nor can they capture the potential characteristics of network packets.Therefore,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network traffic.Instead of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater depth.It regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted feature.Experimental results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%. | Yan Du Shibin Zhang Guogen Wan Daohua Zhou Jiazhong Lu Yuanyuan Huang Xiaoman Cheng Yi Zhang Peilin He | 2023 | Computers, Materials & Continua2023，，6： | 0 |
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