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1,243 results on '"Qin Xing"'

1. Morse Thoery of Saddle Point Reduction with Applications

Author

Ran Yang and Qin Xing

Subjects
Morse index, critical group, Morse theory, critical point theorem, saddle point reduction, Hamiltonian systems, Mathematics, QA1-939
Abstract

In this paper, we demonstrate that when saddle point reduction is applicable, there is a clear relationship between the Morse index and the critical groups before and after the reduction. As an application of this result, we use saddle point reduction along with the critical point theorem to show the existence of periodic solutions in second-order Hamiltonian systems.

Published
2024
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2. A Study on English Translation Skills and Modes of Multimodal Network from the Perspective of Teaching Situation

Author

Xinxin Guan, Qin Xing, and Yuwei Zhang

Subjects
Electronic computers. Computer science, QA75.5-76.95
Abstract

With the in-depth research and continuous exploration of English translation skill patterns by multimodal networks, the scope of English translation skill analysis has been extended to other aspects besides words, and the traditional single-mode English translation skill analysis has gradually changed into a systematic English translation skill analysis that integrates language, images, music, and other symbols. From the perspective of situational teaching, this paper studies the skills and modes of English translation by multimodal network. The research shows that 30 books are randomly selected from preschool children’s books, primary school books, and junior high school books, among which the highest proportion of multimodal books is 82%, 57%, and 54%, respectively, and the proportion of multimodal network books among 120 literature books is 79%. Translators have a full understanding of the translated content in situational teaching, but in the actual process of translation, they will inevitably face the situation of language integration and word order adjustment. If translators have a certain understanding of the corresponding English translation skills, they can lay a good foundation for English translation skills and ensure the accuracy of translation.

Published
2022
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3. Supervised Bayesian joint graphical model for simultaneous network estimation and subgroup identification

Author

Qin, Xing, Liu, Xu, Ma, Shuangge, and Wu, Mengyun

Subjects
Statistics - Methodology
Abstract

Heterogeneity is a fundamental characteristic of cancer. To accommodate heterogeneity, subgroup identification has been extensively studied and broadly categorized into unsupervised and supervised analysis. Compared to unsupervised analysis, supervised approaches potentially hold greater clinical implications. Under the unsupervised analysis framework, several methods focusing on network-based subgroup identification have been developed, offering more comprehensive insights than those restricted to mean, variance, and other simplistic distributions by incorporating the interconnections among variables. However, research on supervised network-based subgroup identification remains limited. In this study, we develop a novel supervised Bayesian graphical model for jointly identifying multiple heterogeneous networks and subgroups. In the proposed model, heterogeneity is not only reflected in molecular data but also associated with a clinical outcome, and a novel similarity prior is introduced to effectively accommodate similarities among the networks of different subgroups, significantly facilitating clinically meaningful biological network construction and subgroup identification. The consistency properties of the estimates are rigorously established, and an efficient algorithm is developed. Extensive simulation studies and a real-world application to TCGA data are conducted, which demonstrate the advantages of the proposed approach in terms of both subgroup and network identification.

Published
2024

11. The genome of homosporous maidenhair fern sheds light on the euphyllophyte evolution and defences

Author

Fang, Yuhan, Qin, Xing, Liao, Qinggang, Du, Ran, Luo, Xizhi, Zhou, Qian, Li, Zhen, Chen, Hengchi, Jin, Wanting, Yuan, Yaning, Sun, Pengbo, Zhang, Rui, Zhang, Jiao, Wang, Li, Cheng, Shifeng, Yang, Xueyong, Yan, Yuehong, Zhang, Xingtan, Zhang, Zhonghua, Bai, Shunong, Van de Peer, Yves, Lucas, William John, Huang, Sanwen, and Yan, Jianbin

Subjects
Human Genome, Genetics, Adiantum, Ferns, Genome, Plant, Phylogeny, Plant Biology, Crop and Pasture Production
Abstract

Euphyllophytes encompass almost all extant plants, including two sister clades, ferns and seed plants. Decoding genomes of ferns is the key to deep insight into the origin of euphyllophytes and the evolution of seed plants. Here we report a chromosome-level genome assembly of Adiantum capillus-veneris L., a model homosporous fern. This fern genome comprises 30 pseudochromosomes with a size of 4.8-gigabase and a contig N50 length of 16.22 Mb. Gene co-expression network analysis uncovered that homospore development in ferns has relatively high genetic similarities with that of the pollen in seed plants. Analysing fern defence response expands understanding of evolution and diversity in endogenous bioactive jasmonates in plants. Moreover, comparing fern genomes with those of other land plants reveals changes in gene families important for the evolutionary novelties within the euphyllophyte clade. These results lay a foundation for studies on fern genome evolution and function, as well as the origin and evolution of euphyllophytes.

Published
2022

14. Two-level Bayesian interaction analysis for survival data incorporating pathway information

Author

Qin, Xing, Ma, Shuangge, and Wu, Mengyun

Subjects
Statistics - Methodology
Abstract

Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer investigations on prognostic survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated to be more effective than single-level analysis, however such analysis is limited to main effects. Pathways are not isolated and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this article, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower-level gene-gene interactions and higher-level pathway-pathway interactions simultaneously. Significantly advancing from existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with favourable priors to account for two-level selection as well as censoring. The computational efficiency is much desirable for high dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. Application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

Published
2021

30. Gene-gene interaction analysis incorporating network information via a structured Bayesian approach

Author

Qin, Xing, Ma, Shuangge, and Wu, Mengyun

Subjects
Statistics - Methodology
Abstract

Increasing evidence has shown that gene-gene interactions have important effects on biological processes of human diseases. Due to the high dimensionality of genetic measurements, existing interaction analysis methods usually suffer from a lack of sufficient information and are still unsatisfactory. Biological networks have been massively accumulated, allowing researchers to identify biomarkers from a system perspective by utilizing network selection (consisting of functionally related biomarkers) as well as network structures. In the main-effect analysis, network information has been widely incorporated, leading to biologically more meaningful and more accurate estimates. However, there is still a big gap in the context of interaction analysis. In this study, we develop a novel structured Bayesian interaction analysis approach, effectively incorporating the network information. This study is among the first to identify gene-gene interactions with the assistance of network selection for phenotype prediction, while simultaneously accommodating the underlying network structures. It innovatively respects the multiple hierarchies among main effects, interactions, and networks. Bayesian method is adopted, which has been shown to have multiple advantages over some other techniques. An efficient variational inference algorithm is developed to explore the posterior distribution. Extensive simulation studies demonstrate the practical superiority of the proposed approach. The analysis of TCGA data on melanoma and lung cancer leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

Published
2020

32. Identification and characterization of a novel fumarase gene by metagenome expression cloning from marine microorganisms

Author

Tang Xian-Lai, Bi You-Li, Qin Xing-Mei, Kang Wen-Long, Hu Guo-Qing, Luo Feng-Feng, Ma Ge-Fei, Li Shuang-Xi, Hao Zhen-Yu, Jin Ke, Shen Pei-Hong, Zhao Gao-Chao, Wu Lan-Lan, Jiang Chengjian, and Wu Bo

Subjects
Microbiology, QR1-502
Abstract

Abstract Background Fumarase catalyzes the reversible hydration of fumarate to L-malate and is a key enzyme in the tricarboxylic acid (TCA) cycle and in amino acid metabolism. Fumarase is also used for the industrial production of L-malate from the substrate fumarate. Thermostable and high-activity fumarases from organisms that inhabit extreme environments may have great potential in industry, biotechnology, and basic research. The marine environment is highly complex and considered one of the main reservoirs of microbial diversity on the planet. However, most of the microorganisms are inaccessible in nature and are not easily cultivated in the laboratory. Metagenomic approaches provide a powerful tool to isolate and identify enzymes with novel biocatalytic activities for various biotechnological applications. Results A plasmid metagenomic library was constructed from uncultivated marine microorganisms within marine water samples. Through sequence-based screening of the DNA library, a gene encoding a novel fumarase (named FumF) was isolated. Amino acid sequence analysis revealed that the FumF protein shared the greatest homology with Class II fumarate hydratases from Bacteroides sp. 2_1_33B and Parabacteroides distasonis ATCC 8503 (26% identical and 43% similar). The putative fumarase gene was subcloned into pETBlue-2 vector and expressed in E. coli BL21(DE3)pLysS. The recombinant protein was purified to homogeneity. Functional characterization by high performance liquid chromatography confirmed that the recombinant FumF protein catalyzed the hydration of fumarate to form L-malate. The maximum activity for FumF protein occurred at pH 8.5 and 55°C in 5 mM Mg2+. The enzyme showed higher affinity and catalytic efficiency under optimal reaction conditions: Km= 0.48 mM, Vmax = 827 μM/min/mg, and kcat/Km = 1900 mM/s. Conclusions We isolated a novel fumarase gene, fumF, from a sequence-based screen of a plasmid metagenomic library from uncultivated marine microorganisms. The properties of FumF protein may be ideal for the industrial production of L-malate under higher temperature conditions. The identification of FumF underscores the potential of marine metagenome screening for novel biomolecules.

Published
2010
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