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RISS 인기검색어
- 검색키워드
- 저자 : Wen Yufeng (검색결과 5 건)
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Forensic biogeographical ancestry inference: recent insights and current trends
Wen Yufeng,Liu Jing,Su Yonglin,Chen Xiacan,Hou Yiping,Liao Linchuan,Wang Zheng 한국유전학회 2023 Genes & Genomics Vol.45 No.10
Background As a powerful complement to the paradigmatic DNA profiling strategy, biogeographical ancestry inference (BGAI) plays a significant part in human forensic investigation especially when a database hit or eyewitness testimony are not available. It indicates one’s biogeographical profile based on known population-specific genetic variations, and thus is crucial for guiding authority investigations to find unknown individuals. Forensic biogeographical ancestry testing exploits much of the recent advances in the understanding of human genomic variation and improving of molecular biology. Objective In this review, recent development of prospective ancestry informative markers (AIMs) and the statistical approaches of inferring biogeographic ancestry from AIMs are elucidated and discussed. Methods We highlight the research progress of three potential AIMs (i.e., single nucleotide polymorphisms, microhaplotypes, and Y or mtDNA uniparental markers) and discuss the prospects and challenges of two methods that are commonly used in BGAI. Conclusion While BGAI for forensic purposes has been thriving in recent years, important challenges, such as ethics and responsibilities, data completeness, and ununified standards for evaluation, remain for the use of biogeographical ancestry information in human forensic investigations. To address these issues and fully realize the value of BGAI in forensic investigation, efforts should be made not only by labs/institutions around the world independently, but also by inter-lab/institution collaborations.
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Development of a Love Wave Based Device for Sensing Icing Process with Fast Response
Wen Wang,Yining Yin,Yana Jia,Mengwei Liu,Yong Liang,Yufeng Zhang,Minghui Lu 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.3
This work addresses the theoretical and experimental investigations of a Love wave based device employing waveguide structure of SiO2/36° YX-LiTaO3 for sensing icing process. The mass loading efect induced by the icing process modulates the acoustic wave propagation, and corresponding changes in device frequency can be collected to evaluate the icing process. The waveguide structure confnes the acoustic wave energy into SiO2 thin-flm, which contributes well to the improvement of the mass loading sensitivity. The corresponding sensing mechanism was analyzed by solving the acoustic propagation equations in layered structure. The sensing device patterned by delay-line on 36° YX-LiTaO3 substrate with SiO2 guiding layer was photolithographically developed as the sensor element, and characterized by using the high-low temperature chamber. The icing process was simulated by dropping appropriate water on top of the device surface. Very clear and fast frequency response was observed from the proposed sensing device in the icing process, and also, the infuence of SiO2 guiding layer thickness on sensor response was also investigated.
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An Octree-Based Two-Step Method of Surface Defects Detection for Remanufacture
Yan He,Wen Ma,Yufeng Li,Chuanpeng Hao,Yulin Wang,Yan Wang 한국정밀공학회 2023 International Journal of Precision Engineering and Vol.10 No.2
Accurate and quick detection has a significant bearing on overall productivity of remanufacture. 3D scanning technologies have been widely applied in defects detection by comparing the damaged model with the nominal model. In this process, a huge amount of point cloud data is required to ensure detection accuracy whereas resulting in large storage space and long processing time of detection. This paper proposed an efficient two-step method based on octree to detect defects accurately and quickly for remanufacturing. In this method, the damaged point cloud and the nominal point cloud are first registered. Then a two-step detection approach is developed to extract the surface defects, coarse detection and detailed extraction, where the octree method is applied to create an effective topology of discrete points and perform the Boolean operation for defects extraction. In coarse detection, rough location and size information of the defects are acquired from the whole point cloud data. Based on coarse detected boundary box containing defects, the detailed extraction step is applied to extract corresponding defects shape accurately. The feasibility of proposed method was validated by using a case to detect defects of a damaged turbine blade and the detection results can be used to generate restoration tool path. The results show that the proposed method outperforms state-of-art defects detection methods, which can reduce time by 74.03% and reduce error by 36.86%, respectively.
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Xin Li,Yancheng Li,Yuanhao Liang,Ruixue Hu,Wenli Xu,Yufeng Liu 대한당뇨병학회 2021 Diabetes and Metabolism Journal Vol.45 No.2
Background: We hypothesized that specific amino acids or acylcarnitines would have benefits for the differential diagnosis of diabetes. Thus, a targeted metabolomics for amino acids and acylcarnitines in patients with diabetes and its complications was carried out. Methods: A cohort of 54 normal individuals and 156 patients with type 2 diabetes mellitus and/or diabetic complications enrolled from the First Affiliated Hospital of Jinzhou Medical University was studied. The subjects were divided into five main groups: normal individuals, impaired fasting glucose, overt diabetes, diabetic microvascular complications, and diabetic peripheral vascular disease. The technique of tandem mass spectrometry was applied to obtain the plasma metabolite profiles. Metabolomics multivariate statistics were applied for the metabolic data analysis and the differential metabolites determination. Results: A total of 10 cross-comparisons within diabetes and its complications were designed to explore the differential metabolites. The results demonstrated that eight comparisons existed and yielded significant metabolic differences. A total number of 24 differential metabolites were determined from six selected comparisons, including up-regulated amino acids, down-regulated medium-chain and long-chain acylcarnitines. Altered differential metabolites provided six panels of biomarkers, which were helpful in distinguishing diabetic patients. Conclusion: Our results demonstrated that the biomarker panels consisted of specific amino acids and acylcarnitines which could reflect the metabolic variations among the different stages of diabetes and might be useful for the differential diagnosis of prediabetes, overt diabetes and diabetic complications.
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Approximate Clustering on Data Streams Using Discrete Cosine Transform
Yu, Feng,Oyana, Damalie,Hou, Wen-Chi,Wainer, Michael Korea Information Processing Society 2010 Journal of information processing systems Vol.6 No.1
In this study, a clustering algorithm that uses DCT transformed data is presented. The algorithm is a grid density-based clustering algorithm that can identify clusters of arbitrary shape. Streaming data are transformed and reconstructed as needed for clustering. Experimental results show that DCT is able to approximate a data distribution efficiently using only a small number of coefficients and preserve the clusters well. The grid based clustering algorithm works well with DCT transformed data, demonstrating the viability of DCT for data stream clustering applications.
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