A new iterative near-field coherent subspace method for rub-impact fault localization using AE technique

Jing Li,Aidong Deng,Yong Yang,Xinmin Cheng,Dongying Liu,Li Zhao 대한기계학회 2017 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.31 No.5

Acoustic emission (AE) localization is an important method to detect defects in bearing of rotatory machine for faults maintenance. However, only the faults near the sensor array can be detected due to severe attenuation in the recorded AE signals. Therefore, we propose a highly reliable new Iterative near-field Coherent subspace method (IN-CSM) for multiple rub-impact faults localization. The proposed approach contains four improved processes: Modal plate wave theory (MPWT) analysis for the multi-modes decomposition and group velocity revision; Discrete wavelet transform (DWT) for the useful narrow band extraction; Near field Multiple signal classification (N-MUSIC) method for the preliminary position estimations; the IN-CSM algorithm for the multiple coherent sources separation and the precise localizations. The simulations based on N-MUSIC and IN-CSM methods were compared by rubbing teston the test rig of rotation machinery. The results indicate that the proposed method can effectively locate multiple coherent rubbing faults at once. Thus, it is an effective analysis tool for rub-impact fault detection.

Discovery and Validation of Salivary Extracellular RNA Biomarkers for Noninvasive Detection of Gastric Cancer

Li, Feng,Yoshizawa, Janice M.,Kim, Kyoung-Mee,Kanjanapangka, Julie,Grogan, Tristan R.,Wang, Xiaoyan,Elashoff, David E.,Ishikawa, Shigeo,Chia, David,Liao, Wei,Akin, David,Yan, Xinmin,Lee, Min-Sun,Choi, American Association for Clinical Chemistry, Inc. 2018 Clinical chemistry Vol.64 No.10

<P><B>BACKGROUND:</B></P><P>Biomarkers are needed for noninvasive early detection of gastric cancer (GC). We investigated salivary extracellular RNA (exRNA) biomarkers as potential clinical evaluation tools for GC.</P><P><B>METHODS:</B></P><P>Unstimulated whole saliva samples were prospectively collected from 294 individuals (163 GC and 131 non-GC patients) who underwent endoscopic evaluation at the Samsung Medical Center in Korea. Salivary transcriptomes of 63 GC and 31 non-GC patients were profiled, and mRNA biomarker candidates were verified with reverse transcription quantitative real-time PCR (RT-qPCR). In parallel, microRNA (miRNA) biomarkers were profiled and verified with saliva samples from 10 GC and 10 non-GC patients. Candidate biomarkers were validated with RT-qPCR in an independent cohort of 100/100 saliva samples from GC and non-GC patients. Validated individual markers were configured into a best performance panel.</P><P><B>RESULTS:</B></P><P>We identified 30 mRNA and 15 miRNA candidates whose expression pattern associated with the presence of GC. Among them, 12 mRNA and 6 miRNA candidates were verified with the discovery cohort by RT-qPCR and further validated with the independent cohort (n = 200). The configured biomarker panel consisted of 3 mRNAs (<I>SPINK7</I>, <I>PPL</I>, and <I>SEMA4B</I>) and 2 miRNAs (<I>MIR140-5p</I> and <I>MIR301a</I>), which were all significantly down-regulated in the GC group, and yielded an area under the ROC curve (AUC) of 0.81 (95% CI, 0.72–0.89). When combined with demographic factors, the AUC of the biomarker panel reached 0.87 (95% CI, 0.80–0.93).</P><P><B>CONCLUSIONS:</B></P><P>We have discovered and validated a panel of salivary exRNA biomarkers with credible clinical performance for the detection of GC. Our study demonstrates the potential utility of salivary exRNA biomarkers in screening and risk assessment for GC.</P>

Sensitivity Analysis of Generalized Gaussian Process Models for Variable Importance Measure

Xinmin Zhang,Manabu Kano,Yuan Li 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10

In machine learning, Gaussian process regression (GPR) has been gaining popularity due to its nonparametric Bayesian form. However, the traditional GPR model is designed for continuous real-valued outputs with a Gaussian assumption, which does not hold in some engineering application studies. For example, when the output variable is count data, it violates the assumptions of the GPR model. Generalized Gaussian process regression (GGPR) can overcome the drawbacks of the conventional GPR, and it allows the model outputs to be any member of the exponential family of distributions. Thus, GGPR is more flexible than GPR. However, since GGPR is a nonlinear kernel-based method, it is not readily accessible to understand the effect of each input variable on the model output. To tackle this issue, the sensitivity analysis of GGPR (SA-GGPR) is proposed in this work. SA-GGPR aims to identify factors that exert higher influence on the model output by utilizing the information from the partial derivative of the GGPR model output with respect to its inputs. The proposed method was applied to a nonlinear count data system. The application results demonstrated that the proposed SA-GGPR is superior to the PLS-Beta, PLS-VIP, and SA-GPR methods in identification accuracy.

Luminescence enhancement by energy transfer from Ce3+ ions in Ba1.6Ca0.4P2O7:Ce3+:Tb3+ phosphor

Xinmin Zhang,Wen-lan Li,장경혁,서효진 한국물리학회 2012 Current Applied Physics Vol.12 No.1

The optical properties of Ba1.6Ca0.4P2O7 doped with Ce3+ and Tb3+ are investigated. Under excitation at 280 nm the emission spectrum of Ba1.6Ca0.4P2O7:Ce3+ consists of a peak at 370 nm and a shoulder at the longer wavelength side. The emission spectra of Ba1.6Ca0.4P2O7:Tb3+ shows the well-known emission lines due to 5D4-7FJ transitions of Tb3+. The green emissions of Tb3+ ions are enhanced upon UV excitation through energy transfer from Ce3+ to Tb3+ ions. The efficiency of such an energy transfer is estimated based on spectroscopic data. The dependence of photoluminescence (PL) intensities of Ce3+ and Tb3+ emissions on Ce3+ or Tb3+ concentrations in the systems (Ba1.6Ca0.4P2O7:0.04Ce3+,xTb3+ and Ba1.6Ca0.4P2O7:xCe3+,0.04Tb3+) and the temperature dependence of PL emission spectra of Ba1.6Ca0.4P2O7:0.06Ce3+,0.04Tb3+ is also investigated.

Concentration Dependence of Energy Transfer between Eu2+ Ions Occupying Two Crystallographic Sites in Ba1.6Ca0.4P2O7

Zhang, Xinmin,Li, Wenlan,Seo, Hyo Jin The Electrochemical Society 2011 Journal of the Electrochemical Society Vol.158 No.5

Physiological and metabolic responses to nitrogen availability of rice (Oryza sativa L.) cultivars with differ in nitrogen efficient

Ruan Xinmin,Du Hongyang,Zhan Xinchun,Cong Xihan,Shi Fuzhi,Li Juan,Luo Zhixiang,Dong Zhaorong 한국식물생명공학회 2023 Plant biotechnology reports Vol.17 No.1

Developing genotypes with high-nitrogen use efficiency is a prerequisite for achieving sustainable agriculture and high yields. In this study, both physiological and metabolic analyses were performed to evaluate the NUE (nitrogen use efficiency) of two elite indica rice cultivars: OM052 and Huanghuazhan (HHZ). The pNUE and gNUE of OM052 were higher than HHZ under three N conditions, and significant (p < 0.05) especially under both LN and MN conditions. The contents of soluble sugar and malate were significantly different between OM052 and HHZ under LN and HN conditions. The GDH activity in OM052 was significantly high than that in HHZ under HN condition. Metabolic analyses showed that 136 and 142 differentially accumulating metabolites were identified between OM052 and HHZ under LN and HN conditions, respectively. KEGG analysis showed that ‘aminoacyl-tRNA biosynthesis’ was also the most significant enriched in LN_OM052 vs. LN_ HHZ and ‘Glyoxylate and dicarboxylate metabolism’ and ‘citrate cycle’ were the most significantly enriched in HN_OM052 vs. HN_ HHZ. The contents of four metabolites (cis-aconitate, isocitrate, fumarate, and malate) involved in the TCA cycle were significantly upregulated in HN condition compared with LN condition, regardless of OM052 and HHZ. Taken together, the results of our study revealed the physiological and metabolic differences underlying the OM052 and HHZ and provide new insights into coordinating C and N metabolism and improving the NUE of rice.

A Derivative of Chrysin Suppresses Two-Stage Skin Carcinogenesis by Inhibiting Mitogen- and Stress-Activated Kinase 1

Liu, Haidan,Hwang, Joonsung,Li, Wei,Choi, Tae Woong,Liu, Kangdong,Huang, Zunnan,Jang, Jae-Hyuk,Thimmegowda, N.R.,Lee, Ki Won,Ryoo, In-Ja,Ahn, Jong-Seog,Bode, Ann M.,Zhou, Xinmin,Yang, Yifeng,Erikson, American Association for Cancer Research 2014 CANCER PREVENTION RESEARCH Vol.7 No.1

<P>Mitogen- and stress-activated kinase 1 (MSK1) is a nuclear serine/threonine protein kinase that acts downstream of both extracellular signal-regulated kinases and p38 mitogen-activated protein kinase in response to stress or mitogenic extracellular stimuli. Increasing evidence has shown that MSK1 is closely associated with malignant transformation and cancer development. MSK1 should be an effective target for cancer chemoprevention and chemotherapy. However, very few MSK1 inhibitors, especially natural compounds, have been reported. We used virtual screening of a natural products database and the active conformation of the C-terminal kinase domain of MSK1 (PDB id 3KN) as the receptor structure to identify chrysin and its derivative, compound 69407, as inhibitors of MSK1. Compared with chrysin, compound 69407 more strongly inhibited proliferation and 12-<I>O</I>-tetradecanoylphorbol-13-acetate (TPA)-induced neoplastic transformation of JB6 P+ cells with lower cytotoxicity. Western blot data demonstrated that compound 69407 suppressed phosphorylation of the MSK1 downstream effector histone H3 in intact cells. Knocking down the expression of MSK1 effectively reduced the sensitivity of JB6 P+ cells to compound 69407. Moreover, topical treatment with compound 69407 before TPA application significantly reduced papilloma development in terms of number and size in a two-stage mouse skin carcinogenesis model. The reduction in papilloma development was accompanied by the inhibition of histone H3 phosphorylation at Ser10 in tumors extracted from mouse skin. The results indicated that compound 69407 exerts inhibitory effects on skin tumorigenesis by directly binding with MSK1 and attenuates the MSK1/histone H3 signaling pathway, which makes it an ideal chemopreventive agent against skin cancer. <I>Cancer Prev Res; 7(1); 74–85. ©2013 AACR</I>.</P>

Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches

( Ning Yu ),( Zeng Yu ),( Feng Gu ),( Tianrui Li ),( Xinmin Tian ),( Yi Pan ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.2

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

Modified Method of the Unified Jacobian-Torsor Model for Tolerance Analysis and Allocation

Hua Chen,Sun Jin,Zhimin Li,Xinmin Lai 한국정밀공학회 2015 International Journal of Precision Engineering and Vol. No.

The unified Jacobian-Torsor model uses the torsor model for tolerance representation and the Jacobian matrix for tolerance propagation. The torsor model is composed of six components, i.e., three translational vectors and three rotational vectors. However, previous studies about this model have only considered the constraint of individual component. In fact, this constraint is the scope of a single component. It is called variation. Relations between these components are constraints which reflect the interaction between them in a tolerance zone. Integrating all limited values of components of torsors into the unified Jacobian-Torsor model may lead to an inaccurate result. Meanwhile, the variations and constraints of torsor for a feature specified by more than one tolerance have been not illustrated clearly. In this paper, a modified method of the unified Jacobian-Torsor model considering constraints between components of torsor is presented. The variations and constraints of torsors for cylindrical and planar features are proposed. These constraints are calculated by means of a modified Monte Carlo method based on the previous work. Moreover, tolerance allocation of this modified method in a statistical way is also introduced. Two case studies have been performed to demonstrate the modified method.

Deep Learning in Genomic and Medical Image Data Analysis: Challenges and Approaches

Yu, Ning,Yu, Zeng,Gu, Feng,Li, Tianrui,Tian, Xinmin,Pan, Yi Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.2

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.