In silico identification and computational characterization of endogenous small interfering RNAs from diverse grapevine tissues and stages

Xudong Zhu,Songtao Jiu,Xiaopeng Li,Kekun Zhang,Mengqi Wang,Chen Wang,Jinggui Fang 한국유전학회 2018 Genes & Genomics Vol.40 No.8

Small interfering RNAs (siRNAs) are effectors of regulatory pathways underlying plant development, metabolism, and stress- and nutrient-signaling regulatory networks. The endogenous siRNAs are generally not conserved between plants; consequently, it is necessary and important to identify and characterize siRNAs from various plants. To address the nature and functions of siRNAs, and understand the biological roles of the huge siRNA population in grapevine (Vitis vinifera L.). The high-throughput sequencing technology was used to identify a large set of putative endogenous siRNAs from six grapevine tissues/organs. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to classify the target genes of siRNA. In total, 520,519 candidate siRNAs were identified and their expression profiles exhibited typical temporal characters during grapevine development. In addition, we identified two grapevine trans-acting siRNA (TAS) gene homologs (VvTAS3 and VvTAS4) and the derived trans-acting siRNAs (tasiRNAs) that could target grapevine auxin response factor (ARF) and myeloblastosis (MYB) genes. Furthermore, the GO and KEGG analysis of target genes showed that most of them covered a broad range of functional categories, especially involving in disease-resistance process. The large-scale and completely genome-wide level identification and characterization of grapevine endogenous siRNAs from the diverse tissues by high throughput technology revealed the nature and functions of siRNAs in grapevine.

Evaluation of Curtain Grouting Efficiency by Cloud Model - based Fuzzy Comprehensive Evaluation Method

Yushan Zhu,Xiaoling Wang,Shaohui Deng,Mengqi Zhao,Xuefei Ao 대한토목학회 2019 KSCE JOURNAL OF CIVIL ENGINEERING Vol.23 No.7

Because of the concealment and uncertainty of dam foundation grouting, the accurate evaluation of the quality of grouting construction is a major concern. Most of the previous studies pertaining to the assessment of grouting efficiency only take a single factor into account, and only a few efforts have been devoted to the comprehensive evaluation of grouting efficiency, which fail to consider the fuzziness problems caused by the complex relationships among indicators as well as the randomness problems caused by subjective consciousness. Therefore, the curtain grouting efficiency evaluation method considering the uncertainty in evaluation process is proposed which includes three main parts: 1) Based on conventional evaluation indicators of grouting efficiency, where groutability is introduced as a new indicator; 2) a fuzzy comprehensive evaluation method based on cloud model is developed, by which the fuzziness and randomness in assessment are organically combined; and 3) the fuzzy entropy was calculated for indicating the complexity of grouting efficiency level. Then, the proposed method is used to assess efficiency of the curtain grouting for a hydropower station in China, and compared with three other methods. The outcomes display the consistency, representativeness, robustness and superiority of this evaluation method, which make the evaluation results more scientific and objective.

Automatic assessment of post-earthquake buildings based on multi-task deep learning with auxiliary tasks

Huamei Zhu,Zhihang Li,Mengqi Huang,Pengxuan Ji,Hongyu Huang,Qianbing Zhang 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.31 No.4

Post-earthquake building condition assessment is crucial for subsequent rescue and remediation and can be automated by emerging computer vision and deep learning technologies. This study is based on an endeavour for the 2nd International Competition of Structural Health Monitoring (IC-SHM 2021). The task package includes five image segmentation objectives - defects (crack/spall/rebar exposure), structural component, and damage state. The structural component and damage state tasks are identified as the priority that can form actionable decisions. A multi-task Convolutional Neural Network (CNN) is proposed to conduct the two major tasks simultaneously. The rest 3 sub-tasks (spall/crack/rebar exposure) were incorporated as auxiliary tasks. By synchronously learning defect information (spall/crack/rebar exposure), the multi-task CNN model outperforms the counterpart single-task models in recognizing structural components and estimating damage states. Particularly, the pixel-level damage state estimation witnesses a mIoU (mean intersection over union) improvement from 0.5855 to 0.6374. For the defect detection tasks, rebar exposure is omitted due to the extremely biased sample distribution. The segmentations of crack and spall are automated by single-task U-Net but with extra efforts to resample the provided data. The segmentation of small objects (spall and crack) benefits from the resampling method, with a substantial IoU increment of nearly 10%.

One-step deep learning-based method for pixel-level detection of fine cracks in steel girder images

Huamei Zhu,Zhihang Li,Mengqi Huang,Pengxuan Ji,Qianbing Zhang 국제구조공학회 2022 Smart Structures and Systems, An International Jou Vol.29 No.1

Identifying fine cracks in steel bridge facilities is a challenging task of structural health monitoring (SHM). This study proposed an end-to-end crack image segmentation framework based on a one-step Convolutional Neural Network (CNN) for pixel-level object recognition with high accuracy. To particularly address the challenges arising from small object detection in complex background, efforts were made in loss function selection aiming at sample imbalance and module modification in order to improve the generalization ability on complicated images. Specifically, loss functions were compared among alternatives including the Binary Cross Entropy (BCE), Focal, Tversky and Dice loss, with the last three specialized for biased sample distribution. Structural modifications with dilated convolution, Spatial Pyramid Pooling (SPP) and Feature Pyramid Network (FPN) were also performed to form a new backbone termed CrackDet. Models of various loss functions and feature extraction modules were trained on crack images and tested on full-scale images collected on steel box girders. The CNN model incorporated the classic U-Net as its backbone, and Dice loss as its loss function achieved the highest mean Intersection-over-Union (mIoU) of 0.7571 on full-scale pictures. In contrast, the best performance on cropped crack images was achieved by integrating CrackDet with Dice loss at a mIoU of 0.7670.

Characterization of Volatile Compounds in Donkey Meat by Gas Chromatography–Ion Mobility Spectrometry (GC–IMS) Combined with Chemometrics

Mengmeng Li,Mengqi Sun,Wei Ren,Limin Man,Wenqiong Chai,Guiqin Liu,Mingxia Zhu,Changfa Wang 한국축산식품학회 2024 한국축산식품학회지 Vol.44 No.1

Volatile compounds (VOCs) are an important factor affecting meat quality. However, the characteristic VOCs in different parts of donkey meat remain unknown. Accordingly, this study represents a preliminary investigation of VOCs to differentiate between different cuts of donkey meat by using headspace–gas chromatography–ion mobility spectrometry (HS–GC–IMS) combined with chemometrics analysis. The results showed that the 31 VOCs identified in donkey meat, ketones, alcohols, aldehydes, and esters were the predominant categories. A total of 10 VOCs with relative odor activity values ≥1 were found to be characteristic of donkey meat, including pentanone, hexanal, nonanal, octanal, and 3-methylbutanal. The VOC profiles in different parts of donkey meat were well differentiated using three- and two-dimensional fingerprint maps. Nine differential VOCs that represent potential markers to discriminate different parts of donkey meat were identified by chemometrics analysis. These include 2-butanone, 2- pentanone, and 2-heptanone. Thus, the VOC profiles in donkey meat and specific VOCs in different parts of donkey meat were revealed by HS–GC–IMS combined with chemometrics, whcih provided a basis and method of investigating the characteristic VOCs and quality control of donkey meat.

Magnetohydrodynamic flows in a dis-aligned duct system under a uniform magnetic field

Luo, Yang,Zhu, Mengqi,Kim, Chang Nyung 대한기계학회 2018 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.32 No.2

<P>In the present study, three-dimensional Magnetohydrodynamic (MHD) Liquid-metal (LM) flows in a dis-aligned duct system under a uniform magnetic field are investigated by numerical method. Computational fluid dynamics (CFD) simulations are carried out to analyzed the characteristics of the MHD flows and to examine the inter-relationship of the LM velocity, current density, electric potential and pressure, using CFX. The duct system consists of two dis-aligned parallel channels (One inflow channel and one outflow channel) and one channel connecting the above channels. In the present study, cases with different lengths of the connecting channel are considered. Because of the inertial force therein, a velocity recirculation is found in the region just after the first turning, resulting in a region of peak value in electric potential together with complex distribution of the current. Also, another velocity recirculation is seen in the region just after the second turning, creating another region of peak value in electric potential. In a situation where the magnetic field is applied in a direction perpendicular to the plane of the main flow in a dis-aligned duct system, until the fluid reaches an edge, the velocity component parallel to the magnetic field converges, with an increasing in the peak value of the side layer velocity, and then, after the fluid passes the edge, the velocity component parallel to the magnetic field diverges, with a decrease in the peak value of the side layer velocity. Oppositely, until the fluid reaches a corner, the velocity component parallel to the magnetic field diverges, with a decrease in the peak value of the side layer velocity, and then, after the fluid passes the corner, the velocity component parallel to the magnetic field converges, with an increase in the peak value of the side layer velocity. It is found that this type of velocity pattern is closely associated with the current distribution in the region of right-angle segments in the sense that the magnitude of the electromotive component of electric current is proportional to the fluid velocity.</P>

A Dynamic Wireless Charging System Based on Transmitter Module Composed of Coplanar Double Bipolar Pads with Hybrid Compensation Topology

Xia Nenghong,Chen Mengqi,Zhu Yimin 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.3

This paper targets to meet three key requirements of dynamic wireless charging system (WCS) for electric vehicles: high output power, misalignment tolerance, and good interoperability. A hybrid wireless charging system with transmitter module composed of coplanar double bipolar pads (BPPs) is designed. The primary side is composed of two BPPs which are driven out of phase to function as bipolar pads for generating larger fundamental magnetic fux height than unipolar pads. A hybrid compensation topology where the decoupled coils in the two BPPs adopt (LCC, S) and (S, LCC) topology, respectively, and that in BPP on the secondary side adopt (LCC, S) topology, forming four coupled coil pairs with LCC-LCC, S–S, S-LCC and LCC-S topologies. This hybrid compensation can efectively restrain the fuctuation of output power caused by the variation of mutual inductance in dynamic charging. And the symmetry of both the pad structure (BPP) and the compensation topologies ensures the transmitter module a good interoperability. The designed system is validated by experimental study on a 6 kW WCS prototype, where the deviations of output power are lower than 2.9% under a 220 mm X-axis misalignment with an efciency higher than 88.6%.

열전 발전과 열전 가열/냉각을 위한 열전 모듈의 설계 모델링 연구

김창녕(Chang Nyung Kim),Yang Lluo,Mengqi Zhu 대한기계학회 2015 대한기계학회 춘추학술대회 Vol.2015 No.6

Serum MicroRNA Levels as a Noninvasive Diagnostic Biomarker for the Early Diagnosis of Hepatitis B Virus-Related Liver Fibrosis

( Suxia Bao ),( Jianming Zheng ),( Ning Li ),( Chong Huang ),( Mingquan Chen ),( Qi Cheng ),( Kangkang Yu ),( Shengshen Chen ),( Mengqi Zhu ),( Guangfeng Shi ) 대한간학회 2017 Gut and Liver Vol.11 No.6

Background/Aims: To investigate the role of selected serum microRNA (miRNA) levels as potential noninvasive biomarkers for differentiating S0-S2 (early fibrosis) from S3-S4 (late fibrosis) in patients with a chronic hepatitis B virus (HBV) infection. Methods: One hundred twenty-three treatment-naive patients with a chronic HBV infection who underwent a liver biopsy were enrolled in this study. The levels of selected miRNAs were measured using a real-time quantitative polymerase chain reaction assay. A logistic regression analysis was performed to assess factors associated with fibrosis progression. Receiver operating characteristic (ROC) curve and discriminant analyses validated these the ability of these predicted variables to discriminate S0-S2 from S3-S4. Results: Serum miR-29, miR-143, miR-223, miR-21, and miR-374 levels were significantly downregulated as fibrosis progressed from S0-S2 to S3-S4 (p<0.05), but not miR-16. The multivariate logistic regression analysis identified a panel of three miRNAs and platelets that were associated with a high diagnostic accuracy in discriminating S0-S2 from S3-S4, with an area under the curve of 0.936. Conclusions: The levels of the studied miRNAs, with the exception of miR-16, varied with fibrosis progression. A panel was identified that was capable of discriminating S0-S2 from S3-S4, indicating that serum miRNA levels could serve as a potential noninvasive biomarker of fibrosis progression. (Gut Liver 2017;11:860-869)