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"Guangde, Zhang"
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Risky-Driving-Image Recognition Based on Visual Attention Mechanism and Deep Learning
Risky driving behavior seriously affects the driver’s ability to react, execute and judge, which is one of the major causes of traffic accidents. The timely and accurate identification of the driving status of drivers is particularly important, since drivers can quickly adjust their driving status to avoid safety accidents. In order to further improve the identification accuracy, this paper proposes a risky-driving image-recognition system based on the visual attention mechanism and deep-learning technology to identify four types of driving status images including normal driving, driving while smoking, driving while drinking and driving while talking. With reference to ResNet, we build four deep-learning models with different depths and embed the proposed visual attention blocks into the image-classification model. The experimental results indicate that the classification accuracy of the ResNet models with lower depth can exceed the ResNet models with higher depth by embedding the visual attention modules, while there is no significant change in model complexity, which could improve the model recognition accuracy without reducing the recognition efficiency.
Journal Article
Viscoacoustic One-Way Fourier Finite Difference Propagator Based on Time Fractional Viscoacoustic Wave Equation
The viscoacoustic properties of the subsurface medium can lead to significant loss of amplitude energy and image quality. Conventional depth migration based on the acoustic wave equation is no longer sufficient to achieve a true reconstruction of the subsurface amplitude. To address this issue, we introduce viscoacoustic migration to fully capture the viscoacoustic characteristics of the subsurface medium. Our article presents a one-way Fourier finite difference (FFD) depth migration method based on the time-fractural viscoacoustic wave equation, which we refer to as one-way viscoacoustic FFD depth migration. Numerical experiments using models such as a dipping layer, BP gas reservoir, and salt models demonstrate the effectiveness of our proposed method in improving the resolution of migration. The application of real seismic data also shows the practical value of our proposed method in restoring deep-stratum information. Overall, our proposed method considers the real information of the medium, leading to improved migration results compared to conventional methods and further advancing the field of one-way wave migration theory.
Journal Article
Circulating MicroRNA-146a and MicroRNA-21 Predict Left Ventricular Remodeling after ST-Elevation Myocardial Infarction
Objectives: MicroRNA (miR)-146a and miR-21 have been reported to participate in inflammatory reactions and fibrosis. Excessive inflammation and cardiac fibrosis may play important roles in the development of left ventricular remodeling (LVR). This study assessed whether miR-146a, miR-21 and other biomarkers could predict LVR after myocardial infarction (MI). Methods: Circulating miR-146a, miR-21 and other biomarker levels were measured in 198 patients with acute MI 5 days after primary percutaneous coronary intervention (PCI). All patients were assessed by transthoracic echocardiography on day 5 and 1 year after primary PCI. Results: Concentrations of circulating miR-146a, miR-21, C-reactive protein, creatine kinase MB type and troponin I, as well as estimated glomerular filtration rate (eGFR) and left ventricular ejection fraction (LVEF), were significantly higher in patients with than in those without LVR (p < 0.05). Multivariate logistic regression analysis showed that circulating miR-146a (odds ratio, OR = 2.127, p < 0.0001), miR-21 (OR = 1.119, p < 0.0001), eGFR (OR = 0.939, p = 0.0137) and LVEF (OR = 0.802, p = 0.0048) were independent predictors of LVR development. The area under the curve for the combination of miR-146a and miR-21 was significantly higher than for either alone. Conclusion: Circulating miR-146a and miR-21 may be novel biomarkers predictive of LVR after acute MI. Their combination may better predict LVR than either alone.
Journal Article
Evidence mapping of traditional Chinese medicine in diabetic peripheral neuropathy treatment
Objective: Diabetic peripheral neuropathy (DPN) stands as a crucial complication of diabetes, significantly affecting patients’ quality of life. This study aims to elucidate the evidence distribution from clinical randomized controlled trials (RCTs) on DPN treatment with traditional Chinese medicine (TCM) through evidence mapping. Methods: A comprehensive search was conducted from January 2017 to October 2022 in databases such as Wanfang (China Online Journals), CNKI (China National Knowledge Infrastructure), VIP (China Science and Technology Journal Database), SinoMed (Chinese Biomedical Literature Database), PubMed, Web of Science, and Cochrane Library. Literature related to the treatment of DPN with TCM was selected. From the 1,229 RCTs identified over the past 6 years, relevant data were extracted. The evidence mapping approach was utilized, and trends in publications, study scales, intervention types, and evaluation indicators were analyzed using descriptive text combined with tables and bubble charts. Results: Research on the treatment of DPN with TCM is extensive. The publication trend remains relatively stable with predominantly smaller sample sizes. The main treatments encompass oral Chinese medicine and traditional external treatments. The most common evaluation indicators are neurophysiological, efficiency rate, symptom signs, neuropathy scores, and traditional Chinese symptoms, with less focus on psychological status and the ankle-brachial index (ABI). Conclusion: Shedding light on contemporary research, this study explores the current RCTs evaluating TCM’s efficacy in treating DPN. The findings not only highlight the potential role of TCM in addressing diabetic complications but also underscore areas that could benefit from refined research approaches, expanded intervention methods, and broader assessment criteria. Our observations aim to inform and inspire future research directions and clinical practices concerning TCM’s role in managing diabetes-associated complications.
Journal Article
MicroRNA and Transcription Factor Mediated Regulatory Network Analysis Reveals Critical Regulators and Regulatory Modules in Myocardial Infarction
Myocardial infarction (MI) is a severe coronary artery disease and a leading cause of mortality and morbidity worldwide. However, the molecular mechanisms of MI have yet to be fully elucidated. In this study, we compiled MI-related genes, MI-related microRNAs (miRNAs) and known human transcription factors (TFs), and we then identified 1,232 feed-forward loops (FFLs) among these miRNAs, TFs and their co-regulated target genes through integrating target prediction. By merging these FFLs, the first miRNA and TF mediated regulatory network for MI was constructed, from which four regulators (SP1, ESR1, miR-21-5p and miR-155-5p) and three regulatory modules that might play crucial roles in MI were then identified. Furthermore, based on the miRNA and TF mediated regulatory network and literature survey, we proposed a pathway model for miR-21-5p, the miR-29 family and SP1 to demonstrate their potential co-regulatory mechanisms in cardiac fibrosis, apoptosis and angiogenesis. The majority of the regulatory relations in the model were confirmed by previous studies, which demonstrated the reliability and validity of this miRNA and TF mediated regulatory network. Our study will aid in deciphering the complex regulatory mechanisms involved in MI and provide putative therapeutic targets for MI.
Journal Article
Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations
MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.
Journal Article
Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells
Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA-TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA-TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.
Journal Article
Time-ordered dysregulated ceRNA networks reveal disease progression and diagnostic biomarkers in ischemic and dilated cardiomyopathy
Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) are the two main causes of heart failure (HF). Despite similar clinical characteristics and common “HF pathways”, ICM and DCM are expected to have different personalized treatment strategies. The underlying mechanisms of ICM and DCM have yet to be fully elucidated. The present study developed a novel computational method for identifying dysregulated long noncoding RNA (lncRNA)–microRNA (miRNA)–mRNA competing endogenous RNA (ceRNA) triplets. Time-ordered dysregulated ceRNA networks were subsequently constructed to reveal the possible disease progression of ICM and DCM based on the method. Biological functional analysis indicated that ICM and DCM had similar features during myocardial remodeling, whereas their characteristics differed during progression. Specifically, disturbance of myocardial energy metabolism may be the main characteristic during DCM progression, whereas early inflammation and response to oxygen are the characteristics that may be specific to ICM. In addition, several panels of diagnostic biomarkers for differentiating non-heart failure (NF) and ICM (NF-ICM), NF-DCM, and ICM-DCM were identified. Our study reveals biological differences during ICM and DCM progression and provides potential diagnostic biomarkers for ICM and DCM.
Journal Article
Orbital maneuver technology of temporary networked satellites for earth exploration
It is very difficult to achieve effective coverage of hot-spot target in a short time period by earth perturbation for orbital maneuver, because the coverage gap of the temporary multi-satellites network in certain period of time is too large. In this paper, it is assumed that wave beam type of the satellite-payload sensor is a simple conic, the beam center of the satellite payload under the maximum lateral pendulum is calculated based on the precise ephemeris, and according to the projection ellipse of the payload wave beam on the ground, the coverage ability of the satellite payload on the ground target direction is obtained. Then the satellite is designed to encounter the ground target in the nodal period between the satellite passing the ground target and the latitude circle. The orbital maneuver control variables are calculated from the change of nodal period, and the optimal orbital maneuver control variables are selected from a specified time period. At last, the orbit maneuver in covering the target in a specified time period is achieved and the maximum revisit interval is greatly shortened. 针对多星以多类型载荷临时联合进行快速勘查存在某些时段勘查覆盖间隙过大、以地球摄动力长时间进行轨道机动难以短时实现对热点目标的有效覆盖等问题，提出基于精密星历计算出卫星在最大侧摆条件下的载荷波束中心，并假设载荷传感器波束类型为简单圆锥，依据载荷波束在地面的投影椭圆给出载荷在地面目标方向的覆盖能力；以卫星过地面目标同纬度圈的交点周期变化来设计卫星与地面目标相遇，由卫星相遇所需交点周期的变化计算出卫星轨道机动控制量；根据勘查需求从指定时间段内筛选出最小轨道机动控制量，从而实现在指定时段内以轨道机动覆盖目标，缩短了多星联合勘查的最大重访间隔。
Journal Article