The Longmenshan Fault Zone (LFZ) is marked by significant neotectonic activity and frequent seismic occurrences, including the Ms 8.0 Wenchuan earthquake in 2008. Numerous investigations into near-surface deformation have been carried out following the earthquake, aiming to clarify the relationship between fault slip mechanisms and seismic activity under the influence of tectonic stress, and to better understand the processes of near-surface deformation. Utilizing strain ellipsoids obtained from fracture measurements, we perform a detailed strain analysis of the LFZ to investigate its strain characteristics and deformation patterns. The findings indicate that the near-horizontal shortening axis of the strain ellipsoid at the periphery of the LFZ is oriented NEE_SWW, indicating a flattening deformation dominated by pure shear. Based on the deformation characteristics, the faults in the vicinity can be categorized into strike-slip and compressional deformation modes. In the strike-slip mode, regional stress in the NEE_SWW direction is decomposed as it approaches the NE-striking fault plane, resulting in compressional deformation dominated by simple shear at the vertical fault strike and horizontal shear slip in the right-lateral direction parallel to the fault strike, leading to the main seismic faults exhibiting right-lateral strike-slip characteristics. In the compressional mode, fault deformation aligns with the direction of regional stress and displays characteristics of flattening, resulting in the formation of folds and reverse faults. The strike-slip characteristics of the faults are minimal, and no seismic activity is recorded. These findings provide new insights into the near-surface deformation mechanisms of active fault zones.

Visual explanations of deep neural networks (DNNs) have gained considerable importance in deep learning due to the lack of interpretability, which constrains human trust in DNNs. This paper proposes a new gradient-free class activation map (CAM) architecture called importance principal-component CAM (IP-CAM). The architecture not only improves the prediction accuracy of networks but also provides simpler and more reliable visual explanations. It adds importance weight layers before the classifier and assigns an importance weight to each activation map. After fine-tuning, it selects images with the highest prediction score for each class, performs principal component analysis (PCA) on activation maps of all channels, and regards the eigenvector of the first principal component as principal-component weights for that class. The final saliency map is obtained by linearly combining the activation maps, importance weights and principal-component weights. IP-CAM is evaluated on the ILSVRC 2012 dataset and RSD46-WHU dataset, whose results show that IP-CAM performs better than most previous CAM variants in recognition and localization tasks. Finally, the method is applied as a tool for interpretability, and the results illustrate that IP-CAM effectively unveils the decision-making process of DNNs through saliency maps.

In many fields, the interpretability of machine learning models holds equal importance to their prediction accuracy. Highly accurate predictions are possible with a multilayer perceptron (MLP) neural network, but its application in high-risk fields is constrained by its lack of interpretability. To solve this issue, this paper introduces an MLP with a presingle-connection layer (SMLP). The SMLP incorporates a single-to-single connection layer with the ReLU function before the original MLP. By examining the weights of the single-connection layer after training the model, the significance of the input features can be determined. The experimental results demonstrate that this method can accurately measure the feature importance with the MLP. It offers advantages such as a straightforward theory, practical implementation, strong stability, and high reliability when compared with other widely used feature importance algorithms. Moreover, this measure effectively reveals the black box of the MLP, indicates the influence of input features on the prediction, and provides a quantitative standard for feature selection in MLP.

Reconstructing cloud-covered regions in remote sensing (RS) images holds great promise for continuous ground object monitoring. A novel lightweight machine-learning method for cloud removal constrained by conditional information (SMLP-CR) is proposed. SMLP-CR constructs a multilayer perceptron with a presingle-connection layer (SMLP) based on multisource conditional information. The method employs multi-scale mean filtering and local neighborhood sampling to gain spatial information while also taking into account multi-spectral and multi-temporal information as well as pixel similarity. Meanwhile, the feature importance from the SMLP provides a selection order for conditional information—homologous images are prioritized over images from the same season as the restoration image, and images with close temporal distances rank last. The results of comparative experiments indicate that SMLP-CR shows apparent advantages in terms of visual naturalness, texture continuity, and quantitative metrics. Moreover, compared with popular deep-learning methods, SMLP-CR samples locally around cloud pixels instead of requiring a large cloud-free training area, so the samples show stronger correlations with the missing data, which demonstrates universality and superiority.

BackgroundCaffeine is widely consumed not only in coffee but also in soft drinks and tea. However, the long-term health effects of caffeine are still controversial, especially in people with high cardiovascular risk such as elderly patients with hypertension.MethodsThis study analyzed data from the National Health and Nutrition Examination Survey 2003–2018. Caffeine intake was calculated by two 24-h dietary recall interviews. Complex sampling-weighted multivariable Cox proportional hazards models were used to compare the hazard ratios (HRs) of all-cause and cardiovascular mortality in elderly hypertensive patients with different caffeine intake (<10, 10 to <100, 100 to <200, 200 to <300, and ≥300 mg/day).ResultsThis study included 6,076 elderly hypertensive patients. The mean ± standard error follow-up duration was 6.86 ± 0.12 years. During this period, a total of 2,200 all-cause deaths occurred, of which 765 were cardiovascular deaths. Taking patients with caffeine intake < 10 mg/day as a reference, patients with moderate caffeine intake (200 to <300 mg/day) had a lower risk of all-cause (HR, 0.70 [95% CI, 0.56–0.87]) and cardiovascular (HR, 0.55 [95% CI, 0.39–0.77]) mortality. The benefit of reducing all-cause mortality risk was significant in female patients (HR, 0.65 [95% CI, 0.50–0.85]) or patients with well-controlled blood pressure (HR, 0.63 [95% CI, 0.46–0.87]), but not in male patients or patients with poorly controlled blood pressure. In addition, non-linear relationship analysis also showed that moderate caffeine intake had the lowest HRs of all-cause (Non-linear p = 0.022) and cardiovascular mortality (Non-linear p = 0.032) in the present study.ConclusionModerate caffeine intake is associated with reduced risk of all-cause and cardiovascular mortality in elderly hypertensive patients.

Satellite Image Time Series (SITS) is a data set that includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two or three scenes in different time phases). However, these methods are unsuitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions, which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, we propose a nonlinear radiometric normalization model (NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algorithm (GA). In this method, GA is used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoids the selection of a single reference image. ANN is used to obtain the optimal solution of error function, which minimizes the radiometric distortion between different images in SITS. The SITS composed of 21 Landsat-8 images in Tianjin, China, from October 2017 to January 2019 was selected to test the method. We compared NMAG with other two contrast methods (Contrast Method 1 (CM1) and Contrast Method 2 (CM2)), and found that the average root mean square error (μRMSE) of NMAG (497.22) is significantly smaller than those of CM1 (641.39) and CM2 (543.47), and the accuracy of normalized SITS obtained using NMAG increases by 22.4% and 8.5% compared with CM1 and CM2, respectively. These experimental results confirm the effectiveness of NMAG in reducing radiometric distortion caused by continuously changing noise between images in SITS.

Automatic detection and segmentation of concrete cracks in tunnels remains a high-priority task for civil engineers. Image-based crack segmentation is an effective method for crack detection in tunnels. With the development of deep learning techniques, especially the development of image segmentation based on convolutional neural networks, new opportunities have been brought to crack detection. In this study, an improved deep fully convolutional neural network, named as CrackSegNet, is proposed to conduct dense pixel-wise crack segmentation. The proposed network consists of a backbone network, dilated convolution, spatial pyramid pooling, and skip connection modules. These modules can be used for efficient multiscale feature extraction, aggregation, and resolution reconstruction which greatly enhance the overall crack segmentation ability of the network. Compared to the conventional image processing and other deep learning-based crack segmentation methods, the proposed network shows significantly higher accuracy and generalization, making tunnel inspection and monitoring highly efficient, low cost, and eventually automatable.

The decomposition of mixed pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) images is essential for the application of MODIS data in many fields. Many existing methods for unmixing mixed pixels use principal component analysis to reduce the dimensionality of the image data and require the extraction of endmember spectra. We propose the pixel spectral unmixing index (PSUI) method for unmixing mixed pixels in MODIS images. In this method, a set of third-order Bernstein basis functions is applied to reduce the dimensionality of the image data and characterize the spectral curves of the mixed pixels in a MODIS image, and then the derived PSUIs (i.e., the coefficients of the basis functions) are calibrated by means of the abundance values of the ground features from the Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) classification images corresponding to the date and region of the MODIS image. The proposed method was tested on MODIS and ETM+/OLI images, and it obtained satisfying unmixing results. We compared the PSUI method with conventional methods, including the pixel purity index, the N-finder algorithm, the sequential maximum angle convex cone, and vertex component analysis and found that the PSUI method outperformed the other four methods. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

BackgroundAlthough coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury.MethodsA weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools.ResultsA total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module.ConclusionsOur study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.

The initiation and development of fractures in rocks is the key part of many problems from academic to industrial, such as faulting, folding, rock mass engineering, reservoir characterization, etc. Conventional ways of evaluating the fracture historical deformations depend on the geologists' visual interpretation of indicating structures such as fault striations, fault steps, plumose structures, etc. on the fracture surface produced by previous deformations, and hence suffer from problems like subjectivity and the absence of obvious indicating structures. In this study, we propose a quantitative method to derive historical shear deformations of rock fractures from digital outcrop models (DOMs) based on the analysis of effects of fault striations and fault steps on the shear strength parameter of the fracture surface. A theoretical model that combines effects of fault striations, fault steps and isotropic base shear strength is fitted to the shear strength parameter. The amount of fault striations and fault steps and their occurrences are estimated, and the historical shear deformations can be inferred. The validity and the effectiveness of the proposed method was proved by testing it on a constructed fracture surface with idealized striations and a fracture surface with clear fault steps. The application of this method on an example outcrop shows an intuitive idea of how the rock mass was deformed and that the distribution, occurrence and mode of new fractures are strictly controlled by preexisting fractures, and hence emphasizes the importance of preexisting fractures in modeling the development of fracture systems.

湖水淹没频率是影响湿地植被生态系统的最重要的水文因素.基于地物波谱特征的MODIS混合像元分解模型,分析了2000-2015年退水期鄱阳湖水体淹没频率的时间变化和空间规律,并在此基础上探讨了湿地植被的空间响应.研究结果表明:(1)鄱阳湖退水期水体的淹没频率总体呈“南低北高”,同时具有大小不一的“斑块式”空间分布特征;(2)15年内湖水的淹没频率经历了先急剧缩减然后恢复到相对稳定的状态,并且不同空间段的变化差异明显:北部河道的淹没频率先急剧降低后回升,中部洲滩不如北部河道段剧烈但大面积的淹没频率下降,南部子湖泊的淹没频率则基本没变;(3)植被丰度对淹没频率具有密切的响应关系,两者呈中间高两边低的“n”形分布,当淹没频率为40％时高植被丰度的像元数最多.(4)淹没频率与植被丰度的关系指示着鄱阳湖湿生植被在空间上的积极演变.

The initiation and development of fractures in rocks is the key part of many problems from academic to industrial, such as fracture network development, faulting, folding, rock mass characterization, reservoir characterization, etc. Conventional ways of evaluating the fracture mechanical origin and historical deformations depend on the geologistsu0027 visual interpretation of the indicating structures such as offsets, plumose structures, fault striation and fault steps, and hence suffer from problems like subjectivity and the absence of obvious indicating structures. In this study, we propose a quantitative method to derive historical shear deformations of rock fractures based on the analysis of effects of indicating structures on the shear strength parameter $theta_{max}^*/C$ (Grasselli et al., 2002) (the three-dimensional description) of the fracture surface. This method fit a model that combines effects of isotropic base shear strength and anisotropic shear deformationsu0027 indicating structures to this shear strength parameter of the fracture. The amount of indicating structures and their occurrences are estimated, and the historical shear deformations can be inferred. The validity of the proposed method was proved by testing it on fracture surfaces with clear indicating structures. The application of this method on an example outcrop shows two different deformation patterns of rock mass with and without preexisting fractures and an intuitive idea of how the rock mass was deformed, and hence demonstrate the effectiveness and high potential utility of the proposed method.

以陕甘宁地区为研究区、2001—2015年的MODIS遥感影像为数据源,基于MODIS像元的植被丰度提出了累积曲线梯度变化法,通过提取沙漠化界线确定了用植被丰度表达的界线指标,讨论了陕甘宁地区的沙漠化变化.结果表明:①基于数学统计分析方法的累积曲线梯度变化法,方法简单、可操作性强,为沙漠化的定量分析与动态监测提供了新方法;②以植被丰度15％为指标作为陕甘宁地区的沙漠化界线,发现在这15年间,陕甘宁地区的沙漠化呈现在波动中不断改善的总体趋势.

The spectral area ratio method (SARM ) was proposed to detect clouds from Landsat 8 images . Landsat 8 visible to near infrared bands (band 1-band 5) and thermal infrared bands (band 10-band 11) were used based on spectral analysis on clouds and other ground objects in order to establish pixels-based spectral area ratio .Scatter plot for images was plotted with normalized difference vegetation index (NDVI) and spectral area ratio .Clouds were detected with different confidence intervals (high ,medium and low level).Three Landsat 8 images of different spatial were employed to demonstrate the accuracy of the method with three representative zones from each image and 10 000 pixels from each zone.Results show that spectral area ratio can enhance the difference between clouds and underlying surface , which is beneficial for the cloud detection .Scatter plot based on NDVI and spectral area ratio can clearly display the cloud distribution features under different ground conditions . The extraction method of adjustable threshold can meet requirements of cloud detection with various objectives .The method can significantly improve the overall accuracy by 10% compared with three previous cloud detection methods .

Conventional manual surveys of rock mass fractures usually require large amounts of time and labor; yet, they provide a relatively small set of data that cannot be considered representative of the study region. Terrestrial laser scanners are increasingly used for fracture surveys because they can efficiently acquire large area, high-resolution, three-dimensional (3D) point clouds from outcrops. However, extracting fractures and other planar surfaces from 3D outcrop point clouds is still a challenging task. No method has been reported that can be used to automatically extract the full extent of every individual fracture from a 3D outcrop point cloud. In this study, we propose a method using a region-growing approach to address this problem; the method also estimates the orientation of each fracture. In this method, criteria based on the local surface normal and curvature of the point cloud are used to initiate and control the growth of the fracture region. In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy. Compared with manually acquired field survey data, our method obtained better-quality fracture data, thereby demonstrating the high potential utility of the proposed method.

Conventional fracture data collection methods are usually implemented on planar surfaces or assuming they are planar; these methods may introduce sampling errors on uneven outcrop surfaces. Consequently, data collected on limited types of outcrop surfaces (mainly bedding surfaces) may not be a sufficient representation of fracture network characteristic in outcrops. Recent development of techniques that obtain DOMs from outcrops and extract the full extent of individual fractures offers the opportunity to address the problem of performing the conventional sampling methods on uneven outcrop surfaces. In this study, we propose a new method that performs outcrop fracture characterization on suppositional planes cutting through DOMs. The suppositional plane is the best fit plane of the outcrop surface, and the fracture trace map is extracted on the suppositional plane so that the fracture network can be further characterized. The amount of sampling errors introduced by the conventional methods and avoided by the new method on 16 uneven outcrop surfaces with different roughnesses are estimated. The results show that the conventional sampling methods don't apply to outcrops other than bedding surfaces or outcrops whose roughness > 0.04 m, and that the proposed method can greatly extend the types of outcrop surfaces for outcrop fracture characterization with the suppositional plane cutting through DOMs.

野外地质教学是提升学生地质专业技能,培养学生地学的情感和兴趣的重要途径,是地学创新人才培养的关键环节.本文通过分析美国亚利桑那大学、加州大学洛杉矶分校等高校野外地质教学理念、教学模式和教学方法,探讨我国综合性大学在野外地质教学中如何提高学生对地学的兴趣和积极性,提升学生的团队合作精神和人际沟通能力,培养高素质的新型地质人才.

Increasing evidence suggests endothelial progenitor cells (EPCs) improve neovascularization and endothelium regeneration. Resveratrol (RSV) is a natural polyphenolic compound, which has been demonstrated to exert multiple protective effects on the cardiovascular system, including inhibition of platelet adhesion and aggregation, reduction of myocardial ischemia‑reperfusion injury, and suppression of neointimal hyperplasia of injured vascular tissue. The present study investigated the role of RSV on levels of oxidative stress and senescence of EPCs, and the effects of RSV on vascular‑promoting and/or vascular‑healing capacity of EPCs. It was demonstrated that EPCs could promote the repair of endothelium of the injured artery. RSV reduced the oxidative reaction of EPCs and inhibited EPC senescence, and these effects may occur via the peroxisome proliferator‑activated receptor‑γ/heme oxygenase‑1 signaling pathways.

A variety of clouds are present in almost all moderate-resolution imaging spectroradiometer (MODIS) images. To extract accurate information from MODIS data, a key preprocessing step is to detect the cloudy pixels. This article proposes a new algorithm to distinguish between cloudy and cloud-free pixels in MODIS images. This algorithm is based on the differences in the spectral areas between clouds and other surface features. It uses as many as 24 of the 36 MODIS spectral bands to obtain the integrated spectral information. The method has been illustrated by an example of 76 MODIS images recorded from 2011 to 2013. The results show that the algorithm is capable of correctly identifying most of the cloud-contaminated pixels except for some thin cloud pixels. We compared the new method with the MODIS Cloud Mask algorithm and found that the new algorithm performs better than the MODIS MOD35 Cloud Mask in some situation, such as coastal area, sun glint, and data with invalid values. ResumeUne variete de nuages est presente dans la quasi-totalite des images du spectroradiometre imageur a resolution moderee (MODIS). Pour extraire des informations precises a partir des donnees MODIS, une etape cle de pretraitement est de detecter les pixels nuageux. Cet article propose un nouvel algorithme pour distinguer les pixels avec nuages des pixels sans nuages dans les images MODIS. Cet algorithme est base sur les differences dans les zones du spectre entre les nuages et d'autres caracteristiques de surface. Il utilise jusqu'a 24 des 36 bandes spectrales de MODIS pour obtenir les informations spectrales integrees. La methode a ete illustree sur un echantillon de 76 images MODIS prises entre 2011 et 2013. Les resultats montrent que l'algorithme est capable d'identifier correctement la plupart des pixels contamines par les nuages a l'exception de quelques pixels avec des nuages minces. Nous avons compare la nouvelle methode avec l'algorithme pour le masquage des nuages de MODIS et nous avons constate que le nouvel algorithme est plus performant que le masque des nuages MOD35 de MODIS dans certaines situations, comme dans les zones cotieres, pour la zone du reflet solaire et pour les donnees avec des valeurs non valides.