期刊文献+
共找到1,506篇文章
< 1 2 76 >
每页显示 20 50 100
GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images
1
作者 Haiyue Li Jing Xie +4 位作者 Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen 《Computers, Materials & Continua》 SCIE EI 2024年第7期487-506,共20页
Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial ne... Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue.Convolutional neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image registration.However,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s disease.We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator inGAN.In this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration.To begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific modalities.Additionally,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise results.Lastly,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s disease.Experimental results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types. 展开更多
关键词 Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision
下载PDF
Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s
2
作者 Anas W.Abulfaraj 《Computers, Materials & Continua》 SCIE EI 2024年第4期1137-1156,共20页
The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when co... The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s. 展开更多
关键词 Differential equations neural-controlled DE image classification attention maps N-CDE’s
下载PDF
A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification
3
作者 Tsu-Yang Wu Haonan Li +1 位作者 Saru Kumari Chien-Ming Chen 《Computers, Materials & Continua》 SCIE EI 2024年第4期19-46,共28页
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol... Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification. 展开更多
关键词 Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification
下载PDF
Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism
4
作者 Yujie Yang Xinyi Li +7 位作者 Jiaying Lu Jingjie Ge Mingjia Chen Ruixin Yao Mei Tian Jian Wang Fengtao Liu Chuantao Zuo 《Neural Regeneration Research》 SCIE CAS 2025年第1期93-106,共14页
Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.... Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders. 展开更多
关键词 aromatic amino acid decarboxylase brain imaging dopamine transporter Parkinson’s disease PARKINsONIsM positron emission tomography presynaptic dopaminergic function vesicle monoamine transporter type 2
下载PDF
Analysis of the Tragic Female Images in Eugene O'Neill's Plays 被引量:1
5
作者 李琳 《海外英语》 2011年第8X期287-288,共2页
Ruth and Mary are two heroines in Eugene O'Neill's plays Beyond the Horizon, and Long Day's Journey into Night. They have some similarities: when they are young, they are beautiful, native and full of hope... Ruth and Mary are two heroines in Eugene O'Neill's plays Beyond the Horizon, and Long Day's Journey into Night. They have some similarities: when they are young, they are beautiful, native and full of hope towards the future life, but both make wrong choices; in the following years, both suffer a lot from these wrong choices, and feel regretful. This paper tries to explore these two tragic female images. 展开更多
关键词 female images Beyond the HORIZON Long Day’s JOURNEY into NIGHT
下载PDF
基于轻量化YOLOX-S与多阈值分割的矿山遥感图像去噪算法
6
作者 沈丹萍 赵爽 《金属矿山》 CAS 北大核心 2024年第9期175-180,共6页
矿山遥感图像普遍存在大量的噪点,给后续图像分析和处理带来了很大困难。提出了一种基于轻量化目标检测模型YOLOX-S和多阈值分割的矿山遥感图像去噪算法。首先使用YOLOX-S模型对矿山遥感图像进行目标检测,得到矿山目标的位置信息。然后... 矿山遥感图像普遍存在大量的噪点,给后续图像分析和处理带来了很大困难。提出了一种基于轻量化目标检测模型YOLOX-S和多阈值分割的矿山遥感图像去噪算法。首先使用YOLOX-S模型对矿山遥感图像进行目标检测,得到矿山目标的位置信息。然后针对矿山目标的特点,设计了一种多阈值分割方法消除图像中的噪声点。通过将图像分为若干个子区域,并对每个子区域采用不同的阈值进行二值化处理,最终将各子区域的二值化结果合并得到去噪后的图像。试验结果表明:该算法能够有效地去除矿山遥感图像中的噪声点,并且在保留目标特征的同时,大幅提升了图像质量。此外,由于采用了轻量化模型和多阈值分割算法,使得该算法具有较快的处理速度和较低的计算成本,适用于大规模图像数据的处理任务。 展开更多
关键词 矿山遥感图像 轻量化 YOLOX-s 阈值分割 图像去噪
下载PDF
基于MRI影像组学构建PD-1/PD-L1抑制剂治疗dMMR/MSI-H直肠癌疗效的预测模型
7
作者 张岚 周彦汝 +3 位作者 韩鼎盛 张嘉诚 何旭 刘鹏 《中国医学计算机成像杂志》 CSCD 北大核心 2024年第3期343-348,共6页
目的:探讨MRI影像组学模型在程序性细胞死亡蛋白-1(PD-1)/程序性细胞死亡-配体1(PD-L1)抑制剂联合全程新辅助治疗(TNT)局部进展期直肠癌(LARC)的疗效预测价值。方法:收集河南中医药大学第一附属医院PD-1/PD-L1抑制剂联合TNT治疗的80例... 目的:探讨MRI影像组学模型在程序性细胞死亡蛋白-1(PD-1)/程序性细胞死亡-配体1(PD-L1)抑制剂联合全程新辅助治疗(TNT)局部进展期直肠癌(LARC)的疗效预测价值。方法:收集河南中医药大学第一附属医院PD-1/PD-L1抑制剂联合TNT治疗的80例错配修复基因缺陷(dMMR)/微卫星高度不稳定(MSI-H)基因型中低位LARC患者的临床和影像资料。将入组患者按7∶3比例分为训练集和测试集,提取影像组学特征,从中筛选并构建影像组学模型。描绘影像组学模型的Rad-score与病理金标准之间的受试者工作特征(ROC)曲线,计算曲线下面积(AUC),并评价模型的诊断效能。采用决策曲线分析(DCA)计算风险阈值的范围,并评估临床获益情况。收集湖南省人民医院25例dMMR/MSI-H基因型LARC患者的影像资料作为外部验证集。结果:训练集、测试集及外部验证集三者之间的临床特征无统计学差异(P>0.05)。经过降维处理、t检验及一致性检验以及LASSO交叉验证后,筛选出一阶偏度特征和体积2个特征构建影像组学模型。训练集、测试集和外部验证集的影像组学预测模型ROC曲线的AUC、灵敏度、特异度、阳性预测值和阴性预测值分别为0.920、97.1%、85.7%、91.9%、94.7%;0.885、80.0%、88.9%、92.3%、72.7%;0.875、87.5%、88.9%、93.3%、80.0%。DCA曲线显示,当风险阈值范围为0%~82%时,采用影像组学模型预测LARC患者为病理完全缓解(pCR)的获益大于将所有患者都视为pCR或者无病理完全缓解(npCR)。结论:基于MRI影像组学构建的dMMR/MSI-H型局部进展期直肠癌PD-1/PD-L1抑制剂联合全程新辅助放化疗疗效预测模型,有较大潜力为不同基因分型的直肠癌患者制定个体化治疗策略提供量化依据。 展开更多
关键词 磁共振成像 影像组学 直肠肿瘤 局部进展期 程序性细胞死亡蛋白-1/程序性细胞死亡-配体1 全程新辅助放化疗
下载PDF
Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning 被引量:2
8
作者 Iroshan Aberathne Don Kulasiri Sandhya Samarasinghe 《Neural Regeneration Research》 SCIE CAS CSCD 2023年第10期2134-2140,共7页
The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectivene... The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset. 展开更多
关键词 deep learning image processing linear mixed effect model NEUROimagING neuroimaging data sources onset of Alzheimer’s disease detection pattern recognition
下载PDF
Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm 被引量:6
9
作者 A.Renugambal K.Selva Bhuvaneswari 《Computers, Materials & Continua》 SCIE EI 2020年第8期681-700,共20页
In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid betwee... In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm. 展开更多
关键词 Hybrid WCMFO algorithm Otsu’s function multilevel thresholding image segmentation brain MR image
下载PDF
Interplay between the glymphatic system and neurotoxic proteins in Parkinson’s disease and related disorders:current knowledge and future directions 被引量:1
10
作者 Yumei Yue Xiaodan Zhang +2 位作者 Wen Lv Hsin-Yi Lai Ting Shen 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第9期1973-1980,共8页
Parkinson’s disease is a common neurodegenerative disorder that is associated with abnormal aggregation and accumulation of neurotoxic proteins,includingα-synuclein,amyloid-β,and tau,in addition to the impaired eli... Parkinson’s disease is a common neurodegenerative disorder that is associated with abnormal aggregation and accumulation of neurotoxic proteins,includingα-synuclein,amyloid-β,and tau,in addition to the impaired elimination of these neurotoxic protein.Atypical parkinsonism,which has the same clinical presentation and neuropathology as Parkinson’s disease,expands the disease landscape within the continuum of Parkinson’s disease and related disorders.The glymphatic system is a waste clearance system in the brain,which is responsible for eliminating the neurotoxic proteins from the interstitial fluid.Impairment of the glymphatic system has been proposed as a significant contributor to the development and progression of neurodegenerative disease,as it exacerbates the aggregation of neurotoxic proteins and deteriorates neuronal damage.Therefore,impairment of the glymphatic system could be considered as the final common pathway to neurodegeneration.Previous evidence has provided initial insights into the potential effect of the impaired glymphatic system on Parkinson’s disease and related disorders;however,many unanswered questions remain.This review aims to provide a comprehensive summary of the growing literature on the glymphatic system in Parkinson’s disease and related disorders.The focus of this review is on identifying the manifestations and mechanisms of interplay between the glymphatic system and neurotoxic proteins,including loss of polarization of aquaporin-4 in astrocytic endfeet,sleep and circadian rhythms,neuroinflammation,astrogliosis,and gliosis.This review further delves into the underlying pathophysiology of the glymphatic system in Parkinson’s disease and related disorders,and the potential implications of targeting the glymphatic system as a novel and promising therapeutic strategy. 展开更多
关键词 atypical parkinsonism glymphatic system magnetic resonance imaging neurotoxic proteins Parkinson’s disease
下载PDF
Range anomaly suppression based on neighborhood pixels detection in ladar range images 被引量:2
11
作者 Mingbo Zhao Jun He +1 位作者 Zaiqi Lu Qiang Fu 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2013年第1期68-75,共8页
Research on the range anomaly suppression algorithm in laser radar (ladar) range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear ... Research on the range anomaly suppression algorithm in laser radar (ladar) range images is significant in the application and development of ladar. But most of existing algorithms cannot protect the edge and linear target well while suppressing the range anomaly. Aiming at this problem, the differences among the edge, linear target, and range anomaly are analyzed and a novel algo- rithm based on neighborhood pixels detection is proposed. Firstly, the range differences between current pixel and its neighborhood pixels are calculated. Then, the number of neighborhood pixels is detected by the range difference threshold. Finally, whether the current pixel is a range anomaly is distinguished by the neighbor- hood pixel number threshold. Experimental results show that the new algorithm not only has a better range anomaly suppression performance and higher efficiency, but also protects the edge and linear target preferably compared with other algorithms. 展开更多
关键词 image processing range anomaly suppression neigh-borhood p xe s detection linear target laser radar (ladar).
下载PDF
Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease
12
作者 Ting Shen Jia-Li Pu +7 位作者 Ya-Si Jiang Yu-Mei Yue Ting-Ting He Bo-Yi Qu Shuai Zhao Ya-Ping Yan Hsin-Yi Lai Bao-Rong Zhang 《Neural Regeneration Research》 SCIE CAS CSCD 2023年第5期1154-1160,共7页
Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclea... Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson’s disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease. Forty-eight Parkinson’s disease patients and 39 matched healthy controls underwent genotyping and 7 T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson’s disease diagnosis. We found that, in Parkinson’s disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein(SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson’s disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson’s disease. 展开更多
关键词 COGNITION imaging genetics magnetic resonance imaging MULTI-MODALITY Parkinson’s disease polygenic risk score single nucleotide polymorphism ultra-high field
下载PDF
Neural stem cell-derived exosomes promote mitochondrial biogenesis and restore abnormal protein distribution in a mouse model of Alzheimer's disease 被引量:1
13
作者 Bo Li Yujie Chen +10 位作者 Yan Zhou Xuanran Feng Guojun Gu Shuang Han Nianhao Cheng Yawen Sun Yiming Zhang Jiahui Cheng Qi Zhang Wei Zhang Jianhui Liu 《Neural Regeneration Research》 SCIE CAS CSCD 2024年第7期1593-1601,共9页
Mitochondrial dysfunction is a hallmark of Alzheimer’s disease.We previously showed that neural stem cell-derived extracellular vesicles improved mitochondrial function in the cortex of AP P/PS1 mice.Because Alzheime... Mitochondrial dysfunction is a hallmark of Alzheimer’s disease.We previously showed that neural stem cell-derived extracellular vesicles improved mitochondrial function in the cortex of AP P/PS1 mice.Because Alzheimer’s disease affects the entire brain,further research is needed to elucidate alterations in mitochondrial metabolism in the brain as a whole.Here,we investigated the expression of several important mitochondrial biogenesis-related cytokines in multiple brain regions after treatment with neural stem cell-derived exosomes and used a combination of whole brain clearing,immunostaining,and lightsheet imaging to clarify their spatial distribution.Additionally,to clarify whether the sirtuin 1(SIRT1)-related pathway plays a regulatory role in neural stem cell-de rived exosomes interfering with mitochondrial functional changes,we generated a novel nervous system-SIRT1 conditional knoc kout AP P/PS1mouse model.Our findings demonstrate that neural stem cell-de rived exosomes significantly increase SIRT1 levels,enhance the production of mitochondrial biogenesis-related fa ctors,and inhibit astrocyte activation,but do not suppress amyloid-βproduction.Thus,neural stem cell-derived exosomes may be a useful therapeutic strategy for Alzheimer’s disease that activates the SIRT1-PGC1αsignaling pathway and increases NRF1 and COXIV synthesis to improve mitochondrial biogenesis.In addition,we showed that the spatial distribution of mitochondrial biogenesis-related factors is disrupted in Alzheimer’s disease,and that neural stem cell-derived exosome treatment can reverse this effect,indicating that neural stem cell-derived exosomes promote mitochondrial biogenesis. 展开更多
关键词 Alzheimer’s disease mitochondrial biogenesis neural stem cell-derived exosome sIRT1-PGC1α regional brain distribution whole brain clearing and imaging
下载PDF
泸定M_(S)6.8地震发震机制研究——来自震前噪声成像和b值分布的共同约束
14
作者 花茜 裴顺平 +5 位作者 杨宜海 薛晓添 李磊 李佳蔚 刘翰林 刘巍 《地球物理学报》 SCIE EI CAS CSCD 北大核心 2024年第5期1767-1780,共14页
2022年9月5日四川省甘孜州泸定县发生了M_(S)6.8地震,打破了鲜水河断裂带东南段的大震"平静期",造成了重大自然灾害.清楚认识泸定地震震源区发震构造、地震活动性和应力状态对研究强震发震机制具有重要作用.本文利用震前在泸... 2022年9月5日四川省甘孜州泸定县发生了M_(S)6.8地震,打破了鲜水河断裂带东南段的大震"平静期",造成了重大自然灾害.清楚认识泸定地震震源区发震构造、地震活动性和应力状态对研究强震发震机制具有重要作用.本文利用震前在泸定地震震源区布设的50台短周期流动地震台阵观测资料及区域地震台网震相走时数据,分别采用背景噪声成像、双差定位和改进的b值成像技术,获得了震前震源区浅层高分辨率S波速度结构、地震空间分布及b值横向变化图像.结果揭示,泸定地震主震初始破裂起始于鲜水河断裂磨西段、具有高速异常和高应力特征的凹凸体内;主震西侧存在一条隐伏的正断型伴生分支断裂,5.0级的最大余震即发生在该断裂上;主震凹凸体的破裂同时造成了东南方向另一个较小高速凹凸体的破裂并形成密集余震群.由此可见,震源区跨断层高速异常"铆钉"结构和震前的高应力积累在整体上控制了泸定地震的发生和强余震活动.通过浅层高分辨率结构成像识别这种特殊的"铆钉"结构,同时通过b值成像识别高应力区,可有效评估断层的发震能力,对重点区域地震危险性研判具有极其重要的意义. 展开更多
关键词 泸定M_(s)6.8地震 s波速度结构 b值成像 高速凹凸体 地震发震机制
下载PDF
Identity Issue and Mysterious Images in the Bonesetter's Daughter
15
作者 张砚 《海外英语》 2013年第11X期226-227,共2页
This paper is going to do a research on the book The Bonesetter's Daughter and will make an analysis of mysterious images appear in the novel which can be considered as symbols that reflect the culture of orient. ... This paper is going to do a research on the book The Bonesetter's Daughter and will make an analysis of mysterious images appear in the novel which can be considered as symbols that reflect the culture of orient. And it also looks into the identity issue of Chinese American women from postcolonial perspective. 展开更多
关键词 IDENTITY IssUE Amy TAN imagE Bonesetter ’s Daughte
下载PDF
Prospects for Jovian seismology with the Lenghu planetary telescope
16
作者 YiQing Zou Fei He +4 位作者 ShanShan Zheng Lei Yu ZhongHua Yao ZhaoJin Rong Yong Wei 《Earth and Planetary Physics》 EI CAS CSCD 2024年第5期703-710,共8页
Jupiter is one of the top priorities for deep space exploration in China and other countries.The structure of Jupiter’s interior,in particular,is a crucial but still unclear scientific topic.This paper discusses curr... Jupiter is one of the top priorities for deep space exploration in China and other countries.The structure of Jupiter’s interior,in particular,is a crucial but still unclear scientific topic.This paper discusses current scientific understanding of Jupiter’s interior by summarizing the history of past and current exploration and data analysis.We review recent space-based and ground-based observation methods and analyze their feasibility.To gain new insight into the internal structure of Jupiter,we propose to study Jupiter’s innards by planetary seismology.Ground-based observation,namely the Jupiter Seismologic Interferometer Polarization Imager(SIPI)in Lenghu,will be developed to obtain the Doppler velocity distribution on the surface of Jupiter and identify oscillation signals.Lenghu has observation conditions that are not only exceptional in China but even in the world,capable of providing novel insight into the interior of Jupiter.This will also be the first study in China of the interior of Jupiter using asteroseismology,which has significant implications for China’s plans to explore Jupiter via spacecraft-mounted instruments. 展开更多
关键词 Jupiter seismology Jupiter’s interior Jupiter model Jupiter seismologic Interferometer Polarization imager(sIPI)
下载PDF
Multisensory mechanisms of gait and balance in Parkinson’s disease:an integrative review
17
作者 Stiven Roytman Rebecca Paalanen +4 位作者 Giulia Carli Uros Marusic Prabesh Kanel Teus van Laar Nico I.Bohnen 《Neural Regeneration Research》 SCIE CAS 2025年第1期82-92,共11页
Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ... Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases. 展开更多
关键词 aging BALANCE encephalography functional magnetic resonance imaging GAIT multisensory integration Parkinson’s disease positron emission tomography sOMATOsENsORY VEsTIBULAR visual
下载PDF
Does shear wave elastography technology provide better value for the assessment of perianal fistulizing Crohn’s disease?
18
作者 Jiong Wu 《World Journal of Gastrointestinal Surgery》 SCIE 2024年第11期3636-3638,共3页
Magnetic resonance imaging is the gold standard compared other clinical fin-dings.But shear wave elastography technique combined with endoscopic ultra-sound can evaluate the degree of fibrosis of fistula tissue in Cr... Magnetic resonance imaging is the gold standard compared other clinical fin-dings.But shear wave elastography technique combined with endoscopic ultra-sound can evaluate the degree of fibrosis of fistula tissue in Crohn’s disease patients.This topic is highly relevant to the current discourse,especially for It shows a certain degree of innovation and practicality and is worthy of study and popularization. 展开更多
关键词 Perianal fistulizing Crohn’s disease shear wave elastography ULTRAsOUND Magnetic resonance imaging
下载PDF
基于特征工程的S-FCN火灾图像检测方法
19
作者 李海 熊升华 孙鹏 《中国安全科学学报》 CAS CSCD 北大核心 2024年第9期191-201,共11页
针对复杂背景下火灾图像检测深度学习算法存在的计算复杂度高、检测实时性差等问题,提出一种基于特征工程的单隐层全连接网络(S-FCN)火灾图像检测方法。首先,从图像中提取多色彩空间颜色特征,并使用互信息量进行多色彩空间颜色特征降维... 针对复杂背景下火灾图像检测深度学习算法存在的计算复杂度高、检测实时性差等问题,提出一种基于特征工程的单隐层全连接网络(S-FCN)火灾图像检测方法。首先,从图像中提取多色彩空间颜色特征,并使用互信息量进行多色彩空间颜色特征降维;其次,简化深度学习模型的网络结构,将单隐层全连接网络作为其主干网络,其中,多色彩空间下的颜色特征能够更好地表征火灾烟雾与火焰,多色彩空间颜色特征降维能够有效降低输入特征的冗余度,单隐层全连接网络能够有效减少模型在传递过程中的参数数量;最后,将该方法在真实的复杂背景火灾图像数据集上进行试验评估。结果表明:所提方法取得的检测精度为93.83%,取得的检测实时性帧率为10869帧/s,能够实现复杂场景下高精度、高速度的火灾图像检测。 展开更多
关键词 特征工程 单隐层全连接网络(s-FCN) 火灾图像 检测方法 色彩空间 特征降维
下载PDF
基于FPGA的AES和ECC算法图像加密
20
作者 方应李 方玉明 《电子科技》 2024年第6期92-97,共6页
随着数字图像的使用次数日益增多,保护机密图像数据免受未经授权的访问较为重要。针对数字图像在通信、存储和传输等领域存在的安全问题,文中基于对称算法模型和非对称算法模型的优点提出一种具有高安全性和高速度性的数字信封技术密码... 随着数字图像的使用次数日益增多,保护机密图像数据免受未经授权的访问较为重要。针对数字图像在通信、存储和传输等领域存在的安全问题,文中基于对称算法模型和非对称算法模型的优点提出一种具有高安全性和高速度性的数字信封技术密码方案。该方案以AES(Advanced Encryption Standard)和ECC(Elliptic Curve Cryptography)为基础,经优化后用于对称密钥共享的ECC硬件架构来提高密钥的安全性。通过加入伪随机数、使用列移位替代列混淆运算以及三维S-box等方式对传统AES进行优化,在保持香农扩散和混淆原理的同时降低了时间复杂性。基于FPGA(Field Programmable Gate Array)实现AES算法的数字图像加密仿真以及性能测试。测试结果表明,所提密码方案具有快速性、高安全性和有效性等优点,能够有效地实现图像加密。 展开更多
关键词 数字图像 数字信封 AEs算法 ECC算法 三维s-box FPGA 信息熵 相关系数
下载PDF
上一页 1 2 76 下一页 到第
使用帮助 返回顶部