Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

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.

GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in 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.

Topcon Image Master软件在日照分析中的应用

针对济南市日照分析中阳台内的窗户测量比较困难的情况,本文提出了一种采用Topcon Image Master软件与数码相机相结合的测量方法,并从其工作原理、项目应用、精度分析及存在的问题等方面进行了详细阐述,解决了工作中的难题,为相关项目的开展提供了新思路。

Optimum selection of common master image for ground deformation monitoring based on PS-DInSAR technique

Considering the joint effects of various factors such as temporal baseline, spatial baseline, thermal noise, the difference of Doppler centroid frequency and the error of data processing on the interference correlation, an optimum selection method of common master images for ground deformation monitoring based on the permanent scatterer and differential SAR interferometry （PS-DInSAR） technique is proposed, in which the joint correlation coeficient is used as the evaluation function. The principle and realization method of PS-DInSAR technology is introduced, the factors affecting the DInSAR correlation are analysed, and the joint correlation function model and its solution are presented. Finally an experiment for the optimum selection of common master images is performed by using 25 SAR images over Shanghai taken by the ERS-1/2 as test data. The results indicate that the optimum selection method for PS-DInSAR common master images is effective and reliable.

Analysis of the Tragic Female Images in Eugene O'Neill's Plays

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.

基于轻量化YOLOX-S与多阈值分割的矿山遥感图像去噪算法

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。

Tourism Image Plan for Wunvfeng National Forest Park

After the introduction of tourist resources in Wunvfeng National Forest Park, the paper had planed its overall image from the perspectives of concept design, visual identity, behavioral norms and audio identity. The slogan of Wunvfeng National Forest Park had been identified as "tour of nature and mythology-Wunvfeng", and the park's emblem, symbolic mascots, spokesman of tourism image and tourist souvenirs had been set, so as to better display tourist advantages of Wunvfeng National Forest Park and create more economic and social benefits.

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

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。

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

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.

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

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.

Interplay between the glymphatic system and neurotoxic proteins in Parkinson’s disease and related disorders:current knowledge and future directions

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.

A hybrid quantum encoding algorithm of vector quantization for image compression

Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.

Range anomaly suppression based on neighborhood pixels detection in ladar range images

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.

Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson’s disease

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.

Application of Preoperative CT/MRI Image Fusion in Target Positioning for Deep Brain Stimulation

Objective To explore the efficacy of target positioning by preoperative CT/MRI image fusion technique in deep brain stimulation.Methods We retrospectively analyzed the clinical data and images of 79 cases(68 with Parkinson's disease,11 with dystonia) who received preoperative CT/MRI image fusion in target positioning of subthalamic nucleus in deep brain stimulation.Deviation of implanted electrodes from the target nucleus of each patient were measured.Neurological evaluations of each patient before and after the treatment were performed and compared.Complications of the positioning and treatment were recorded.Results The mean deviations of the electrodes implanted on X,Y,and Z axis were 0.5 mm,0.6 mm,and 0.6 mm,respectively.Postoperative neurologic evaluations scores of unified Parkinson's disease rating scale(UPDRS) for Parkinson's disease and Burke-Fahn-Marsden Dystonia Rating Scale(BFMDRS) for dystonia patients improved significantly compared to the preoperative scores(P<0.001); Complications occurred in 10.1%(8/79) patients,and main side effects were dysarthria and diplopia.Conclusion Target positioning by preoperative CT/MRI image fusion technique in deep brain stimulation has high accuracy and good clinical outcomes.

A Content-Based Parallel Image Retrieval System on Cluster Architectures

We propose a content-based parallel image retrieval system to achieve high responding ability. Our system is developed on cluster architectures. It has several retrieval. servers to supply the service of content-based image retrieval. It adopts the Browser/Server (B/S) mode. The users could visit our system though web pages. It uses the symmetrical color-spatial features (SCSF) to represent the content of an image. The SCSF is effective and efficient for image matching because it is independent of image distortion such as rotation and flip as well as it increases the matching accuracy. The SCSF was organized by M-tree, which could speedup the searching procedure. Our experiments show that the image matching is quickly and efficiently with the use of SCSF. And with the support of several retrieval servers, the system could respond to many users at mean time. Key words content-based image retrieval - cluster architecture - color-spatial feature - B/S mode - task parallel - WWW - Internet CLC number TP391 Foundation item: Supported by the National Natural Science Foundation of China (60173058)Biography: ZHOU Bing (1975-), male, Ph. D candidate, reseach direction: data mining, content-based image retrieval.

Neural stem cell-derived exosomes promote mitochondrial biogenesis and restore abnormal protein distribution in a mouse model of Alzheimer's disease

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.

基于等效结构应力法的高强钢焊接结构低温主S-N曲线

现有的疲劳S-N曲线已不再适用于北极的低温环境,为了评估北极海洋工程设备材料的低温疲劳寿命,有必要建立金属结构尤其是焊接结构的低温疲劳S-N曲线。本文基于等效结构应力法,计算出Q690高强钢管环焊缝的主S-N曲线,并通过共振疲劳试验对该方法进行了验证。在此基础上,结合大量的高强钢焊接结构试验结果,将温度敏感因子c引入到低温疲劳主S-N曲线的推导中,首次建立了基于低温金属焊接结构的主S-N曲线,并对文献中的疲劳S-N试验数据和ASME中的修正方法进行了对比和验证。结果表明,等效结构应力法能够较准确地计算出焊缝疲劳S-N曲线,推导出的金属焊接结构低温主S-N曲线与试验曲线吻合较好,能够满足低温地区工程要求,可为高强钢焊接结构在北极地区低温环境中的广泛应用提供理论指导。该方法可以为金属焊接结构的低温疲劳研究节省大量的成本,减少非标准试验操作造成的不必要的误差。这对北极海洋工程设备的设计、安全运行和疲劳评估具有重要意义。