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.

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.

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.

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.

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.

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.

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.

Identity Issue and Mysterious Images in the Bonesetter's Daughter

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.

A novel pseudo-random coupled LP spatiotemporal chaos and its application in image encryption

In this paper, first, we investigate a novel one-dimensional logistic-PWLCM（LP） modulation map which is derived from the logistic and PWLCM maps. Second, we propose a novel PCLML spatiotemporal chaos in pseudo-random coupling method that can accelerate the system behavior of the fully spatial chaos. Here, because the better chaotic properties include a wide range of parameter settings and better ergodicity than a logistic map, the LP is used in PCLML as f（x）. The Kolmogorov–Sinai entropy density and universality and the bifurcation diagram are employed to investigate the chaotic behaviors of the proposed PCLML model. Finally, we apply the LP and PCLML chaotic systems to image encryption to improve the effectiveness and security of the encryption scheme. By combining self-generating matrix model M and dynamic substitution box（S-Box） methods, we design a new image encryption algorithm. Numerical simulations and security analysis have been carried out to demonstrate that the proposed algorithm has a high security level and can efficiently encrypt several different kinds of images into random-like images.

Efficient image encryption scheme with synchronous substitution and diffusion based on double S-boxes

Single or multiple S-boxes are widely used in image encryption schemes, and in many image encryption schemes the asynchronous encryption structure is utilized, which separates the processes of substitution and diffusion. In this paper, we analyze the defects of this structure based on the example of an article and crack it using a simpler method. To address the defects of the asynchronous encryption structure, a novel encryption scheme is proposed, in which the structure of synchronous substitution and diffusion based on double S-boxes is utilized, so the processes of substitution and diffusion are combined together and the attackers cannot crack the cryptosystem by any of the processes. The simulation results and security analysis show that the proposed encryption scheme is safer and more efficient to expediently use in the real-time system.

Multisensory mechanisms of gait and balance in Parkinson’s disease:an integrative review

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.

A Comparative Image Study of two Love Songs——The Sun Rising by John Donne and The Love Song of J.Alfred Prufrock by T.S Eliot

The Sun Rising is one of the most famous metaphysical poems of John Donne.The Love Song of J.Alfred Prufrock is known as the first Modernist poem of T.S Eliot.This paper mainly aims to analyze the strikingly unconventional images use in the two poems,and make comments on their effect on expressing the poet emotions.

Spectrometer of hard X-ray imager payload onboard the ASO-S mission

A spaceborne hard X-ray spectrometer, composed of an array of 99 scintillation detectors and associated readout electronics, has been developed for the hard X-ray imager(HXI). The HXI is one of the three payloads onboard the advanced space-based solar observatory(ASO-S), which is scheduled to be launched in early 2022 as the first Chinese solar satellite. LaBr3 scintillators and photomultiplier tubes with a super bialkali cathode are used to achieve an energy resolution better than 20% at 30 keV.Further, a new multi-channel charge-sensitive readout application-specific integrated circuit guarantees high-frequency data acquisition with low power consumption. This paper presents a detailed design of the spectrometer for the engineering model of the HXI and discusses its noise and linearity performance.

China’s National Image in Western Travel Writings in the Age of Discovery

Western travel writings about China provided material driving and theoretical guidance for the Age of Discovery, and became the main spiritual motive of the Westerners voyage to the East. In the Age of Discovery, the image of China in the eyes of Western envoys, merchants and missionaries begun to fade away from traditional mythological and psychedelic colors, and the image of cultural elements such as material, spirit and history become clear. These Western travelers tried to shape the image of China as a state of civilization and wisdom, a state of moral order, and a state of wealth and power. On the basis of the continued description of the kingship and wealth of the Middle Ages, they constructed an overall image of China as a powerful state of civilization and wealth, thus forming a new frame of reference for cognition of China.

The Images in The Rime of the Ancient Mariner

The paper mainly talks about the background of S. T. Coleridge and the images of The Rime of the AncientMariner. As for the images, the wind, the albatross and the snake will be thoroughly discussed. There are manydifferent images which also have different meanings and connotations in the context. What＇s more, the poemexpresses the theme of the metabolic relationships between men and nature： harmony, conflict or compromise.

Real-time parameters monitoring system for underground equipment based on panoramic images

Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.