Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in de...Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in demand.In this work,we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution.The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy,convolutional neural network(CNN),and multi-head self-attention mechanism.To address the limited size of the original spectra dataset,Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training.Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism,the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29±0.82%to 99.43±0.15%.To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN,the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve.In the process of spectra augmentation from 0 to 1000,the distribution of relative classification weight varied from a discrete state to a more concentrated phase.More importantly,these highlighted four Raman bands(1017,1449,1576,and 1660 cm^(-1))were assigned large weights,showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy.It can be foreseen that,our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained promine...Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.展开更多
Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowad...Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.展开更多
Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have becom...Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.展开更多
Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by le...Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.展开更多
A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumor...A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.展开更多
Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features i...Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.展开更多
Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the pun...Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.展开更多
For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high com...For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.展开更多
As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additi...As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.展开更多
The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiment...The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.展开更多
Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and...Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and treatment planning.This research presents a significant advancement in the multi-classification of brain tumors.This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d,a Convolutional Neural Network(CNN)with a multi-head self-attention(MHSA)mechanism.This combination harnesses the strengths of the feature extraction,feature representation by CNN,and long-range dependencies by MHSA.Magnetic Resonance Imaging(MRI)datasets were employed to check the effectiveness of the proposed architecture.The first dataset(DS-1,Msoud)included four brain tumor classes,and the second dataset(DS-2)contained seven brain tumor classes.This methodology effectively distinguished various tumor classes,achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2.These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification.Evaluationmetrics such as accuracy,precision,recall,F1 score,and ROC(receiver operating characteristic)curve were utilized to comprehensively evaluate model validity.The performance results showed that the model is well-suited for clinical applications,with reduced errors and high accuracy.展开更多
Synaptic plasticity is essential for maintaining neuronal function in the central nervous system and serves as a critical indicator of the effects of neurodegenerative disease.Glaucoma directly impairs retinal ganglio...Synaptic plasticity is essential for maintaining neuronal function in the central nervous system and serves as a critical indicator of the effects of neurodegenerative disease.Glaucoma directly impairs retinal ganglion cells and their axons,leading to axonal transport dysfuntion,subsequently causing secondary damage to anterior or posterior ends of the visual system.Accordingly,recent evidence indicates that glaucoma is a degenerative disease of the central nervous system that causes damage throughout the visual pathway.However,the effects of glaucoma on synaptic plasticity in the primary visual cortex remain unclear.In this study,we established a mouse model of unilateral chronic ocular hypertension by injecting magnetic microbeads into the anterior chamber of one eye.We found that,after 4 weeks of chronic ocular hypertension,the neuronal somas were smaller in the superior colliculus and lateral geniculate body regions of the brain contralateral to the affected eye.This was accompanied by glial cell activation and increased expression of inflammatory factors.After 8 weeks of ocular hypertension,we observed a reduction in the number of excitatory and inhibitory synapses,dendritic spines,and activation of glial cells in the primary visual cortex contralateral to the affected eye.These findings suggest that glaucoma not only directly damages the retina but also induces alterations in synapses and dendritic spines in the primary visual cortex,providing new insights into the pathogenesis of glaucoma.展开更多
AIM:To compare the visual outcomes between bilateral implantation of Tecnis ZXR00 extended depth-of-focus(EDOF)intraocular lenses(IOLs)and mixed implantation of Tecnis ZXR00(EDOF)with Tecnis ZMB00(bifocal)IOLs.METHODS...AIM:To compare the visual outcomes between bilateral implantation of Tecnis ZXR00 extended depth-of-focus(EDOF)intraocular lenses(IOLs)and mixed implantation of Tecnis ZXR00(EDOF)with Tecnis ZMB00(bifocal)IOLs.METHODS:This postoperative cross-sectional study enrolled patients who underwent phacoemulsification combined with IOL implantation.Patients were divided into two groups:the bilateral ZXR00 group(ZXR00-only group)and the mixed IOL group(ZXR00+ZMB00 group).Primary outcome measures included uncorrected and corrected distance visual acuity(UDVA,CDVA),uncorrected and distance-corrected near visual acuity(UNVA,DCNVA),uncorrected and distance-corrected intermediate visual acuity(UIVA,DCIVA),and defocus curves.Secondary outcome measures were visual quality,spectacle independence,patient satisfaction,photic phenomena,and stereopsis.RESULTS:A total of 47 patients(94 eyes)were included,with 26 patients(11 males,15 females)in the ZXR00-only group(mean age:62.73±7.24y)and 21 patients(7 males,14 females)in the mixed group(mean age:65.71±9.16y).There was no statistically significant difference in age between the two groups(P=0.218).The mixed group showed significantly better binocular DCNVA compared to the ZXR00-only group(P=0.002).Defocus curve analysis revealed that the mixed group exhibited superior performance at−2.5 to−4.0 D but inferior performance at−0.5 and−1.5 D.Near stereoacuity was significantly poorer in the mixed group(Randot:5.589±0.744 vs 6.240±0.394 ln arcsec;Contour:4.966±0.973 vs 5.740±0.833 ln arcsec;both P<0.01).Both groups achieved high levels of spectacle independence and patient satisfaction,with no significant differences in photic phenomena or questionnaire scores.CONCLUSION:Mixed implantation of EDOF and bifocal IOLs improve near visual acuity but may compromise near stereopsis.This approach provides a viable option for patients prioritizing near vision;however,caution is recommended for individuals requiring fine stereoscopic vision for daily or professional tasks.展开更多
Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follow...Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.展开更多
Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for art...Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for artificial intelligence sensing.For instance,the incorporation of low-dimensional materials(e.g.,quantum dots,carbon nanotubes,and two-dimensional materials)optimizes device optoelectronic properties,while the synergistic design of organic semiconductors and oxide materials balances flexibility with complementary metal-oxide-semiconductor(CMOS)compatibility.Representative neuromorphic devices such as memristors and neuromorphic transistors address traditional vision system bottlenecks via near-sensor and in-sensor architectures in data transmission latency and energy consumption,offering a new paradigm for highly integrated,energy-efficient real-time perception.However,critical challenges—including device non-uniformity caused by material interface defects,system instability induced by memristor conductance drift,and environmental adaptability under complex illumination—remain barriers to scalable applications.This review comprehensively examines neuromorphic visual perception devices from the perspectives of device structure,operational mechanisms,materials,and applications.It explores the pivotal roles of memristors,electrolyte-gated transistors,and other neuromorphic devices in optical signal perception and information processing,with a focus on their implementations in visual perception tasks and future prospects.展开更多
While degenerative diseases of the central nervous system are commonly linked to age-related macular degeneration and glaucoma,they have also been infrequently associated with retinitis pigmentosa,a condition defined ...While degenerative diseases of the central nervous system are commonly linked to age-related macular degeneration and glaucoma,they have also been infrequently associated with retinitis pigmentosa,a condition defined by retinal degeneration that can be caused by an isoform of receptor expression enhancing protein 6(REEP6)expressed in rod photoreceptors.In this study,we used REEP6 knockout mice(REEP6^(-/-))and wild-type mice(REEP6^(+/+))to examine neurodegenerative pathology within the visual pathways and neural activity in the primary visual cortex(V1)at three specific time points(1,6,and 10 months)during retinitis pigmentosa progression.Microglial activation was observed in both the retina and the primary visual cortex starting at 1 month of age,but no such activation was detected in the lateral geniculate nucleus at any time point.Not only was increased microglial activation observed at 6 and 10 months within the primary visual cortex of REEP6^(-/-)mice,but also coinciding with elevated levels of phosphorylated Tau expression.At 6 and 10 months of age,primary visual cortex neurons in REEP6^(-/-)mice exhibited reduced responses to grating stimuli and increased spontaneous activity compared with neurons in the primary visual cortex of mice in the control group.Our findings show that retinitis pigmentosa induces neurodegenerative pathology within the visual pathway of mice,particularly in the primary visual cortex,suggesting that ocular disease contributes substantially to central nervous system degeneration.It may provide new clues for the selection of treatment opportunities and the development of therapeutic measures for the subsequent treatment of retinitis pigmentosa or even other retinal degenerative diseases.展开更多
This study extends the self-propelled particle(SPP)model by incorporating a limited vision cone and local density sensing.The results reveal that clusters can simultaneously exhibit velocity polarization and spatial c...This study extends the self-propelled particle(SPP)model by incorporating a limited vision cone and local density sensing.The results reveal that clusters can simultaneously exhibit velocity polarization and spatial cohesion within specific ranges of vision angle and density threshold.The dependence of the dynamical features,including the order parameter and density variation,on the threshold and visual cone is investigated.Furthermore,a critical threshold is identified,which governs the transition between ordered and disordered states and is closely linked to density fluctuations and noise intensity.The clustering results show that the model is explained by the chasing mechanism responsible for cluster formation,density,and shape.These results may stimulate practical applications in swarm maneuvering.展开更多
AIM:To identify early biomarkers associated with glaucomatous visual field(VF)progression in patients with normal-tension glaucoma(NTG).METHODS:This study included patients were divided into two groups based on diseas...AIM:To identify early biomarkers associated with glaucomatous visual field(VF)progression in patients with normal-tension glaucoma(NTG).METHODS:This study included patients were divided into two groups based on disease progression status.Tear samples were collected for proteomic analysis.Dataindependent acquisition(DIA)mass spectrometry combined with bioinformatic analyses was performed to identify and validate potential protein biomarkers for NTG progression.Additionally,differentially expressed proteins(DEPs)were evaluated using mediating effect models and receiver operating characteristic(ROC)curve analysis.RESULTS:A total of 19 patients(20 eyes)with NTG participated in this study,including 10 patients(4 males and 6 females;10 eyes)in the progression group with mean age of 67.70±9.03y and 10 patients(4 males and 6 females;10 eyes)in the non-progression group with mean age of 68.60±7.58y.A total of 158 significantly differentially expressed proteins were detected.UniProt database annotation identified 3 upregulated proteins and 12 downregulated proteins.Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis showed that these DEPs were mainly enriched in pathways such as oocyte meiosis.Gene Ontology(GO)enrichment analysis revealed functional clusters related to cellular processes.Weighted gene coexpression network analysis(WGCNA)indicated that the core proteins were primarily involved in the neurodegenerationmultiple diseases pathway and cellular processes.Mediating effect analysis identified PRDX4(L)as a potential protein biomarker.ROC curve analysis showed that GNAI1 had the largest area under the curve(AUC=0.889).CONCLUSION:This study identifies 15 differentially expressed proteins in the tear fluid of NTG patients,including PRDX4(L).PRDX4(L)plays a key role in oxidative stress.展开更多
基金partially supported by the National Natural Science Foundation of China(62075137)the Guangdong Basic and Applied Basic Research Foundation(2023A1515140161)+3 种基金the Guangxi Natural Science Foundation of China(2021JJB 110003)the Dongguan Science and Technology of Social Development Program(20231800936312)the high-level talent program of Dongguan University of Technology(No.221110080)the Sanming Project of Medicine in Shenzhen(No.SZSM202103014).
文摘Many spore-forming Bacillus species can cause serious human diseases,because of accidental Bacillusspore infection.Thus,developing an identification strategy with both high sensitivity and specificity is greatly in demand.In this work,we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution.The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy,convolutional neural network(CNN),and multi-head self-attention mechanism.To address the limited size of the original spectra dataset,Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training.Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism,the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29±0.82%to 99.43±0.15%.To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN,the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve.In the process of spectra augmentation from 0 to 1000,the distribution of relative classification weight varied from a discrete state to a more concentrated phase.More importantly,these highlighted four Raman bands(1017,1449,1576,and 1660 cm^(-1))were assigned large weights,showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy.It can be foreseen that,our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
基金supported by the National Natural Science Foundation of China(No.52277055).
文摘Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals,which has certain limitations.Conversely,deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency.Recently,utilizing the respective advantages of convolution neural network(CNN)and Transformer in local and global feature extraction,research on cooperating the two have demonstrated promise in the field of fault diagnosis.However,the cross-channel convolution mechanism in CNN and the self-attention calculations in Transformer contribute to excessive complexity in the cooperative model.This complexity results in high computational costs and limited industrial applicability.To tackle the above challenges,this paper proposes a lightweight CNN-Transformer named as SEFormer for rotating machinery fault diagnosis.First,a separable multiscale depthwise convolution block is designed to extract and integrate multiscale feature information from different channel dimensions of vibration signals.Then,an efficient self-attention block is developed to capture critical fine-grained features of the signal from a global perspective.Finally,experimental results on the planetary gearbox dataset and themotor roller bearing dataset prove that the proposed framework can balance the advantages of robustness,generalization and lightweight compared to recent state-of-the-art fault diagnosis models based on CNN and Transformer.This study presents a feasible strategy for developing a lightweight rotating machinery fault diagnosis framework aimed at economical deployment.
基金supported by the National Key Research and Development Program of China(No.2021YFA0715900).
文摘Located in northern China,the Hetao Plain is an important agro-economic zone and population centre.The deterioration of local groundwater quality has had a serious impact on human health and economic development.Nowadays,the groundwater vulnerability assessment(GVA)has become an essential task to identify the current status and development trend of groundwater quality.In this study,the Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)models are integrated to realize the spatio-temporal prediction of regional groundwater vulnerability by introducing the Self-attention mechanism.The study firstly builds the CNN-LSTM modelwith self-attention(SA)mechanism and evaluates the prediction accuracy of the model for groundwater vulnerability compared to other common machine learning models such as Support Vector Machine(SVM),Random Forest(RF),and Extreme Gradient Boosting(XGBoost).The results indicate that the CNNLSTM model outperforms thesemodels,demonstrating its significance in groundwater vulnerability assessment.It can be posited that the predictions indicate an increased risk of groundwater vulnerability in the study area over the coming years.This increase can be attributed to the synergistic impact of global climate anomalies and intensified local human activities.Moreover,the overall groundwater vulnerability risk in the entire region has increased,evident fromboth the notably high value and standard deviation.This suggests that the spatial variability of groundwater vulnerability in the area is expected to expand in the future due to the sustained progression of climate change and human activities.The model can be optimized for diverse applications across regional environmental assessment,pollution prediction,and risk statistics.This study holds particular significance for ecological protection and groundwater resource management.
基金supported by the National Natural Science Foundation of China under Grant 62177029the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX21_0740),China.
文摘Visual object tracking plays a crucial role in computer vision.In recent years,researchers have proposed various methods to achieve high-performance object tracking.Among these,methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information.However,current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information.In this paper,we introduce self-calibration multi-head self-attention Transformer(SMSTracker)as a solution to these challenges.It employs a hybrid tensor decomposition self-organizing multihead self-attention transformermechanism,which not only compresses and accelerates Transformer operations but also significantly reduces redundant data,thereby enhancing the accuracy and efficiency of tracking.Additionally,we introduce a self-calibration attention fusion block to resolve common issues of attention ambiguities and inconsistencies found in traditional trackingmethods,ensuring the stability and reliability of tracking performance across various scenarios.By integrating a hybrid tensor decomposition approach with a self-organizingmulti-head self-attentive transformer mechanism,SMSTracker enhances the efficiency and accuracy of the tracking process.Experimental results show that SMSTracker achieves competitive performance in visual object tracking,promising more robust and efficient tracking systems,demonstrating its potential to providemore robust and efficient tracking solutions in real-world applications.
文摘Lightweight convolutional neural networks(CNNs)have simple structures but struggle to comprehensively and accurately extract important semantic information from images.While attention mechanisms can enhance CNNs by learning distinctive representations,most existing spatial and hybrid attention methods focus on local regions with extensive parameters,making them unsuitable for lightweight CNNs.In this paper,we propose a self-attention mechanism tailored for lightweight networks,namely the brief self-attention module(BSAM).BSAM consists of the brief spatial attention(BSA)and advanced channel attention blocks.Unlike conventional self-attention methods with many parameters,our BSA block improves the performance of lightweight networks by effectively learning global semantic representations.Moreover,BSAM can be seamlessly integrated into lightweight CNNs for end-to-end training,maintaining the network’s lightweight and mobile characteristics.We validate the effectiveness of the proposed method on image classification tasks using the Food-101,Caltech-256,and Mini-ImageNet datasets.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia under Grant No.(GPIP:1055-829-2024).
文摘A healthy brain is vital to every person since the brain controls every movement and emotion.Sometimes,some brain cells grow unexpectedly to be uncontrollable and cancerous.These cancerous cells are called brain tumors.For diagnosed patients,their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans.Nowadays,Physicians and radiologists rely on Magnetic Resonance Imaging(MRI)pictures for their clinical evaluations of brain tumors.These evaluations are time-consuming,expensive,and require expertise with high skills to provide an accurate diagnosis.Scholars and industrials have recently partnered to implement automatic solutions to diagnose the disease with high accuracy.Due to their accuracy,some of these solutions depend on deep-learning(DL)methodologies.These techniques have become important due to their roles in the diagnosis process,which includes identification and classification.Therefore,there is a need for a solid and robust approach based on a deep-learning method to diagnose brain tumors.The purpose of this study is to develop an intelligent automatic framework for brain tumor diagnosis.The proposed solution is based on a novel dense dynamic residual self-attention transfer adaptive learning fusion approach(NDDRSATALFA),carried over two implemented deep-learning networks:VGG19 and UNET to identify and classify brain tumors.In addition,this solution applies a transfer learning approach to exchange extracted features and data within the two neural networks.The presented framework is trained,validated,and tested on six public datasets of MRIs to detect brain tumors and categorize these tumors into three suitable classes,which are glioma,meningioma,and pituitary.The proposed framework yielded remarkable findings on variously evaluated performance indicators:99.32%accuracy,98.74%sensitivity,98.89%specificity,99.01%Dice,98.93%Area Under the Curve(AUC),and 99.81%F1-score.In addition,a comparative analysis with recent state-of-the-art methods was performed and according to the comparative analysis,NDDRSATALFA shows an admirable level of reliability in simplifying the timely identification of diverse brain tumors.Moreover,this framework can be applied by healthcare providers to assist radiologists,pathologists,and physicians in their evaluations.The attained outcomes open doors for advanced automatic solutions that improve clinical evaluations and provide reasonable treatment plans.
基金funded by the Ongoing Research Funding Program(ORF-2025-102),King Saud University,Riyadh,Saudi Arabiaby the Science and Technology Research Programof Chongqing Municipal Education Commission(Grant No.KJQN202400813)by the Graduate Research Innovation Project(Grant Nos.yjscxx2025-269-193 and CYS25618).
文摘Medical image analysis based on deep learning has become an important technical requirement in the field of smart healthcare.In view of the difficulties in collaborative modeling of local details and global features in multimodal image analysis of ophthalmology,as well as the existence of information redundancy in cross-modal data fusion,this paper proposes amultimodal fusion framework based on cross-modal collaboration and weighted attention mechanism.In terms of feature extraction,the framework collaboratively extracts local fine-grained features and global structural dependencies through a parallel dual-branch architecture,overcoming the limitations of traditional single-modality models in capturing either local or global information;in terms of fusion strategy,the framework innovatively designs a cross-modal dynamic fusion strategy,combining overlappingmulti-head self-attention modules with a bidirectional feature alignment mechanism,addressing the bottlenecks of low feature interaction efficiency and excessive attention fusion computations in traditional parallel fusion,and further introduces cross-domain local integration technology,which enhances the representation ability of the lesion area through pixel-level feature recalibration and optimizes the diagnostic robustness of complex cases.Experiments show that the framework exhibits excellent feature expression and generalization performance in cross-domain scenarios of ophthalmic medical images and natural images,providing a high-precision,low-redundancy fusion paradigm for multimodal medical image analysis,and promoting the upgrade of intelligent diagnosis and treatment fromsingle-modal static analysis to dynamic decision-making.
文摘Deep Learning-based systems for Finger vein recognition have gained rising attention in recent years due to improved efficiency and enhanced security.The performance of existing CNN-based methods is limited by the puny generalization of learned features and deficiency of the finger vein image training data.Considering the concerns of existing methods,in this work,a simplified deep transfer learning-based framework for finger-vein recognition is developed using an EfficientNet model of deep learning with a self-attention mechanism.Data augmentation using various geometrical methods is employed to address the problem of training data shortage required for a deep learning model.The proposed model is tested using K-fold cross-validation on three publicly available datasets:HKPU,FVUSM,and SDUMLA.Also,the developed network is compared with other modern deep nets to check its effectiveness.In addition,a comparison of the proposed method with other existing Finger vein recognition(FVR)methods is also done.The experimental results exhibited superior recognition accuracy of the proposed method compared to other existing methods.In addition,the developed method proves to be more effective and less sophisticated at extracting robust features.The proposed EffAttenNet achieves an accuracy of 98.14%on HKPU,99.03%on FVUSM,and 99.50%on SDUMLA databases.
基金supported by National Natural Science Foundation of China(Nos.61261016,61661025)Science and Technology Plan of Gansu Province(No.20JR10RA273).
文摘For image compression sensing reconstruction,most algorithms use the method of reconstructing image blocks one by one and stacking many convolutional layers,which usually have defects of obvious block effects,high computational complexity,and long reconstruction time.An image compressed sensing reconstruction network based on self-attention mechanism(SAMNet)was proposed.For the compressed sampling,self-attention convolution was designed,which was conducive to capturing richer features,so that the compressed sensing measurement value retained more image structure information.For the reconstruction,a self-attention mechanism was introduced in the convolutional neural network.A reconstruction network including residual blocks,bottleneck transformer(BoTNet),and dense blocks was proposed,which strengthened the transfer of image features and reduced the amount of parameters dramatically.Under the Set5 dataset,when the measurement rates are 0.01,0.04,0.10,and 0.25,the average peak signal-to-noise ratio(PSNR)of SAMNet is improved by 1.27,1.23,0.50,and 0.15 dB,respectively,compared to the CSNet+.The running time of reconstructing a 256×256 image is reduced by 0.1473,0.1789,0.2310,and 0.2524 s compared to ReconNet.Experimental results showed that SAMNet improved the quality of reconstructed images and reduced the reconstruction time.
基金supported by the National Key Research and Development Program of China(2020YFC2200901)。
文摘As the complexity of scientific satellite missions increases,the requirements for their magnetic fields,magnetic field fluctuations,and even magnetic field gradients and variations become increasingly stringent.Additionally,there is a growing need to address the alternating magnetic fields produced by the spacecraft itself.This paper introduces a novel modeling method for spacecraft magnetic dipoles using an integrated self-attention mechanism and a transformer combined with Kolmogorov-Arnold Networks.The self-attention mechanism captures correlations among globally sparse data,establishing dependencies b.etween sparse magnetometer readings.Concurrently,the Kolmogorov-Arnold Network,proficient in modeling implicit numerical relationships between data features,enhances the ability to learn subtle patterns.Comparative experiments validate the capability of the proposed method to precisely model magnetic dipoles,achieving maximum Root Mean Square Errors of 24.06 mA·m^(2)and 0.32 cm for size and location modeling,respectively.The spacecraft magnetic model established using this method accurately computes magnetic fields and alternating magnetic fields at designated surfaces or points.This approach facilitates the rapid and precise construction of individual and complete spacecraft magnetic models,enabling the verification of magnetic specifications from the spacecraft design phase.
文摘The development of deep learning has made non-biochemical methods for molecular property prediction screening a reality,which can increase the experimental speed and reduce the experimental cost of relevant experiments.There are currently two main approaches to representing molecules:(a)representing molecules by fixing molecular descriptors,and(b)representing molecules by graph convolutional neural networks.Currently,both of these Representative methods have achieved some results in their respective experiments.Based on past efforts,we propose a Dual Self-attention Fusion Message Neural Network(DSFMNN).DSFMNN uses a combination of dual self-attention mechanism and graph convolutional neural network.Advantages of DSFMNN:(1)The dual self-attention mechanism focuses not only on the relationship between individual subunits in a molecule but also on the relationship between the atoms and chemical bonds contained in each subunit.(2)On the directed molecular graph,a message delivery approach centered on directed molecular bonds is used.We test the performance of the model on eight publicly available datasets and compare the performance with several models.Based on the current experimental results,DSFMNN has superior performance compared to previous models on the datasets applied in this paper.
文摘Brain tumor identification is a challenging task in neuro-oncology.The brain’s complex anatomy makes it a crucial part of the central nervous system.Accurate tumor classification is crucial for clinical diagnosis and treatment planning.This research presents a significant advancement in the multi-classification of brain tumors.This paper proposed a novel architecture that integrates Enhanced ResNeXt 101_32×8d,a Convolutional Neural Network(CNN)with a multi-head self-attention(MHSA)mechanism.This combination harnesses the strengths of the feature extraction,feature representation by CNN,and long-range dependencies by MHSA.Magnetic Resonance Imaging(MRI)datasets were employed to check the effectiveness of the proposed architecture.The first dataset(DS-1,Msoud)included four brain tumor classes,and the second dataset(DS-2)contained seven brain tumor classes.This methodology effectively distinguished various tumor classes,achieving high accuracies of 99.75% on DS-1 and 98.80% on DS-2.These impressive results indicate the superior performance and adaptability of our model for multiclass brain tumor classification.Evaluationmetrics such as accuracy,precision,recall,F1 score,and ROC(receiver operating characteristic)curve were utilized to comprehensively evaluate model validity.The performance results showed that the model is well-suited for clinical applications,with reduced errors and high accuracy.
基金supported by the National Natural Science Foundation of China,No.82271115(to MY).
文摘Synaptic plasticity is essential for maintaining neuronal function in the central nervous system and serves as a critical indicator of the effects of neurodegenerative disease.Glaucoma directly impairs retinal ganglion cells and their axons,leading to axonal transport dysfuntion,subsequently causing secondary damage to anterior or posterior ends of the visual system.Accordingly,recent evidence indicates that glaucoma is a degenerative disease of the central nervous system that causes damage throughout the visual pathway.However,the effects of glaucoma on synaptic plasticity in the primary visual cortex remain unclear.In this study,we established a mouse model of unilateral chronic ocular hypertension by injecting magnetic microbeads into the anterior chamber of one eye.We found that,after 4 weeks of chronic ocular hypertension,the neuronal somas were smaller in the superior colliculus and lateral geniculate body regions of the brain contralateral to the affected eye.This was accompanied by glial cell activation and increased expression of inflammatory factors.After 8 weeks of ocular hypertension,we observed a reduction in the number of excitatory and inhibitory synapses,dendritic spines,and activation of glial cells in the primary visual cortex contralateral to the affected eye.These findings suggest that glaucoma not only directly damages the retina but also induces alterations in synapses and dendritic spines in the primary visual cortex,providing new insights into the pathogenesis of glaucoma.
文摘AIM:To compare the visual outcomes between bilateral implantation of Tecnis ZXR00 extended depth-of-focus(EDOF)intraocular lenses(IOLs)and mixed implantation of Tecnis ZXR00(EDOF)with Tecnis ZMB00(bifocal)IOLs.METHODS:This postoperative cross-sectional study enrolled patients who underwent phacoemulsification combined with IOL implantation.Patients were divided into two groups:the bilateral ZXR00 group(ZXR00-only group)and the mixed IOL group(ZXR00+ZMB00 group).Primary outcome measures included uncorrected and corrected distance visual acuity(UDVA,CDVA),uncorrected and distance-corrected near visual acuity(UNVA,DCNVA),uncorrected and distance-corrected intermediate visual acuity(UIVA,DCIVA),and defocus curves.Secondary outcome measures were visual quality,spectacle independence,patient satisfaction,photic phenomena,and stereopsis.RESULTS:A total of 47 patients(94 eyes)were included,with 26 patients(11 males,15 females)in the ZXR00-only group(mean age:62.73±7.24y)and 21 patients(7 males,14 females)in the mixed group(mean age:65.71±9.16y).There was no statistically significant difference in age between the two groups(P=0.218).The mixed group showed significantly better binocular DCNVA compared to the ZXR00-only group(P=0.002).Defocus curve analysis revealed that the mixed group exhibited superior performance at−2.5 to−4.0 D but inferior performance at−0.5 and−1.5 D.Near stereoacuity was significantly poorer in the mixed group(Randot:5.589±0.744 vs 6.240±0.394 ln arcsec;Contour:4.966±0.973 vs 5.740±0.833 ln arcsec;both P<0.01).Both groups achieved high levels of spectacle independence and patient satisfaction,with no significant differences in photic phenomena or questionnaire scores.CONCLUSION:Mixed implantation of EDOF and bifocal IOLs improve near visual acuity but may compromise near stereopsis.This approach provides a viable option for patients prioritizing near vision;however,caution is recommended for individuals requiring fine stereoscopic vision for daily or professional tasks.
基金supported by the National Natural Science Foundation of China(62421004,U24A20279,62473243,62533004)。
文摘Dear Editor,This letter addresses the formation control problem for unmanned surface vehicles(USVs)under GPS-denied environments.A novel visual servo formation control scheme,utilizing a monocular camera on the follower to obtain the leader’s global position,is developed,which is also capable of guaranteeing collision avoidance and visibility maintenance(CA&VM)raised by the requirement of actual formation navigation.
基金supported by Post-Moore Major Project of the National Natural Science Foundation of China(Grant No.92364204)Zhejiang Province introduces and cultivates leading innovation and entrepreneurship teams(Grant No.2023R01011)+1 种基金Zhejiang Provincial Natural Science Foundation of China(Grant No.LMS25F040005)the Key R&D Program of Zhejiang(Grant No.2024SSYS0042)。
文摘Neuromorphic visual perception,by emulating the efficient information processing mechanisms of biological vision systems and integrating innovations in materials and device architectures,offers novel solutions for artificial intelligence sensing.For instance,the incorporation of low-dimensional materials(e.g.,quantum dots,carbon nanotubes,and two-dimensional materials)optimizes device optoelectronic properties,while the synergistic design of organic semiconductors and oxide materials balances flexibility with complementary metal-oxide-semiconductor(CMOS)compatibility.Representative neuromorphic devices such as memristors and neuromorphic transistors address traditional vision system bottlenecks via near-sensor and in-sensor architectures in data transmission latency and energy consumption,offering a new paradigm for highly integrated,energy-efficient real-time perception.However,critical challenges—including device non-uniformity caused by material interface defects,system instability induced by memristor conductance drift,and environmental adaptability under complex illumination—remain barriers to scalable applications.This review comprehensively examines neuromorphic visual perception devices from the perspectives of device structure,operational mechanisms,materials,and applications.It explores the pivotal roles of memristors,electrolyte-gated transistors,and other neuromorphic devices in optical signal perception and information processing,with a focus on their implementations in visual perception tasks and future prospects.
基金supported by STI 2030-Major Projects,No.2022ZD0208503(to DY)the Fund of Chinese Academy of Science(“Xi Bu Zhi Guang”Project)(to YY)the Sichuan Science and Technology Program,No.2023YFS0312(to YY).
文摘While degenerative diseases of the central nervous system are commonly linked to age-related macular degeneration and glaucoma,they have also been infrequently associated with retinitis pigmentosa,a condition defined by retinal degeneration that can be caused by an isoform of receptor expression enhancing protein 6(REEP6)expressed in rod photoreceptors.In this study,we used REEP6 knockout mice(REEP6^(-/-))and wild-type mice(REEP6^(+/+))to examine neurodegenerative pathology within the visual pathways and neural activity in the primary visual cortex(V1)at three specific time points(1,6,and 10 months)during retinitis pigmentosa progression.Microglial activation was observed in both the retina and the primary visual cortex starting at 1 month of age,but no such activation was detected in the lateral geniculate nucleus at any time point.Not only was increased microglial activation observed at 6 and 10 months within the primary visual cortex of REEP6^(-/-)mice,but also coinciding with elevated levels of phosphorylated Tau expression.At 6 and 10 months of age,primary visual cortex neurons in REEP6^(-/-)mice exhibited reduced responses to grating stimuli and increased spontaneous activity compared with neurons in the primary visual cortex of mice in the control group.Our findings show that retinitis pigmentosa induces neurodegenerative pathology within the visual pathway of mice,particularly in the primary visual cortex,suggesting that ocular disease contributes substantially to central nervous system degeneration.It may provide new clues for the selection of treatment opportunities and the development of therapeutic measures for the subsequent treatment of retinitis pigmentosa or even other retinal degenerative diseases.
基金Project supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX240139)funded by the Youth Independent Innovation Fund of PLA Army Engineering University(Grant No.KYJBJKQTZQ23006)。
文摘This study extends the self-propelled particle(SPP)model by incorporating a limited vision cone and local density sensing.The results reveal that clusters can simultaneously exhibit velocity polarization and spatial cohesion within specific ranges of vision angle and density threshold.The dependence of the dynamical features,including the order parameter and density variation,on the threshold and visual cone is investigated.Furthermore,a critical threshold is identified,which governs the transition between ordered and disordered states and is closely linked to density fluctuations and noise intensity.The clustering results show that the model is explained by the chasing mechanism responsible for cluster formation,density,and shape.These results may stimulate practical applications in swarm maneuvering.
基金Supported by The Eye Hospital of Wenzhou Medical University(No.KYQD20220304)The Fifth Batch of Provincial Ten Thousand Personnel Program Outstanding Talents Funding(No.474092204)+1 种基金Innovative Talents and Teams(2024)-The Fifth Batch of Funding Funds for Scientific and Technological Innovation Leading Talents Under the Provincial Ten Thousand Personnel Program(No.4240924003G)The Key R&D Program of Zhejiang(No.2022C03112).
文摘AIM:To identify early biomarkers associated with glaucomatous visual field(VF)progression in patients with normal-tension glaucoma(NTG).METHODS:This study included patients were divided into two groups based on disease progression status.Tear samples were collected for proteomic analysis.Dataindependent acquisition(DIA)mass spectrometry combined with bioinformatic analyses was performed to identify and validate potential protein biomarkers for NTG progression.Additionally,differentially expressed proteins(DEPs)were evaluated using mediating effect models and receiver operating characteristic(ROC)curve analysis.RESULTS:A total of 19 patients(20 eyes)with NTG participated in this study,including 10 patients(4 males and 6 females;10 eyes)in the progression group with mean age of 67.70±9.03y and 10 patients(4 males and 6 females;10 eyes)in the non-progression group with mean age of 68.60±7.58y.A total of 158 significantly differentially expressed proteins were detected.UniProt database annotation identified 3 upregulated proteins and 12 downregulated proteins.Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis showed that these DEPs were mainly enriched in pathways such as oocyte meiosis.Gene Ontology(GO)enrichment analysis revealed functional clusters related to cellular processes.Weighted gene coexpression network analysis(WGCNA)indicated that the core proteins were primarily involved in the neurodegenerationmultiple diseases pathway and cellular processes.Mediating effect analysis identified PRDX4(L)as a potential protein biomarker.ROC curve analysis showed that GNAI1 had the largest area under the curve(AUC=0.889).CONCLUSION:This study identifies 15 differentially expressed proteins in the tear fluid of NTG patients,including PRDX4(L).PRDX4(L)plays a key role in oxidative stress.
