BACKGROUND This case report demonstrates the simultaneous development of a gastrointestinal stromal tumour(GIST)with arteriovenous malformations(AVMs)within the jejunal mesentery.A 74-year-old male presented to the de...BACKGROUND This case report demonstrates the simultaneous development of a gastrointestinal stromal tumour(GIST)with arteriovenous malformations(AVMs)within the jejunal mesentery.A 74-year-old male presented to the department of surgery at our institution with a one-month history of abdominal pain.Contrast-enhanced computed tomography revealed an AVM.During exploratory laparotomy,hyperspectral imaging(HSI)and indocyanine green(ICG)fluorescence were used to evaluate the extent of the tumour and determine the resection margins.Intraoperative imaging confirmed AVM,while histopathological evaluation showed an epithelioid,partially spindle cell GIST.CASE SUMMARY This is the first case reporting the use of HSI and ICG to image GIST intermingled with an AVM.The resection margins were planned using intraoperative analysis of additional optical data.Image-guided surgery enhances the clinician’s knowledge of tissue composition and facilitates tissue differentiation.CONCLUSION Since image-guided surgery is safe,this procedure should increase in popularity among the next generation of surgeons as it is associated with better postoperative outcomes.展开更多
Fingerprints are unique and life-long to everyone, so they occupy very important statuses in forensic science. However, due to the limit of current imaging technologies and instruments, recognition and matching of fin...Fingerprints are unique and life-long to everyone, so they occupy very important statuses in forensic science. However, due to the limit of current imaging technologies and instruments, recognition and matching of fingerprints are mostly based on their level 2 structures（bifurcation, crossover, and etc.）.Moreover, in real-world cases, fingerprints collected in the field are often incomplete or damaged, which adds further difficulty in fingerprint analysis. Quantum dots（QDs） are superior fluorescent imaging agents for latent fingerprints, which can provide both level 2 and level 3（sweat pores） details. Here, we used red-emitting N-acetylcysteine-capped Cd Te QDs as imaging agent for staining of eccrine LFPs. The numbers of level 2 and level 3 features that can be mapped are significantly larger than those obtained by cyanoacrylate fuming, a standard technique being adopted at forensic scene. Therefore, the level 2 and level 3 characteristics from QD-staining were simultaneously extracted for improved fingerprint analysis.A preliminary fingerprint matching based modified Pore Matching algorithm was thus developed based on the integration of both level 2 and level 3 characteristics. Satisfactory results of fingerprint matching were obtained, demonstrating the advantage of the QD-staining for advanced fingerprint analysis.展开更多
Abnormal accumulation of intracellular and extrac-ellularβ-amyloid(Aβ)aggregates is closely related to the pathogenesis of Alzheimer’s disease(AD).In this work,we use quinolinium derivatives with electron-rich anil...Abnormal accumulation of intracellular and extrac-ellularβ-amyloid(Aβ)aggregates is closely related to the pathogenesis of Alzheimer’s disease(AD).In this work,we use quinolinium derivatives with electron-rich aniline substituents as the skeletons to develop a set of spontaneous blinking ffuorophores by the formation of long-lived radicals.These probes can target Aβ_(1−40) aggregates and exhibit strong deep-red emission upon binding to Aβ_(1−40) aggregates.More importantly,at the single-molecule level,these probes display spontaneous blinking,low duty cycle,and high photon output,which are suitable for the nanoscopic imaging of Aβ aggregates in living cells.The assembly process of the Aβ aggregates was then tracked with nanoscopic imaging.The elongation rate on the cell membrane was noticeably fast over that in the solution.This work provides a feasible strategy for the design of spontaneous blinking ffuorophores for Aβ aggregates.展开更多
Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
Terahertz biotechnology has been increasingly applied in various biomedical fields and has especially shown great potential for application in brain sciences.In this article,we review the development of terahertz biot...Terahertz biotechnology has been increasingly applied in various biomedical fields and has especially shown great potential for application in brain sciences.In this article,we review the development of terahertz biotechnology and its applications in the field of neuropsychiatry.Available evidence indicates promising prospects for the use of terahertz spectroscopy and terahertz imaging techniques in the diagnosis of amyloid disease,cerebrovascular disease,glioma,psychiatric disease,traumatic brain injury,and myelin deficit.In vitro and animal experiments have also demonstrated the potential therapeutic value of terahertz technology in some neuropsychiatric diseases.Although the precise underlying mechanism of the interactions between terahertz electromagnetic waves and the biosystem is not yet fully understood,the research progress in this field shows great potential for biomedical noninvasive diagnostic and therapeutic applications.However,the biosafety of terahertz radiation requires further exploration regarding its two-sided efficacy in practical applications.This review demonstrates that terahertz biotechnology has the potential to be a promising method in the field of neuropsychiatry based on its unique advantages.展开更多
Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have ...Understanding the neural underpinning of human gait and balance is one of the most pertinent challenges for 21st-century translational neuroscience due to the profound impact that falls and mobility disturbances have on our aging population.Posture and gait control does not happen automatically,as previously believed,but rather requires continuous involvement of central nervous mechanisms.To effectively exert control over the body,the brain must integrate multiple streams of sensory information,including visual,vestibular,and somatosensory signals.The mechanisms which underpin the integration of these multisensory signals are the principal topic of the present work.Existing multisensory integration theories focus on how failure of cognitive processes thought to be involved in multisensory integration leads to falls in older adults.Insufficient emphasis,however,has been placed on specific contributions of individual sensory modalities to multisensory integration processes and cross-modal interactions that occur between the sensory modalities in relation to gait and balance.In the present work,we review the contributions of somatosensory,visual,and vestibular modalities,along with their multisensory intersections to gait and balance in older adults and patients with Parkinson’s disease.We also review evidence of vestibular contributions to multisensory temporal binding windows,previously shown to be highly pertinent to fall risk in older adults.Lastly,we relate multisensory vestibular mechanisms to potential neural substrates,both at the level of neurobiology(concerning positron emission tomography imaging)and at the level of electrophysiology(concerning electroencephalography).We hope that this integrative review,drawing influence across multiple subdisciplines of neuroscience,paves the way for novel research directions and therapeutic neuromodulatory approaches,to improve the lives of older adults and patients with neurodegenerative diseases.展开更多
Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism....Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.展开更多
Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination...Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.展开更多
The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is i...The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.展开更多
0 INTRODUCTION.According to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 a.m.local time.The epicenter is located at 28.5°N,...0 INTRODUCTION.According to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 a.m.local time.The epicenter is located at 28.5°N,87.45°E,with a depth of~10 km.展开更多
Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ul...Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.展开更多
To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.Howeve...To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.展开更多
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
文摘BACKGROUND This case report demonstrates the simultaneous development of a gastrointestinal stromal tumour(GIST)with arteriovenous malformations(AVMs)within the jejunal mesentery.A 74-year-old male presented to the department of surgery at our institution with a one-month history of abdominal pain.Contrast-enhanced computed tomography revealed an AVM.During exploratory laparotomy,hyperspectral imaging(HSI)and indocyanine green(ICG)fluorescence were used to evaluate the extent of the tumour and determine the resection margins.Intraoperative imaging confirmed AVM,while histopathological evaluation showed an epithelioid,partially spindle cell GIST.CASE SUMMARY This is the first case reporting the use of HSI and ICG to image GIST intermingled with an AVM.The resection margins were planned using intraoperative analysis of additional optical data.Image-guided surgery enhances the clinician’s knowledge of tissue composition and facilitates tissue differentiation.CONCLUSION Since image-guided surgery is safe,this procedure should increase in popularity among the next generation of surgeons as it is associated with better postoperative outcomes.
基金supported by the National Natural Science Foundation of China (Nos. 21475090 and 21522505)
文摘Fingerprints are unique and life-long to everyone, so they occupy very important statuses in forensic science. However, due to the limit of current imaging technologies and instruments, recognition and matching of fingerprints are mostly based on their level 2 structures（bifurcation, crossover, and etc.）.Moreover, in real-world cases, fingerprints collected in the field are often incomplete or damaged, which adds further difficulty in fingerprint analysis. Quantum dots（QDs） are superior fluorescent imaging agents for latent fingerprints, which can provide both level 2 and level 3（sweat pores） details. Here, we used red-emitting N-acetylcysteine-capped Cd Te QDs as imaging agent for staining of eccrine LFPs. The numbers of level 2 and level 3 features that can be mapped are significantly larger than those obtained by cyanoacrylate fuming, a standard technique being adopted at forensic scene. Therefore, the level 2 and level 3 characteristics from QD-staining were simultaneously extracted for improved fingerprint analysis.A preliminary fingerprint matching based modified Pore Matching algorithm was thus developed based on the integration of both level 2 and level 3 characteristics. Satisfactory results of fingerprint matching were obtained, demonstrating the advantage of the QD-staining for advanced fingerprint analysis.
基金supported by National Natural Science Foundation of China(NSFC,Project No.22174079,21974073)Natural Science Foundation of Hunan Province(2022JJ40266)the Scientific Research Foundation of Hunan Provincial Education Department(20A299).
文摘Abnormal accumulation of intracellular and extrac-ellularβ-amyloid(Aβ)aggregates is closely related to the pathogenesis of Alzheimer’s disease(AD).In this work,we use quinolinium derivatives with electron-rich aniline substituents as the skeletons to develop a set of spontaneous blinking ffuorophores by the formation of long-lived radicals.These probes can target Aβ_(1−40) aggregates and exhibit strong deep-red emission upon binding to Aβ_(1−40) aggregates.More importantly,at the single-molecule level,these probes display spontaneous blinking,low duty cycle,and high photon output,which are suitable for the nanoscopic imaging of Aβ aggregates in living cells.The assembly process of the Aβ aggregates was then tracked with nanoscopic imaging.The elongation rate on the cell membrane was noticeably fast over that in the solution.This work provides a feasible strategy for the design of spontaneous blinking ffuorophores for Aβ aggregates.
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金supported by grants from the National Key R&D Program of China,No.2017YFC0909200(to DC)the National Natural Science Foundation of China,No.62075225(to HZ)+1 种基金Zhejiang Provincial Medical Health Science and Technology Project,No.2023XY053(to ZP)Zhejiang Provincial Traditional Chinese Medical Science and Technology Project,No.2023ZL703(to ZP).
文摘Terahertz biotechnology has been increasingly applied in various biomedical fields and has especially shown great potential for application in brain sciences.In this article,we review the development of terahertz biotechnology and its applications in the field of neuropsychiatry.Available evidence indicates promising prospects for the use of terahertz spectroscopy and terahertz imaging techniques in the diagnosis of amyloid disease,cerebrovascular disease,glioma,psychiatric disease,traumatic brain injury,and myelin deficit.In vitro and animal experiments have also demonstrated the potential therapeutic value of terahertz technology in some neuropsychiatric diseases.Although the precise underlying mechanism of the interactions between terahertz electromagnetic waves and the biosystem is not yet fully understood,the research progress in this field shows great potential for biomedical noninvasive diagnostic and therapeutic applications.However,the biosafety of terahertz radiation requires further exploration regarding its two-sided efficacy in practical applications.This review demonstrates that terahertz biotechnology has the potential to be a promising method in the field of neuropsychiatry based on its unique advantages.
文摘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.
基金supported by the Research Project of the Shanghai Health Commission,No.2020YJZX0111(to CZ)the National Natural Science Foundation of China,Nos.82021002(to CZ),82272039(to CZ),82171252(to FL)+1 种基金a grant from the National Health Commission of People’s Republic of China(PRC),No.Pro20211231084249000238(to JW)Medical Innovation Research Project of Shanghai Science and Technology Commission,No.21Y11903300(to JG).
文摘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.
基金supports by the National Natural Science Foundation of China(Nos.82201135)"2015"Cultivation Program for Reserve Talents for Academic Leaders of Nanjing Stomatological School,Medical School of Nanjing University(No.0223A204).
文摘Early correction of childhood malocclusion is timely managing morphological,structural,and functional abnormalities at different dentomaxillofacial developmental stages.The selection of appropriate imaging examination and comprehensive radiological diagnosis and analysis play an important role in early correction of childhood malocclusion.This expert consensus is a collaborative effort by multidisciplinary experts in dentistry across the nation based on the current clinical evidence,aiming to provide general guidance on appropriate imaging examination selection,comprehensive and accurate imaging assessment for early orthodontic treatment patients.
基金supported by the National Natural Science Foundation of China(Grant Nos.82272955 and 22203057)the Natural Science Foundation of Fujian Province(Grant No.2021J011361).
文摘The presence of a positive deep surgical margin in tongue squamous cell carcinoma(TSCC)significantly elevates the risk of local recurrence.Therefore,a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection.In this study,we integrate Raman imaging technology with an artificial intelligence(AI)generative model,proposing an innovative approach for intraoperative margin status diagnosis.This method utilizes Raman imaging to swiftly and non-invasively capture tissue Raman images,which are then transformed into hematoxylin-eosin(H&E)-stained histopathological images using an AI generative model for histopathological diagnosis.The generated H&E-stained images clearly illustrate the tissue’s pathological conditions.Independently reviewed by three pathologists,the overall diagnostic accuracy for distinguishing between tumor tissue and normal muscle tissue reaches 86.7%.Notably,it outperforms current clinical practices,especially in TSCC with positive lymph node metastasis or moderately differentiated grades.This advancement highlights the potential of AI-enhanced Raman imaging to significantly improve intraoperative assessments and surgical margin evaluations,promising a versatile diagnostic tool beyond TSCC.
基金funded by the National Key R&D Program of China(No.2020YFC150071)partly supported by the Shaanxi Province Geoscience Big Data and Geohazard Prevention Innovation Team(2022)and the Research Funds for the Interdisciplinary Projects,CHU(No.300104240914)。
文摘0 INTRODUCTION.According to the China Earthquake Networks Center,an M6.8 earthquake struck Dingri County,Xizang Autonomous Region,China,on 7 January 2025 at 9:05 a.m.local time.The epicenter is located at 28.5°N,87.45°E,with a depth of~10 km.
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
基金upported by the National Natural Science Foundation of China(Grant No.62305184)the Major Key Project of Pengcheng Laboratory(Grant No.PCL2024A1)+1 种基金the Basic and Applied Basic Research Foundation of Guangdong Province(Grant No.2023A1515012932)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.WDZC20220818100259004).
文摘Single-pixel imaging(SPI)enables efficient sensing in challenging conditions.However,the requirement for numerous samplings constrains its practicality.We address the challenge of high-quality SPI reconstruction at ultra-low sampling rates.We develop an alternative optimization with physics and a data-driven diffusion network(APD-Net).It features alternative optimization driven by the learned task-agnostic natural image prior and the task-specific physics prior.During the training stage,APD-Net harnesses the power of diffusion models to capture data-driven statistics of natural signals.In the inference stage,the physics prior is introduced as corrective guidance to ensure consistency between the physics imaging model and the natural image probability distribution.Through alternative optimization,APD-Net reconstructs data-efficient,high-fidelity images that are statistically and physically compliant.To accelerate reconstruction,initializing images with the inverse SPI physical model reduces the need for reconstruction inference from 100 to 30 steps.Through both numerical simulations and real prototype experiments,APD-Net achieves high-quality,full-color reconstructions of complex natural images at a low sampling rate of 1%.In addition,APD-Net’s tuning-free nature ensures robustness across various imaging setups and sampling rates.Our research offers a broadly applicable approach for various applications,including but not limited to medical imaging and industrial inspection.
基金supported by the National Natural Science Foundation of China(No.22276139)the Shanghai’s Municipal State-owned Assets Supervision and Administration Commission(No.2022028).
文摘To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.
基金supported by the National Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
