In recent years,the rapid advancement of artificial intelligence(AI)technology has enabled AI-assisted negative screening to significantly enhance physicians'efficiency through image feature analysis and multimoda...In recent years,the rapid advancement of artificial intelligence(AI)technology has enabled AI-assisted negative screening to significantly enhance physicians'efficiency through image feature analysis and multimodal data modeling,allowing them to focus more on diagnosing positive cases.Meanwhile,multispectral imaging(MSI)integrates spectral and spatial resolution to capture subtle tissue features invisible to the human eye,providing high-resolution data support for pathological analysis.Combining AI technology with MSI and employing quantitative methods to analyze multiband biomarkers(such as absorbance differences in keratin pearls)can effectively improve diagnostic specificity and reduce subjective errors in manual slide interpretation.To address the challenge of identifying negative tissue sections,we developed a discrimination algorithm powered by MSI.We demonstrated its efficacy using cutaneous squamous cell carcinoma(cSCC)as a representative case study.The algorithm achieved 100%accuracy in excluding negative cases and effectively mitigated the false-positive problem caused by cSCC heterogeneity.We constructed a multispectral image(MSI)dataset acquired at 520 nm,600 nm,and 630 nm wavelengths.Subsequently,we employed an optimized MobileViT model for tissue classification and performed comparative analyses against other models.The experimental results showed that our optimized MobileViT model achieved superior performance in identifying negative tissue sections,with a perfect accuracy rate of 100%.Thus,our results confirm the feasibility of integrating MSI with AI to exclude negative cases with perfect accuracy,offering a novel solution to alleviate the workload of pathologists.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
Sulfur dioxide(SO_(2)) and its derivatives have been recognized as harmful environmental pollutants.However,they are often produced during the processing of traditional Chinese medicines,potentially compromising the q...Sulfur dioxide(SO_(2)) and its derivatives have been recognized as harmful environmental pollutants.However,they are often produced during the processing of traditional Chinese medicines,potentially compromising the quality of these medicinal materials and contributing to various health issues.Due to a lack of effective monitoring and imaging tools,the physiological effects of excessive SO_(2) residues in traditional Chinese medicine remain unclear.Therefore,developing a rapid and effective tool for detecting SO_(2) is crucial for understanding its metabolic pathways and effects in vivo.In this study,we developed a near infrared(NIR) and ratiometric fluorescent probe,NIR-RS,which exhibits high sensitivity,selectivity,and rapid response for SO_(2) detection.Notably,NIR-RS accurately quantifies SO_(2) contents in Pinelliae rhizoma(P.rhizoma) samples,with recovery rates from 98.46 % to 102.40 %,and relative standard deviations(RSDs)< 5.0 %.For bioimaging applications,NIR-RS has low cytotoxicity and good mitochondrial-targeting ability,making it suitable for imaging exogenous and endogenous SO_(2) in mitochondria.Additionally,NIR-RS was successfully applied to image SO_(2) content of P.rhizoma samples within cells,revealing that high SO_(2) residue elevated mitochondria adenosine triphosphate(ATP) content,these findings reveal that P.rhizoma with excessive SO_(2) can affect the organism's growth mechanisms through alterations in ATP pathways.In vivo,SO_(2) was found to predominantly accumulate in the liver following gavage with P.rhizoma solution,with accumulation levels increasing in proportion to SO_(2) residue concentration.High SO_(2) concentrations in P.rhizoma can cause pulmonary fibrosis and gastric mucosal damage.This work provides a valuable tool for regulating SO_(2) content in P.rhizoma and may help researcher better understand the metabolism of SO_(2) derivatives and explore their physiological roles in biological systems.展开更多
In vivo imaging of neurodegenerative diseases provides valuable insights into disease mechanisms and potential therapeutic interventions.Many ocular diseases are closely linked to neurodegenerative conditions affectin...In vivo imaging of neurodegenerative diseases provides valuable insights into disease mechanisms and potential therapeutic interventions.Many ocular diseases are closely linked to neurodegenerative conditions affecting the brain,making the eye a unique and accessible model for studying these disorders.The transparency of eyes allows researchers to monitor disease progression non-invasively,offering a window into neural health.展开更多
Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight N...Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.展开更多
Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspectio...Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspection,which requires extensive experience,suffers from low efficiency,and is highly subjective.As the results are presented as radar images,image processing methods can be applied for fast and objective identification.Deep learning-based approaches now offer a robust solution for automated roadbed disease detection.This study proposes an enhanced Faster Region-based Convolutional Neural Networks(R-CNN)framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation(2D-DFT)for frequency-domain feature fusion.A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering,histogram equalization,and binarization.The proposed model segments defect regions,applies binary masking,and fuses frequency-domain features to improve small-target detection under noisy backgrounds.Experimental results show that the improved Faster R-CNN achieves a mean Average Precision(mAP)of 0.92,representing a 0.22 increase over the baseline.Precision improved by 26%while recall remained stable at 87%.The model was further validated on real urban road data,demonstrating robust detection capability even under interference.These findings highlight the potential of combining GPR with deep learning for efficient,non-destructive roadbed health monitoring.展开更多
Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks(GANs).Deepfake technology has many useful uses in education and entertainment,but it also raises a lot of ethical,socia...Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks(GANs).Deepfake technology has many useful uses in education and entertainment,but it also raises a lot of ethical,social,and security issues,such as identity theft,the dissemination of false information,and privacy violations.This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes,with a particular focus on image-based Deepfakes.There are three main types of detection methods:classical,machine learning(ML)and deep learning(DL)-based,and hybrid methods.There are three main types of preventative methods:technical,legal,and moral.The study investigates the effectiveness of several detection approaches,such as convolutional neural networks(CNNs),frequency domain analysis,and hybrid CNN-LSTM models,focusing on the respective advantages and disadvantages of each method.We also look at new technologies like Explainable Artificial Intelligence(XAI)and blockchain-based frameworks.The essay looks at the use of algorithmic protocols,watermarking,and blockchain-based content verification as possible ways to stop certain things from happening.Recent advancements,including adversarial training and anti-Deepfake data generation,are essential because of their potential to alleviate rising concerns.This reviewshows that there aremajor problems,such as the difficulty of improving the capabilities of existing systems,the high running expenses,and the risk of being attacked by enemies.It stresses the importance of working together across fields,including academia,business,and government,to create robust,scalable,and ethical solutions.Themain goals of futurework should be to create lightweight,real-timedetection systems,connect them to large language models(LLMs),and put in place worldwide regulatory frameworks.This essay argues for a complete and varied plan to keep digital information real and build confidence in a time when media is driven by artificial intelligence.It uses both technical and non-technical means.展开更多
With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods ...With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.展开更多
Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patient...Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patients.Methods We conducted a single-center,prospective,parallel-group feasibility randomized controlled trial.We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City,Zhejiang Province,between March 2025 and June 2025,and they were randomly assigned to either the control group(routine care)or the intervention group(routine care plus image recognition-based detection system).The system continuously tracked patients’hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones,notifying on-duty nurses to enable early intervention.System stability was assessed by continuous system uptime;system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies(ADMS).Results All 80 participants completed the intervention,with 40 patients in each group.The baseline characteristics and median observation time of the two groups were balanced(intervention group:48 h/patient vs.control group:49 h/patient).Compared with the control group,the intervention group showed fewer ADMS(2/40 vs.9/40)and detected more risk actions per 100 h(36 vs.25);all system-detected events had corroborating images with complete concordance on manual review,and all nurse-recorded hand-contact events were accurately captured.Conclusions The study demonstrated that the image recognition-based detection system can function stably in clinical settings,providing accurate and continuous surveillance while supporting the early detection of risk actions.By reducing the observation burden and offering real-time cognitive support,the system complements routine nursing care and serves as an additional safety measure in ICU practice.With further optimization and larger multicenter validation,this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.展开更多
Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-...Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.展开更多
To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints ...To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.展开更多
The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textile...The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.展开更多
Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while ob...Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results.The authors have(1)carefully compared performances of most-developed segmentation and object detection methods in localising prostate imaging reporting and data system(PIRADS)-labelled prostate lesions on MRI scans;(2)proposed an additional customised set of lesion-level localisation sensitivity and precision;(3)proposed efficient ways to ensemble the segmentation and object detection methods for improved performances.The ground-truth(GT)perspective lesion-level sensitivity and prediction-perspective lesion-level precision are reported,to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions.The two networks are trained independently on 549 clinical patients data with PIRADS-V2 as GT labels,and tested on 161 internal and 100 external MRI scans.At the lesion level,nnDetection outperforms nnUNet for detecting both PIRADS≥3 and PIRADS≥4 lesions in majority cases.For example,at the average false positive prediction per patient being 3,nnDetection achieves a greater Intersection-of-Union(IoU)-based sensitivity than nnUNet for detecting PIRADS≥3 lesions,being 80.78%�1.50%versus 60.40%�1.64%(p<0.01).At the voxel level,nnUnet is in general superior or comparable to nnDetection.The proposed ensemble methods achieve improved or comparable lesion-level accuracy,in all tested clinical scenarios.For example,at 3 false positives,the lesion-wise ensemble method achieves 82.24%�1.43%sensitivity versus 80.78%�1.50%(nnDetection)and 60.40%�1.64%(nnUNet)for detecting PIRADS≥3 lesions.Consistent conclusions are also drawn from results on the external data set.展开更多
Butyrylcholinesterase(BChE)is a key enzyme in the metabolism of cholinergic compounds.It has been recognized as a key biomarker for many diseases,including liver diseases and Alzheimer’s disease.However,classical met...Butyrylcholinesterase(BChE)is a key enzyme in the metabolism of cholinergic compounds.It has been recognized as a key biomarker for many diseases,including liver diseases and Alzheimer’s disease.However,classical methods for detecting BChE activity suffer from low sensitivity,cumbersome pre-treatment,and poor stability.Chemiluminescence is a promising new method for detecting and imaging the activity of BChE.It has several advantages over traditional methods,including low background interference,high sensitivity,and the absence of external illumination.In this study,we developed a novel BChE-activatable chemiluminescent probe(CL-BChE).It exhibited a significant chemiluminescence enhancement at 525nm upon incubation with BChE.It had a low limit of detection(6.25×10^(−3)U/mL)and was highly selective for BChE.CL-BChE was used to image BChE activity in living cells and tumor-bearing animal models.It was also successfully applied to detect pesticide residue,even under the interference of representative phytochromes and real vegetable samples.Given its high sensitivity,selectivity,and versatility,we believe that CL-BChE will be a promising tool for investigating BChE’s activity in biomedical research as well as other BChE-related scenarios.展开更多
Aggregation-induced emission(AIE)is a unique phenomenon where certain organic materials exhibit enhanced luminescence in their aggregated states,overcoming the typical quenching observed in conventional organic materi...Aggregation-induced emission(AIE)is a unique phenomenon where certain organic materials exhibit enhanced luminescence in their aggregated states,overcoming the typical quenching observed in conventional organic materials.Since its discovery in 2001,AIE has driven significant advances in fields like OLEDs and biological imaging,earning recognition in fundamental research.However,its application in high-energy radiation detection remains underexplored.Organic scintillators,though widely used,face challenges such as low light yield and poor radiation attenuation.AIE materials offer promising solutions by improving light yield,response speed,and radiation attenuation.This review summarizes the design strategies behind AIE scintillators and their very recent applications in X-ray,γ-ray,and fast neutron detection.We highlight their advantages in enhancing detection sensitivity,reducing background noise,and achieving high-resolution imaging.By addressing the current challenges,we believe AIE materials will play a pivotal role in advancing future radiation detection and imaging technologies.展开更多
The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations...The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection,lesion segmentation,and microscopic image analysis,thereby accelerating the development of clinical decision support systems.This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024.It compares YOLO’s performance with othermodels(e.g.,Faster R-CNN,RetinaNet)inmedical contexts,summarizes standard evaluation metrics(e.g.,mean Average Precision(mAP),sensitivity),and analyzes hardware deployment strategies using public datasets such as LUNA16,BraTS,andCheXpert.Thereviewhighlights the impressive performance of YOLO models,particularly from YOLOv5 to YOLOv8,in achieving high precision(up to 99.17%),sensitivity(up to 97.5%),and mAP exceeding 95%in tasks such as lung nodule,breast cancer,and polyp detection.These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications,indicating their ability to enhance clinical workflows.However,the study also identifies key challenges,including high small-object miss rates,limited generalization in low-contrast images,scarcity of annotated data,and model interpretability issues.Finally,the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.展开更多
This study explores the application of X-ray-induced photochromism and photoluminescence in optical storage,anti-counterfeiting,non-destructive testing,and high-resolution X-ray detection and imaging.Ba_(2)LaNbO_(6):B...This study explores the application of X-ray-induced photochromism and photoluminescence in optical storage,anti-counterfeiting,non-destructive testing,and high-resolution X-ray detection and imaging.Ba_(2)LaNbO_(6):Bi,Eu phosphors were synthesized,with Bi enhancing X-ray-induced photochromic prop-erties.Under X-ray irradiation,the phosphors transfer from white to red in bright field conditions and emit red photoluminescence in dark field conditions.Exposure to 470 nm ultraviolet light induces rapid bleaching.The mechanisms of photochromism and photoluminescence,particularly Bi's role as a colorant,were systematically investigated.The Ba_(2)LaNbO_(6):Bi,Eu phosphors film achieves high resolution,high-lighting its potential for X-ray imaging and non-destructive testing.Furthermore,the flexible Ba_(2)LaNbO_(6):Bi,Eu film supports dual-mode imaging and detection,addressing the limitations of traditional flat dis-plays in 3D imaging.展开更多
Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribu...Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.展开更多
Manganese(Mn),an essential trace element in the human body,plays critical roles in many biological processes.Recent studies have discovered that Mn^(2+)may promote or directly activate the cGAS-STING pathway,thereby s...Manganese(Mn),an essential trace element in the human body,plays critical roles in many biological processes.Recent studies have discovered that Mn^(2+)may promote or directly activate the cGAS-STING pathway,thereby subsequently initiating the natural immune response and augmenting antitumor therapy.However,the current lack of accurate methods for Mn^(2+)determination in cells significantly limits their mechanism investigation;hence,it is urgent to establish novel tools to detect Mn^(2+)in cells.In this study,the dual-emission carbon dots were initially synthesized via the one-pot hydrothermal method employing L-aspartic acid and p-phenylenediamine as raw materials.In the presence of Mn^(2+),the emission peak centered at 350 nm exhibited significant enhancement,whereas another peak at 610 nm remained stable.Consequently,a ratiometric sensor for Mn^(2+)determination was established using the signal at 350 nm as the responsive signal and the signal at 610 nm as an internal reference.Under the optimal condition,a good linear relationship was achieved between the F350/F610 value and Mn^(2+)concentration ranging from 0.9 to 15μmol/L,with a calculated LOD of 61 nmol/L.Benefiting from the special Mn^(2+)-induced ratiometric approach,this method demonstrates outstanding sensitivity,selectivity,and stability,rendering it applicable for Mn^(2+)determination in complex biological samples,as well as Mn^(2+)imaging in MKN-45 and LO2 cells.展开更多
基金funded by the Natural Science Foundation of Shanghai Municipality(No.21ZR1440500)the Shanghai Science and Technology Commission(Grant No.21S31902700).
文摘In recent years,the rapid advancement of artificial intelligence(AI)technology has enabled AI-assisted negative screening to significantly enhance physicians'efficiency through image feature analysis and multimodal data modeling,allowing them to focus more on diagnosing positive cases.Meanwhile,multispectral imaging(MSI)integrates spectral and spatial resolution to capture subtle tissue features invisible to the human eye,providing high-resolution data support for pathological analysis.Combining AI technology with MSI and employing quantitative methods to analyze multiband biomarkers(such as absorbance differences in keratin pearls)can effectively improve diagnostic specificity and reduce subjective errors in manual slide interpretation.To address the challenge of identifying negative tissue sections,we developed a discrimination algorithm powered by MSI.We demonstrated its efficacy using cutaneous squamous cell carcinoma(cSCC)as a representative case study.The algorithm achieved 100%accuracy in excluding negative cases and effectively mitigated the false-positive problem caused by cSCC heterogeneity.We constructed a multispectral image(MSI)dataset acquired at 520 nm,600 nm,and 630 nm wavelengths.Subsequently,we employed an optimized MobileViT model for tissue classification and performed comparative analyses against other models.The experimental results showed that our optimized MobileViT model achieved superior performance in identifying negative tissue sections,with a perfect accuracy rate of 100%.Thus,our results confirm the feasibility of integrating MSI with AI to exclude negative cases with perfect accuracy,offering a novel solution to alleviate the workload of pathologists.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
基金supported by the Natural Science Foundation of Hubei Province (Nos.2023AFB376 and 2024AFD287)National Key Research and Development Program (No.2023YFC3503804)the National Natural Science Foundation of China (No.22077044)。
文摘Sulfur dioxide(SO_(2)) and its derivatives have been recognized as harmful environmental pollutants.However,they are often produced during the processing of traditional Chinese medicines,potentially compromising the quality of these medicinal materials and contributing to various health issues.Due to a lack of effective monitoring and imaging tools,the physiological effects of excessive SO_(2) residues in traditional Chinese medicine remain unclear.Therefore,developing a rapid and effective tool for detecting SO_(2) is crucial for understanding its metabolic pathways and effects in vivo.In this study,we developed a near infrared(NIR) and ratiometric fluorescent probe,NIR-RS,which exhibits high sensitivity,selectivity,and rapid response for SO_(2) detection.Notably,NIR-RS accurately quantifies SO_(2) contents in Pinelliae rhizoma(P.rhizoma) samples,with recovery rates from 98.46 % to 102.40 %,and relative standard deviations(RSDs)< 5.0 %.For bioimaging applications,NIR-RS has low cytotoxicity and good mitochondrial-targeting ability,making it suitable for imaging exogenous and endogenous SO_(2) in mitochondria.Additionally,NIR-RS was successfully applied to image SO_(2) content of P.rhizoma samples within cells,revealing that high SO_(2) residue elevated mitochondria adenosine triphosphate(ATP) content,these findings reveal that P.rhizoma with excessive SO_(2) can affect the organism's growth mechanisms through alterations in ATP pathways.In vivo,SO_(2) was found to predominantly accumulate in the liver following gavage with P.rhizoma solution,with accumulation levels increasing in proportion to SO_(2) residue concentration.High SO_(2) concentrations in P.rhizoma can cause pulmonary fibrosis and gastric mucosal damage.This work provides a valuable tool for regulating SO_(2) content in P.rhizoma and may help researcher better understand the metabolism of SO_(2) derivatives and explore their physiological roles in biological systems.
基金supported[in part]by the IntramuralResearch Program of the National Institutes ofHealth(NIH)(to KJM),and also supported by theOffice by the Office of the Assistant Secretary ofDefense for Health Affairs and the Defense HealthAgency J9,Research and Development Directorate,through the Vision Research Program under AwardNo.(CDMRPL-18-0-VR180205 to KJM and FMN-N).
文摘In vivo imaging of neurodegenerative diseases provides valuable insights into disease mechanisms and potential therapeutic interventions.Many ocular diseases are closely linked to neurodegenerative conditions affecting the brain,making the eye a unique and accessible model for studying these disorders.The transparency of eyes allows researchers to monitor disease progression non-invasively,offering a window into neural health.
基金funded by the Natural Science Foundation of Hunan Province(Grant No.2025JJ80352)the National Natural Science Foundation Project of China(Grant No.32271879).
文摘Detecting small forest fire targets in unmanned aerial vehicle(UAV)images is difficult,as flames typically cover only a very limited portion of the visual scene.This study proposes Context-guided Compact Lightweight Network(CCLNet),an end-to-end lightweight model designed to detect small forest fire targets while ensuring efficient inference on devices with constrained computational resources.CCLNet employs a three-stage network architecture.Its key components include three modules.C3F-Convolutional Gated Linear Unit(C3F-CGLU)performs selective local feature extraction while preserving fine-grained high-frequency flame details.Context-Guided Feature Fusion Module(CGFM)replaces plain concatenation with triplet-attention interactions to emphasize subtle flame patterns.Lightweight Shared Convolution with Separated Batch Normalization Detection(LSCSBD)reduces parameters through separated batch normalization while maintaining scale-specific statistics.We build TF-11K,an 11,139-image dataset combining 9139 self-collected UAV images from subtropical forests and 2000 re-annotated frames from the FLAME dataset.On TF-11K,CCLNet attains 85.8%mAP@0.5,45.5%mean Average Precision(mAP)@[0.5:0.95],87.4%precision,and 79.1%recall with 2.21 M parameters and 5.7 Giga Floating-point Operations Per Second(GFLOPs).The ablation study confirms that each module contributes to both accuracy and efficiency.Cross-dataset evaluation on DFS yields 77.5%mAP@0.5 and 42.3%mAP@[0.5:0.95],indicating good generalization to unseen scenes.These results suggest that CCLNet offers a practical balance between accuracy and speed for small-target forest fire monitoring with UAVs.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金supported by the Second Batch of Key Textbook Construction Projects of“14th Five-Year Plan”of Zhejiang Vocational Colleges(SZDJC-2412).
文摘Roadbed disease detection is essential for maintaining road functionality.Ground penetrating radar(GPR)enables non-destructive detection without drilling.However,current identification often relies on manual inspection,which requires extensive experience,suffers from low efficiency,and is highly subjective.As the results are presented as radar images,image processing methods can be applied for fast and objective identification.Deep learning-based approaches now offer a robust solution for automated roadbed disease detection.This study proposes an enhanced Faster Region-based Convolutional Neural Networks(R-CNN)framework integrating ResNet-50 as the backbone and two-dimensional discrete Fourier spectrum transformation(2D-DFT)for frequency-domain feature fusion.A dedicated GPR image dataset comprising 1650 annotated images was constructed and augmented to 6600 images via median filtering,histogram equalization,and binarization.The proposed model segments defect regions,applies binary masking,and fuses frequency-domain features to improve small-target detection under noisy backgrounds.Experimental results show that the improved Faster R-CNN achieves a mean Average Precision(mAP)of 0.92,representing a 0.22 increase over the baseline.Precision improved by 26%while recall remained stable at 87%.The model was further validated on real urban road data,demonstrating robust detection capability even under interference.These findings highlight the potential of combining GPR with deep learning for efficient,non-destructive roadbed health monitoring.
基金funded by the Arab Open University,Riyadh,Saudi Arabia.
文摘Deepfake is a sort of fake media made by advanced AI methods like Generative Adversarial Networks(GANs).Deepfake technology has many useful uses in education and entertainment,but it also raises a lot of ethical,social,and security issues,such as identity theft,the dissemination of false information,and privacy violations.This study seeks to provide a comprehensive analysis of several methods for identifying and circumventing Deepfakes,with a particular focus on image-based Deepfakes.There are three main types of detection methods:classical,machine learning(ML)and deep learning(DL)-based,and hybrid methods.There are three main types of preventative methods:technical,legal,and moral.The study investigates the effectiveness of several detection approaches,such as convolutional neural networks(CNNs),frequency domain analysis,and hybrid CNN-LSTM models,focusing on the respective advantages and disadvantages of each method.We also look at new technologies like Explainable Artificial Intelligence(XAI)and blockchain-based frameworks.The essay looks at the use of algorithmic protocols,watermarking,and blockchain-based content verification as possible ways to stop certain things from happening.Recent advancements,including adversarial training and anti-Deepfake data generation,are essential because of their potential to alleviate rising concerns.This reviewshows that there aremajor problems,such as the difficulty of improving the capabilities of existing systems,the high running expenses,and the risk of being attacked by enemies.It stresses the importance of working together across fields,including academia,business,and government,to create robust,scalable,and ethical solutions.Themain goals of futurework should be to create lightweight,real-timedetection systems,connect them to large language models(LLMs),and put in place worldwide regulatory frameworks.This essay argues for a complete and varied plan to keep digital information real and build confidence in a time when media is driven by artificial intelligence.It uses both technical and non-technical means.
文摘With the rapid expansion of drone applications,accurate detection of objects in aerial imagery has become crucial for intelligent transportation,urban management,and emergency rescue missions.However,existing methods face numerous challenges in practical deployment,including scale variation handling,feature degradation,and complex backgrounds.To address these issues,we propose Edge-enhanced and Detail-Capturing You Only Look Once(EHDC-YOLO),a novel framework for object detection in Unmanned Aerial Vehicle(UAV)imagery.Based on the You Only Look Once version 11 nano(YOLOv11n)baseline,EHDC-YOLO systematically introduces several architectural enhancements:(1)a Multi-Scale Edge Enhancement(MSEE)module that leverages multi-scale pooling and edge information to enhance boundary feature extraction;(2)an Enhanced Feature Pyramid Network(EFPN)that integrates P2-level features with Cross Stage Partial(CSP)structures and OmniKernel convolutions for better fine-grained representation;and(3)Dynamic Head(DyHead)with multi-dimensional attention mechanisms for enhanced cross-scale modeling and perspective adaptability.Comprehensive experiments on the Vision meets Drones for Detection(VisDrone-DET)2019 dataset demonstrate that EHDC-YOLO achieves significant improvements,increasing mean Average Precision(mAP)@0.5 from 33.2%to 46.1%(an absolute improvement of 12.9 percentage points)and mAP@0.5:0.95 from 19.5%to 28.0%(an absolute improvement of 8.5 percentage points)compared with the YOLOv11n baseline,while maintaining a reasonable parameter count(2.81 M vs the baseline’s 2.58 M).Further ablation studies confirm the effectiveness of each proposed component,while visualization results highlight EHDC-YOLO’s superior performance in detecting objects and handling occlusions in complex drone scenarios.
文摘Objectives This study aimed to design and evaluate a detection system for the accidental dislodgement of head-and-neck medical supplies through hand position recognition and tracking in Intensive Care Unit(ICU)patients.Methods We conducted a single-center,prospective,parallel-group feasibility randomized controlled trial.We recruited 80 participants using convenience sampling from the ICU of a hospital in Ningbo City,Zhejiang Province,between March 2025 and June 2025,and they were randomly assigned to either the control group(routine care)or the intervention group(routine care plus image recognition-based detection system).The system continuously tracked patients’hand positions via bedside cameras and generated real-time alarms when hands entered predefined risk zones,notifying on-duty nurses to enable early intervention.System stability was assessed by continuous system uptime;system performance and clinical feasibility were evaluated by the frequencies of risk actions and accidental dislodgement of medical supplies(ADMS).Results All 80 participants completed the intervention,with 40 patients in each group.The baseline characteristics and median observation time of the two groups were balanced(intervention group:48 h/patient vs.control group:49 h/patient).Compared with the control group,the intervention group showed fewer ADMS(2/40 vs.9/40)and detected more risk actions per 100 h(36 vs.25);all system-detected events had corroborating images with complete concordance on manual review,and all nurse-recorded hand-contact events were accurately captured.Conclusions The study demonstrated that the image recognition-based detection system can function stably in clinical settings,providing accurate and continuous surveillance while supporting the early detection of risk actions.By reducing the observation burden and offering real-time cognitive support,the system complements routine nursing care and serves as an additional safety measure in ICU practice.With further optimization and larger multicenter validation,this approach could have the potential to make a significant contribution to the development of smart ICUs and the broader digital transformation of nursing care.
文摘Wind turbine blade defect detection faces persistent challenges in separating small,low-contrast surface faults from complex backgrounds while maintaining reliability under variable illumination and viewpoints.Conven-tional image-processing pipelines struggle with scalability and robustness,and recent deep learning methods remain sensitive to class imbalance and acquisition variability.This paper introduces TurbineBladeDetNet,a convolutional architecture combining dual-attention mechanisms with multi-path feature extraction for detecting five distinct blade fault types.Our approach employs both channel-wise and spatial attention modules alongside an Albumentations-driven augmentation strategy to handle dataset imbalance and capture condition variability.The model achieves 97.14%accuracy,98.65%precision,and 98.68%recall,yielding a 98.66%F1-score with 0.0110 s inference time.Class-specific analysis shows uniformly high sensitivity and specificity;lightning damage reaches 99.80%for sensitivity,precision,and F1-score,and crack achieves perfect precision and specificity with a 98.94%F1-score.Comparative evaluation against recent wind-turbine inspection approaches indicates higher performance in both accuracy and F1-score.The resulting balance of sensitivity and specificity limits both missed defects and false alarms,supporting reliable deployment in routine unmanned aerial vehicle(UAV)inspection.
基金supported by the National Key R&D Program of China(No.2023YFC3081200)the National Natural Science Foundation of China(No.42077264)。
文摘To map the rock joints in the underground rock mass,a method was proposed to semiautomatically detect the rock joints from borehole imaging logs using a deep learning algorithm.First,450 images containing rock joints were selected from borehole ZKZ01 in the Rumei hydropower station.These images were labeled to establish ground truth which was subdivided into training,validation,and testing data.Second,the YOLO v2 model with optimal parameter settings was constructed.Third,the training and validation data were used for model training,while the test data was used to generate the precision-recall curve for prediction evaluation.Fourth,the trained model was applied to a new borehole ZKZ02 to verify the feasibility of the model.There were 12 rock joints detected from the selected images in borehole ZKZ02 and four geometric parameters for each rock joint were determined by sinusoidal curve fitting.The average precision of the trained model reached 0.87.
基金supported in part by the Six Talent Peaks Project in Jiangsu Province under Grant 013040315in part by the China Textile Industry Federation Science and Technology Guidance Project under Grant 2017107+1 种基金in part by the National Natural Science Foundation of China under Grant 31570714in part by the China Scholarship Council under Grant 202108320290。
文摘The cleanliness of seed cotton plays a critical role in the pre-treatment of cotton textiles,and the removal of impurity during the harvesting process directly determines the quality and market value of cotton textiles.By fusing band combination optimization with deep learning,this study aims to achieve more efficient and accurate detection of film impurities in seed cotton on the production line.By applying hyperspectral imaging and a one-dimensional deep learning algorithm,we detect and classify impurities in seed cotton after harvest.The main categories detected include pure cotton,conveyor belt,film covering seed cotton,and film adhered to the conveyor belt.The proposed method achieves an impurity detection rate of 99.698%.To further ensure the feasibility and practical application potential of this strategy,we compare our results against existing mainstream methods.In addition,the model shows excellent recognition performance on pseudo-color images of real samples.With a processing time of 11.764μs per pixel from experimental data,it shows a much improved speed requirement while maintaining the accuracy of real production lines.This strategy provides an accurate and efficient method for removing impurities during cotton processing.
基金National Natural Science Foundation of China,Grant/Award Number:62303275International Alliance for Cancer Early Detection,Grant/Award Numbers:C28070/A30912,C73666/A31378Wellcome/EPSRC Centre for Interventional and Surgical Sciences,Grant/Award Number:203145Z/16/Z。
文摘Automated prostate cancer detection in magnetic resonance imaging(MRI)scans is of significant importance for cancer patient management.Most existing computer-aided diagnosis systems adopt segmentation methods while object detection approaches recently show promising results.The authors have(1)carefully compared performances of most-developed segmentation and object detection methods in localising prostate imaging reporting and data system(PIRADS)-labelled prostate lesions on MRI scans;(2)proposed an additional customised set of lesion-level localisation sensitivity and precision;(3)proposed efficient ways to ensemble the segmentation and object detection methods for improved performances.The ground-truth(GT)perspective lesion-level sensitivity and prediction-perspective lesion-level precision are reported,to quantify the ratios of true positive voxels being detected by algorithms over the number of voxels in the GT labelled regions and predicted regions.The two networks are trained independently on 549 clinical patients data with PIRADS-V2 as GT labels,and tested on 161 internal and 100 external MRI scans.At the lesion level,nnDetection outperforms nnUNet for detecting both PIRADS≥3 and PIRADS≥4 lesions in majority cases.For example,at the average false positive prediction per patient being 3,nnDetection achieves a greater Intersection-of-Union(IoU)-based sensitivity than nnUNet for detecting PIRADS≥3 lesions,being 80.78%�1.50%versus 60.40%�1.64%(p<0.01).At the voxel level,nnUnet is in general superior or comparable to nnDetection.The proposed ensemble methods achieve improved or comparable lesion-level accuracy,in all tested clinical scenarios.For example,at 3 false positives,the lesion-wise ensemble method achieves 82.24%�1.43%sensitivity versus 80.78%�1.50%(nnDetection)and 60.40%�1.64%(nnUNet)for detecting PIRADS≥3 lesions.Consistent conclusions are also drawn from results on the external data set.
基金the financial support from the National Natural Science Foundation of China(Nos.82272067,81974386,and M-0696)Natural Science Foundation of Hunan Province(Nos.2022JJ80052 and 2022JJ40656)the Innovation Fund for Postgraduate Students of Central South University(No.2023ZZTS0609)。
文摘Butyrylcholinesterase(BChE)is a key enzyme in the metabolism of cholinergic compounds.It has been recognized as a key biomarker for many diseases,including liver diseases and Alzheimer’s disease.However,classical methods for detecting BChE activity suffer from low sensitivity,cumbersome pre-treatment,and poor stability.Chemiluminescence is a promising new method for detecting and imaging the activity of BChE.It has several advantages over traditional methods,including low background interference,high sensitivity,and the absence of external illumination.In this study,we developed a novel BChE-activatable chemiluminescent probe(CL-BChE).It exhibited a significant chemiluminescence enhancement at 525nm upon incubation with BChE.It had a low limit of detection(6.25×10^(−3)U/mL)and was highly selective for BChE.CL-BChE was used to image BChE activity in living cells and tumor-bearing animal models.It was also successfully applied to detect pesticide residue,even under the interference of representative phytochromes and real vegetable samples.Given its high sensitivity,selectivity,and versatility,we believe that CL-BChE will be a promising tool for investigating BChE’s activity in biomedical research as well as other BChE-related scenarios.
基金financial support from National Natural Science Foundation of China(No.22175156)the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(No.162301202692).
文摘Aggregation-induced emission(AIE)is a unique phenomenon where certain organic materials exhibit enhanced luminescence in their aggregated states,overcoming the typical quenching observed in conventional organic materials.Since its discovery in 2001,AIE has driven significant advances in fields like OLEDs and biological imaging,earning recognition in fundamental research.However,its application in high-energy radiation detection remains underexplored.Organic scintillators,though widely used,face challenges such as low light yield and poor radiation attenuation.AIE materials offer promising solutions by improving light yield,response speed,and radiation attenuation.This review summarizes the design strategies behind AIE scintillators and their very recent applications in X-ray,γ-ray,and fast neutron detection.We highlight their advantages in enhancing detection sensitivity,reducing background noise,and achieving high-resolution imaging.By addressing the current challenges,we believe AIE materials will play a pivotal role in advancing future radiation detection and imaging technologies.
基金supported by the National Natural Science Foundation of China under grant number 62066016the Natural Science Foundation of Hunan Province of China under grant number 2024JJ7395+2 种基金the Scientific Research Project of Education Department of Hunan Province of China under grant number 22B0549International and Regional Science and Technology Cooperation and Exchange Program of the Hunan Association for Science and Technology under grant number 025SKX-KJ-04Hunan Province Undergraduate Innovation and Entrepreneurship Training Program(grant number S202410531015).
文摘The YOLO(You Only Look Once)series,a leading single-stage object detection framework,has gained significant prominence in medical-image analysis due to its real-time efficiency and robust performance.Recent iterations of YOLO have further enhanced its accuracy and reliability in critical clinical tasks such as tumor detection,lesion segmentation,and microscopic image analysis,thereby accelerating the development of clinical decision support systems.This paper systematically reviews advances in YOLO-based medical object detection from 2018 to 2024.It compares YOLO’s performance with othermodels(e.g.,Faster R-CNN,RetinaNet)inmedical contexts,summarizes standard evaluation metrics(e.g.,mean Average Precision(mAP),sensitivity),and analyzes hardware deployment strategies using public datasets such as LUNA16,BraTS,andCheXpert.Thereviewhighlights the impressive performance of YOLO models,particularly from YOLOv5 to YOLOv8,in achieving high precision(up to 99.17%),sensitivity(up to 97.5%),and mAP exceeding 95%in tasks such as lung nodule,breast cancer,and polyp detection.These results demonstrate the significant potential of YOLO models for early disease detection and real-time clinical applications,indicating their ability to enhance clinical workflows.However,the study also identifies key challenges,including high small-object miss rates,limited generalization in low-contrast images,scarcity of annotated data,and model interpretability issues.Finally,the potential future research directions are also proposed to address these challenges and further advance the application of YOLO models in healthcare.
基金supported by the Key Project of the National Natural Science Foundation of China-Yunnan Joint Fund(No.U2102215)National Natural Science Foundation(No.52472002)+5 种基金Science and Technology Project of Southwest Joint Graduate School of Yunnan Province(No.202302A0370008)2024 Industrial Innovation Talent Support Project(Preparation of luminous materials,performance control and application in plateau agriculture,No.YFGRC202407)National Natural Science Foundation of High-End Foreign Expert Introduction Plan(No.G2022039008L)Academician Workstation of Cherkasova Tatiana in Yunnan Province(No.202305AF150099)Yunnan Province Major Science and Technology Special Plan(No.202302AB080005)and UTS Chancellor’s Research Fellowship Program(No.J.L.,PRO22-15457)the National Health and Medical Research Council(No.J.L.,2025442).
文摘This study explores the application of X-ray-induced photochromism and photoluminescence in optical storage,anti-counterfeiting,non-destructive testing,and high-resolution X-ray detection and imaging.Ba_(2)LaNbO_(6):Bi,Eu phosphors were synthesized,with Bi enhancing X-ray-induced photochromic prop-erties.Under X-ray irradiation,the phosphors transfer from white to red in bright field conditions and emit red photoluminescence in dark field conditions.Exposure to 470 nm ultraviolet light induces rapid bleaching.The mechanisms of photochromism and photoluminescence,particularly Bi's role as a colorant,were systematically investigated.The Ba_(2)LaNbO_(6):Bi,Eu phosphors film achieves high resolution,high-lighting its potential for X-ray imaging and non-destructive testing.Furthermore,the flexible Ba_(2)LaNbO_(6):Bi,Eu film supports dual-mode imaging and detection,addressing the limitations of traditional flat dis-plays in 3D imaging.
基金supported by the National Natural Science Foundation of China(Nos.12205271,12075217,U20B2011,and 51978218)Sichuan Science and Technology Program(No.2019ZDZX0010)the National Key R&D Program of China(No.2022YFA1604002).
文摘Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.
基金Supported by National Natural Science Foundation of China(22264023)Natural Science Foundation of Shaanxi Province(2024JC-YBQN-0150)+2 种基金Yan'an Science and Technology Bureau Project(2023-SFGG-057)Scientific Research Projects of Education Department of Shaanxi Province(22JK0614)PhD Start Fund of Yan'an University(YDBK2022-15)。
文摘Manganese(Mn),an essential trace element in the human body,plays critical roles in many biological processes.Recent studies have discovered that Mn^(2+)may promote or directly activate the cGAS-STING pathway,thereby subsequently initiating the natural immune response and augmenting antitumor therapy.However,the current lack of accurate methods for Mn^(2+)determination in cells significantly limits their mechanism investigation;hence,it is urgent to establish novel tools to detect Mn^(2+)in cells.In this study,the dual-emission carbon dots were initially synthesized via the one-pot hydrothermal method employing L-aspartic acid and p-phenylenediamine as raw materials.In the presence of Mn^(2+),the emission peak centered at 350 nm exhibited significant enhancement,whereas another peak at 610 nm remained stable.Consequently,a ratiometric sensor for Mn^(2+)determination was established using the signal at 350 nm as the responsive signal and the signal at 610 nm as an internal reference.Under the optimal condition,a good linear relationship was achieved between the F350/F610 value and Mn^(2+)concentration ranging from 0.9 to 15μmol/L,with a calculated LOD of 61 nmol/L.Benefiting from the special Mn^(2+)-induced ratiometric approach,this method demonstrates outstanding sensitivity,selectivity,and stability,rendering it applicable for Mn^(2+)determination in complex biological samples,as well as Mn^(2+)imaging in MKN-45 and LO2 cells.
