AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A...AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.展开更多
Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step...Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.展开更多
AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbi...AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbil,Iraq.The target population consisted of high school students selected through a multi-stage cluster sampling method.Comprehensive visual examinations were performed for all students,including measurement of uncorrected and corrected visual acuity,objective and subjective refraction,and distance and near cover tests.NPC was evaluated using a single 6/12 visual target mounted on a centrally positioned Gulden fixation stick.Ocular symptoms were investigated through interviews.RESULTS:Of the 996 selected students,921 participated in the study.Of them,543(58.96%)were female,and their ages ranged from 13 to 22y.The prevalence of tropia was 3.58%[95%confidence interval(CI):2.38%-4.78%],observed in 3.44%of males and 3.68%of females.Exotropia(1.95%,95%CI:1.06%-2.85%)was more common than esotropia(1.52%,95%CI:0.73%-2.31%).The 15.42%(95%CI:13.09%-17.75%)of students had phoria.Exophoria(13.79%,95%CI:11.56%-16.02%)was significantly more prevalent than esophoria(1.63%,95%CI:0.81%-2.45%).The prevalence of NPC abnormality in the total study population was 24.97%(95%CI:22.18%-27.77%).It was 26.72%(95%CI:22.26%-31.18%)in males and 23.76%(95%CI:20.18%-27.34%)in females(P=0.307).The most common symptom in phoria was headache(86.62%,95%CI:81.02%-92.22%),followed by tired or sore eyes(61.97%,95%CI:53.99%-69.96%).The most common symptoms in tropia were blurry vision(93.94%,95%CI:79.77%-99.26%)and difficulty concentrating(87.88%,95%CI:76.74%-99.01%).CONCLUSION:Among Erbil’s high school students,the prevalence of strabismus,particularly the exodeviation type,is relatively high,and a significant percentage of students have NPC abnormalities.Addressing and correcting these binocular vision problems,due to their associated visual symptoms,can lead to an improvement in students’quality of life and academic performance.展开更多
Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone t...Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.展开更多
基金Supported by Ongoing Research Funding Program(ORFFT-2025-054-1),King Saud University,Riyadh,Saudi Arabia.
文摘AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.
文摘Over the past decade,large-scale pre-trained autoregressive and diffusion models rejuvenated the field of text-guided image generation.However,these models require enormous datasets and parameters,and their multi-step generation processes are often inefficient and difficult to control.To address these challenges,we propose CAFE-GAN,a CLIP-Projected GAN with Attention-Aware Generation and Multi-Scale Discrimination,which incorporates a pretrained CLIP model along with several key architectural innovations.First,we embed a coordinate attention mechanism into the generator to capture long-range dependencies and enhance feature representation.Second,we introduce a trainable linear projection layer after the CLIP text encoder,which aligns textual embeddings with the generator’s semantic space.Third,we design a multi-scale discriminator that leverages pre-trained visual features and integrates a feature regularization strategy,thereby improving training stability and discrimination performance.Experiments on the CUB and COCO datasets demonstrate that CAFE-GAN outperforms existing text-to-image generation methods,achieving lower Fréchet Inception Distance(FID)scores and generating images with superior visual quality and semantic fidelity,with FID scores of 9.84 and 5.62 on the CUB and COCO datasets,respectively,surpassing current state-of-the-art text-to-image models by varying degrees.These findings offer valuable insights for future research on efficient,controllable text-to-image synthesis.
文摘AIM:To determine the prevalence of tropia,phoria,and abnormality of near point of convergence(NPC),along with associated ocular symptoms,in high school students.METHODS:This cross-sectional study was conducted in Erbil,Iraq.The target population consisted of high school students selected through a multi-stage cluster sampling method.Comprehensive visual examinations were performed for all students,including measurement of uncorrected and corrected visual acuity,objective and subjective refraction,and distance and near cover tests.NPC was evaluated using a single 6/12 visual target mounted on a centrally positioned Gulden fixation stick.Ocular symptoms were investigated through interviews.RESULTS:Of the 996 selected students,921 participated in the study.Of them,543(58.96%)were female,and their ages ranged from 13 to 22y.The prevalence of tropia was 3.58%[95%confidence interval(CI):2.38%-4.78%],observed in 3.44%of males and 3.68%of females.Exotropia(1.95%,95%CI:1.06%-2.85%)was more common than esotropia(1.52%,95%CI:0.73%-2.31%).The 15.42%(95%CI:13.09%-17.75%)of students had phoria.Exophoria(13.79%,95%CI:11.56%-16.02%)was significantly more prevalent than esophoria(1.63%,95%CI:0.81%-2.45%).The prevalence of NPC abnormality in the total study population was 24.97%(95%CI:22.18%-27.77%).It was 26.72%(95%CI:22.26%-31.18%)in males and 23.76%(95%CI:20.18%-27.34%)in females(P=0.307).The most common symptom in phoria was headache(86.62%,95%CI:81.02%-92.22%),followed by tired or sore eyes(61.97%,95%CI:53.99%-69.96%).The most common symptoms in tropia were blurry vision(93.94%,95%CI:79.77%-99.26%)and difficulty concentrating(87.88%,95%CI:76.74%-99.01%).CONCLUSION:Among Erbil’s high school students,the prevalence of strabismus,particularly the exodeviation type,is relatively high,and a significant percentage of students have NPC abnormalities.Addressing and correcting these binocular vision problems,due to their associated visual symptoms,can lead to an improvement in students’quality of life and academic performance.
文摘Lung cancer remains a major global health challenge,with early diagnosis crucial for improved patient survival.Traditional diagnostic techniques,including manual histopathology and radiological assessments,are prone to errors and variability.Deep learning methods,particularly Vision Transformers(ViT),have shown promise for improving diagnostic accuracy by effectively extracting global features.However,ViT-based approaches face challenges related to computational complexity and limited generalizability.This research proposes the DualSet ViT-PSO-SVM framework,integrating aViTwith dual attentionmechanisms,Particle Swarm Optimization(PSO),and SupportVector Machines(SVM),aiming for efficient and robust lung cancer classification acrossmultiple medical image datasets.The study utilized three publicly available datasets:LIDC-IDRI,LUNA16,and TCIA,encompassing computed tomography(CT)scans and histopathological images.Data preprocessing included normalization,augmentation,and segmentation.Dual attention mechanisms enhanced ViT’s feature extraction capabilities.PSO optimized feature selection,and SVM performed classification.Model performance was evaluated on individual and combined datasets,benchmarked against CNN-based and standard ViT approaches.The DualSet ViT-PSO-SVM significantly outperformed existing methods,achieving superior accuracy rates of 97.85%(LIDC-IDRI),98.32%(LUNA16),and 96.75%(TCIA).Crossdataset evaluations demonstrated strong generalization capabilities and stability across similar imagingmodalities.The proposed framework effectively bridges advanced deep learning techniques with clinical applicability,offering a robust diagnostic tool for lung cancer detection,reducing complexity,and improving diagnostic reliability and interpretability.
