FQ-UWF: Unpaired Generative Image Enhancement for Fundus Quality Ultra-Widefield Retinal Images.

Ultra-widefield (UWF) retinal imaging stands as a pivotal modality for detecting major eye diseases such as diabetic retinopathy and retinal detachment. However, UWF exhibits a well-documented limitation in terms of low resolution and artifacts in the macular area, thereby constraining its clinical diagnostic accuracy, particularly for macular diseases like age-related macular degeneration. Conventional supervised super-resolution techniques aim to address this limitation by enhancing the resolution of the macular region through the utilization of meticulously paired and aligned fundus image ground truths. However, obtaining such refined paired ground truths is a formidable challenge. To tackle this issue, we propose an unpaired, degradation-aware, super-resolution technique for enhancing UWF retinal images. Our approach leverages recent advancements in deep learning: specifically, by employing generative adversarial networks and attention mechanisms. Notably, our method excels at enhancing and super-resolving UWF images without relying on paired, clean ground truths. Through extensive experimentation and evaluation, we demonstrate that our approach not only produces visually pleasing results but also establishes state-of-the-art performance in enhancing and super-resolving UWF retinal images. We anticipate that our method will contribute to improving the accuracy of clinical assessments and treatments, ultimately leading to better patient outcomes.

Learning to Learn Task-Adaptive Hyperparameters for Few-Shot Learning.

The objective of few-shot learning is to design a system that can adapt to a given task with only few examples while achieving generalization. Model-agnostic meta-learning (MAML), which has recently gained the popularity for its simplicity and flexibility, learns a good initialization for fast adaptation to a task under few-data regime. However, its performance has been relatively limited especially when novel tasks are different from tasks previously seen during training. In this work, instead of searching for a better initialization, we focus on designing a better fast adaptation process. Consequently, we propose a new task-adaptive weight update rule that greatly enhances the fast adaptation process. Specifically, we introduce a small meta-network that can generate per-step hyperparameters for each given task: learning rate and weight decay coefficients. The experimental results validate that learning a good weight update rule for fast adaptation is the equally important component that has drawn relatively less attention in the recent few-shot learning approaches. Surprisingly, fast adaptation from random initialization with ALFA can already outperform MAML. Furthermore, the proposed weight-update rule is shown to consistently improve the task-adaptation capability of MAML across diverse problem domains: few-shot classification, cross-domain few-shot classification, regression, visual tracking, and video frame interpolation.

Learning Controllable ISP for Image Enhancement.

We present a plug-and-play Image Signal Processor (ISP) for image enhancement to better produce diverse image styles than the previous works. Our proposed method, ContRollable Image Signal Processor (CRISP), explicitly controls the parameters of the ISP that determine output image styles. ISP parameters for high-quality (HQ) image styles are encoded into low-dimensional latent codes, allowing fast and easy style adjustments. We empirically show that CRISP covers a wide range of image styles with high efficiency. On the MIT-Adobe FiveK dataset, CRISP can very closely estimate the reference styles produced by human experts and achieves better MOS with diverse image styles. Compared with the state-of-the-art method, our ISP comprises only 19 parameters, allowing CRISP to have 2× smaller parameters and 100× reduced FLOPs for an image output. CRISP outperforms previous works in PSNR and FLOPs with several scenarios for style adjustments.

A deep learning-based framework for retinal fundus image enhancement.

PROBLEM: Low-quality fundus images with complex degredation can cause costly re-examinations of patients or inaccurate clinical diagnosis. AIM: This study aims to create an automatic fundus macular image enhancement framework to improve low-quality fundus images and remove complex image degradation. METHOD: We propose a new deep learning-based model that automatically enhances low-quality retinal fundus images that suffer from complex degradation. We collected a dataset, comprising 1068 pairs of high-quality (HQ) and low-quality (LQ) fundus images from the Kangbuk Samsung Hospital's health screening program and ophthalmology department from 2017 to 2019. Then, we used these dataset to develop data augmentation methods to simulate major aspects of retinal image degradation and to propose a customized convolutional neural network (CNN) architecture to enhance LQ images, depending on the nature of the degradation. Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), r-value (linear index of fuzziness), and proportion of ungradable fundus photographs before and after the enhancement process are calculated to assess the performance of proposed model. A comparative evaluation is conducted on an external database and four different open-source databases. RESULTS: The results of the evaluation on the external test dataset showed an significant increase in PSNR and SSIM compared with the original LQ images. Moreover, PSNR and SSIM increased by over 4 dB and 0.04, respectively compared with the previous state-of-the-art methods (P < 0.05). The proportion of ungradable fundus photographs decreased from 42.6% to 26.4% (P = 0.012). CONCLUSION: Our enhancement process improves LQ fundus images that suffer from complex degradation significantly. Moreover our customized CNN achieved improved performance over the existing state-of-the-art methods. Overall, our framework can have a clinical impact on reducing re-examinations and improving the accuracy of diagnosis.

Concentrations of blood and urinary arsenic species and their characteristics in general Korean population.

Arsenic (As) exposure has been extensively studied by investigating As species (e.g., inorganic arsenic (iAs), monomethylarsonic acid (MMA), and dimethylarsinic acid (DMA)) in urine, yet recent research suggests that blood could be a possible biomarker of As exposure. These investigations, however, were conducted on iAs-contaminated areas, and evidence on populations exposed to low levels of iAs is limited. This study aimed to describe the levels and distributions of As species in urine and blood, as well as to estimate methylation efficiency and related factors in the Korean population. Biological samples were obtained by the Korean Ministry of Food and Drug Safety. A total of 2025 urine samples and 598 blood samples were utilized in this study. Six As species were measured using ultra-high-performance liquid chromatography with inductively coupled plasma mass spectrometry (UPLC-ICP-MS): As(V), As(III), MMA, DMA, arsenobetaine (AsB), and arsenocholine (AsC). Multiple linear regression models were used to examine the relationship between As species (concentrations and proportions) and covariates. AsB was the most prevalent species in urine and blood. The relative composition of iAs, MMA, DMA, and AsC in urine and blood differed significantly. Consumption of blue-backed fish was linked to higher levels of AsB in urine and blood. Type of drinking water and multigrain rice consumption were associated with increased iAs concentration in urine. Except for iAs, every species had correlations in urine and blood in both univariate and multivariate analyses. Adolescents and smokers presented a lower methylation efficiency (higher %MMA and lower %DMA in urine) and females presented a higher methylation efficiency (lower %iAs, %MMA, and higher %DMA in urine). In conclusion, blood iAs concentration cannot represent urinary iAs; nonetheless, different compositions of urine and blood might reflect distinct information about iAs exposure. Further investigations on exposure factors and health are needed using low-exposure groups.

Test-Time Adaptation for Video Frame Interpolation via Meta-Learning.

Video frame interpolation is a challenging problem that involves various scenarios depending on the variety of foreground and background motions, frame rate, and occlusion. Therefore, generalizing across different scenes is difficult for a single network with fixed parameters. Ideally, one could have a different network for each scenario, but this will be computationally infeasible for practical applications. In this work, we propose MetaVFI, an adaptive video frame interpolation algorithm that uses additional information readily available at test time but has not been exploited in previous works. We initially show the benefits of test-time adaptation through simple fine-tuning of a network and then greatly improve its efficiency by incorporating meta-learning. Thus, we obtain significant performance gains with only a single gradient update without introducing any additional parameters. Moreover, the proposed MetaVFI algorithm is model-agnostic which can be easily combined with any video frame interpolation network. We show that our adaptive framework greatly improves the performance of baseline video frame interpolation networks on multiple benchmark datasets.

Learning to Forget for Meta-Learning via Task-and-Layer-Wise Attenuation.

Few-shot learning is an emerging yet challenging problem in which the goal is to achieve generalization from only few examples. Meta-learning tackles few-shot learning via the learning of prior knowledge shared across tasks and using it to learn new tasks. One of the most representative meta-learning algorithms is the model-agnostic meta-learning (MAML), which formulates prior knowledge as a common initialization, a shared starting point from where a learner can quickly adapt to unseen tasks. However, forcibly sharing an initialization can lead to conflicts among tasks and the compromised (undesired by tasks) location on optimization landscape, thereby hindering task adaptation. Furthermore, the degree of conflict is observed to vary not only among the tasks but also among the layers of a neural network. Thus, we propose task-and-layer-wise attenuation on the compromised initialization to reduce its adverse influence on task adaptation. As attenuation dynamically controls (or selectively forgets) the influence of the compromised prior knowledge for a given task and each layer, we name our method Learn to Forget (L2F). Experimental results demonstrate that the proposed method greatly improves the performance of the state-of-the-art MAML-based frameworks across diverse domains: few-shot classification, cross-domain few-shot classification, regression, reinforcement learning, and visual tracking.

Toward Real-World Super-Resolution via Adaptive Downsampling Models.

Most image super-resolution (SR) methods are developed on synthetic low-resolution (LR) and high-resolution (HR) image pairs that are constructed by a predetermined operation, e.g., bicubic downsampling. As existing methods typically learn an inverse mapping of the specific function, they produce blurry results when applied to real-world images whose exact formulation is different and unknown. Therefore, several methods attempt to synthesize much more diverse LR samples or learn a realistic downsampling model. However, due to restrictive assumptions on the downsampling process, they are still biased and less generalizable. This study proposes a novel method to simulate an unknown downsampling process without imposing restrictive prior knowledge. We propose a generalizable low-frequency loss (LFL) in the adversarial training framework to imitate the distribution of target LR images without using any paired examples. Furthermore, we design an adaptive data loss (ADL) for the downsampler, which can be adaptively learned and updated from the data during the training loops. Extensive experiments validate that our downsampling model can facilitate existing SR methods to perform more accurate reconstructions on various synthetic and real-world examples than the conventional approaches.

A comparison of meta-analysis results with and without adjustment for the healthy worker effect: cancer mortality among workers in the semiconductor industry.

OBJECTIVES: This study compared the results of meta-analysis with and without adjustment for the healthy worker effect on the association between working in the semiconductor industry and cancer mortality. METHODS: Six studies that reported standardized mortality ratios (SMRs) for cancers were selected for meta-analysis. Using a random-effects model, the SMR results from each study were combined for all cancers and leukemias to estimate the summary SMRs (95% confidence interval, CI). To adjust for the healthy worker effect, the relative standardized mortality ratio (rSMR=SMRx/SMRnot x) were calculated using observed and expected counts for the specific cause of interest (i.e., all cancers and leukemias) and the observed and expected counts for all other causes of mortality. Then, the rSMR results were combined to estimate the summary rSMRs (95% CIs). RESULTS: The SMRs for all causes of mortality among semiconductor industry workers ranged from 0.25 to 0.80, which reflects a significant healthy worker effect. A remarkable difference was found between the summary SMRs and the summary rSMRs. The summary SMR for all cancers was 0.70 (95% CI, 0.63 to 0.79) whereas the summary rSMR was 1.38 (95% CI, 1.20 to 1.59). The summary SMR for leukemia was 0.88 (95% CI, 0.72 to 1.07), and the summary rSMR was 1.88 (95% CI, 1.20 to 2.95). CONCLUSIONS: Our results suggest that adjustment for the healthy worker effect (i.e., rSMR) may be useful in meta-analyses of cohort studies reporting SMRs.

Joint segmentation and classification of hepatic lesions in ultrasound images using deep learning.

OBJECTIVES: To develop a convolutional neural network system to jointly segment and classify a hepatic lesion selected by user clicks in ultrasound images. METHODS: In total, 4309 anonymized ultrasound images of 3873 patients with hepatic cyst (n = 1214), hemangioma (n = 1220), metastasis (n = 1001), or hepatocellular carcinoma (HCC) (n = 874) were collected and annotated. The images were divided into 3909 training and 400 test images. Our network is composed of one shared encoder and two inference branches used for segmentation and classification and takes the concatenation of an input image and two Euclidean distance maps of foreground and background clicks provided by a user as input. The performance of hepatic lesion segmentation was evaluated based on the Jaccard index (JI), and the performance of classification was based on accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC). RESULTS: We achieved performance improvements by jointly conducting segmentation and classification. In the segmentation only system, the mean JI was 68.5%. In the classification only system, the accuracy of classifying four types of hepatic lesions was 79.8%. The mean JI and classification accuracy were 68.5% and 82.2%, respectively, for the proposed joint system. The optimal sensitivity and specificity and the AUROC of classifying benign and malignant hepatic lesions of the joint system were 95.0%, 86.0%, and 0.970, respectively. The respective sensitivity, specificity, and the AUROC for classifying four hepatic lesions of the joint system were 86.7%, 89.7%, and 0.947. CONCLUSIONS: The proposed joint system exhibited fair performance compared to segmentation only and classification only systems. KEY POINTS: • The joint segmentation and classification system using deep learning accurately segmented and classified hepatic lesions selected by user clicks in US examination. • The joint segmentation and classification system for hepatic lesions in US images exhibited higher performance than segmentation only and classification only systems. • The joint segmentation and classification system could assist radiologists with minimal experience in US imaging by characterizing hepatic lesions.

Deep vessel segmentation by learning graphical connectivity.

We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective use of the strong relationship that exists between vessel neighborhoods can help improve the vessel segmentation accuracy. To this end, we incorporate a graph neural network into a unified CNN architecture to jointly exploit both local appearances and global vessel structures. We extensively perform comparative evaluations on four retinal image datasets and a coronary artery X-ray angiography dataset, showing that the proposed method outperforms or is on par with current state-of-the-art methods in terms of the average precision and the area under the receiver operating characteristic curve. Statistical significance on the performance difference between the proposed method and each comparable method is suggested by conducting a paired t-test. In addition, ablation studies support the particular choices of algorithmic detail and hyperparameter values of the proposed method. The proposed architecture is widely applicable since it can be applied to expand any type of CNN-based vessel segmentation method to enhance the performance.

Association between occupational exposure to trichloroethylene and serum levels of microRNAs: a cross-sectional molecular epidemiology study in China.

OBJECTIVES: The objective of our study was to evaluate the association between occupational exposure to trichloroethylene (TCE), a suspected lymphomagen, and serum levels of miRNAs in a cross-sectional molecular epidemiology study of TCE-exposed workers and comparable unexposed controls in China. METHODS: Serum levels of 40 miRNAs were compared in 74 workers exposed to TCE (median: 12 ppm) and 90 unexposed control workers. Linear regression models were used to test for differences in serum miRNA levels between exposed and unexposed workers and to evaluate exposure-response relationships across TCE exposure categories using a three-level ordinal variable [i.e., unexposed, < 12 ppm, the median value among workers exposed to TCE) and ≥ 12 ppm)]. Models were adjusted for sex, age, current smoking, current alcohol use, and recent infection. RESULTS: Seven miRNAs showed significant differences between exposed and unexposed workers at FDR (false discovery rate) < 0.20. miR-150-5p and let-7b-5p also showed significant inverse exposure-response associations with TCE exposure (Ptrend= 0.002 and 0.03, respectively). The % differences in serum levels of miR-150-5p relative to unexposed controls were - 13% and - 20% among workers exposed to < 12 ppm and ≥ 12 ppm TCE, respectively. CONCLUSIONS: miR-150-5p is involved in B cell receptor pathways and let-7b-5p plays a role in the innate immune response processes that are potentially important in the etiology of non-Hodgkin lymphoma (NHL). Further studies are needed to replicate these findings and to directly test the association between serum levels of these miRNAs and risk of NHL in prospective studies.

Association between family history of diabetes and clusters of adherence to healthy behaviors: cross-sectional results from the Health Examinees-Gem (HEXA-G) study.

OBJECTIVES: This study evaluated whether individuals with affected family member adhered to healthy behaviours. DESIGN AND SETTING: This was a cross-sectional study of participants selected from health examinees who underwent the national health check-up programme of Korea in 39 centres between 2004 and 2013. PARTICIPANTS: The baseline data of 128 520 participants enrolled in the Health Examinees-Gem study were used for analysis. MAIN OUTCOMES AND MEASURES: Associations of family history of diabetes with adherence to regular exercise, healthy diet and body composition, and clusters of healthy behaviours were evaluated while adjusting for potential confounders selected by a directed acyclic graph. RESULTS: Participants with a family history of diabetes were more likely to adhere to a regular exercise regimen (OR=1.12, 95% CI 1.06 to 1.18 for men and OR=1.10, 95% CI 1.07 to 1.14 for women) and healthy diet (OR=1.06, 95% CI 1.01 to 1.12 for men and OR=1.06, 95% CI 1.01 to 1.12 for women) but were less likely to have a normal body composition (OR=0.83, 95% CI 0.78 to 0.87 for men and OR=0.83, 95% CI 0.80 to 0.86 for women). These associations were strengthened when the affected family members were siblings, the number of affected members was increased or the age at diagnosis of the affected member was younger than 50 years. In men and women, having a normal body composition is important in determining the cluster of behaviours, and those with a family history of diabetes were less likely to adhere to the normal body composition cluster. CONCLUSIONS: The group with high risk of diabetes showed healthy behaviors, but they did not have a normal body composition. Policies and campaigns targeting integrated health behaviors will be needed to reduce the burden of diseases and improve public health.

Alterations in immune and renal biomarkers among workers occupationally exposed to low levels of trichloroethylene below current regulatory standards.

OBJECTIVES: The occupational exposure limit for trichloroethylene (TCE) in different countries varies from 1 to 100 ppm as an 8-hour time-weighted average (TWA). Many countries currently use 10 ppm as the regulatory standard for occupational exposures, but the biological effects in humans at this level of exposure remain unclear. The objective of our study was to evaluate alterations in immune and renal biomarkers among workers occupationally exposed to low levels of TCE below current regulatory standards. METHODS: We conducted a cross-sectional molecular epidemiology study of 80 healthy workers exposed to a wide range of TCE (ie, 0.4-229 ppm) and 96 comparable unexposed controls in China, and previously reported that TCE exposure was associated with multiple candidate biological markers related to immune function and kidney toxicity. Here, we conducted further analyses of all of the 31 biomarkers that we have measured to determine the magnitude and statistical significance of changes in the subgroup of workers (n=35) exposed to <10 ppm TCE compared with controls. RESULTS: Six immune biomarkers (ie, CD4+ effector memory T cells, sCD27, sCD30, interleukin-10, IgG and IgM) were significantly decreased (% difference ranged from -16.0% to -72.1%) and one kidney toxicity marker (kidney injury molecule-1, KIM-1) was significantly increased (% difference: +52.5%) among workers exposed to <10 ppm compared with the control group. These associations remained noteworthy after taking into account multiple comparisons using the false discovery rate (ie, <0.20). CONCLUSION: Our results suggest that occupational exposure to TCE below 10 ppm as an 8-hour TWA may alter levels of key markers of immune function and kidney toxicity.

Trends and Correlates of High-Risk Alcohol Consumption and Types of Alcoholic Beverages in Middle-Aged Korean Adults: Results From the HEXA-G Study.

BACKGROUND: We aimed to report the prevalence and correlates of high-risk alcohol consumption and types of alcoholic beverages. METHODS: The baseline data of the Health Examinees-Gem (HEXA-G) study participants, including 43,927 men and 85,897 women enrolled from 2005 through 2013, were used for analysis. Joinpoint regression was performed to estimate trends in the age-standardized prevalence of alcohol consumption. Associations of demographic and behavioral factors, perceived health-related effects, social relationships, and the diagnostic history of diseases with alcohol consumption were assessed using multinomial logistic regression. RESULTS: The prevalence of alcohol consumption remained higher in men during the study period and increased in women. The amount of alcohol consumed has increased in women, especially that from beer and makgeolli, a traditional Korean fermented rice wine. Older participants were less likely to be high-risk drinkers (men and women who drink more than 40 or 20 g/day of alcohol, respectively) and drink Soju, a distilled liquor, and beer, and more likely to drink makgeolli. Educational level was negatively associated with high-risk drinking. However, it was positively associated with the consumption of strong spirits and wine. Smoking was associated with high-risk drinking and the consumption of soju and strong spirits. Engaging in regular exercise and having stress were associated with drinking all types of beverages except for soju. CONCLUSIONS: Sex-specific trends in alcohol consumption were influenced by demographic, behavioral, and perceived health-related factors. The findings will help improve the understanding of alcohol-related problems and provide evidence for establishing country-specific policies and campaigns in Korea.

Mortality among diagnostic medical radiation workers in South Korea, 1996-2015.

OBJECTIVES: To evaluate the risk for all-cause and cause-specific mortality in diagnostic medical radiation workers in South Korea. METHODS: The study population included all diagnostic medical radiation workers enrolled in the National Dosimetry Registry (NDR) between 1996 and 2011. NDR data were linked with mortality data obtained from national registries through 2015. Standardised mortality ratios (SMRs) and relative standardised mortality ratios (rSMRs) were calculated for external comparison and for adjustment of the cohort's overall healthiness. RESULTS: A total of 1099 deaths (974 in men and 125 in women) were reported from among 80 837 medical radiation workers. The SMRs for all causes of death were significantly lower than expected in both men (SMR 0.45, 95% CI 0.42 to 0.48) and women (SMR 0.49, 95% CI 0.41 to 0.58). No excesses were observed for any specific cause of death. The findings were similar by job title, calendar year of entry and year of birth. However, relative to all causes of death, mortality from all cancers (rSMR 1.60, 95% CI 1.41 to 1.82), leukaemia, colon cancer, stomach cancer and diseases of the circulatory system increased significantly among male workers. The results for female workers were limited due to small number of deaths; however, the rSMR for all cancers was significantly elevated (rSMR 1.70, 95% CI 1.17 to 2.46). CONCLUSIONS: This cohort showed lower mortality among diagnostic medical radiation workers than in the general population. However, occupational factors may have been involved in the increased relative mortality for several causes of death.

Directions for and prospects of the Environmental Health Study in Korean National Industrial Complexes (EHSNIC): A proposal for the third phase of the EHSNIC.

The Environmental Health Study in the Korean National Industrial Complexes (EHSNIC) is a project that aims to monitor the exposure and health effects of environmental pollution among residents of national industrial complexes, as well as propose appropriate environmental health measures. Since its launch in 2003, this project has been initiated in eight national industrial complexes. Currently, it is necessary to review the accomplishments and limitations of the phases 1 and 2 of this project, and establish the direction of the upcoming the phase 3. Thus, the present study has developed principles and goals for the phase 3, considering the rationale and justification of the EHSNIC, and presented specific research contents accordingly. In the phase 3, it is important to improve the methods for exposure assessment and evaluation of health effects, in order to identify clearly the association between the pollutants released from industrial complexes and their health impacts, to develop and to reinforce communication strategies to promote participation of residents of communities near industrial complexes. Nonetheless, it is also important to maintain the basic goal of continuously monitoring the level of exposure to and health effects of environmental pollutants.

Robust Light Field Depth Estimation Using Occlusion-Noise Aware Data Costs.

Depth estimation is essential in many light field applications. Numerous algorithms have been developed using a range of light field properties. However, conventional data costs fail when handling noisy scenes in which occlusion is present. To address this problem, we introduce a light field depth estimation method that is more robust against occlusion and less sensitive to noise. Two novel data costs are proposed, which are measured using the angular patch and refocus image, respectively. The constrained angular entropy cost (CAE) reduces the effects of the dominant occluder and noise in the angular patch, resulting in a low cost. The constrained adaptive defocus cost (CAD) provides a low cost in the occlusion region, while also maintaining robustness against noise. Integrating the two data costs is shown to significantly improve the occlusion and noise invariant capability. Cost volume filtering and graph cut optimization are applied to improve the accuracy of the depth map. Our experimental results confirm the robustness of the proposed method and demonstrate its ability to produce high-quality depth maps from a range of scenes. The proposed method outperforms other state-of-the-art light field depth estimation methods in both qualitative and quantitative evaluations.

Dynamic Video Deblurring Using a Locally Adaptive Blur Model.

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental results on publicly available challenging videos demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation.

Longitudinal trends of blood lead levels before and after leaded gasoline regulation in Korea.

The objective of this study was to verify a change in the longitudinal trend of blood lead levels for the Korean population, before and after the regulation of leaded gasoline- which occurred between 1987 and 1993 in Korea. A total of 77 reports on blood lead levels among general Korean population between 1981 and 2014 were selected, and the results were summarized to have the variables of year, number of subjects, the subjects' range in age, gender, and blood lead concentrations (arithmetic mean). The annual average atmospheric lead levels for four major cities (i.e., Seoul, Busan, Daegu and Gwangju) were collected from the Air Pollution Monitoring Database from 1991, and pilot studies from 1985 to 1990 before the national air quality monitoring system was launched in 1991. Blood lead levels were visualized in a bubble plot in which the size of each bubble represented the sample size of each study, and the annual average concentrations in ambient air were depicted on line graphs. Blood lead levels in the Korean population tended to gradually increase from the early 1980s (approximately 15-20 µg/dL) until 1990-1992 (20-25 µg/dL). Blood lead levels then began to rapidly decrease until 2014 (<2 µg/dL). Similar patterns were observed for both adults (≥20 years) and younger children/adolescents. The same longitudinal trend was observed in annual average atmospheric lead concentration, which suggests a significant correlation between air lead concentration and blood lead concentration in the general population. In conclusion, the regulation of leaded gasoline has significantly contributed to the rapid change in blood lead concentrations. And, the regulation of other sources of lead exposure should be considered to further decrease blood lead levels in the Korean population.