Edge-Triggered Three-Dimensional Object Detection Using a LiDAR Ring.

Autonomous driving recognition technology that can quickly and accurately recognize even small objects must be developed in high-speed situations. This study proposes an object point extraction method using rule-based LiDAR ring data and edge triggers to increase both speed and performance. The LiDAR's ring information is interpreted as a digital pulse to remove the ground, and object points are extracted by detecting discontinuous edges of the z value aligned with the ring ID and azimuth. A bounding box was simply created using DBSCAN and PCA to check recognition performance from the extracted object points. Verification of the results of removing the ground and extracting points through Ring Edge was conducted using SemanticKITTI and Waymo Open Dataset, and it was confirmed that both F1 scores were superior to RANSAC. In addition, extracting bounding boxes of objects also showed higher PDR index performance when verified in open datasets, virtual driving environments, and actual driving environments.

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.

OBJECTIVES: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres. METHODS: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings. Four radiology residents read all CXRs twice with and without AI assistance. The performances of the AI and readers with and without AI assistance were measured in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: The prevalence of clinically significant lesions was 2.2% (68 of 3047). The AUROC, sensitivity, and specificity of the AI were 0.648 (95% confidence interval [CI] 0.630-0.665), 35.3% (CI, 24.7-47.8), and 94.2% (CI, 93.3-95.0), respectively. AI detected 12 of 41 pneumonia, 3 of 5 tuberculosis, and 9 of 22 tumours. AI-undetected lesions tended to be smaller than true-positive lesions. The readers' AUROCs ranged from 0.534-0.676 without AI and 0.571-0.688 with AI (all p values < 0.05). For all readers, the mean reading time was 2.96-10.27 s longer with AI assistance (all p values < 0.05). CONCLUSIONS: The performance of commercial AI in these high-volume, low-prevalence settings was poorer than expected, although it modestly boosted the performance of less-experienced readers. The technical prowess of AI demonstrated in experimental settings and approved by regulatory bodies may not directly translate to real-world practice, especially where the demand for AI assistance is highest. KEY POINTS: • This study shows the limited applicability of commercial AI software for detecting abnormalities in CXRs in a health screening population. • When using AI software in a specific clinical setting that differs from the training setting, it is necessary to adjust the threshold or perform additional training with such data that reflects this environment well. • Prospective test accuracy studies, randomised controlled trials, or cohort studies are needed to examine AI software to be implemented in real clinical practice.

Identifying fragile calcifications of the aortic valve in transcatheter aortic valve replacement: iodine concentration of aortic valvular calcification by spectral CT.

OBJECTIVE: To demonstrate the relationship between spectral computed tomography (CT) measured iodine concentration and strength of aortic valvular calcification (AVC) in patients with aortic valve stenosis (AVS). METHODS: A retrospective study was performed on patients who underwent transcatheter aortic valve replacement (TAVR) for symptomatic AVS and underwent both pre and postprocedural electrocardiogram gated CT scans using a spectral CT system. Preprocedural CT was used to evaluate the volume and iodine concentration (IC) in the AVC. Postprocedural CT data were used to calculate the volume reduction percentage (VRP) of AVC. Multiple linear regression analysis was used to identify the independent variables related to the VRP in AVCs. RESULTS: A total of 94 AVCs were selected from 22 patients. The mean volume and IC of the AVCs before TAVR were 0.37 mL ± 0.15 mL and 7 mg/mL ± 10.5 mg/mL, respectively. After TAVR, a median VRP of all 94 AVCs was 18.5%. Multiple linear regression analysis showed that the IC was independently associated with the VRP (coefficient = 1.64, p < 0.001). When an optimal IC cutoff point was set at 4 mg/mL in the assessment of a fragile AVC which showed the VRP was > 18.5%, the sensitivity was 63%; specificity, 91%; positive predictive value, 88%; and negative predictive value, 71%. CONCLUSIONS: When using spectral CT to prepare the TAVR, measuring the IC of the AVC may be useful to assess the probability of AVC deformity after TAVR. KEY POINTS: • A dual-layer detector-based spectral CT enables quantifying iodine of contrast media in the aortic valve calcification (AVC) on contrast-enhanced CT images. • The AVC including iodine of contrast media on contrast-enhanced CT image may have loose compositions, associated with the deformity of AVC after TAVR. • Measuring the iodine concentration in AVC may have the potential to assess the probability of AVC deformity, which may be associated with the outcome and complications after TAVR.2.

Evaluation of fully automated commercial software for Agatston calcium scoring on non-ECG-gated low-dose chest CT with different slice thickness.

OBJECTIVES: To evaluate commercial deep learning-based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). METHODS: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. RESULTS: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, -244.0, 283.9] vs. 45.26 [-248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). CONCLUSIONS: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. KEY POINTS: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mm than with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mm than with LDCT2.5-mm.

Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography.

BACKGROUND: To propose a deep learning architecture for automatically detecting the complex structure of the aortic annulus plane using cardiac computed tomography (CT) for transcatheter aortic valve replacement (TAVR). METHODS: This study retrospectively reviewed consecutive patients who underwent TAVR between January 2017 and July 2020 at a tertiary medical center. Annulus Detection Permuted AdaIN network (ADPANet) based on a three-dimensional (3D) U-net architecture was developed to detect and localize the aortic annulus plane using cardiac CT. Patients (N = 72) who underwent TAVR between January 2017 and July 2020 at a tertiary medical center were enrolled. Ground truth using a limited dataset was delineated manually by three cardiac radiologists. Training, tuning, and testing sets (70:10:20) were used to build the deep learning model. The performance of ADPANet for detecting the aortic annulus plane was analyzed using the root mean square error (RMSE) and dice similarity coefficient (DSC). RESULTS: In this study, the total dataset consisted of 72 selected scans from patients who underwent TAVR. The RMSE and DSC values for the aortic annulus plane using ADPANet were 55.078 ± 35.794 and 0.496 ± 0.217, respectively. CONCLUSION: Our deep learning framework was feasible to detect the 3D complex structure of the aortic annulus plane using cardiac CT for TAVR. The performance of our algorithms was higher than other convolutional neural networks.

Two Years of Experience and Methodology of Korean COVID-19 Living Clinical Practice Guideline Development.

BACKGROUND: In Korea, during the early phase of the coronavirus disease 2019 (COVID-19) pandemic, we responded to the uncertainty of treatments under various conditions, consistently playing catch up with the speed of evidence updates. Therefore, there was high demand for national-level evidence-based clinical practice guidelines for clinicians in a timely manner. We developed evidence-based and updated living recommendations for clinicians through a transparent development process and multidisciplinary expert collaboration. METHODS: The National Evidence-based Healthcare Collaborating Agency (NECA) and the Korean Academy of Medical Sciences (KAMS) collaborated to develop trustworthy Korean living guidelines. The NECA-supported methodological sections and 8 professional medical societies of the KAMS worked with clinical experts, and 31 clinicians were involved annually. We developed a total of 35 clinical questions, including medications, respiratory/critical care, pediatric care, emergency care, diagnostic tests, and radiological examinations. RESULTS: An evidence-based search for treatments began in March 2021 and monthly updates were performed. It was expanded to other areas, and the search interval was organized by a steering committee owing to priority changes. Evidence synthesis and recommendation review was performed by researchers, and living recommendations were updated within 3-4 months. CONCLUSION: We provided timely recommendations on living schemes and disseminated them to the public, policymakers and various stakeholders using webpages and social media. Although the output was successful, there were some limitations. The rigor of development issues, urgent timelines for public dissemination, education for new developers, and spread of several new COVID-19 variants have worked as barriers. Therefore, we must prepare systematic processes and funding for future pandemics.

Indoor exposure assessment for levels of dust mite and total volatile organic compounds (TVOCs) in living houses.

The purpose of this study is to measure the levels of dust mites (Dermatophagoides farina group 1 [Der f1] and Dermatophagoides pteronyssinus group 1 [Der p1]) and total volatile organic compounds (TVOCs) in the resident environments with living conditions such as the number of ventilation; the number of window, floor, and temperature; and relative humidity (RH) from vulnerable class in South Korea. Information on residents is designed to be prepared by residents on their general characteristics (daily residence time, heating and cooking type, and cleaning on the day of sampling). Dust mites levels ranged from 24.0 to 1087.5 ng/g with a mean of 215.4 ng/g for Der f1 and from 0.6 to 489.9 ng/g with a mean of 489.9 ng/g for Der p1. Total TVOC levels ranged from 15.3 to 1642.1 µg/m3 with a mean of 219.7 µg/m3. The correlation analysis showed a positive association between Der f1 levels and xylene levels in fall (r = 0.49, r < 0.05) of this study.

Association between levels of radon and bioaerosols (bacteria and fungi) by living conditions.

This study aimed to measure the levels of airborne radon Rn and bioaerosols - culturable airborne bacteria (CAB) and culturable airborne fungi (CAF)-in South Korea's residential environments, considering living conditions such as the number of ventilations, number of windows, floors, temperature, and relative humidity. . The range of Rn levels was 0.43-7.439 pCi/L with a median of 0.70 pCi/L. The CAB levels were 239-488 colony-forming unit (CFU)/m3 with a median of 309 CFU/m3, and CAF levels were 174-366 CFU/m3 with a median of 233 CFU/m3. Thus, this study found that semi-basement residential indoor environments negatively affected Rn and bioaerosol levels, and living in such residences resulted in high health condition scores on the bad side. Given the correlation between airborne Rn and bioaerosol levels, further large-scale studies are needed to identify more reliable and representative of research.

Differentiating pulmonary metastasis from benign lung nodules in thyroid cancer patients using dual-energy CT parameters.

OBJECTIVES: To explore the importance of quantitative characteristics of dual-energy CT (DECT) between pulmonary metastasis and benign lung nodules in thyroid cancer. METHODS: In this retrospective study, we identified 63 patients from our institution's database with pathologically proven thyroid cancer who underwent DECT to assess pulmonary metastasis. Among these patients, 22 had 55 pulmonary metastases, and 41 had 97 benign nodules. If nodules showed increased iodine uptake on I-131 single-photon emission computed tomography-computed tomography or increased size in follow-up CT, they were considered metastatic. We compared the clinical findings and DECT parameters of both groups and performed a receiver operating characteristic analysis to evaluate the optimal cutoff values of the DECT parameters. RESULTS: Patients with metastases were significantly older than patients with benign nodules (p = 0.048). The DECT parameters of the metastatic nodules were significantly higher than those of the benign nodules (iodine concentration [IC], 5.61 ± 2.02 mg/mL vs. 1.61 ± 0.98 mg/mL; normalized IC [NIC], 0.60 ± 0.20 vs. 0.16 ± 0.11; NIC using pulmonary artery [NICPA], 0.60 ± 0.44 vs. 0.15 ± 0.11; slope of the spectral attenuation curves [λHU], 5.18 ± 2.54 vs. 2.12 ± 1.39; and Z-effective value [Zeff], 10.0 ± 0.94 vs. 8.79 ± 0.75; all p < 0.001). In the subgroup analysis according to nodule size, all DECT parameters of the metastatic nodules in all subgroups were significantly higher than those of the benign nodules (all p < 0.05). The cutoff values for IC, NIC, λHU, NICPA, and Zeff for diagnosing metastases were 3.10, 0.29, 3.57, 0.28, and 9.34, respectively (all p < 0.001). CONCLUSIONS: DECT parameters can help to differentiate metastatic and benign lung nodules in thyroid cancer. KEY POINTS: • DECT parameters can help to differentiate metastatic and benign lung nodules in patients with thyroid cancer. • DECT parameters showed a significant difference between benign lung nodules and lung metastases, even for nodules with diameters ≥ 3 mm and < 5 mm. • Among the DECT parameters, the highest diagnostic accuracy for differentiating pulmonary metastases from benign lung nodules was achieved with the NIC and IC, followed by the NICPA and λHU, and their cutoff values were 0.29, 3.10, 0.28, and 3.57, respectively.

Review of Inhalation Health Risks Involving Chloromethylisothiazolinone (CMIT) and Methylisothiazolinone (MIT) Used as Disinfectants in Household Humidifiers.

The association between lung injury and exposure to humidifier disinfectant (HD) containing a mixture of chloromethylisothiazolinone (CMIT) and methylisothiazolinone (MIT) has been controversial in South Korea. This study conducts a literature review in order to evaluate the likelihood of CMIT/MIT reaching the lower part of the respiratory tract and causing lung injury. A literature review focused on the inhalation risk of HD containing a mixture of CMIT and MIT. The major contents included the physicochemical properties of CMIT and MIT contained in HDs and methodological reviews on substance analysis, toxicity tests and clinical cases. HD products marketed in South Korea have been reported to contain approximately 1-2% CMIT and 0.2-0.6% MIT along with magnesium nitrate (20-25%), magnesium chloride (0.2-1.0%), and water (70-75%). The types of CMIT and MIT dispersed into the air and deposited in the respiratory tract are assumed to be either gaseous substances or nanoparticles mixed with magnesium salts. The result of the literature review including clinical cases of lung injury among CMIT/MIT HD product users, demonstrated that these chemicals likely reach the lower respiratory tract and accordingly cause lung injury. A number of humidifier disinfectant-associated lung injury cases with clinical evidence should be prioritized in risk assessment of HD containing CMIT and MIT, even though there might be insufficient evidence in all related areas, including inhalation exposure assessment studies, animal testing, and epidemiological studies.

Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging.

BACKGROUND: To propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI). METHODS: An active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step. RESULTS: The Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (-14.90-27.61), 6.21% (-9.62-22.03), and 2.68% (-8.57-13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively. CONCLUSION: Deep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.

Blood flow volume of left atrial appendage measured by magnetic resonance imaging is improved after radiofrequency catheter ablation of atrial fibrillation.

INTRODUCTION: Hemodynamics of left atrial appendage (LAA) is an important factor for future risk of ischemic stroke in atrial fibrillation (AF) patients. Velocity encoded cardiac magnetic resonance imaging (VENC-MRI) can evaluate blood flow volume of LAA without any invasive procedures. We aimed to evaluate the association between radiofrequency catheter ablation (RFCA) and LAA hemodynamics measured by MRI. METHODS AND RESULTS: Consecutive RFCA cases in a single arrhythmia center were retrospectively analyzed. A total of 3120 AF patients who underwent first RFCA were analyzed. Among these patients 360 patients had both pre- and post-RFCA VENC-MRI evaluation. Atrial fibrillation was non-paroxysmal in 174 (48.3%) patients. Mean VENC-MRI (ml/sec) was significantly improved after RFCA with 49.93 ± 32.92 and 72.00 ± 34.82 for pre- and post-RFCA, respectively. Patients with non-paroxysmal AF (∆VENC-MRI = 14.63 ± 40.67 vs. 30.03 ± 35.37; p < .001) and low pre-RFCA VENC-MRI (∆VENC-MRI = 17.19 ± 38.35 vs. 50.35 ± 29.12; p < .001) had significantly higher improvement in VENC-MRI. Those who experienced late recurrence before post-RFCA MRI had significantly less improvement in LAA flow volume (∆VENC-MRI = 15.55 ± 41.41 vs. 26.18 ± 36.77; p = .011). Late recurrence and pre-RFCA VENC-MRI were significantly associated with ∆VENC-MRI after adjusting covariates. Patients who were AF before RFCA but maintained sinus rhythm after RFCA showed greatest improvement in VENC-MRI. CONCLUSION: Effective rhythm control through RFCA can be associated with significant improvement in LAA hemodynamics. Low pre-RFCA VENC-MRI and absence of late recurrence were associated with greater improvement in LAA hemodynamics.

Nitrates administered by spray versus tablet: comparison of coronary vasodilation on CT angiography.

OBJECTIVES: To compare the coronary vasodilation effects of spray with those induced by tablet administration in coronary CT angiography (CCTA). METHODS: A total of 2024 patients who underwent CCTA were identified for this retrospective study, including 828 patients with spray (spray group) and 1169 with tablets (tablet group). Of these, 93 patients underwent CCTA at least twice using both spray and tablets. The number of measurable segments and diameters of all 18 segments was measured. The number of measurable segments was compared between groups. RESULTS: No statistically significant differences were evident between these two groups in terms of clinical characteristics. All coronary segments except the ramus intermedius (RI) and left posterior descending artery (L-PDA) were significantly larger in the spray group than in the tablet group (all p < 0.001). In peripheral and branch vessels, as well as in central and main coronary arteries, the diameters were significantly larger in the spray group than in the tablet group (all p < 0.001). Although not always statistically significant, all coronary segments tended to be more measurable on CCTA with spray than with tablet. In the subgroup that underwent CCTA twice using both spray and tablets, all coronary segments except the RI, obtuse marginal artery 2 (OM2), and L-PDA were significantly larger in the spray group than in the tablet group (all p < 0.05). CONCLUSION: Lingual isosorbide dinitrate (ISDN) spray was more efficacious than sublingual nitroglycerin (NTG) tablets in coronary vasodilation for CCTA. Therefore, lingual ISDN spray should be preferred over sublingual NTG tablets for CCTA. KEY POINTS: • Lingual ISDN spray was more efficacious than sublingual NTG tablet for coronary vasodilation in coronary CT angiography, even in elderly patients. • The diameters of all coronary segments except RI and L-PDA were significantly larger, and there were significantly more coronary segments greater than 1.5 mm, except RI and L-PDA, in the spray group than in the tablet group in the whole study group. • Even in peripheral and branch vessels, the diameters of coronary arteries were significantly larger in the spray group than in the tablet group, and they were also larger in elderly patients.

Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization.

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder-decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles' trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle's dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model's performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

Indoor air concentrations of carbon dioxide (CO2), nitrogen dioxide (NO2), and ozone (O3) in multiple healthcare facilities.

This study evaluates indoor air concentrations of CO2, NO2, and O3 and their relationship to other indoor environmental factors in facilities with occupants susceptible to air contaminants, such as hospitals, senior specialized hospitals, elderly care facilities, and postnatal care centers. Indoor air samples were collected from 82 indoor facilities in South Korea and organized by region. Spearman's correlation and Kruskal-Wallis analyses were employed to examine the relationship among and differences between contaminants in the indoor facilities and indoor/outdoor differences of NO2 and O3 concentrations. Significant correlations were found between CO2 and NO2 concentrations (r2 = 0.176, p < 0.01), as well as NO2 and O3 concentrations (r2 = - 0.289, p < 0.0001). The indoor/outdoor concentration ratios in the indoor facilities were 0.73 for NO2 and 0.25 for O3. CO2 and NO2 displayed the highest mean concentrations during spring, while O3 displayed the highest and lowest mean concentrations during fall and summer, respectively. The calculated hazard quotient (HQ) for NO2 was higher than the acceptable level of 1 in postnatal care centers, thus posing a health risk for children. Study results indicate that efficient ventilation is required to reduce indoor contaminants in multiple healthcare facilities. This study provides a novel approach toward health risk assessment for indoor facilities with susceptible occupants on a large geographical scale.

Left Atrial Fibrosis Assessed with Cardiac MRI in Patients with Paroxysmal and Those with Persistent Atrial Fibrillation.

Background Electrophysiology studies have demonstrated that left atrial late gadolinium enhancement (LGE) is associated with the chronicity of atrial fibrillation (AF). To date, cardiac MRI has been used to assess the extent of atrial LGE but not the distribution pattern of LGE in the left atrium. Purpose To determine whether the MRI pattern of left atrial fibrosis is associated with the chronicity of AF. Materials and Methods This retrospective study included patients with AF who underwent LGE MRI between June 2017 and May 2018. The presence of left atrial LGE was assessed at nine left atrial segments; the extent was determined by the number of segments involved. According to the chronicity of AF, patients were separated into paroxysmal AF (PAF) and persistent AF (PeAF) groups. The location and extent of left atrial LGE were compared between PAF and PeAF by using the χ2 test and logistic regression analysis. Results Of the 195 patients (mean age, 55 years ± 10 [standard deviation], 161 men), 74 (38%) had PAF and 121 (62%) had PeAF. Of all patients, 114 (58.4%) had at least one left atrial LGE segment. The mean number of LGE segments was higher (1.4 ± 1.1 vs 0.6 ± 0.7, P = .002) in the PeAF group than in the PAF group. The incidence of LGE at the left inferior pulmonary vein (LIPV) antrum was higher in the PeAF group than in the PAF group (39.2% [29 of 74] vs 7.4% [nine of 121]; P < .001). In multivariable analysis, LGE at the LIPV antrum was independently associated with PeAF (odds ratio = 4.2; 95% confidence interval: 1.7, 10.5; P < .001). Conclusion The presence of fibrosis assessed with late gadolinium enhancement MRI of the left inferior pulmonary vein antrum was associated with persistent atrial fibrillation. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Almeida in this issue.

Cu/CuO@ZnO Hollow Nanofiber Gas Sensor: Effect of Hollow Nanofiber Structure and P-N Junction on Operating Temperature and Sensitivity.

For the fast and easy detection of carbon monoxide (CO) gas, it was necessary to develop a CO gas sensor to operate in low temperatures. Herein, a novel Cu/CuO-decorated ZnO hollow nanofiber was prepared with the electrospinning, calcination, and photodeposition methods. In the presence of 100 ppm CO gas, the Cu/CuO-photodeposited ZnO hollow nanofiber (Cu/CuO@ZnO HNF) showed twice higher sensitivity than that of pure ZnO nanofiber at a relatively low working temperature of 300 °C. The hollow structure and p-n junction between Cu/CuO and ZnO would be considered to contribute to the enhancement of sensitivity to CO gas at 300 °C due to the improved specific surface area and efficient electron transfer.

Dynamic Deflection of a Railroad Sleeper from the Coupled Measurements of Acceleration and Strain.

Dynamic deflection of a railroad sleeper works as an indicator of ballast stiffness, reflecting the health conditions of a ballast track. However, difficulty exists in measuring dynamic deflection of a railroad sleeper by conventional deflection transducers such as a linear variable differential transformer (LVDT) or a potentiometer. This is because a fixed reference point is unattainable due to ground vibrations during train passage. In this paper, a patented signal processing technique for evaluation of pseudo-deflection is presented to recover dynamic deflection of a railroad sleeper using a coupled measurement of acceleration and strain at the concrete sleeper. The presented technique combines high-frequency deflections calculated from double integration of acceleration and low-frequency deflections determined from strains. Validity of the combined deflections was shown by the deflections measured with a camera target on a concrete sleeper, captured by a high-resolution DSLR camera with superb video capturing features and processed by computer vision techniques, such as Canny edge detection and Blob analysis.

Exposure assessment of particulate matter and blood chromium levels in people living near a cement plant.

This study evaluates the effect of air pollution caused by cement plants on nearby residential areas and performs an exposure assessment of particulate matter (PM) and total Cr, Cr6+, Pb, and Al. Further, the blood Cr levels of residents exposed to PM released by cement plants are also assessed. Nine buildings (eight residential and one elementary school building) close to cement plants were selected for this study, which were located in Pyeongtaek port, in west of Gyeonggi Province, South Korea. A total of 51 suspended particulate samples were collected at a flow rate of 2.0 L/min. Total Cr was more widely detected in residents' houses and elementary schools. PM levels were higher at distances of 4.1 and 4.8 km than those at closer distances of 2.7 km. This was due to the influence of wind direction. The estimated mean blood level of Cr for the study participants was 3.80 µg/L, which is higher than levels estimated by other studies on Cr blood levels. Therefore, cement plants could cause an increase in total Cr and blood Cr levels in residential areas, and more continuous monitoring is necessary to better understand their impacts.

Meropenem-resistant bacteria in hospital effluents in Seoul, Korea.

This study aimed to understand the prevalence, diversity, antibiotic resistance, ß-lactamase gene types, and possibility of environmental survival of meropenem-resistant bacteria present in hospital effluents in Seoul, Korea. Water sampling was performed at five general hospitals in Seoul, Korea, in January 2017. Water samples were plated in triplicate on tryptic soy agar plates with 16 mg/L meropenem. Meropenem-resistant bacteria were selected and subjected to 16S rRNA analysis for species determination and PCR for identification of ß-lactamase gene types. Resistant bacteria were cultured in sterilized surface water. Meropenem-resistant bacteria exhibited resistance to more than 12 antibiotics and possessed several ß-lactamase genes, such as those encoding OXT-M, NDM-1, AmpC, and OXA. They were able to multiply and survive in sterilized surface water for up to 60 days. Multidrug-resistant bacteria represent an environmental health risk, as they can survive in the environment for an extended period of time. Therefore, these bacteria should be monitored before discharge.