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Purpose Assessing the metastasis status of the sentinel lymph nodes (SLNs) for hematoxylin and eosin–stained frozen tissue sections by pathologists is an essential but tedious and time-consuming task that contributes to accurate breast cancer staging. This study aimed to review a challenge competition (HeLP 2019) for the development of automated solutions for classifying the metastasis status of breast cancer patients.Materials and Methods A total of 524 digital slides were obtained from frozen SLN sections: 297 (56.7%) from Asan Medical Center (AMC) and 227 (43.4%) from Seoul National University Bundang Hospital (SNUBH), South Korea. The slides were divided into training, development, and validation sets, where the development set comprised slides from both institutions and training and validation set included slides from only AMC and SNUBH, respectively. The algorithms were assessed for area under the receiver operating characteristic curve (AUC) and measurement of the longest metastatic tumor diameter. The final total scores were calculated as the mean of the two metrics, and the three teams with AUC values greater than 0.500 were selected for review and analysis in this study.Results The top three teams showed AUC values of 0.891, 0.809, and 0.736 and major axis prediction scores of 0.525, 0.459, and 0.387 for the validation set. The major factor that lowered the diagnostic accuracy was micro-metastasis.Conclusion In this challenge competition, accurate deep learning algorithms were developed that can be helpful for making a diagnosis on intraoperative SLN biopsy. The clinical utility of this approach was evaluated by including an external validation set from SNUBH.

To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia.This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification.The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000.The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia.• A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.

Epilepsy is a neurological disorder in which transient alteration of brain. Predicting outcomes in epilepsy is essential since the prediction could provide feedback that can foster improvement in the outcomes. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalogram (EEG) signals could improve the prediction of patients' outcomes after antiseizure medication for temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (seizure-free (SF, n = 22) or nonseizure-free (NSF, n = 24)) with TLE and reviewed their clinical data retrospectively. We dissected spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, and zero-crossing rate) and compared their performance by applying optimal frequency only, optimal duration only, and both. For all time-domain features, optimal frequency and time strategy (OFTS) showed the highest performance in distinguishing SF patients from NSF patients (0.759 ± 0.148 AUC). In addition, the best performance using statistical parameters as a feature vector was a frequency band of 39–41 Hz at a window length of 210s, with an AUC of 0.748. By identifying the optimal parameters, we improved the prediction model’s performance. These parameters can function as standard parameters for outcome prediction using resting-state EEG signals.

Although plain radiographs have declined in importance since the advent of magnetic resonance imaging (MRI), their diagnostic ability has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing lumbar spinal stenosis (LSS). In this study, we aimed to improve and generalize the performance of CNN models using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using four pre-trained algorithms and three single-pose-based CNN (SP-CNN) using extension, flexion, and neutral postures. The MP-CNN model underwent additional internal and external validation to measure generalization performance. The ResNet50-based MP-CNN model achieved the largest area under the receiver operating characteristic curve (AUROC) of 91.4% (95% confidence interval [CI] 90.9–91.8%). In the extra validation, the AUROC of the MP-CNN model was 91.3% (95% CI 90.7–91.9%) and 79.5% (95% CI 78.2–80.8%) for the extra-internal and external validation, respectively. The MP-based heatmap offered a logical decision-making direction through optimized visualization. This model holds potential as a screening tool for LSS diagnosis, offering an explainable rationale for its prediction.

Background Sequence-based identification is one of the most effective methods for species-level identification of nontuberculous mycobacteria (NTM). However, it is time-consuming because of the bioinformatics processes involved, including sequence trimming, consensus sequence generation, and public database searches. We developed a simple and fully automated software that enabled species-level identification of NTM from trace files, SnackNTM (https://github.com/Young-gonKim/SnackNTM). Methods JAVA programing language was used for software development. The SnackNTM diagnostic algorithm utilized 16S rRNA gene sequences, according to the Clinical & Laboratory Standards Institute guidelines, and an rpoB gene region was adjunctively utilized to narrow down the species. The software performance was validated using trace files of 234 clinical cases, comprising 217 consecutive cases and 17 additionally selected cases of unique species. Results SnackNTM could analyze multiple cases at once, and all the bioinformatics processes required for sequence-based NTM identification were automatically performed with a single mouse click. SnackNTM successfully identified 95.9% (208/217) of consecutive clinical cases, and the results showed 99.0% (206/208) agreement with manual classification results. SnackNTM successfully identified all 17 cases of unique species. In a processing time comparison test, the analysis and reporting of 30 cases, which took 150 minutes manually, took only 40 minutes with SnackNTM. Conclusions SnackNTM is expected to reduce the workload for NTM identification, especially in clinical laboratories that process large numbers of cases.

Titin truncating variants (TTNtvs) are the most common genetic cause of dilated cardiomyopathy (DCM). Among four regions of titin, A-band enrichment of DCM-causing TTNtvs is widely accepted but the underlying mechanism is still unknown. Meanwhile, few reports have identified exon 327 as a highly mutated A-band exon but the degree of exon 327 enrichment has not been quantitatively investigated. To find the real hotspot of DCM-causing TTNtvs, we aimed to reassess the degree of TTNtv enrichment in known titin regions and in exon 327, separately. In addition, we tried to explain exon 327 clustering in terms of nonsense-mediated mRNA decay (NMD) efficiency and a dominant negative mechanism recently proposed. Research papers focusing on TTNtvs found in patients with DCM were collected. A total of 612 patients with TTNtv-realated DCM were obtained from 10 studies. In the four regions of TTN and exon 327, the degree of TTNtvs enrichment was calculated in a way that the effect of distribution of highly expressed exons was normalized. As a result, exon 327 was the only region that showed significant enrichment for DCM-related TTNtv (p < .001). On the other hand, other A-band exons had almost the same number of TTNtv of random distribution. A review of RNAseq data revealed that the median allelic imbalance deviation of exon 327 TTNtvs was .04, indicating almost zero NMD. From these findings, we propose that the widely accepted A-band enrichment of DCM-related TTNtv is mostly attributable to exon 327 enrichment. In addition, based on the recently demonstrated dominant negative mechanism, the extremely low NMD efficiency seems to contribute to exon 327 enrichment.

Whole-genome sequencing is the most comprehensive form of next-generation sequencing method. We aimed to assess the additional diagnostic yield of whole-genome sequencing in patients with clinically diagnosed Charcot–Marie–Tooth disease when compared with whole-exome sequencing, which has not been reported in the literature. Whole-genome sequencing was performed on 72 families whose genetic cause of clinically diagnosed Charcot–Marie–Tooth disease was not revealed after the whole-exome sequencing and 17p12 duplication screening. Among the included families, 14 (19.4%) acquired genetic diagnoses that were compatible with their phenotypes. The most common factor that led to the additional diagnosis in the whole-genome sequencing was genotype-driven analysis (four families, 4/14), in which a wider range of genes, not limited to peripheral neuropathy-related genes, were analysed. Another four families acquired diagnosis due to the inherent advantage of whole-genome sequencing such as better coverage than the whole-exome sequencing (two families, 2/14), structural variants (one family, 1/14) and non-coding variants (one family, 1/14). In conclusion, an evident gain in diagnostic yield was obtained from whole-genome sequencing of the whole-exome sequencing-negative cases. A wide range of genes, not limited to inherited peripheral neuropathy-related genes, should be targeted during whole-genome sequencing.

Despite the wide application of next-generation sequencing, Sanger sequencing still plays a necessary role in clinical laboratories. However, recent developments in the field of bioinformatics have focused mostly on next-generation sequencing, while tools for Sanger sequencing have shown little progress. In this study, SnackVar (https://github.com/Young-gonKim/SnackVar, last accessed June 22, 2020), a novel graphical user interface-based software for Sanger sequencing, was developed. All types of variants, including heterozygous insertion/deletion variants, can be identified by SnackVar with minimal user effort. The featured reference sequences of all of the genes are prestored in SnackVar, allowing for detected variants to be precisely described based on coding DNA references according to the nomenclature of the Human Genome Variation Society. Among 88 previously reported variants from four insertion/deletion-rich genes (BRCA1, APC, CALR, and CEBPA), the result of SnackVar agreed with reported results in 87 variants [98.9% (93.0%; 99.9%)]. The cause of one incorrect variant calling was proven to be erroneous base callings from poor-quality trace files. Compared with commercial software, SnackVar required less than one-half of the time taken for the analysis of a selected set of test cases. We expect SnackVar to be a cost-effective option for clinical laboratories performing Sanger sequencing.

Fast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

The flow functions for plastic deformation have been developed to describe the plastic behavior of sheet metals. In order to explain the plastic behavior of material in metal forming processes via finite element analyses, two basic input functions should be applied. One is the yield function that determines the yielding behavior. The other is flow function to describe the hardening property of sheet metal. To describe the hardening properties of sheet materials under quasi-static tension condition in a wide range of plastic straining, various different equations are known such as classical Swift, Voce, Holloman, combined Swift-Voce, and recently proposed Kim-Tuan equations, etc. Those hardening equations are based on metallurgical or phenomenological investigations, and however the application of each equation has some limitation. In this study, the random growth of the binary tree method is introduced to develop the reliable hardening equations of various sheet metals (i.e. DP980, Pure Ti, AA5052-O, STS304, Ti-Gr2, and Mg-AZ31B) with no knowledge of existing hardening equation types. To evaluate the proposed method, the proposed equations developed by new approach are compared with the Voce, Swift, and Kim-Tuan hardening equations for stress-strain curve and the plastic instability point. Consequently, the proposed approach was proven to be very efficient to find the reliable and accurate hardening equation for any kind of materials.

We evaluated the reproducibility of computer-aided detections (CADs) with a convolutional neural network (CNN) on chest radiographs (CXRs) of abnormal pulmonary patterns in patients, acquired within a short-term interval. Anonymized CXRs (n = 9792) obtained from 2010 to 2016 and comprising five types of disease patterns, including the nodule (N), consolidation (C), interstitial opacity (IO), pleural effusion (PLE), and pneumothorax (PN), were included. The number of normal and abnormal CXRs was 6068 and 3724, respectively. The number of CXRs (region of interests, ROIs) of N, C, IO, PLE, and PN was 944 (1092), 550 (721), 280 (538), 1361 (1661), and 589 (622), respectively. CXRs were randomly allocated to training, tuning, and test sets in 70:10:20 ratios. Two thoracic radiologists labeled and delineated the ROIs of each disease pattern. The CAD system was developed using eDenseYOLO. For the reproducibility evaluation of developed CAD, paired CXRs of various diseases (N = 121, C = 28, IO = 12, PLE = 67, and PN = 20), acquired within a short-term interval from the test sets without any changes confirmed by thoracic radiologists, were used to evaluate CAD reproducibility. Percent positive agreement (PPAs) and Chamberlain’s percent positive agreement (CPPAs) were used to evaluate CAD reproducibility. The figure of merit (FOM) of five classes based on eDenseYOLO showed N-0.72 (0.68–0.75), C-0.41 (0.33–0.43), IO-0.97 (0.96–0.98), PLE-0.94 (0.92–95), and PN-0.87 (0.76–0.93). The PPAs of the five disease patterns including N, C, IO, PLE, and PN were 83.39%, 74.14%, 95.12%, 96.84%, and 84.58%, respectively, whereas the values of CPPAs were 71.70%, 59.13%, 91.16%, 93.91%, and 74.17%, respectively. The reproducibility of abnormal pulmonary patterns from CXRs, based on deep learning-based CAD, showed different results; this is important for assessing the reproducible performance of CAD in clinical settings.

A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based detection algorithm for C4d immunostaining in renal allografts. The objective of this study is to assess the diagnostic performance of the algorithm by comparing pathologists' diagnoses and analyzing the associations of the algorithm with clinical data. C4d immunostaining slides of renal allografts were obtained from two different institutions (100 slides from the Asan Medical Center and 86 slides from the Seoul National University Hospital) and scanned using two different slide scanners. Three pathologists and the algorithm independently evaluated each slide according to the Banff 2017 criteria. Subsequently, they jointly reviewed the results for consensus scoring. The result of the algorithm was compared with that of each pathologist and the consensus diagnosis. Clinicopathological associations of the results of the algorithm with allograft survival, histologic evidence of microvascular inflammation, and serologic results for donor-specific antibodies were also analyzed. As a result, the reproducibility between the pathologists was fair to moderate (kappa 0.36-0.54), which is comparable to that between the algorithm and each pathologist (kappa 0.34-0.51). The C4d scores predicted by the algorithm achieved substantial concordance with the consensus diagnosis (kappa = 0.61), and they were significantly associated with remarkable microvascular inflammation (P = 0.001), higher detection rate of donor-specific antibody (P = 0.003), and shorter graft survival (P < 0.001). In conclusion, the deep learning-based C4d detection algorithm showed a diagnostic performance similar to that of the pathologists.

To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. We retrospectively collected 2088 abnormal (nodule/mass) and 352 normal chest radiographs from two institutions. Three thoracic radiologists drew 2758 abnormalities regions. A total of 1736 abnormal chest radiographs were used for training and tuning convolutional neural networks (CNNs). The remaining 352 abnormal and 352 normal chest radiographs were used as a test set. Two CNNs (Mask R-CNN and RetinaNet) were selected to validate the effects of the squared different matrix size of chest radiograph (256, 448, 896, 1344, and 1792). For comparison, figure of merit (FOM) of jackknife free-response receiver operating curve and sensitivity were obtained. In Mask R-CNN, matrix size 896 and 1344 achieved significantly higher FOM (0.869 and 0.856, respectively) for detecting abnormalities than 256, 448, and 1792 (0.667–0.820) (p

Background Cardiorespiratory fitness (CRF) is a fundamental component of physical fitness. While maximal oxygen uptake (VO2max) is the gold standard for quantifying CRF, standard maximal exercise tests using direct measurements VO2max are dependent on the availability of laboratory equipment, and thereby expensive and time consuming. Recently, an equation was formulated to indirectly estimate VO2max using the YMCA 3-minute step test. Methods The study included 15 Korean (KR) and 15 Vietnamese (VN) healthy adults aged 19-35 years. All subjects completed a YMCA 3-minute step test (YMCA 3MST) and a maximal exercise treadmill test to predict VO2max and VO2max measures, respectively. Results There was a significant relationship between VO2max predicted from the YMCA 3MST and actual VO2max measurements from the treadmill test (r = 0.80, p < 0.0001; KR group: r = 0.81, p < 0.0001; VN group: r = 0.93, p < 0.0001). Bland-Altman analysis revealed statistical agreement between tests, although there was a systematic overestimation of 3.36 mL/kg/min for the KR group. Conclusion The equation for predicting VO2max from the YMCA 3MST was validated among the study subjects. However, future research should explore the validity and reliability of the YMCA 3MST equation for estimating VO2max in other populations.

Soft polymer nanoparticles can be functionalized with end-tethered polymer chains to control their solvent compatibility and stability. Controlling and understanding the behaviour of such functionalized latex suspensions are critical for their comprehensive applications. To investigate the effect of the nanoparticle architecture on their rheological behaviour, a library of polystyrene nanoparticles functionalized with a canopy of end-tethered poly(methyl acrylate) chains with different degrees of polymerization and grafting densities was prepared. When the end-tethered polymer chains were long enough, the suspensions of polymer-functionalized nanoparticles underwent a liquid to gel transition when the concentration of the nanoparticles was increased. The architecture of the polymer canopy was the determining factor for the mechanical properties of the resulting gels; nanoparticles with moderate grafting density where the polymer chains adopt a relaxed polymer brush conformation led to the formation of the strongest and most robust gels. In comparison with suspensions prepared with polymer functionalized nanoparticles, particles with a soft and swollen core formed gels with higher yield stress at a lower solid content.

South Korea is surrounded by the sea on three sides. The characteristics of offshore wind resources vary from region to region due to the influence of the distribution of the coastline and differences in roughness length and atmospheric stability between the coast and the sea. In particular, turbulent gusts and low-level wind shear occurring near the hub height of the wind turbine within the atmospheric boundary layer have a significant effect on the load of wind turbines. These severe weather phenomena are closely related to atmospheric stability. Therefore, the objective of this study is to determine differences in wind resource characteristics in the South Korean offshore and coast in relation to variations in atmospheric stability using observation data from the HeMOSU-1 meteorological tower in the West Sea and the Boseong meteorological observation tower on the southern coast. On the southern coast, changes in sea and land breezes are observed throughout diurnal and nocturnal periods, with an atmospheric stability distribution similar to that of land, which is unstable during the day and becomes more stable at night. On the other hand, the stable ratio continues to dominate in the west offshore. In the case of coastal areas, low-level wind shear occasionally occurs near the general wind turbine hub height approximately over 100 m due to the influence of winds from the sea. This study shows that when constructing an offshore wind farm, it is necessary to first analyze the characteristics of local coastal and offshore wind resources for more efficient and safe wind farm construction and operation.

Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false positive). Here we showed that transfer learning was more effective than scratch learning in terms of area under curve, sensitivity, specificity and negative/positive predictive value, that about 3900 images were minimally required to demonstrate an acceptable performance, and that algorithm performance can be customized according to the population characteristics by adjusting threshold value.

Parkinson's disease (PD) is a neurodegenerative disorder with only symptomatic treatments currently available. Although correct, early diagnoses of PD are important, the existing diagnostic method based on pathologic examinations only has an accuracy of approximately 80.6%. Although electroencephalography (EEG)-based assistive technology has been introduced, it has been difficult to implement in practice due to the high computational complexity and low accuracy of the analysis methods. This study proposed a fast, accurate PD prediction method using the Hjorth parameter and the gradient boosting decision tree (GBDT) algorithm.We used an open EEG dataset with 41 PD patients and 41 healthy controls (HCs); EEG signals were recorded from participants at the University of New Mexico (PD: 27 vs. HC: 27) and University of Iowa (PD: 14 vs. HC: 14). We explored the analytic time segment and frequency range in which the Hjorth parameter best represents the EEG characteristics of PD patients.Our best model (CatBoost-based) distinguished PD patients from controls with an accuracy of 89.3%, an area under the receiver operating characteristics curve (AUC) of 0.912, an F-score of 0.903, and an odds ratio of 115.5. These results showed that our models outperformed those of all other previous works and were even superior to previously known pathologic examination-based diagnoses with long-term follow-up (accuracy = 83.9%).The proposed methods are expected to be utilized as an effective method for improving the diagnosis of PD.

Scanning electron microscopy (SEM) is a high-resolution imaging technique with subnanometer spatial resolution that is widely used in materials science, basic science, and nanofabrication. However, conducting SEM is rather complex due to the nature of using an electron beam and the many parameters that must be adjusted to acquire high-quality images. Only trained operators can use SEM equipment properly, meaning that the use of SEM is restricted. To broaden the usability of SEM, we propose an autofocus method for a SEM system based on a dual deep learning network, which consists of an autofocusing-evaluation network (AENet) and an autofocusing-control network (ACNet). The AENet was designed to evaluate the quality of given images, with scores ranging from 0 to 9 regardless of the magnification. The ACNet can delicately control the focus of SEM online based on the AENet’s outputs for any lateral sample position and magnification. The results of these dual networks showed successful autofocus performance on three trained samples. Moreover, the robustness of the proposed method was demonstrated by autofocusing on unseen samples. We expect that our autofocusing system will not only contribute to expanding the versatility of SEM but will also be applicable to various microscopes.

Ryu, G.H.; Kim, H.; Kim, Y.-G.; Chon, K.-H.; Joo, J.Y., and Moon, C.-J., 2021. GIS-based site analysis of an optimal offshore wind farm for minimizing coastal disasters. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 246–250. Coconut Creek (Florida), ISSN 0749–0208. The horizontal force transmitted to the turbine and the substructure of a wind power system is very important factor in system safety, and such a system is particularly vulnerable to large-scale coastal disasters such as earthquakes and typhoons. Wind power systems built offshore and on coasts have reduced economic efficiency due to the increase in initial investment costs, because they require a more robust design when installed in areas vulnerable to coastal disasters. The western and southern seas of Korea have a relatively shallow water depth, which is advantageous for the construction of offshore wind farms. However, since it is close to the Pacific Rim, the probability of an earthquake is relatively high compared to those in other regions, and the frequency of typhoons is higher as well, so a more detailed site analysis is necessary. In this study, the GIS technique was used to select an optimal site for wind farms with a focus on reducing the risk of coastal disasters. The current state of earthquakes in the western and southern seas of Korea as well as the movement path and intensity of typhoons affecting or passing through the western and southern seas were also analyzed in a complex manner. As a result, an optimal site for an offshore wind farm with the lowest risk of coastal disasters was analyzed.

Kim, H.; Moon, C.-J.; Kim, Y.-G.; Chon, K.-H.; Joo, J.Y., and Ryu, G.H., 2021. Analysis of atmospheric stability for the prevention of coastal disasters and the development of efficient coastal renewable energy. In: Lee, J.L.; Suh, K.S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 241–245. Coconut Creek (Florida), ISSN 0749–0208. The ground friction force is lower in coastal and marine areas than it is on land, and there is a relatively high frequency of strong winds in such areas, which substantially affects the life and stability of wind turbines. In particular, the strong wind at the bottom of the atmospheric boundary layer is generated by a combination of thermal and mechanical factors, so there is always a risk due to vertical wind shear and strong turbulent gusts. Therefore, to prevent physical loss of wind turbines located in the atmospheric boundary layer, it is necessary to analyze the wind condition through a more accurate atmospheric stability analysis. In this study, observation data from the Boseong Standard Meteorological Tower are examined to select strong wind cases in the lower atmospheric boundary layer, and the vertical distribution characteristics of the wind resources are analyzed according to the Monin-Obukhov length (MOL). The results confirmed that the vertical wind velocity gradient caused by atmospheric stability mainly changes when the atmosphere is in the near-neutral state at night during the winter.

OBJECTIVE Magnetic resonance imaging (MRI) is the gold-standard tool for diagnosing lumbar spinal stenosis (LSS), but it is difficult to promptly examine all suspected cases with MRI considering the modality’s high cost and limited accessibility. Although radiography is an efficient screening technique owing to its low cost, rapid operability, and wide availability, its diagnostic accuracy is relatively poor. In this study, the authors aimed to develop a deep learning model with a convolutional neural network (CNN) for diagnosing severe central LSS using radiography and to evaluate radiological diagnostic features using gradient-weighted class activation mapping (Grad-CAM). METHODS Patients who had undergone both spinal MRI and radiography in the period from May 1, 2005, to December 31, 2017, were screened. According to the formal MRI report, participants were consecutively included in the severe central LSS or healthy control group, and radiographs for both groups were collected. A CNN-based transfer learning algorithm was developed to classify radiographic findings as LSS or normal (binary classification). The proposed models were evaluated using six performance metrics: area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and positive and negative predictive values. RESULTS The VGG19 model achieved the highest accuracy with an AUROC of 90.0% (95% CI 89.8%–90.3%) by training 12,442 images. Accuracy was 82.8% (95% CI 82.5%–83.1%) by averaging 5-fold models. Feature points on Grad-CAM were reasonable, and the features could be categorized into reduced disc height, narrow foramina, short pedicle, and hyperdense facet joint. The AUROC in the extra validation was 89.3% (95% CI 88.7%–90.0%). Accuracy was 81.8% (95% CI 80.6%–83.0%) by averaging 5-fold models. Multivariate logistic regression analysis showed that a combination of demographic factors (age and sex) did not improve the model performance. CONCLUSIONS The algorithm trained by a CNN to identify central LSS on radiographs showed high diagnostic accuracy and is expected to be useful as a triage tool. The algorithm could accurately localize the stenotic lesion to assist physicians in the identification of LSS.

In this letter, a quasi-coaxial through-silicon-via (TSV) is presented for millimeter-wave integrated circuit (IC) packaging. The quasi-coaxial-via (Q-COV) structure in which one side ground metal is removed can minimize the interconnect length when it is mounted, in comparison to the coaxial-via (COV) structure. Simulation analysis shows that the Q-COV has similar electrical characteristics as the COV up to 100 GHz even though there is no GND on one side. To make the small signal core of the Q-COV, the silicon-core metallization process was used and a Si-interposer with Q-COVs of 50- $\mu \text{m}$ core diameter was fabricated and mounted on the glass board for signal transition analysis. The measured transition loss in the mounted Si-interposer was very small, only about 0.6 dB at 100 GHz, and the return loss was more than 20 dB for the entire measured frequency band (0–110 GHz).

In this study, K(Ta0.6Nb0.4)O3 thin films were fabricated by sol–gel method and their structural and electrical properties were measured to investigate the applicability for electrocaloric devices. As the thickness of thin films increased, crystallinity was improved. The average thickness of the thin films with a single coating was about 130–140 nm. Dielectric constant, dielectric loss, remanent polarization, and coercive field of the 6 times coated thin KTN films at 30 °C were 4920, 0.492, 18.49 μC/cm2 and 54.7 kV/cm, respectively. As the number of coatings increased, the temperature at which the decrease in remanent polarization began also subsided. Likewise, the rate of change in remanent polarization increased as the temperature became higher. When a voltage of 220 kV/cm was applied to the 3 times coated KTN films, the electrocaloric property was 2.95 °C. When the 3 times coated thin KTN films were at 75 °C, electrocaloric property for a unit electric field was about 1.47 °C.

본 연구는 우리나라 원자력 발전소가 입지해 있는 지역을 대상으로 주민들의 참여에 관한 인식이 민·관 협력적 참여행동에 미치는 영향을 실증분석 함으로써 지역 주민의 원자력 발전에 대한 수용성을 향상시킬 수 있는 방안을 모색해 보고자 하였다. 이를 위해 원자력 발전소가 입지해 있는 5개 지역(기장, 울주, 울진, 경주, 영광)에 거주 중인 주민 600명을 대상으로 설문조사를 실시하여 변수 간 인과관계의 추세와 영향력의 크기를 검증하였다. 현재 원자력 발전소 입지 지역에는 근접거리 5km 안팎을 기준으로 보상의 유무가 결정되고 있기 때문에 보상지역과 비 보상지역 간의 원자력 및 참여에 관한 인식에도 차이가 있을 것으로 예상된다. 이에 주변지역을 거리에 따라 보상 및 비 보상지역으로 구분하여 비교분석 하였다. 분석결과, 보상 및 비 보상지역 모두에서 주민참여의 필요성 인식이 민·관 협력적 참여행동에 통계적으로 유의미한 정(+)의 영향을 미치는 것으로 나타났고, 주민참여의 비용 인식은 비 보상지역에서만 협력적 참여행동에 통계적으로 유의미한 부(-)의 영향을 미치는 것으로 나타났다. 주민참여의 편익 인식은 보상 및 비 보상지역 모두에서 민·관 협력적 참여행동에 통계적으로 유의미한 영향을 미치지 않는 것으로 나타났다. 이상의 분석결과를 통해 유추해보면 원자력 발전소 주변지역 주민들에 대한 직·간접적인 보상 이외에도 주민참여의 장려를 통하여 원자력 수용성의 제고가 가능할 것으로 사료된다. 이에 주민들의 참여의 필요성에 대한 공감대를 확산하고, 참여에 우호적인 분위기를 형성 및 유지하기 위해서 필요한 제반 요소들을 꾸준히 보강하기 위한 노력을 기울여야 할 것이다.

Pathologic diagnoses mainly depend on visual scoring by pathologists, a process that can be time-consuming, laborious, and susceptible to inter- and/or intra-observer variations. This study proposes a novel method to enhance pathologic scoring of renal allograft rejection. A fully automated system using a convolutional neural network (CNN) was developed to identify regions of interest (ROIs) and to detect C4d positive and negative peritubular capillaries (PTCs) in giga-pixel immunostained slides. The performance of faster R-CNN was evaluated using optimal parameters of the novel method to enlarge the size of labeled masks. Fifty and forty pixels of the enlarged size images showed the best performance in detecting C4d positive and negative PTCs, respectively. Additionally, the feasibility of deep-learning-assisted labeling as independent dataset to enhance detection in this model was evaluated. Based on these two CNN methods, a fully automated system for renal allograft rejection was developed. This system was highly reliable, efficient, and effective, making it applicable to real clinical workflow.

If the wind speed that passed through a wind turbine rotor disk area is constant, the hub height wind speed (HHWS) could be representative of the wind speed over the rotor disk area. However, this assumption cannot be applied to the large wind turbine, because of the wind shear effect by atmospheric stability. This is because the hub height wind speed cannot represent the vertical wind shear effect from the aerodynamics characteristic on the wind turbine. Using SCADA and offshore LiDAR observation data of the Anholt offshore wind farm, it is investigated whether the rotor equivalent wind speed (REWS) introduced in IEC61400-12-1 can contribute to the improvement of power output forecasting accuracy. The weighted value by separated sector area and vertical wind shear effect by difference between heights can explain the role of energy flux and atmospheric stability on the exact wind energy calculation. The commercial CFD model WindSim is used to calculate power production according to the HHWS and the REWS, and to compare them with the actual AEP of the local wind farm. The classification of atmospheric stability is carried out by Richardson number, which well represents the thermal and physical properties of the atmosphere below the atmospheric boundary layer, along with the wind shear coefficient and turbulence intensity. When atmospheric stability was classified by each stability index, the REWS-based predicted power output was sometimes more accurate than HHWS, but sometimes inferior. However, in most cases, using the REWS, it was possible to calculate an estimate closer to the actual power output. Through the results of this study, it is possible to provide a rationale for which method, REWS or HHWS, can more accurately calculate the expected power output and effectively derive the economic feasibility of the project by identifying the characteristics of local atmospheric stability before the wind farm project.

Rotor syndrome is caused by digenic loss-of-function variants in SLCO1B1 and SLCO1B3 but only a few studies have reported co-occurring inactivating variants from both genes. A rotor syndrome-causing long interspersed element-1 (LINE-1) insertion in SLCO1B3 had been reported to be highly prevalent in the Japanese population but there has been no additional report. In spite of its known association with various human diseases, LINE-1 is hard to detect with current sequencing technologies. In this study, we aimed to devise a method to screen the LINE-1 insertion variant and investigate the frequency of this variant in various populations. A chimeric sequence, that was generated by concatenating the reference sequence at the junction and a part of inserted LINE-1 sequence, was searched from 725 raw sequencing data files. In cases containing the chimeric sequence, confirmatory long-range PCR and gap-PCR were performed. In total, 95 (13.1%) of 725 patients were positive for the chimeric sequence, and all were confirmed to have the SLCO1B3 LINE-1 insertion by PCR-based tests. The same chimeric sequence was searched from the 1000 Genomes Project data repository and the carrier frequency was remarkably high in the East Asian populations (10.1%), especially in Southern Han Chinese (18.5%), but almost absent in other populations. This SLCO1B3 LINE-1 insertion should be screened in a population-specific manner under suspicion of Rotor syndrome and the methods proposed in this study would enable this in a simple way.

In managing patients with coronavirus disease 2019 (COVID‐19), early identification of those at high risk and real‐time monitoring of disease progression to severe COVID‐19 is a major challenge. We aimed to identify potential early prognostic protein markers and to expand understanding of proteome dynamics during clinical progression of the disease. We performed in‐depth proteome profiling on 137 sera, longitudinally collected from 25 patients with COVID‐19 (non‐severe patients, n = 13; patients who progressed to severe COVID‐19, n = 12). We identified 11 potential biomarkers, including the novel markers IGLV3‐19 and BNC2, as early potential prognostic indicators of severe COVID‐19. These potential biomarkers are mainly involved in biological processes associated with humoral immune response, interferon signalling, acute phase response, lipid metabolism, and platelet degranulation. We further revealed that the longitudinal changes of 40 proteins persistently increased or decreased as the disease progressed to severe COVID‐19. These 40 potential biomarkers could effectively reflect the clinical progression of the disease. Our findings provide some new insights into host response to SARS‐CoV‐2 infection, which are valuable for understanding of COVID‐19 disease progression. This study also identified potential biomarkers that could be further validated, which may support better predicting and monitoring progression to severe COVID‐19.