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Abstract Despite its importance for regulating gene expression, nonsense-mediated mRNA decay (NMD) remains poorly understood. Here, we extend the findings of a previous landmark study that proposed several factors associated with NMD efficiency using matched genome and transcriptome data from The Cancer Genome Atlas Program (TCGA) by incorporating additional data including Genotype-Tissue Expression (GTEx), gnomAD, and metrics for mutational constraints. Factors affecting NMD efficiency are analyzed using an allele-specific expression (ASE)-based measure to reduce noise caused by random variations. Additionally, by combining our data with the updated allele frequency database of gnomAD, we demonstrate the spectrum of NMD efficiency according to the degree of gene-level mutational constraints (degree of a gene-tolerating loss-of-function variants). The NMD prediction model, trained on TCGA data, shows that gene-level mutational constraint is an important predictor of NMD efficiency. Findings of this study suggest the potential role of NMD on shaping the landscape of mutational constraints.

Abstract Recently, a founder Alu insertion in exon 4 of RP1 was detected in Japanese and Korean patients with inherited retinal diseases (IRDs). However, carrier frequency and diagnostic challenges for detecting AluY insertion are not established. We aim to investigate the frequency of AluY in individuals with or without IRDs and to overcome common diagnostic pitfalls associated with AluY insertion. A total of 1,072 subjects comprising 411 patients with IRD (IRD group) and 661 patients with other suspected Mendelian genetic disease (non-IRD group) was screened for AluY insertion. Targeted panel sequencing and whole-genome sequencing were used for detection of AluY insertion, and an optimized allele-specific PCR (AS-PCR) was used for validation. The AluY insertion was detected in 1.5% in IRD group (6/411). The AluY insertion was not observed in non-IRD group (0/661). All patients with AluY were confirmed to have RP1 pathogenic variants on the paired allele. We identified AluY allele dropout leading to false homozygosity for c.4196del pathogenic variant in Sanger sequencing. The allelic relationship between variants of RP1 was accurately determined by AluY AS-PCR. Delineating diagnostic challenges of AluY insertion and strategies to avoid potential pitfalls could aid clinicians in an accurate molecular diagnosis for patients with IRD.

Abstract Distinguishing between pathologic complete response and residual cancer after neoadjuvant chemotherapy (NAC) is crucial for treatment decisions, but the current imaging methods face challenges. To address this, we developed deep-learning models using post-NAC dynamic contrast-enhanced MRI and clinical data. A total of 852 women with human epidermal growth factor receptor 2 (HER2)-positive or triple-negative breast cancer were randomly divided into a training set (n = 724) and a validation set (n = 128). A 3D convolutional neural network model was trained on the training set and validated independently. The main models were developed using cropped MRI images, but models using uncropped whole images were also explored. The delayed-phase model demonstrated superior performance compared to the early-phase model (area under the receiver operating characteristic curve [AUC] = 0.74 vs. 0.69, P = 0.013) and the combined model integrating multiple dynamic phases and clinical data (AUC = 0.74 vs. 0.70, P = 0.022). Deep-learning models using uncropped whole images exhibited inferior performance, with AUCs ranging from 0.45 to 0.54. Further refinement and external validation are necessary for enhanced accuracy.

Abstract Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms.

IntroductionNumerous studies have suggested high concordance between tissue and circulating tumor DNA (ctDNA) comprehensive genomic profiling (CGP) tests but only few of them focused on fusions. In addition, atypical breakpoints occasionally detected from DNA-based fusion detection make interpretation difficult, and their clinical significance remains unclear. This study evaluated the clinical utility of ctDNA CGP for fusion detection.MethodsThe results of ctDNA CGP tests performed on patients with stage IV non-small cell lung cancer during routine clinical care were retrospectively reviewed. The concordance between ctDNA CGP and combined tissue test results was analyzed using CGP, immunohistochemistry, fluorescence in situ hybridization, and reverse transcription polymerase chain reaction. The clinical significance of fusions detected by ctDNA CGP, including those with atypical breakpoints at the DNA level, was assessed.ResultsIn total, 264 patients were tested with ctDNA CGP. Fusions were detected in 27 patients (10.2%), and the fusion drivers were RET (n=12, 4.6%), ALK (n=9, 3.4%), ROS1 (n=4, 1.5%), and FGFR2 (n=2, 0.8%). The overall prevalence of fusion in tissue CGP was comparable to that in ctDNA CGP. A total of 371 ctDNA-tissue test pairs were available, and the overall positive and negative percent agreement rates were 92.9% (13/14) and 100.0% (357/357), respectively. One ALK IHC-positive and ctDNA CGP-negative case did not respond to ALK-targeted therapy. Response to targeted therapy was assessed in 16 patients, and a partial response was achieved in all patients, including four with atypical breakpoints.ConclusionFusion detection using ctDNA CGP showed high concordance with tissue tests and accuracy in predicting therapeutic responses in patients with non-small cell lung cancer. ctDNA CGP may provide an important diagnostic tool for fusion detection.

The main challenge of pose estimation for six degrees of freedom (6DoF) is the lack of labeled data in real environment. In order to overcome this problem, many studies recently have trained deep learning models with synthetic data. However, a domain gap between real and synthetic environments exists, prompting various approaches to address this issue. In this work, we propose domain adaptation for self-supervised 6DoF pose estimation, which leverages the components and introduces an effective method to reduce domain discrepancy. First, we adopt a multi-level domain adaptation module, on image level and instance level, to learn domain-invariant features. Second, we used entropy-based alignment to minimize the entropy of representation embedding. Finally, we evaluate our approach on LineMOD and Occlusion-LineMOD datasets. Experiments show that our proposed method achieves higher performance compared to the prior methods and demonstrate effectiveness in domain shift scenarios on 6DoF pose estimation.

Abstract Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in importance since the advent of magnetic resonance imaging (MRI), diagnostic ability of plain radiographs has improved dramatically when combined with deep learning. Previously, we developed a convolutional neural network (CNN) model using a radiograph for diagnosing LSS. In this study, we aimed to improve and generalize the performance of CNN models and overcome the limitation of the single-pose-based CNN (SP-CNN) model using multi-pose radiographs. Individuals with severe or no LSS, confirmed using MRI, were enrolled. Lateral radiographs of patients in three postures were collected. We developed a multi-pose-based CNN (MP-CNN) model using the encoders of the three SP-CNN model (extension, flexion, and neutral postures). We compared the validation results of the MP-CNN model using four algorithms pretrained with ImageNet. The MP-CNN model underwent additional internal and external validations 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%) for internal validation. The AUROC of the MP-CNN model were 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-CNN 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.

Abstract Epilepsy is a neurological disorder in which the brain is transiently altered. Predicting outcomes in epilepsy is essential for providing feedback that can foster improved outcomes in the future. This study aimed to investigate whether applying spectral and temporal filters to resting-state electroencephalography (EEG) signals could improve the prediction of outcomes for patients taking antiseizure medication to treat temporal lobe epilepsy (TLE). We collected EEG data from a total of 46 patients (divided into a seizure-free group (SF, n = 22) and a non-seizure-free group (NSF, n = 24)) with TLE and retrospectively reviewed their clinical data. We segmented spectral and temporal ranges with various time-domain features (Hjorth parameters, statistical parameters, energy, zero-crossing rate, inter-channel correlation, inter-channel phase locking value and spectral information derived from Fourier transform, Stockwell transform, and wavelet transform) and compared their performance by applying an optimal frequency strategy, an optimal duration strategy, and a combination strategy. For all time-domain features, the optimal frequency and time combination strategy showed the highest performance in distinguishing SF patients from NSF patients (area under the curve (AUC) = 0.790 ± 0.159). Furthermore, optimal performance was achieved by utilizing a feature vector derived from statistical parameters within the 39- to 41-Hz frequency band with a window length of 210 s, as evidenced by an AUC of 0.748. By identifying the optimal parameters, we improved the performance of the prediction model. These parameters can serve as standard parameters for predicting outcomes based on resting-state EEG signals.

Abstract Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories—signal property, inter-/intra-position correlation, peak value/interval variability, and demographics—were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

Abstract Background To predict, using deep learning, the first recurrence in patients with neovascular age-related macular degeneration (nAMD) after three monthly loading injections of intravitreal anti-vascular endothelial growth factor (anti-VEGF). Methods Optical coherence tomography (OCT) images were obtained at baseline and after the loading phase. The first recurrence was defined as the initial appearance of a new retinal hemorrhage or intra/subretinal fluid accumulation after the initial resolution of exudative changes after three loading injections. Standard U-Net architecture was used to identify the three retinal fluid compartments, which include pigment epithelial detachment, subretinal fluid, and intraretinal fluid. To predict the first recurrence of nAMD, classification learning was conducted to determine whether the first recurrence occurred within three months after the loading phase. The recurrence classification architecture was built using ResNet50. The model with retinal regions of interest of the entire region and fluid region on OCT at baseline and after the loading phase is presented. Results A total of 1,444 eyes of 1,302 patients were included. The mean duration until the first recurrence after the loading phase was 8.20 ± 15.56 months. The recurrence classification system revealed that the model with the fluid region of OCT after the loading phase provided the highest classification performance, with an area under the receiver operating characteristic curve (AUC) of 0.725 ± 0.012. Heatmap analysis revealed that three pathological fluids, subsided choroidal neovascularization lesions, and hyperreflective foci were important areas for the first recurrence. Conclusions The deep learning algorithm allowed for the prediction of the first recurrence for three months after the loading phase with adequate feasibility. An automated prediction system may assist in establishing patient-specific treatment plans and the provision of individualized medical care for patients with nAMD.

Introduction: RNA sequence analysis can be effectively used to identify aberrant splicing, and tumor suppressor genes are adequate targets considering their loss-of-function mechanisms. Sanger sequencing is the simplest method for RNA sequence analysis; however, because of its insufficient sensitivity in cases with nonsense-mediated mRNA decay (NMD), the use of cultured specimens with NMD inhibition has been recommended, hindering its wide adoption.Method: The results of Sanger sequencing of peripheral blood RNA without NMD inhibition performed on potential splicing variants of tumor suppressor genes were retrospectively reviewed. For negative cases, in which no change was identified in the transcript, the possibility of false negativity caused by NMD was assessed through a review of the up-to-date literature.Results: Eleven potential splice variants of various tumor suppressor genes were reviewed. Six variants were classified as pathogenic or likely pathogenic based on the nullifying effect identified by Sanger RNA sequencing. Four variants remained as variants of uncertain significance because of identified in-frame changes or normal expression of both alleles. The result of one variant was suspected to be a false negative caused by NMD after reviewing a recent study that reported the same variant as causing a nullifying effect on the affected transcript.Conclusion: Although RNA changes found in the majority of cases were expected to undergo NMD by canonical rules, most cases (10/11) were interpretable by Sanger RNA sequencing without NMD inhibition due to incomplete NMD efficiency or allele-specific expression despite highly efficient NMD.

The research on multi-task learning has been steadily increasing due to its advantages, such as preventing overfitting, averting catastrophic forgetting, solving multiple inseparable tasks, and coping with data shortage. Here, we question whether to incorporate different orderings of feature levels based on distinct characteristics of tasks and their interrelationships in multitask learning. While in many classification tasks, leveraging the features extracted from the last layer is common, we thought that given different characteristics of tasks there might be a need to encompass different representation levels, i.e., different orderings of feature levels. Hence, we utilized the knowledge of different representation levels by features extracted from the various blocks of the main module and applied trainable parameters as weights on the features. This indicates that we optimized the solution to the question by learning to weigh the features in a task-specific manner and solving tasks with a combination of newly weighted features. Our method SimPara presents a modular topology of multitask learning that is efficient in terms of memory and computation, effective, and easily applicable to diverse tasks or models. To show that our approach is task-agnostic and highly applicable, we demonstrate its effectiveness in auxiliary task learning, active learning, and multilabel learning settings. This work underscores that by simply learning weights to better order the features learned by a single backbone, we can incur better task-specific performance of the model.

Abstract 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.

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.

Abstract The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer variabilities. Therefore, we proposed a fully automated cephalometry analysis with a cascade convolutional neural net (CNN). One thousand cephalometric x-ray images (2 k × 3 k) pixel were used. The dataset was split into training, validation, and test sets as 8:1:1. The 43 landmarks from each image were identified by an expert orthodontist. To evaluate intra-observer variabilities, 28 images from the dataset were randomly selected and measured again by the same orthodontist. To improve accuracy, a cascade CNN consisting of two steps was used for transfer learning. In the first step, the regions of interest (ROIs) were predicted by RetinaNet. In the second step, U-Net detected the precise landmarks in the ROIs. The average error of ROI detection alone was 1.55 ± 2.17 mm. The model with the cascade CNN showed an average error of 0.79 ± 0.91 mm (paired t-test, p = 0.0015). The orthodontist’s average error of reproducibility was 0.80 ± 0.79 mm. An accurate and fully automated cephalometric analysis was successfully developed and evaluated.

Objectives This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. Methods We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. Results 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model’s accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. Conclusions In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.

Abstract 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.

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.

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.

Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.

In recent years, photovoltaic (PV) power generation has attracted considerable attention as a new eco-friendly and renewable energy generation technology. With the recent development of semiconductor manufacturing technologies, PV power generation is gradually increasing. In this paper, we analyze the types of defects that form in PV power generation panels and propose a method for enhancing the productivity and efficiency of PV power stations by determining the defects of aging PV modules based on their temperature, power output, and panel images. The method proposed in the paper allows the replacement of individual panels that are experiencing a malfunction, thereby reducing the output loss of solar power generation plants. The aim is to develop a method that enables users to immediately check the type of failures among the six failure types that frequently occur in aging PV panels—namely, hotspot, panel breakage, connector breakage, busbar breakage, panel cell overheating, and diode failure—based on thermal images by using the failure detection system. By comparing the data acquired in the study with the thermal images of a PV power station, efficiency is increased by detecting solar module faults in deteriorated photovoltaic power plants.

In order to reduce the weight of parts in the automobile and electronic industries, various research on dissimilar welding techniques of aluminum and steel is being carried out. Since dissimilar materials have different physical and electrochemical characteristics, joining through conventional fusion welding is challenging, and there is a high probability of a decrease in strength of the welded joints. To solve this problem, a mechanical fastening method is mainly applied to join dissimilar parts with different material properties, but this process has disadvantages in terms of productivity improvement and cost reduction because additional consumables, such as rivets, are required. In this research, we investigated the optimization of the weld bonding conditions of joints using epoxy-based adhesive bonding and DeltaSpot welding for Al/Fe dissimilar materials. For each experimental condition, the corrosion resistance and tensile shear strength of the welded joints were evaluated according to salt spray test times of 0 h, 640 h, 1280 h, and 1920 h. As a whole, as the salt spray test time increased, the tensile shear strength of the welded joints decreased. It was confirmed that weld bonding, after manual polishing of the aluminum side, resulted in the highest average tensile shear strength of 5.88 kN at 1920 h, which was an increase compared with other conditions.

Background: The rate of metabolic syndrome (MetS) is exceptionally high in Korea, and the health risks and dietary implications of MetS have been reported extensively. Although most meals include combination foods, little is known about metabolic responses to meals containing various foods (such as a hamburger, which typically includes meat, bread, and vegetables). The purpose of this study was to compare glycemic responses and postprandial-triglyceride (PTG) concentrations after consuming each of four test meals: bibimbap, kimbap, pork cutlet, and hamburger. Methods: A clinical trial was carried out with 32 men using an open 4 × 4 × 4 treatment, sequence, and period crossover design. The blood-glucose concentrations, incremental area under the curve (iAUC), glycemic index (GI), insulinogenic index, and PTG after each test meal were determined. Results: The iAUC2h and Cmax responses were proportional to the amount of carbohydrates in the experimental diets [i.e., higher (p

Background: To investigate the antidiabetic effects of hydrolyzed ginseng extract (HGE) for Korean participants in an 8-wk, randomized, double-blinded, placebo-controlled clinical trial. Methods: Impaired fasting glucose participants [fasting plasma glucose (FPG) ≥ 5.6mM or

A design method of an ultra-wideband transition from double-sided parallel stripline (DSPSL) to coplanar waveguide (CPW) is proposed based on analytical expressions of characteristic impedance. The conformal mapping is used to obtain the characteristic impedance for each section of the transition within 3.7% accuracy as compared with the EM simulation results. An efficient and clear guideline for the design of the transition is proposed. The implemented transition performs less than 0.6 dB insertion loss per transition for frequencies from 40 MHz to 12 GHz and less than 1.2 dB insertion loss to 27 GHz, which well exceeds the previous results in the literature.

A clear and efficient design method for ultra-wideband microstrip-to-suspended stripline transition, which is based on the analytical expressions of the whole transitional structure, is presented. The conformal mapping is applied to obtain the characteristic impedance of the transitional structure within 2.85% accuracy as compared with the EM-simulation results. The transition is designed to provide broadband impedance matching and smooth field conversion. The implemented transition performs less than 0.6 dB insertion loss per transition for frequencies up to 30 GHz.

Circulating tumor DNA (ctDNA) has emerged as a promising tool for various clinical applications, including early diagnosis, therapeutic target identification, treatment response monitoring, prognosis evaluation, and minimal residual disease detection. Consequently, ctDNA assays have been incorporated into clinical practice. In this review, we offer an indepth exploration of the clinical implementation of ctDNA assays. Notably, we examined existing evidence related to pre-analytical procedures, analytical components in current technologies, and result interpretation and reporting processes. The primary objective of this guidelines is to provide recommendations for the clinical utilization of ctDNA assays. [ABSTRACT FROM AUTHOR]

Rationale: Differential diagnosis of pleural effusion is challenging in clinical practice. Objectives: We aimed to develop a machine learning model to classify the five common causes of pleural effusions. Methods: This retrospective study collected 49 features from clinical information, blood, and pleural fluid of adult patients who underwent diagnostic thoracentesis between October 2013 and December 2018. Pleural effusions were classified into the following five categories: transudative, malignant, parapneumonic, tuberculous, and other. The performance of five different classifiers, including multinomial logistic regression, support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LGB), was evaluated in terms of accuracy and area under the receiver operating characteristic curve through fivefold cross-validation. Hybrid feature selection was applied to determine the most relevant features for classifying pleural effusion. Results: We analyzed 2,253 patients (training set, n = 1,459; validation set, n = 365; extra-validation set, n = 429) and found that the LGB model achieved the best performance in both validation and extra-validation sets. After feature selection, the accuracy of the LGB model with the selected 18 features was equivalent to that with all 49 features (mean6standard deviation): 0.81860.012 and 0.77760.007 in the validation and extra-validation sets, respectively. The model's mean area under the receiver operating characteristic curve was as high as 0.93060.042 and 0.91660.044 in the validation and extra-validation sets, respectively. In our model, pleural lactate dehydrogenase, protein, and adenosine deaminase levels were the most important factors for classifying pleural effusions. Conclusions: Our LGB model showed satisfactory performance for differential diagnosis of the common causes of pleural effusions. This model could provide clinicians with valuable information regarding the major differential diagnoses of pleural diseases. [ABSTRACT FROM AUTHOR]