• Molecules and Cells
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• v.23 no.3
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• pp.357-362
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• 2007

There is an increasing demand for high throughput (HTP) methods for gene analysis on a genome-wide scale. However, the current repertoire of HTP detection methodologies allows only a limited range of cellular phenotypes to be studied. We have constructed two HTP-optimized expression vectors generated from the red fluorescent reporter protein (RFP) gene. These vectors produce RFP-tagged target proteins in a multiple expression system using gateway cloning technology (GCT). The RFP tag was fused with the cloned genes, thereby allowing us localize the expressed proteins in mammalian cells. The effectiveness of the vectors was evaluated using an HTP-screening system. Sixty representative human C2 domains were tagged with RFP and overexpressed in HiB5 neuronal progenitor cells, and we studied in detail two C2 domains that promoted the neuronal differentiation of HiB5 cells. Our results show that the two vectors developed in this study are useful for functional gene analysis using an HTP-screening system on a genome-wide scale.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.15-22
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• 2019

Highly pathogenic avian influenza (HPAI) is among the top infectious disease priorities in Korea and the leading cause of economic loss in relevant poultry industry. An understanding of the spatial epidemiology of HPAI outbreak is essential in assessing and managing the risk of the infection. Though previous studies have reported the majority of outbreaks occurred clustered in what are preferred to as densely populated poultry regions, especially in southwest coast of Korea, little is known about the spatial distribution of risk areas vulnerable to HPAI occurrence based on geographic information system (GIS). The main aim of the present study was to develop a GIS-based risk index model for defining potential high-risk areas of HPAI outbreaks and to explore spatial distribution in relative risk index for each 252 Si-Gun-Gu (administrative unit) in Korea. The risk index was derived incorporating seven GIS database associated with risk factors of HPAI in a standardized five-score scale. Scale 1 and 5 for each database represent the lowest and the highest risk of HPAI respectively. Our model showed that Jeollabuk-do, Chungcheongnam-do, Jeollanam-do and Chungcheongbuk-do regions will have the highest relative risk from HPAI. Areas with risk index value over 4.0 were Naju, Jeongeup, Anseong, Cheonan, Kochang, Iksan, Kyeongju and Kimje, indicating that Korea is at risk of HPAI introduction. Management and control of HPAI becomes difficult once the virus are established in domestic poultry populations; therefore, early detection and development of nationwide monitoring system through targeted surveillance of high-risk spots are priorities for preventing the future outbreaks.

• Journal of Applied Biological Chemistry
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• v.62 no.1
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• pp.31-38
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• 2019

Scutellaria baicalensis Georgi (Scutellariae Radix) has been widely used as a dietary ingredient and traditional herbal medicine such as diuretic, hyperlipidemia, antibacterial, anti-allergy, anti-inflammatory and anticancer properties. In this study, the isolation of biomarkers or bioactive compounds from complex S. baicalensis extracts represents an essential step for de novo identification and bioactivity assessment. The bioactive fraction consisted of eight compounds which was chromatographed on an analytical high performance liquid chromatography column using two different gradient runs. A simulative replacement of the analytical column with a medium pressure liquid chromatography and open column allowed the determination of gradient profile to allow sufficient separation in the preparative scale. From the optimized method, eight standard compounds have been identified in the fractions. In addition, MS, UV, HRMS detection was provided by ultraperformance liquid chromatographyequadrupole time-of-flight mass spectrometry (UPLC-QTof-MS) of all fractions. Therefore, this scale up procedure was successfully applied to a S. baicalensis extract.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1424-1436
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• 2020

Although huge progress has been made in current image segmentation work, there are still no efficient segmentation strategies for tree image which is taken from natural environment and contains complex background. To improve those problems, we propose a method for tree image segmentation combining adaptive mean shifting with image abstraction. Our approach perform better than others because it focuses mainly on the background of image and characteristics of the tree itself. First, we abstract the original tree image using bilateral filtering and image pyramid from multiple perspectives, which can reduce the influence of the background and tree canopy gaps on clustering. Spatial location and gray scale features are obtained by step detection and the insertion rule method, respectively. Bandwidths calculated by spatial location and gray scale features are then used to determine the size of the Gaussian kernel function and in the mean shift clustering. Furthermore, the flood fill method is employed to fill the results of clustering and highlight the region of interest. To prove the effectiveness of tree image abstractions on image clustering, we compared different abstraction levels and achieved the optimal clustering results. For our algorithm, the average segmentation accuracy (SA), over-segmentation rate (OR), and under-segmentation rate (UR) of the crown are 91.21%, 3.54%, and 9.85%, respectively. The average values of the trunk are 92.78%, 8.16%, and 7.93%, respectively. Comparing the results of our method experimentally with other popular tree image segmentation methods, our segmentation method get rid of human interaction and shows higher SA. Meanwhile, this work shows a promising application prospect on visual reconstruction and factors measurement of tree.

• Smart Structures and Systems
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• v.31 no.4
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• pp.421-436
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• 2023

This paper presents a vibration displacement measurement and damage identification method for a space truss structure from its vibration videos. Features from Accelerated Segment Test (FAST) algorithm is combined with adaptive threshold strategy to detect the feature points of high quality within the Region of Interest (ROI), around each node of the truss structure. Then these points are tracked by Kanade-Lucas-Tomasi (KLT) algorithm along the video frame sequences to obtain the vibration displacement time histories. For some cases with the image plane not parallel to the truss structural plane, the scale factors cannot be applied directly. Therefore, these videos are processed with homography transformation. After scale factor adaptation, tracking results are expressed in physical units and compared with ground truth data. The main operational frequencies and the corresponding mode shapes are identified by using Subspace Stochastic Identification (SSI) from the obtained vibration displacement responses and compared with ground truth data. Structural damages are quantified by elemental stiffness reductions. A Bayesian inference-based objective function is constructed based on natural frequencies to identify the damage by model updating. The Success-History based Adaptive Differential Evolution with Linear Population Size Reduction (L-SHADE) is applied to minimise the objective function by tuning the damage parameter of each element. The locations and severities of damage in each case are then identified. The accuracy and effectiveness are verified by comparison of the identified results with the ground truth data.

• Current Optics and Photonics
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• v.7 no.1
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• pp.38-46
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• 2023

Optical Fourier-domain reflectometry (OFDR) sensors have been widely used to measure distances with high resolution and speed in a noncontact state. In the electroplating process of a printed circuit board, it is critically important to monitor the copper-plating thickness, as small deviations can lead to defects, such as an open or short circuit. In this paper we employ a phase-based OFDR sensor for in situ relative distance sensing of a sample with nanometer-scale resolution, during electroplating. We also develop an optical-path difference (OPD)-regulated sensing probe that can maintain a preset distance from the sample. This function can markedly facilitate practical measurements in two aspects: Optimal distance setting for high signal-to-noise ratio OFDR sensing, and protection of a fragile probe tip via vertical evasion movement. In a sample with a centimeter-scale structure, a conventional OFDR sensor will probably either bump into the sample or practically out of the detection range of the sensing probe. To address this limitation, a novel OPD-regulated OFDR system is designed by combining the OFDR sensing probe and linear piezo motors with feedback-loop control. By using multiple OFDR sensors, it is possible to effectively monitor copper-plating thickness in situ and uniformize it at various positions.

• Anxiety and mood
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• v.18 no.2
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• pp.65-71
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• 2022

Objective : This study was to examine the impact of adverse childhood experiences on social anxiety symptoms in young adults and verify the mediating effects of positive resources. Methods : Data from 1,317 young adults aged to 18 to 29 years who took part in the university-based cross-sectional survey were analyzed. All participants completed Adverse Childhood Experience (ACE) scale, Social Avoidance and Distress Scale (SAD), and Positive Resources Test (POREST). Results : In young adults, 9.3% had severe social anxiety symptoms. Based on 10 ACE categories, 32.7% of participants reported one or more adverse childhood experience, and 4.5% reported four or more different forms of adverse childhood experiences. Young adults with higher social anxiety symptoms were likely to report more adverse childhood experiences, and less positive resources. Multivariate regression analysis indicated that positive resources moderated the association between adverse childhood experiences and social anxiety symptoms. Conclusion : Based on the results, professionals need to consider early detection of adverse childhood experiences and comorbid social anxiety symptoms. In addition, various positive psychological interventions for individuals with adverse childhood experiences are needed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.456-477
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• 2024

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively.

• Korean Journal of Radiology
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• v.2 no.4
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• pp.197-203
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• 2001

Objective: To compare the clinical utility of contrast-enhanced color Doppler US in the differentiation of retinal detachment (RD) from vitreous membrane (VM) with that of various conventional US modalities, and to analyze the enhancement patterns in cases showing an enhancement effect. Materials and Methods: In 32 eyes examined over a recent two-year period, RD (n=14) and VM (n=18) were confirmed by surgery (n=28) or clinical follow-up (n=4). In all cases, gray-scale, color Doppler, and power Doppler US were performed prior to contrast injection, and after the intravenous injection of Levovist (Schering, Berlin) by hand for 30 seconds at a dose of 2.5 g and a concentration of 300 mg/mL via an antecubital vein, contrast-enhanced color Doppler US was performed. At Doppler US, the diagnostic criterion for RD and VM was whether or not color signals were visualized in membranous structures. Results: Diagnostic accuracy was 78% at gray-scale US, 81% at color Doppler US, 59% at power Doppler US, and 97% at contrast-enhanced color Doppler US. The sensitivity of color Doppler US to color signals in RD increased from 57% to 93% after contrast enhancement. The enhancement patterns observed were signal accentuation (n=3), signal extension (n=2), signal addition (n=3), and new signal visualization (n=5). Conclusion: Contrast-enhanced color Doppler US was the most accurate US modality for differentiating RD from VM, showing a significantly increased signal detection rate in RD.

• Smart Structures and Systems
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• v.33 no.2
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.