• Korean Journal of Radiology
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• v.23 no.10
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• pp.959-975
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• 2022

Objective: To investigate the agreement and reliability of estimating the volumes and normative percentiles (N%) of segmented brain regions among NeuroQuant (NQ), DeepBrain (DB), and FreeSurfer (FS) software programs, focusing on the comparison between NQ and DB. Materials and Methods: Three-dimensional T1-weighted images of 145 participants (48 healthy participants, 50 patients with mild cognitive impairment, and 47 patients with Alzheimer's disease) from a single medical center (SMC) dataset and 130 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset were included in this retrospective study. All images were analyzed with DB, NQ, and FS software to obtain volume estimates and N% of various segmented brain regions. We used Bland-Altman analysis, repeated measures ANOVA, reproducibility coefficient, effect size, and intraclass correlation coefficient (ICC) to evaluate inter-method agreement and reliability. Results: Among the three software programs, the Bland-Altman plot showed a substantial bias, the ICC showed a broad range of reliability (0.004-0.97), and repeated-measures ANOVA revealed significant mean volume differences in all brain regions. Similarly, the volume differences of the three software programs had large effect sizes in most regions (0.73-5.51). The effect size was largest in the pallidum in both datasets and smallest in the thalamus and cerebral white matter in the SMC and ADNI datasets, respectively. N% of NQ and DB showed an unacceptably broad Bland-Altman limit of agreement in all brain regions and a very wide range of ICC values (-0.142-0.844) in most brain regions. Conclusion: NQ and DB showed significant differences in the measured volume and N%, with limited agreement and reliability for most brain regions. Therefore, users should be aware of the lack of interchangeability between these software programs when they are applied in clinical practice.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.35-49
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• 2024

Video captioning technology, as a significant outcome of the integration between computer vision and natural language processing, has emerged as a key research direction in the field of artificial intelligence. This technology aims to achieve automatic understanding and language expression of video content, enabling computers to transform visual information in videos into textual form. This paper provides an initial analysis of the research trends in deep learning-based video captioning and categorizes them into four main groups: CNN-RNN-based Model, RNN-RNN-based Model, Multimodal-based Model, and Transformer-based Model, and explain the concept of each video captioning model. The features, pros and cons were discussed. This paper lists commonly used datasets and performance evaluation methods in the video captioning field. The dataset encompasses diverse domains and scenarios, offering extensive resources for the training and validation of video captioning models. The model performance evaluation method mentions major evaluation indicators and provides practical references for researchers to evaluate model performance from various angles. Finally, as future research tasks for video captioning, there are major challenges that need to be continuously improved, such as maintaining temporal consistency and accurate description of dynamic scenes, which increase the complexity in real-world applications, and new tasks that need to be studied are presented such as temporal relationship modeling and multimodal data integration.

• Journal of Pharmacopuncture
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• v.27 no.1
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• pp.1-13
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• 2024

Background: Diabetic nephropathy (DN) is one of the major complications of chronic hyperglycaemia affecting normal kidney functioning. The ayurvedic medicine curcumin (CUR) is pharmaceutically accepted for its vast biological effects. Objectives: The Curcuma-derived diferuloylmethane compound CUR, loaded on Poly (lactide-co-glycolic) acid (PLGA) nanoparticles was utilized to combat DN-induced renal apoptosis by selectively targeting and modulating Bcl2. Methods: Upon in silico molecular docking and screening study CUR was selected as the core phytocompound for nanoparticle formulation. PLGA-nano-encapsulated-curcumin (NCUR) were synthesized following standard solvent displacement method. The NCUR were characterized for shape, size and other physico-chemical properties by Atomic Force Microscopy (AFM), Dynamic Light Scattering (DLS) and Fourier-Transform Infrared (FTIR) Spectroscopy studies. For in vivo validation of nephro-protective effects, Mus musculus were pre-treated with CUR at a dose of 50 mg/kg b.w. and NCUR at a dose of 25 mg/kg b.w. (dose 1), 12.5 mg/kg b.w (dose 2) followed by alloxan administration (100 mg/kg b.w) and serum glucose levels, histopathology and immunofluorescence study were conducted. Results: The in silico study revealed a strong affinity of CUR towards Bcl2 (dock score -10.94 Kcal/mol). The synthesized NCUR were of even shape, devoid of cracks and holes with mean size of ~80 nm having -7.53 mV zeta potential. Dose 1 efficiently improved serum glucose levels, tissue-specific expression of Bcl2 and reduced glomerular space and glomerular sclerosis in comparison to hyperglycaemic group. Conclusion: This study essentially validates the potential of NCUR to inhibit DN by reducing blood glucose level and mitigating glomerular apoptosis by selectively promoting Bcl2 protein expression in kidney tissue.

• Steel and Composite Structures
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• v.50 no.6
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• pp.705-720
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• 2024

Analyzing the collapse behavior of thin-walled steel structures holds significant importance in ensuring their safety and longevity. Geometric imperfections present on the surface of metal materials can diminish both the durability and mechanical integrity of steel shells. These imperfections, encompassing local geometric irregularities and deformations such as holes, cavities, notches, and cracks localized in specific regions of the shell surface, play a pivotal role in the assessment. They can induce stress concentration within the structure, thereby influencing its susceptibility to buckling. The intricate relationship between the buckling behavior of these structures and such imperfections is multifaceted, contingent upon a variety of factors. The buckling analysis of thin-walled steel shell structures, similar to other steel structures, commonly involves the determination of crucial material properties, including elastic modulus, shear modulus, tensile strength, and fracture toughness. An established method involves the emulation of distributed geometric imperfections, utilizing real test specimen data as a basis. This approach allows for the accurate representation and assessment of the diversity and distribution of imperfections encountered in real-world scenarios. Utilizing defect data obtained from actual test samples enhances the model's realism and applicability. The sizes and configurations of these defects are employed as inputs in the modeling process, aiding in the prediction of structural behavior. It's worth noting that there is a dearth of experimental studies addressing the influence of geometric defects on the buckling behavior of cylindrical steel shells. In this particular study, samples featuring geometric imperfections were subjected to experimental buckling tests. These same samples were also modeled using Finite Element Analysis (FEM), with results corroborating the experimental findings. Furthermore, the initial geometrical imperfections were measured using digital image correlation (DIC) techniques. In this way, the response of the test specimens can be estimated accurately by applying the initial imperfections to FE models. After validation of the test results with FEA, a numerical parametric study was conducted to develop more generalized design recommendations for the stainless-steel shell structures with the initial geometric imperfection. While the load-carrying capacity of samples with perfect surfaces was up to 140 kN, the load-carrying capacity of samples with 4 mm defects was around 130 kN. Likewise, while the load carrying capacity of samples with 10 mm defects was around 125 kN, the load carrying capacity of samples with 14 mm defects was measured around 120 kN.

• Information Systems Review
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• v.24 no.1
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• pp.1-19
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• 2022

Social media-based communication has become crucial part of our personal and official lives. Therefore, it is no surprise that social media sentiment analysis has emerged an important way of detecting potential customers' sentiment trends for all kinds of companies. However, social media sentiment analysis suffers from huge number of sentiment features obtained in the process of conducting the sentiment analysis. In this sense, this study proposes a novel method by using Bayesian Network. In this model MBFS (Markov Blanket-based Feature Selection) is used to reduce the number of sentiment features. To show the validity of our proposed model, we utilized online review data from Yelp, a famous social media about restaurant, bars, beauty salons evaluation and recommendation. We used a number of benchmarking feature selection methods like correlation-based feature selection, information gain, and gain ratio. A number of machine learning classifiers were also used for our validation tasks, like TAN, NBN, Sons & Spouses BN (Bayesian Network), Augmented Markov Blanket. Furthermore, we conducted Bayesian Network-based what-if analysis to see how the knowledge map between target node and related explanatory nodes could yield meaningful glimpse into what is going on in sentiments underlying the target dataset.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.413-421
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• 2020

Objective: A widely applicable, non-invasive screening method for non-alcoholic fatty liver disease (NAFLD) is needed. We aimed to develop and validate an index combining computed tomography (CT) and routine clinical data for screening for NAFLD in a large cohort of adults with pathologically proven NAFLD. Materials and Methods: This retrospective study included 2218 living liver donors who had undergone liver biopsy and CT within a span of 3 days. Donors were randomized 2:1 into development and test cohorts. CTL-S was measured by subtracting splenic attenuation from hepatic attenuation on non-enhanced CT. Multivariable logistic regression analysis of the development cohort was utilized to develop a clinical-CT index predicting pathologically proven NAFLD. The diagnostic performance was evaluated by analyzing the areas under the receiver operating characteristic curve (AUC). The cutoffs for the clinical-CT index were determined for 90% sensitivity and 90% specificity in the development cohort, and their diagnostic performance was evaluated in the test cohort. Results: The clinical-CT index included CTL-S, body mass index, and aspartate transaminase and triglyceride concentrations. In the test cohort, the clinical-CT index (AUC, 0.81) outperformed CTL-S (0.74; p < 0.001) and clinical indices (0.73-0.75; p < 0.001) in diagnosing NAFLD. A cutoff of ≥ 46 had a sensitivity of 89% and a specificity of 41%, whereas a cutoff of ≥ 56.5 had a sensitivity of 57% and a specificity of 89%. Conclusion: The clinical-CT index is more accurate than CTL-S and clinical indices alone for the diagnosis of NAFLD and may be clinically useful in screening for NAFLD.

• Korean Journal of Radiology
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• v.22 no.1
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• pp.131-138
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• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

• Journal of The Korean Association For Science Education
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• v.29 no.1
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• pp.43-53
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• 2009

The teacher's role in education is important. Science education majors must be able to solve problems effectively and pertinently when facing new ones in various situations and complicated human relations in order to become successful science teacher. The purpose of this research is to develop a test that measures the Practical Ability of pre-service science teachers and to apply this to them. The Practical Efficacy Scale for Science Education Majors was also developed in order to be used for validation. In this research, Practical Ability of Science Education Majors consisted of four sub-domains: subject education, business administration, relations and self-development. The result of the correlations between the scores of four sub-domains and the composite score of Practical Ability Test for Preservice Science Teacher(PATPST) is relevant. Subject education and administration business is the highest correlation with PATPSP score specially, and correlation between two areas appeared high. The result of applying PATPSP scores differed according to the grade of science education majors, but not according to their majors. This study's limitation is that the subjects consisted only of female students. However, PATPSP could be a new method that will help science education majors be successful science teachers.

• Analytical Science and Technology
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• v.31 no.4
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• pp.161-170
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• 2018

The epidemic of disorders associated with synthetic stimulants, such as methamphetamine (MA) and amphetamine (AP), is a health, social, legal, and financial problem. Owing to the high potential of their abuse and addiction, reliable analytical methods are required to detect and identify MA, AP, and their metabolites in biological samples. Thus, a dilute-and-shoot liquid chromatography-tandem mass spectrophotometry (LC-MS/MS) was developed for simultaneous determination of MA, 4-hydroxymethamphetamine (4HMA), AP, and 4-hydroxyamphetamine (4HA) in urine. Urine sample ($100{\mu}L$) was mixed with $50{\mu}L$ of mobile phase consisting of 0.4 % formic acid and methanol and $50{\mu}L$ of working internal-standard solution. Aliquots of $8{\mu}L$ diluted urine was injected into the LC-MS/MS system. For all analytes, chromatographic separation was performed using a C18 reversed-phase column with gradient elution and a total run time of 5 min. The identification and quantification were performed by multiple reaction monitoring (MRM). Linear least-squares regression was conducted to generate a calibration curve, with $1/x^2$ as the weighting factor. The linear ranges were 2.0-200, 1.0-800, and 10-2500 ng/mL for 4HA and 4HMA, AP, and MA, respectively. The inter- and intraday precisions were within 6.6 %, whereas the inter- and intraday accuracies ranged from -14.9 to 11.3 %. The low limits of quantification were 2.0 ng/mL (4HA and 4HMA), 1.0 ng/mL (AP), and 10 ng/mL (MA). The proposed method exhibited satisfactory selectivity, dilution integrity, matrix effect, and stability, which are required for validation. Moreover, the purification efficiency of high-speed centrifugation was clearly higher than 6-15 % for QC samples (n=5), which was higher than that of the membrane-filtration method. The applicability of the proposed method was tested by forensic analysis of urine samples from drug abusers.

• Korean Journal of Food Science and Technology
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• v.42 no.3
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• pp.287-291
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• 2010

In this study, a simple and rapid pre-treatment method based on liquid extraction was applied for the simultaneous determination of three macrolides (spiramycin, tylosin, and tilmicosin) residues. In these studies, the stock farm products was used as a matrix sample. When the liquid extraction method was compared with the solid phase extraction (SPE) method, the former showed higher recovery percentages and simpler steps than the latter. The macrolids were separated using a reverse-phase C18 ($250\;mm{\times}4.6\;mm$, $5\;{\mu}m$) column and a gradient elution with mobile phases consisting of phosphate buffer (pH 2.5) and acetonitrile. Tylosin and tilmicosin were detected at 288 nm and spiramycin was detected at 232 nm. The average recovery percentage ranged between 83.0-90.2% for samples spiked with the three macrolids at 50 and 100 ng/g The validation results showed that the limit of detection (7 (spiramycin), 12 (tilmiconsin), 12 (tylosin) ng/g)) was under the regulatory tolerances and the linearity from calibration curves was satisfactory for determining the multi-residue of three macrolids in farm products. Monitoring samples were collected at the main cities in Korea as Seoul, Busan, Deajeon, Incheon, Deagu, and Gwangju. Microlide antibiotics were not detected in most samples.