• Korean Journal of Radiology
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• v.22 no.7
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• pp.1163-1171
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• 2021

Objective: To analyze the correlations between intraoperative ultrasound and MRI metrics of the spinal cord in degenerative cervical myelopathy and identify novel potential predictive ultrasonic indicators of neurological recovery for degenerative cervical myelopathy. Materials and Methods: Twenty-two patients who underwent French-door laminoplasty for multilevel degenerative cervical myelopathy were followed up for 12 months. The Japanese Orthopedic Association (JOA) scores were assessed preoperatively and 12 months postoperatively. Maximum spinal cord compression and compression rates were measured and calculated using both intraoperative ultrasound imaging and preoperative T2-weight (T2W) MRI. Signal change rates of the spinal cord on preoperative T2W MRI and gray value ratios of dorsal and ventral spinal cord hyperechogenicity on intraoperative ultrasound imaging were measured and calculated. Correlations between intraoperative ultrasound metrics, MRI metrics, and the recovery rate JOA scores were analyzed using Spearman correlation analysis. Results: The postoperative JOA scores improved significantly, with a mean recovery rate of 65.0 ± 20.3% (p < 0.001). No significant correlations were found between the operative ultrasound metrics and MRI metrics. The gray value ratios of the spinal cord hyperechogenicity was negatively correlated with the recovery rate of JOA scores (ρ = -0.638, p = 0.001), while the ventral and dorsal gray value ratios of spinal cord hyperechogenicity were negatively correlated with the recovery rate of JOA-motor scores (ρ = -0.582, p = 0.004) and JOA-sensory scores (ρ = -0.452, p = 0.035), respectively. The dorsal gray value ratio was significantly higher than the ventral gray value ratio (p < 0.001), while the recovery rate of JOA-motor scores was better than that of JOA-sensory scores at 12 months post-surgery (p = 0.028). Conclusion: For degenerative cervical myelopathy, the correlations between intraoperative ultrasound and preoperative T2W MRI metrics were not significant. Gray value ratios of the spinal cord hyperechogenicity and dorsal and ventral spinal cord hyperechogenicity were significantly correlated with neurological recovery at 12 months postoperatively.

• Journal of Ecology and Environment
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• v.38 no.4
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• pp.529-536
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• 2015

This study was performed to provide important basic data for the preservation and management of Scopura laminata, a species endemic to Korea, by elucidating the spatial characteristics of its present, potential, and future distribution areas. Currently, this species is found in the Odaesan National Park area of South Korea and has been known to be restricted in its habitat due to its poor mobility, as even fully grown insects do not have wings. Utilizing the MaxEnt model, 20 collection points around Odaesan National Park were assessed to analyze and predict spatial distribution characteristics. The precision of the MaxEnt model was excellent, with an AUC value of 0.833. Variables affecting the potential distribution area of S. laminata by more than 10% included the range of annual temperature, seasonality of precipitation, and precipitation of the driest quarter, in order of greatest to least impact. Compared to the current potential distribution area, no significant difference in the overall habitable area was predicted for the 2050s or 2070s. It was, however, demonstrated that the potential habitable area would be reduced in the 2050s by up to 270.3 km from the current area of 403.9 km; further, no potential habitable area was anticipated by the 2070s according to our predictive model. Taken together, it is anticipated that this endemic species could be significantly affected by climate changes, and hence effective countermeasures are strongly warranted for the preservation of habitats and species management.

• Journal of Environmental Science International
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• v.18 no.2
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• pp.129-139
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• 2009

Development of an artificial neural network model was presented to predict the daily maximum $SO_2$ concentration in the urban-industrial area of Ulsan. The network model was trained during April through September for 2000-2005 using $SO_2$ potential parameters estimated from meteorological and air quality data which are closely related to daily maximum $SO_2$ concentrations. Meteorological data were obtained from regional modeling results, upper air soundings and surface field measurements and were then used to create the $SO_2$ potential parameters such as synoptic conditions, mixing heights, atmospheric stabilities, and surface conditions. In particular, two-stage clustering techniques were used to identify potential index representing major synoptic conditions associated with high $SO_2$ concentration. Two neural network models were developed and tested in different conditions for prediction: the first model was set up to predict daily maximum $SO_2$ at 5 PM on the previous day, and the second was 10 AM for a given forecast day using an additional potential factors related with urban emissions in the early morning. The results showed that the developed models can predict the daily maximum $SO_2$ concentrations with good simulation accuracy of 87% and 96% for the first and second model. respectively, but the limitation of predictive capability was found at a higher or lower concentrations. The increased accuracy for the second model demonstrates that improvements can be made by utilizing more recent air quality data for initialization of the model.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.3
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• pp.173-183
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• 2000

This study measured the zeta potential of both latex colloidal particles with carboxylate surface groups and glass beads (collectors) with silanol surface group employing various solution with different chemical characteristics. The results have been compared with the surface chemistry theory. The zeta potential of the particle and collector increased with increasing pH up to 5.0 regardless of the solution chemistry. For a monovalent electrolyte solution(sodium chloride solution) the zeta potential steadily increased until the pH reached 9.5. In contrast, little change in zeta potential was made between 5.0 and 9.5 for a divalent electrolyte solution (sodium chloride solution) the zeta potential steadily increased until the pH reached 9.5. In contrast, little change in zeta potential was made between 5.0 and 9.5 for a divalent electrolyte solution (calcium chloride solution). In other words, the more the pH decreases, the larger the effect of neutral salts, such as NaCl and CaCl$_2$, have on the ζ-potential values. In this study, the PZPC(point of zero proton condition) of the particle and collector occurred below a pH of 3.1, H(sup)+ and OH(sup)- acted as a PDI (potential determining ion), and Na(sup)+ acted as an IDI(indifferent ion). The magnitude of the negative ζ-potential values of the particle and collector monotonically increased as the concentrations of Na(sup)+ or Ca(sup)2+([Na(sup)+] or [Ca(sup)2+]) decreased (the values of pNa or pCa increased). In the case of latex particles, the ζ-potential should aproach zero (isoelectric point; IEP) asymptotically as the pNa approaches zero, while in the case of calcium chloride electrolyte, ζ-potential reversal may be expected to occur around 3.16$\times$10(sup)-2MCaCl$_2$(pCa=1.5). pH, valance and ionic strength can be used in various ways to improve the water treatment efficiency by modifying the charge characteristics of the particle and collector. Predictive capability is far less certain when EDL(electrical double layer) repulsive forces exist between the particle and collector.

• Progress in Medical Physics
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• v.21 no.1
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• pp.22-28
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• 2010

To evaluate the potential value of $^1H$ Magnetic resonance spectroscopy (MRS) for detecting and characterizing invasive ductal carcinoma of breast. We conducted $^1H$ Magnetic resonance spectroscopy (MRS), using a 3.0T MR scanner, on 40 patients who were histologically diagnosed to have invasive ductal carcinoma (IDC); tumor areas of the patients were designated as experimental samples, and non-tumor areas as control samples. The peak at 3.2 ppm is characteristically intense and observed in 34 cases of the total 40 invasive ductal carcinoma (sensitivity 86.2%; specificity 100%; positive predictive value 100%; negative predictive value 60%). In constrast peak at 1.3 ppm is characteristically intense and observed in normal breast (sensitivity 86.2%; specificity 100%; positive predictive value 100%; negative predictive value 60%). The study shows that $^1H$ MRS can effectively discriminate invasive ductal carcinoma from normal breast in most cases. It also demonstrates the feasibility of localized in vivo $^1H$ MRS technique as a new diagnostic modality in the detection of breast tumor.

• Food Science of Animal Resources
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• v.30 no.3
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• pp.487-494
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• 2010

Cooked pork can be easily contaminated with Staphylococcus aureus during carriage and serving after cooking. This study was performed to develop growth prediction models of S. aureus to assure the safety of cooked pork. The Baranyi and Gompertz primary predictive models were compared. These growth models for S. aureus in cooked pork were developed at storage temperatures of 5, 15, and $25^{\circ}C$. The specific growth rate (SGR) and lag time (LT) values were calculated. The Baranyi model, which displayed a $R^2$ of 0.98 and root mean square error (RMSE) of 0.27, was more compatible than the Gompertz model, which displayed 0.84 in both $R^2$ and RMSE. The Baranyi model was used to develop a response surface secondary model to indicate changes of LT and SGR values according to storage temperature. The compatibility of the developed model was confirmed by calculating $R^2$, $B_f$, $A_f$, and RMSE values as statistic parameters. At 5, 15 and $25^{\circ}C$, $R^2$ was 0.88, 0.99 and 0.99; RMSE was 0.11, 0.24 and 0.10; $B_f$ was 1.12, 1.02 and 1.03; and $A_f$ was 1.17, 1.03 and 1.03, respectively. The developed predictive growth model is suitable to predict the growth of S. aureus in cooked pork, and so has potential in the microbial risk assessment as an input value or model.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.129-148
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• 2023

Bitcoin is a blockchain technology-based digital currency that has been recognized as a representative cryptocurrency and a financial investment asset. Due to its highly volatile nature, Bitcoin has gained a lot of attention from investors and the public. Based on this popularity, numerous studies have been conducted on price and trend prediction using machine learning and deep learning. This study employed LSTM (Long Short Term Memory) and CNN (Convolutional Neural Networks), which have shown potential for predictive performance in the finance domain, to enhance the classification accuracy in Bitcoin price trend prediction. XAI(eXplainable Artificial Intelligence) techniques were applied to the predictive model to enhance its explainability and interpretability by providing a comprehensive explanation of the model. In the empirical experiment, CNN was applied to technical indicators and Google trend data to build a Bitcoin price trend prediction model, and the CNN model using both technical indicators and Google trend data clearly outperformed the other models using neural networks, SVM, and LSTM. Then SHAP(Shapley Additive exPlanations) was applied to the predictive model to obtain explanations about the output values. Important prediction drivers in input variables were extracted through global interpretation, and the interpretation of the predictive model's decision process for each instance was suggested through local interpretation. The results show that our proposed research framework demonstrates both improved classification accuracy and explainability by using CNN, Google trend data, and SHAP.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.344-353
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• 2021

Objective: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of this study was to develop a predictive model for the mitotic index of local primary GIST, based on deep learning algorithm. Materials and Methods: Abdominal contrast-enhanced CT images of 148 pathologically confirmed GIST cases were retrospectively collected for the development of a deep learning classification algorithm. The areas of GIST masses on the CT images were retrospectively labelled by an experienced radiologist. The postoperative pathological mitotic count was considered as the gold standard (high mitotic count, > 5/50 high-power fields [HPFs]; low mitotic count, ≤ 5/50 HPFs). A binary classification model was trained on the basis of the VGG16 convolutional neural network, using the CT images with the training set (n = 108), validation set (n = 20), and the test set (n = 20). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both, the image level and the patient level. The receiver operating characteristic curves were generated on the basis of the model prediction results and the area under curves (AUCs) were calculated. The risk categories of the tumors were predicted according to the Armed Forces Institute of Pathology criteria. Results: At the image level, the classification prediction results of the mitotic counts in the test cohort were as follows: sensitivity 85.7% (95% confidence interval [CI]: 0.834-0.877), specificity 67.5% (95% CI: 0.636-0.712), PPV 82.1% (95% CI: 0.797-0.843), NPV 73.0% (95% CI: 0.691-0.766), and AUC 0.771 (95% CI: 0.750-0.791). At the patient level, the classification prediction results in the test cohort were as follows: sensitivity 90.0% (95% CI: 0.541-0.995), specificity 70.0% (95% CI: 0.354-0.919), PPV 75.0% (95% CI: 0.428-0.933), NPV 87.5% (95% CI: 0.467-0.993), and AUC 0.800 (95% CI: 0.563-0.943). Conclusion: We developed and preliminarily verified the GIST mitotic count binary prediction model, based on the VGG convolutional neural network. The model displayed a good predictive performance.

• Journal of Chest Surgery
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• v.57 no.1
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• pp.44-52
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• 2024

Background: Visceral pleural invasion (VPI) is a poor prognostic factor that contributes to the upstaging of early lung cancers. However, the preoperative assessment of VPI presents challenges. This study was conducted to examine intraoperative pleural carcinoembryonic antigen (pCEA) level and maximum standardized uptake value (SUVmax) as predictive markers of VPI in patients with clinical T1N0M0 lung adenocarcinoma. Methods: A retrospective review was conducted of the medical records of 613 patients who underwent intraoperative pCEA sampling and lung resection for non-small cell lung cancer. Of these, 390 individuals with clinical stage I adenocarcinoma and tumors ≤30 mm were included. Based on computed tomography findings, these patients were divided into pleural contact (n=186) and non-pleural contact (n=204) groups. A receiver operating characteristic (ROC) curve was constructed to analyze the association between pCEA and SUVmax in relation to VPI. Additionally, logistic regression analysis was performed to evaluate risk factors for VPI in each group. Results: ROC curve analysis revealed that pCEA level greater than 2.565 ng/mL (area under the curve [AUC]=0.751) and SUVmax above 4.25 (AUC=0.801) were highly predictive of VPI in patients exhibiting pleural contact. Based on multivariable analysis, pCEA (odds ratio [OR], 3.00; 95% confidence interval [CI], 1.14-7.87; p=0.026) and SUVmax (OR, 5.25; 95% CI, 1.90-14.50; p=0.001) were significant risk factors for VPI in the pleural contact group. Conclusion: In patients with clinical stage I lung adenocarcinoma exhibiting pleural contact, pCEA and SUVmax are potential predictive indicators of VPI. These markers may be helpful in planning for lung cancer surgery.

• Journal of Ecology and Environment
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• v.43 no.4
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• pp.427-437
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• 2019

Background: Climate change is believed to be continuously affecting ticks by influencing their habitat suitability. However, we attempted to model the climate change-induced impacts on future genus Rhipicephalus distribution considering the major environmental factors that would influence the tick. Therefore, 50 tick occuance points were taken to model the potential distribution using maximum entropy (MaxEnt) software and 19 climatic variables, taking into account the ability for future climatic change under representative concentration pathways (RCPs) 4.5 and 8.5, were used. Results: MaxEnt model performance was tested and found with the AUC value of 0.99 which indicates excellent goodness-of-fit and predictive accuracy. Current models predict increased temperatures, both in the mid and end terms together with possible changes of other climatic factors like precipitation which may lead to higher tick-borne disease risks associated with expansion of the range of the targeted tick distribution. Distribution maps were constructed for the current, 2050, and 2070 for the two greenhouse gas scenarios and the most dramatic scenario; RCP 8.5 produced the highest increase probable distribution range. Conclusions: The future potential distribution of the genus Rhipicephalus show potential expansion to the new areas due to the future climatic suitability increase. These results indicate that the genus population of the targeted tick could emerge in areas in which they are currently lacking; increased incidence of tick-borne diseases poses further risk which can affect cattle production and productivity, thereby affecting the livelihood of smallholding farmers. Therefore, it is recommended to implement climate change adaptation practices to minimize the impacts.