• Korean Journal of Radiology
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• v.1 no.3
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• pp.142-151
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• 2000

Objective: To determine the optimal scan timing for contrast-enhanced magnetic resonance angiography and to evaluate a new timing method based on the arteriovenous circulation time. Materials and Methods: Eighty-nine contrast-enhanced magnetic resonance angiographic examinations were performed mainly in the extremities. A 1.5T scanner with a 3-D turbo-FLASH sequence was used, and during each study, two consecutive arterial phases and one venous phase were acquired. Scan delay time was calculated from the time-intensity curve by the traditional (n = 48) and/or the new (n = 41) method. This latter was based on arteriovenous circulation time rather than peak arterial enhancement time, as used in the traditional method. The numbers of first-phase images showing a properly enhanced arterial phase were compared between the two methods. Results: Mean scan delay time was 5.4 sec longer with the new method than with the traditional. Properly enhanced first-phase images were found in 65% of cases (31/48) using the traditional timing method, and 95% (39/41) using the new method. When cases in which there was mismatch between the target vessel and the time-intensity curve acquisition site are excluded, erroneous acquisition occurred in seven cases with the traditional method, but in none with the new method. Conclusion: The calculation of scan delay time on the basis of arteriovenous circulation time provides better timing for arterial phase acquisition than the traditional method.

• Journal of Microbiology and Biotechnology
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• v.34 no.3
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• pp.710-724
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• 2024

Flavobacterium can synthesize xanthophyll, particularly the pigment zeaxanthin, which has significant economic value in nutrition and pharmaceuticals. Recently, the use of carotenoid biosynthesis by bacteria and yeast fermentation technology has shown to be very efficient and offers significant advantages in large-scale production, cost-effectiveness, and safety. In the present study, JSWR-1 strain capable of producing xanthophyll pigment was isolated from a freshwater reservoir in Wanju-gun, Republic of Korea. Based on the morphological, physiological, and molecular characteristics, JSWR-1 classified as belonging to the Flavobacterium species. The bacterium is strictly aerobic, Gram-negative, rod-shaped, and psychrophilic. The completed genome sequence of the strain Flavobacterium sp. JSWR-1 is predicted to be a single circular 3,425,829-bp chromosome with a G+C content of 35.2% and 2,941 protein-coding genes. The optimization of carotenoid production was achieved by small-scale cultivation, resulting in zeaxanthin being identified as the predominant carotenoid pigment. The enhancement of zeaxanthin biosynthesis by applying different light-irradiation, variations in pH and temperature, and adding carbon and nitrogen supplies to the growth medium. A significant increase in intracellular zeaxanthin concentrations was also recorded during fed-batch fermentation achieving a maximum of 16.69 ± 0.71 mg/l, corresponding to a product yield of 4.05 ± 0.15 mg zeaxanthin per gram cell dry weight. Batch and fed-batch culture extracts exhibit significant antioxidant activity. The results demonstrated that the JSWR-1 strain can potentially serve as a source for zeaxanthin biosynthesis.

• The Journal of Bigdata
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• v.9 no.1
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• pp.141-152
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• 2024

In the biopharmaceutical contract manufacturing organization (CMO) business, establishing a production schedule that satisfies the due date for various customer orders is crucial for competitiveness. In a CMO process, each order consists of multiple batches that can be allocated to multiple production lines in small batch units for parallel production. This study proposes a meta-heuristic algorithm to establish a scheduling plan that minimizes the total delivery delay of orders in a CMO process with identical parallel machine. Inspired by biological evolution, the proposed algorithm generates random data structures similar to chromosomes to solve specific problems and effectively explores various solutions through operations such as crossover and mutation. Based on real-world data provided by a domestic CMO company, computer experiments were conducted to verify that the proposed algorithm produces superior scheduling plans compared to expert algorithms used by the company and commercial optimization packages, within a reasonable computation time.

• Journal of the Korean Society of Radiology
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• v.9 no.1
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• pp.47-53
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• 2015

Quantitative analysis of MR spectrum depending on mole concentration of the contrast media in cereberal metabolite phantom was performed. PRESS pulse sequence was used to obtain MR spectrum at 3.0T MRI system (Archieva, Philips Healthcare, Best, Netherland), and the phantom contains brain metabolites such as N-Acetyl Asparatate (NAA), Choline (Cho), Creatine (Cr) and Lactate (Lac). In this study, optimization of MRS PRESS pulse sequency depending on the concentration of contrast media (0, 0.1 and $0.3mmol/{\ell}$) was evaluated for various repetition time(TR; 1500, 1700 and 2000 ms). In control (cotrast-media-free) group, NAA and Cho signals were the highest at TR 2000 ms than at 1700 and 1500 ms. Cr had the highest peak signal at TR 1500 ms. When concentration of contrast media was $0.1mmol/{\ell}$, the metabolites were increased NAA 73%, Cho 249%, Cr 37% at TR 1700 ms compared with other TR, and also signal increased at $0.3mmol/{\ell}$, In $0.5mmol/{\ell}$ of contrast agent, cerebral metabolite peaks reduced, especially when TR 1500 ms and 2000 ms they decreased below those of control group. The ratio of metabolite peaks such as NAA/Cr and Cho/Cr decreased as the concentration of the contrast agent increased from 0.1 to $0.5mmol/{\ell}$. Authors found that the optimization of PRESS sequence for 0.3T MRS was as follows: low density of contrast agent ($0.1mmol/{\ell}$ and $0.3mmol/{\ell}$) made the highest signal intensity, while high density of contrast agent reveals the least reduction of signal intensity at 1700 ms. In conclusion, authors believe that it is helpful to reduce TR for acquiring maximum signal intensity.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5179-5186
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• 2012

Miglitol, a well-known therapeutic intervention agents for diabetes, exhibits competitive inhibitory activity against ${\alpha}$-glucosidase and it is usually produced through three sequential steps including chemical and bioconversion processes. Gluconobactor oxydans (G. oxydans) belonging to acetic acid bacteria biologically, converts 1-deoxy-1-(2-hydroxyethylamino)-D-glucitol (P1) into a key intermidiate, 6-(2-hydroxyetyl) amino-6-deoxy-${\alpha}$-L-sorbofuranose (P2) by incomplete oxidation. In this study, we identified and optimized fermentation conditions of CK-2165, that was selected in soil samples by comparing the bioconversion yield. CK-2165 strain was found to be closely related to G. oxydans based on the result of phylogenetic analysis using 16S rDNA sequence. Utilization of API 20 kits revealed that this strain could use glucose, mannose, inositol, sorbitol, rhamnose, sucrose, melibiose, amygdalin and arabinose as carbon sources. The culture conditions were optimized for industrial production and several important factors affecting bioconversion rate were also tested using mycelial cake. Cell harvested at the late-stationary phase showed the highest bioconversion yield and $MgSO_4$ was critically required for the catalytic activity.

• Journal of the Korea Society for Simulation
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• v.26 no.4
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• pp.35-41
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• 2017

With the advances in Internet over Things, the demand in diverse electronic devices such as mobile phones and sensors has been rapidly increasing and boosting up the researches on those products. Semiconductor materials, devices, and fabrication processes are becoming more diverse and complicated, which accompanies finding parameters for an optimal fabrication process. In order to find the parameters, a process simulation before fabrication or a real-time process control system during fabrication can be used, but they lack incorporating the feedback from post-fabrication data and compatibility with older equipment. In this research, we have developed an artificial intelligence based simulator, which finds parameters for an optimal process and controls process equipment. In order to apply the control concept to all the equipment in a fabrication sequence, we have developed a prototype for a manipulator which can be installed over an existing buttons and knobs in the equipment and controls the equipment communicating with the AI over the Internet. The AI is based on the deep learning to find process parameters that will produce a device having target electrical characteristics. The proposed simulator can control existing equipment via the Internet to fabricate devices with desired performance and, therefore, it will help engineers to develop new devices efficiently and effectively.

• Investigative Magnetic Resonance Imaging
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• v.13 no.2
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• pp.117-126
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• 2009

Purpose : A numerical method of designing a multiple quantum filter (MQF) is presented for the optimum detection of myo-inositol (mI), an important brain metabolite, by using in vivo proton nuclear magnetic resonance spectroscopy ($^1$-HMRS). Materials and Methods : Starting from the characterization of the metabolite, the filter design includes the optimization of the sequence parameters such as the two echo times (TEs), the mixing time (TM), and the flip angle and offset frequency of the 3rd $90^{\circ}$ pulse which converts multiple quantum coherences (MQCs) back into single quantum coherences (SQCs). The optimized filter was then tested both in phantom and in human brains. Results : The results demonstrate that the proposed MQF can improve the signal-to-background ratio of the target metabolite by a factor of more than three by effectively suppressing the signal from the background metabolites. Conclusion : By incorporating a numerical method into the design of MQFs in $^1$-HMRS the spectral integrity of a target metabolite, in particular, with a complicated spin system can be substantially enhanced.

• Analytical Science and Technology
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• v.24 no.6
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• pp.510-518
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• 2011

Recent developments and improvements of multiple technological elements including mass spectrometry (MS) instrument, multi-dimensional chromatographic separation, and software tools processing MS data resulted in benefits of large scale proteomics analysis. However, its throughput is limited by the speed and reproducibility of the protein digestion process. In this study, we demonstrated a new method for rapid proteolytic digestion of proteins using acoustic technology. Tryptic digests of BSA prepared at various conditions by super acoustic for optimization time and intensity were analyzed by LC-MS/MS showed higher sequence coverage in compared with traditional 16 hrs digestion method. The method was applied successfully for complex proteins of a breast cancer cells at 30 min of digestion at intensity 2. This new application reduces time-consuming of sample preparation with better efficiency, even with large amount of proteins, and increases high-throughput process in sample preparation state.

• Journal of Microbiology and Biotechnology
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• v.15 no.4
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• pp.880-886
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• 2005

Using carotenoid genes of Erwinia herbicola, metabolic engineering was carried out for lycopene production with the pAC-LYCO4 plasmid, which was composed of a chromosomal DNA fragment of E. herbicola containing the crtE, crtB, and crtI genes under the control of the tetracycline promoter and the ipi gene of Haematococcus pluvialis with the trc promoter. Plasmid pAC-LYCm4 was constructed for efficient expression of the four exogenous genes using a strong RBS sequence and the same tetracycline promoter. The optimized expression construct of pAC-LYCm4 increased Iycopene production three times as compared with pAC-LYCO4. pAC-LYCm5 containing ispA behind the four exogenous genes was constructed. There was no significant difference in Iycopene production and cell growth between pAC-LYCm4 and pAC-LYCm5. FPP synthase encoded by ispA was not rate-limiting for Iycopene production. Each gene of crtE, crtB, crtI, and ipi was overexpressed, using pBAD-crtE, pBAD-crtIB, and pBAD-ipiHPI, in addition to their expression from pAC-LYCm4. However, there was no increase oflycopene production with the additional overexpression of each exogenous gene. The four exogenous genes appeared to be not rate-limiting in cells harboring pAC-LYCm4. When pDdxs, pBAD24 containing dxs, was introduced into cells harboring lycopene synthetic plasmids, lycopene production of pAC-LYCO4, pAC-LYCm4, and pAC-LYCm5 was increased by 4.7-, 2.2-, and 2.2-fold, respectively. Lycopene production of pBAD-DXm4 containing crtE, crtB, crtI, ipi, and dxs was 5.2 mg/g dry cell weight with $0.2\%$ arabinose, which was 8.7-fold higher than that of the initial strain with pAC-LYC04. Therefore, the present study showed that proper regulation of a metabolically engineered pathway is important for Iycopene production.