• Journal of fish pathology
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• v.35 no.2
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• pp.205-214
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• 2022

In recent years, there have been many studies investigating changes in animal behavior using video tracking technology to track motion. However, there have been very few studies and results on changes in the behavior of fish infected with a pathogen. Therefore, the present study attempted to analyze the behavior of angelfish (Pterophyllum scalare) infected with bacterial pathogens using video tracking. Two cameras were placed in front of the water tank to obtain behavior data, and tracking was performed for three days until the day of death. Data such as average speed, changes in speed, the locations of the fish in the tank, and fractal dimension were statistically analyzed based on the fish speed and location in the tank of the fish. For bacterial infection, an individual angelfish was intraperitoneally injected with approximately 10⁶ CFU ml^-1 of Aeromonas hydrophila or Edwardsiella piscicida. The experiment was carried out five times for each group. Fish infected with the bacterial pathogens showed a tendency to increase in speed and to spend more time in the upper part of the tank one or two days before death. On the day the fish died, the average speed, changes in speed, and the fractal dimension value were significantly lower than the corresponding values in the control group, and the fish also remained in the lower part of the tank. Our results indicated that behavioral changes in fish could be successfully detected earlier than death using video tracking technology, and that this method presents potential for disease monitoring in aquaculture.

• Nuclear Engineering and Technology
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• v.44 no.1
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• pp.53-60
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• 2012

The aim of this study was to explore the spatial and energy resolutions of a PET scanner that we have recently developed. The scanner, which consists of six detector modules with 1-layer LGSO crystals, has a hexagonal configuration with a faceto- face distance of 86.4 mm between two opposite PET modules; such properties facilitate the imaging of small animals. A $^{22}Na$ point source was employed to estimate horizontal and vertical spatial resolutions. To assess the energy resolution, a uniform $^{18}F$ cylindrical phantom was scanned. A software-based spectrum analysis of list-mode data was used to assign a local energy window centered on the photopeak position for every single crystal. For the image reconstruction, an ML-EM algorithm was used. The spatial resolutions at the center of the scanner were 0.99 mm in the horizontal direction and 1.13 mm in the vertical direction. The energy resolution averaged over each PMT ranged from 13.3%-14.3%, which gave an average value of 13.8%. These results show that this simple system is promising for small animal imaging with excellent spatial and energy resolutions.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.307-325
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• 2023

Due to the prolonged COVID-19 pandemic, the frequency of people who are tired of living indoors visiting nearby mountains and national parks to relieve depression and lethargy has exploded. There is a place where thousands of people who came out of nature stop walking and breathe and rest, that is the mineral spring. Even in mountains or national parks, there are about 600 mineral springs that can be found occasionally in neighboring parks or trails in the metropolitan area. However, due to irregular and manual water quality tests, people drink mineral water without knowing the test results in real time. Therefore, in this study, we intend to develop a model that can predict the quality of the spring water in real time by exploring the factors affecting the quality of the spring water and collecting data scattered in various places. After limiting the regions to Seoul and Gyeonggi-do due to the limitations of data collection, we obtained data on water quality tests from 2015 to 2020 for about 300 mineral springs in 18 cities where data management is well performed. A total of 10 factors were finally selected after two rounds of review among various factors that are considered to affect the suitability of the mineral spring water quality. Using AutoML, an automated machine learning technology that has recently been attracting attention, we derived the top 5 models based on prediction performance among about 20 machine learning methods. Among them, the catboost model has the highest performance with a prediction classification accuracy of 75.26%. In addition, as a result of examining the absolute influence of the variables used in the analysis through the SHAP method on the prediction, the most important factor was whether or not a water quality test was judged nonconforming in the previous water quality test. It was confirmed that the temperature on the day of the inspection and the altitude of the mineral spring had an influence on whether the water quality was unsuitable.

• Journal of the korean society of oceanography
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• v.39 no.3
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• pp.172-185
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• 2004

LSU rDNA D1-D2 and SSU rDNA genes of 23 strains in seven Alexandrium (Halim) species, A. tamarense (Lebour) Balech, A. catenella (Whedon et Kofoid), A. fraterculus (Balech) Balech, A. affine (Inoue et Fukuyo) Balech, A. insuetum Balech, A. pseudogonyaulax (Biecheler) Horiguchi ex Yuki et Fukuyo and A. tamiyavanichii Balech, were sequenced and the data were used for molecular phylogenetic analysis. The sequence data revealed 11 and 7 ribotypes in the LSU rDNA D1-D2 region and 4 and 17 ribotypes in the SSU rDNA region of A. catenella and A. tamarense, respectively. Other Alexandrium species had also 1 to 5 ribotypes in the two regions. With the exception of CMC2 and CMC3 of A. catenella, all A. tamarense and A. catenella strains had a common ribotype, a functionally expressed rRNA gene (here termed type A), in both gene regions. In addition to the functionally expressed gene, several pseudogenes were obtained that were found to be good tools to analyze the population designation of regional isolates by grouping them according to shared ribotypes. From the phylogenetic analysis of the sequence data determined in this study and retrieved from GenBank, the genus Alexandrium was divided into 14 groups: 1) A. tamarense, 2) A. excavatum, 3) A. catenella, 4) Tasmanian A. tamarense, 5) A. affine (and/or A. concavum), 6) Thai A. tamarense, 7) A. tamiyavanichii, 8) A. fraterculus, 9) A. margalefii, 10) A. andersonii, 11) A. ostenfeldii, 12) A. minutum (or A. lusitanicum), 13) A. insuetum, and 14) A. pseudogonyaulax. The SSU rDNA gene sequence of A. fundyense was so similar to those of A. tamarense used in this study that the two species were difficult to discriminate each other. A. tamiyavanichii was closest to the A. tamarense strain isolated in Thailand and close to the long chain-forming species of A. affine and A. fraterculus. The phylogenetic tree showed that A. margalefii, A. andersonii, A. ostenfeldii, A. minutum and A. insuetum constituted the basal relative complex, and that A. pseudogonyaulax is an ancestral taxon in the genus Alexandrium.

• Journal of Conservation Science
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• v.35 no.6
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• pp.600-611
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• 2019

Yellow to reddish brown soil is generally referred to as hwangto and is used in various industries in Korea. Despite the fact that it is used as an inorganic pigment in dancheong, limited studies have been conducted on the properties of pigments associated with soil and on the mineralogical characteristics of hwangto. This study examines how the pedological and mineralogical features of hwangto affect pigment properties. Results indicate that reddish and yellowish soils have differences in terms of soil texture, mineral composition, oil absorption and stability under light. Reddish soil is mostly found in clay regions, whereas Ulleungdo hwangto is found in loam regions. Yellowish soil is mostly present in the clay loam to loam zones. whereas Haenam hwangto exists in the sandy clay loam zone. As a result of a mineralogical analysis, reddish soil is classified into the feldspar group and clay soil. The major minerals in the yellowish soils are similar however these soils differ in terms of clay mineral compositions. results of the characteristics of pigments prepared by the traditional method revealed that the average particle size is in the range of 10-20 ㎛, reddish soil has an average of 20 ml/100 g higher oil absorption than yellowish soil. In addition, reddish soil is more susceptible to discoloration and deterioration under light than yellowish soil. This study confirms that the soil and mineral characteristics of hwangto affect the physical properties and stability of produced pigments. These result can be used as basic data in future studies natural inorganic pigments using hwangto.

• Korean Journal of Animal Reproduction
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• v.9 no.2
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• pp.133-139
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• 1985

An immunoassay may be defined as an analytical procedure involving the competitive reaction between a limiting concentration of specific antibody and two populations of antigen, one of which is labelled or immobillized. The advent of immunoassay has revolutionised our knowledge of reproductive physiology and the practice of veterinary and clinical medicine. Radioimmunoassay (RIA) was the first of these methods to be developed, which meausred the analyte with good sensitivity, accuracy and precision (1,2). The essential components of RIA are:-(i) a limited concentration of antibodies, (ii) a reference preparation, and (iii) an antigen labelled with a radioisotope (usually tritium or iodine-125). Most procedures invelove isolating the antibody-bound fraction and measuring the amount of labelled antigen. Good facilities are available for scintilltion counting, data reduction nd statistical analysis. RIA is undergoing refinement through:-(i) the introduction of new techniques to separate the antibody-bound and free fractions which minimize the misclassification of labelled antigen into these compartments, and the amount of non-specfic binding. (3), (ii) the development of non-extration for the measurement of haptens (4), (iii) the determination of a, pp.rent free (i.e. non-protein bound) analytes (5), and (iv) the use of monoclonal antibodies(6). In 1968, Miles and Hales introduced in important new type of immunoassay which they termed immunora-diometric assay (IRMA) based on t도 use of isotopically labelled specific antibodies(7) in a move from limited to excess reagent systems. The concept of two-site IRMAs (with a capture antibody on a solid-phase, and a second labelled antibody to a different antigenic determinant of the analyte) has enabled the development of more sensitive and less-time consuming methods for the measurement of protein hormones ovar wide concentration of analyte (8). The increasing use of isotopic methos for diverse a, pp.ications has exposed several problems. For example, the radioactive half-life and radiolysis of the labelled reagent limits assay sensitivity and imposes a time limit on the usefulness of a kit. In addition, the potential health hazards associated with the use and disposal of radioactive cmpounds and the solvents and photofluors necessary for liquid scientillation counting are incompatable with the development of extra-laboratory tests. To date, the most practical alternative labels to radioisotopes, for the measurement of analytes in a concentration > 1 ng/ml, are erythrocytes, polystyrene particiles, gold sols, dyes and enzymes or cofactors with a visual or colorimetric end-point(9). Increased sensitivity to<1 pg/ml may be obtained with fluorescent and chemiluminescent labels, or enzymes with a fluorometric, chemiluminometric or bioluminometric end-point. The sensitivity of any immunoassay or immunometric assay depends on the affinity of the antibody-antigen reaction, the specific activity of the label, the precision with which the reagents are manipulated and the nonspecific background signal (10). The sensitivity of a limited reagent system for the measurement of haptens or proteins is mainly dependent upon the affinity of the antibodies and the smalleest amount of reagent that may be manipulated. Consequently, it is difficult in practice to improve on the sensitivity obtained with iodine-125 as the label. Conversely, with excess reagent systems for the measurement of proteins it is theoretically possible to increase assay sensitivity at least 1000 fold with alternative luminescent labels. To date, a 10-fold improvement has been achieved, and attempts are being made to reduce the influence of other variables on the specific signal from the immunoreaction.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• Proceedings of the Korean Society of Toxicology Conference
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• 2006.11a
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• pp.65-74
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• 2006

Modem drug discovery requires rapid pharmacokinetic evaluation of chemically diverse compounds for early candidate selection. This demands the development of analytical methods that offer high-throughput of samples. Naturally, liquid chromatography / tandem mass spectrometry (LC-MS/MS) is choice of the analytical method because of its superior sensitivity and selectivity. As a result of the short analysis time(typically 3-5min) by LC-MS/MS, sample preparation has become the rate- determining step in the whole analytical cycle. Consequently tremendous efforts are being made to speed up and automate this step. In a typical automated 96-well SPE(solid-phase extraction) procedure, plasma samples are transferred to the 96-well SPE plate, internal standard and aqueous buffer solutions are added and then vacuum is applied using the robotic liquid handling system. It takes only 20-90 min to process 96 samples by automated SPE and the analyst is physically occupied for only approximately 10 min. Recently, the ultra-high flow rate liquid chromatography (turbulent-flow chromatography)has sparked a huge interest for rapid and direct quantitation of drugs in plasma. There is no sample preparation except for sample aliquotting, internal standard addition and centrifugation. This type of analysis is achieved by using a small diameter column with a large particle size(30-5O ${\mu}$m) and a high flow rate, typically between 3-5 ml/min. Silica-based monolithic HPLC columns contain a novel chromatographic support in which the traditional particulate packing has been replaced with a single, continuous network (monolith) of pcrous silica. The main advantage of such a network is decreased backpressure due to macropores (2 ${\mu}$m) throughout the network. This allows high flow rates, and hence fast analyses that are unattainable with traditional particulate columns. The reduction of particle diameter in HPLC results in increased column efficiency. use of small particles (<2 urn), however, requires p.essu.es beyond the traditional 6,000 psi of conventional pumping devices. Instrumental development in recent years has resulted in pumping devices capable of handling the requirements of columns packed with small particles. The staggered parallel HPLC system consists of four fully independent binary HPLC pumps, a modified auto sampler, and a series of switching and selector valves all controlled by a single computer program. The system improves sample throughput without sacrificing chromatographic separation or data quality. Sample throughput can be increased nearly four-fold without requiring significant changes in current analytical procedures. The process of Bioanalytical Method Validation is required by the FDA to assess and verify the performance of a chronlatographic method prior to its application in sample analysis. The validation should address the selectivity, linearity, accuracy, precision and stability of the method. This presentation will provide all overview of the work required to accomplish a full validation and show how a chromatographic method is suitable for toxirokinetic sample analysis. A liquid chromatography/tandem mass spectrometry (LC-MS/MS) method developed to quantitate drug levels in dog plasma will be used as an example of tile process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.6
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• pp.438-448
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• 2017

This study was a systematic review and meta-analysis that evaluated the results of research on the pain reduction effects of lidocaine gel for urethral catheterization in adults. A literature search was conducted using seven electronic databases, gray literature and other resources based on the guidelines of Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA). A Risk of Bias (RoB) tool was applied to assess the quality of selected studies. Data were analyzed using the RevMan 5.3.-program. Sixteen randomized controlled trials involving 1904 adults were included. RoB was not observed in the funnel plot. Overall, lidocaine gel was effective for pain reduction during urethral catheterization (Standard Mean Difference[SMD] -0.96;95% CI: -1.43, -0.49). To explore the cause of heterogeneity (I2=95%, p<.001), subgroup analysis was conducted according to three catheter types (urinary catheter, flexible cystoscopy, and rigid cystoscopy) and the SMDs were -0.88 (95% CI:-1.51, -0.26), -0.31 (95% CI:-0.63, 0.01), and -1.93 (95% CI:-2.88, -0.97), respectively. A significant pain reduction effect was observed regardless of gender in urinary catheterization. However, in rigid cystoscopy, a significant pain reduction effect was observed only in male subjects. Pain reduction effects were observed when 10~11ml lidocaine gel was used during rigid cystoscopy and when lubrication was used during urinary catheterization, irrespective of application time. These findings suggest that lidocaine gel is a useful anesthetic lubricant for urinary catheterization and rigid cystoscopy in male adults.