• International Journal of Stem Cells
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• v.16 no.1
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• pp.52-65
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• 2023

Background and Objectives: Epithelial-Mesenchymal transition (EMT) is one of the origins of myofibroblasts in renal interstitial fibrosis. Mesenchymal stem cells (MSCs) alleviating EMT has been proved, but the concrete mechanism is unclear. To explore the mechanism, serum-free MSCs conditioned medium (SF-MSCs-CM) was used to treat rat renal tubular epithelial cells (NRK-52E) fibrosis induced by transforming growth factor-β1 (TGF-β1) which ameliorated EMT. Methods and Results: Galectin-3 knockdown (Gal-3 KD) and overexpression (Gal-3 OE) lentiviral vectors were established and transfected into NRK-52E. NRK-52E fibrosis model was induced by TGF-β1 and treated with the SF-MSCs-CM for 24 h after modelling. Fibrosis and autophagy related indexes were detected by western blot and immunocytochemistry. In model group, the expressions of α-smooth muscle actin (α-SMA), fibronectin (FN), Galectin-3, Snail, Kim-1, and the ratios of P-Akt/Akt, P-GSK3β/GSK3β, P-PI3K/PI3K, P-mTOR/mTOR, TIMP1/MMP9, and LC3B-II/I were obviously increased, and E-Cadherin (E-cad) and P62 decreased significantly compared with control group. SF-MSCs-CM showed an opposite trend after treatment compared with model group. Whether in Gal-3 KD or Gal-3 OE NRK-52E cells, SF-MSCs-CM also showed similar trends. However, the effects of anti-fibrosis and enhanced autophagy in Gal-3 KD cells were more obvious than those in Gal-3 OE cells. Conclusions: SF-MSCs-CM probably alleviated the EMT via inhibiting Galectin-3/Akt/GSK3β/Snail pathway. Meanwhile, Gal-3 KD possibly enhanced autophagy via inhibiting Galectin-3/Akt/mTOR pathway, which synergistically ameliorated renal fibrosis. Targeting galectin-3 may be a potential target for the treatment of renal fibrosis.

• Research in Plant Disease
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• v.29 no.4
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• pp.363-376
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• 2023

Alternaria black spots or blights in cruciferous crops have been devastating diseases worldwide and led to economic losses in broccoli, Chinese cabbage, kale, radish, rapeseed, etc. These diseases are caused by different Alternaria spp., including A. brassicae, A. brassicicola and A. raphani transmitted from infected seeds or insect vectors. Efforts to excavate disease resistance traits of cruciferous crops against Alternaria black spots or blights have been demonstrated. Genetic resource of disease resistance was investigated in the wild relatives of cruciferous crops, and different cultivars were screened under different inoculation conditions. Development of the disease-resistant lines against Alternaria black spots or blights was also tried via genetic transformation of the cruciferous crops using diverse plant defence-associated genes. Plant immunity activated by pre-treatment with chemicals, i. e. β-amino-n-butyric acid and melatonin, was suggested for reducing Alternaria black spots or blights in cruciferous crops. The disease resistance traits have also been evaluated in model plant Arabidopsis originating from different habitats. Various plant immunity-related mutants showing different disease responses from wild-type Arabidopsis provided valuable information for managing Alternaria black spots or blights in cruciferous crops. In particular, redox regulation and antioxidant responses altered in the Alternaria-infected mutants were discussed in this review.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.

• The Journal of Natural Sciences
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• v.4
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• pp.103-142
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• 1991

These studies were carried out to select somatic hybrid using selectable marker genes of Nicotiana glauca transformed by NPTII gene and Solanum tuberosum transformed by T- DNA, and to study characteristics of transformant. The results are summarized as follows. 1. Crown gall tumors and hairy roots were formed on potato tuber disc infected by A. tumefaciens Ach5 and A. rhizogenes ATCC15834. These tumors and roots could be grown on the phytohormone free media. 2. Callus formation from hairy root was prompted on the medium containing 2, 4 D 2mg/I with casein hydrolysate lg/l. 3. The survival ratio of crown gall tumor callus derived from potato increased on the medium containing the activated charcoal 0. 5-2. 0mg/I because of the preventions on the other hand, hairy roots were necrosis on the same medium. 4. Callus derived from hairy root were excellently grown for a short time by suspension culture on liquid medium containing 2, 4-D 2mg/I and casein hydrolysate lg/l. 5. The binary vector pGA643 was mobilized from E. coli MC1000 into wild type Agrobacteriurn tumefaciens Ach5, A. tumefaciens $A_4T$ and disarmed A. tuniefaciens LBA4404 using a triparental mating method with E. ccli HB1O1/pRK2013. Transconjugants were obtained on the minimal media containing tetracycline and kanamycin. pGA643 vectors were confirmed by electrophoresis on 0.7% agarose gel. 6. Kanamycin resistant calli were selected on the media supplemented with 2, 4-D 0.5mg/1 and kanamycin $100\mug$/ml after co- cultivating with tobacco stem explants and A. tumefaciens LBA4404/pGA643, and selected calli propagated on the same medium. 7. The multiple shoots were regenerated from kanamycin resistant calli on the MS medium containing BA 2mg/l. 8. Leaf segments of transformed shoot were able to grow vigorusly on the medium supplemented with high concentration of kanamycin $1000\mug$/ml. 9. Kanamycin resistant shoots were rooting and elongated on medium containing kanamycin $100\mug$/ml, but normal shoot were not. 10. For the production of protoplast from potato calli transformed by T-DNA and mesophyll tissue transformed by NPTII gene, the former was isolated in the enzyme mixture of 2.0% celluase Onozuka R-10, 1.0% dricelase, 1.0% macerozyme. and 0.5M mannitol, the latter was isolated in the enzyme mixture 1.0% Celluase Onozuka R-10, 0.3% macerozyme, and 0.7M mannitol. 11. The optimal concentrationn of mannitol in the enzyme mixture for high protoplast yield was 0.8M at both transformed tobacco mesophyll and potato callus. The viabilities of protoplast were shown above 90%, respectively. 12. Both tobacco mesophyll and potato callus protoplasts were fused by using PEG solution. Cell walls were regenerated on hormone free media supplemented with kanamycin after 5 days, and colonies were observed after 4 weeks culture.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.139-155
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• 2009

For the last few decades, many studies have tried to explore and unveil venture companies' success factors and unique features in order to identify the sources of such companies' competitive advantages over their rivals. Such venture companies have shown tendency to give high returns for investors generally making the best use of information technology. For this reason, many venture companies are keen on attracting avid investors' attention. Investors generally make their investment decisions by carefully examining the evaluation criteria of the alternatives. To them, credit rating information provided by international rating agencies, such as Standard and Poor's, Moody's and Fitch is crucial source as to such pivotal concerns as companies stability, growth, and risk status. But these types of information are generated only for the companies issuing corporate bonds, not venture companies. Therefore, this study proposes a method for evaluating venture businesses by presenting our recent empirical results using financial data of Korean venture companies listed on KOSDAQ in Korea exchange. In addition, this paper used multi-class SVM for the prediction of DEA-based efficiency rating for venture businesses, which was derived from our proposed method. Our approach sheds light on ways to locate efficient companies generating high level of profits. Above all, in determining effective ways to evaluate a venture firm's efficiency, it is important to understand the major contributing factors of such efficiency. Therefore, this paper is constructed on the basis of following two ideas to classify which companies are more efficient venture companies: i) making DEA based multi-class rating for sample companies and ii) developing multi-class SVM-based efficiency prediction model for classifying all companies. First, the Data Envelopment Analysis(DEA) is a non-parametric multiple input-output efficiency technique that measures the relative efficiency of decision making units(DMUs) using a linear programming based model. It is non-parametric because it requires no assumption on the shape or parameters of the underlying production function. DEA has been already widely applied for evaluating the relative efficiency of DMUs. Recently, a number of DEA based studies have evaluated the efficiency of various types of companies, such as internet companies and venture companies. It has been also applied to corporate credit ratings. In this study we utilized DEA for sorting venture companies by efficiency based ratings. The Support Vector Machine(SVM), on the other hand, is a popular technique for solving data classification problems. In this paper, we employed SVM to classify the efficiency ratings in IT venture companies according to the results of DEA. The SVM method was first developed by Vapnik (1995). As one of many machine learning techniques, SVM is based on a statistical theory. Thus far, the method has shown good performances especially in generalizing capacity in classification tasks, resulting in numerous applications in many areas of business, SVM is basically the algorithm that finds the maximum margin hyperplane, which is the maximum separation between classes. According to this method, support vectors are the closest to the maximum margin hyperplane. If it is impossible to classify, we can use the kernel function. In the case of nonlinear class boundaries, we can transform the inputs into a high-dimensional feature space, This is the original input space and is mapped into a high-dimensional dot-product space. Many studies applied SVM to the prediction of bankruptcy, the forecast a financial time series, and the problem of estimating credit rating, In this study we employed SVM for developing data mining-based efficiency prediction model. We used the Gaussian radial function as a kernel function of SVM. In multi-class SVM, we adopted one-against-one approach between binary classification method and two all-together methods, proposed by Weston and Watkins(1999) and Crammer and Singer(2000), respectively. In this research, we used corporate information of 154 companies listed on KOSDAQ market in Korea exchange. We obtained companies' financial information of 2005 from the KIS(Korea Information Service, Inc.). Using this data, we made multi-class rating with DEA efficiency and built multi-class prediction model based data mining. Among three manners of multi-classification, the hit ratio of the Weston and Watkins method is the best in the test data set. In multi classification problems as efficiency ratings of venture business, it is very useful for investors to know the class with errors, one class difference, when it is difficult to find out the accurate class in the actual market. So we presented accuracy results within 1-class errors, and the Weston and Watkins method showed 85.7% accuracy in our test samples. We conclude that the DEA based multi-class approach in venture business generates more information than the binary classification problem, notwithstanding its efficiency level. We believe this model can help investors in decision making as it provides a reliably tool to evaluate venture companies in the financial domain. For the future research, we perceive the need to enhance such areas as the variable selection process, the parameter selection of kernel function, the generalization, and the sample size of multi-class.

• Reproductive and Developmental Biology
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• v.32 no.3
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• pp.153-158
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• 2008

Tissue-specific and temporal regulation of milk protein gene expression is advantageous when creating transgenic animal that produces foreign protein into milk. Gene expression, i.e. protein production, is regulated not only by promoter strength but also mRNA stability. Especially, poly A tail length by polyadenylation affects in vivo and in vitro mRNA stability and translation efficiency of the target gene. In the present study, nucleotide sequence of 3'-UTR was analyzed to evaluate the effects of mRNA stability on the target gene expression. Based on the poly A signal of 3' -untranslated region (UTR), nucleotide sequences of putative cytoplasmic polyadenylation elements (CPEs) and downstream elements (DSEs: U-rich, G-rich, GU-rich) were analyzed and used to construct 15 luciferase reporter vectors. Each vector was transfected to HC11 and porcine mammary gland cell (PMGC) and measured for dual luciferase expression levels after 48 hours of incubation. Luciferase expression was significantly higher in construct #6 (with CPE 2, 3 and DSE 1 of exon 9) and #11 (with CPE 2, 3 and DSE 1, 2 and 3 of exon 9) than construct #1 in the PMGC. These results suggest that expression of target genes in PMGC may be effectively expressed by using the construct #6 and #11 on production of transgenic pig.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• The Korean Journal of Physiology
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• v.18 no.1
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• pp.51-65
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• 1984

In an effort to elucidate the effect of physical training on the electrocardiographic amplitudes, QRS vector, axis and QRS vector amplitude, electrocardiograms were recorded before and 1, 5 and 10 minutes after 3 minute rebounder exercise in 23 healthy male students aged between 18 and 21 years in two groups of athletes and non-athletes. ECG amplitudes were measured from lead I, $V_1$ and $V_5$ and axis and amplitudes of QRS vectors were measured from lead I and III in frontal plane, from lead $V_2$ and lead $V_6$ in horizontal plane. The results obtained are summarized as follows. ECG amplitudes: The R wave amplitude was $23.38{\pm}1.14\;mm$ in athletes which was higher than $17.91{\pm}2.00\;mm$ in non-athletes. After exercise, the difference in two groups remained significant throughout the recovery period. The S wave amplitude was increased significantly, and the T wave amplitude was decreased in both groups after exercise. The P wave amplitude was increased in both groups after exercise, and it was lower in athletes than in non-athletes. The PQ segment amplitude was zero in athletes but negative in non-athletes than in the resting state. The J point amplitude was positive in resting state and was negative after exercise in both groups. J+0.08 sec point amplitude was also lowered after exercise, and it was higher in athletes than in non-athletes. Therefore the whole ST segment was proved to be decreased after exercise. The summated amplitude of R in $V_5$ plus S in $V_1$ was $38.74{\pm}2.71\;mm$ in athletes which was higher than $32.82{\pm}2.90\;mm$ in non-athletes. After exercise, it was also significantly higher in athletes than in non-athletes. Axis of QRS vector: In frontal plane, axis of QRS vector was $62.7{\pm}7.36^{\circ}$ in athletes, it showed no significant difference between the two groups. In horizontal plane, axis of QRS vector was $-23.5{\pm}7.2^{\circ}$ in athletes which was significantly higher than $-38.8{\pm}8.2^{\circ}$ in non-athletes. After exercise, it was significantly higher than the resting state in both groups. Amplitude of QRS vector : In frontal plane, amplitude of QRS vector was $13.86{\pm}1.44\;mm$ in athletes which was significantly higher than $9.62{\pm}0.97\;mm$ in non-athletes. After exercise, it was also significantly higher in athletes than in non-athletes. In horizontal plane, amplitude of QRS vector was $19.82{\pm}2.10\;mm$ in athletes which was significantly higher than $16.90{\pm}1.39\;mm$ in non-athletes. After exercise, it was also significantly higher in athletes than in non-athletes. From the above, these results indicate that R wave amplitude in athletes was significantly higher than in non-athletes before and after exercise, and that the summated amplitude of R in $V_5$ plus S in $V_1$ in athletes was also $38.74{\pm}2.71\;mm$ suggesting a left ventricular hypertrophy We should note that the PQ segment and ST segment amplitude were higher in athletes than in non-athletes, and they were decreased with exercise in both groups. In particular, the fact that amplitudes of QRS vector in frontal plane or in horizontal plane were significantly greater in athletes than in non-athletes may be an index in evaluating athletes.