• Journal of radiological science and technology
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• v.28 no.1
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• pp.25-32
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• 2005

Purpose : To evaluate the degree of the artifact which is caused by the mascara and the eye shadow when acquiring MR images and compare the difference of the image distortion according to the change of various pulse sequence. Material and Method : The popular domestic mascara and eye shadow products were selected from three different companies respectively and divided into two groups mascara (M1, M2, M3 ), eye shadow (E1, E2, E3). Self-designed quadrature type saddle coil which has 4 cm inside diameter, 8 cm length and which is for both Tx and Rx was used. MR image was acquired respectively after applying the mascara to the tape from study 1, the eye shadow to the tape from study 2 and adding the eye shadow to the mascara from study 3. The FSE(fast spin echo), the SE(spin echo), the GE(gradient echo) were used as pulse sequences. The degree of the image distortion which was measured from each sequence was analyzed in quality and quantity. Result : The mascara and the eye shadow caused the artifacts to the MR images partially and induced the image distortion. There was a little difference in terms of the degree of artifact according to the change of pulse sequence. From the study 3 in which the eye shadow was applied to the mascara, on the axial plane image, the width of artifact was 16.73 mm in the GE pulse sequence, 6.64 mm in the SE pulse sequence, and 6.19 mm in the FSE pulse sequence. The degree of the artifact appeared highly in order of the GE, the SE and the FSE. On the sagittal plane image, the length of artifact was 22.84 mm in the GE, 17.81 mm in the SE and it appeared highly with the SE and the FSE technique order. Conclusion : When examining the eyeball and the brain of a woman with the mascara and the eye shadow, we have to consider that the artifact caused by them can have an effect on the image diagnosis. We concluded that it is more suitable for a brain and a eyeball T2 emphasizing image to use the FSE technique than the GE technique.

• Korean Journal of Microbiology
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• v.54 no.1
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• pp.60-68
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• 2018

A cold-active and alkaline serine protease (Pro21717) was partially purified from the Antarctic marine bacterium Pseudoalteromonas arctica PAMC 21717. On a zymogram gel containing skim milk, Pro21717 produced two distinct clear-zones of approximately 37 kDa (low intensity) and 74 kDa (high intensity). These were found to have identical N-terminal sequences, suggesting they arose from an identical precursor and that the 37 kDa protease might homodimerize to the more active 74 kDa form of the protein. Pro21717 displayed proteolytic activity at $0-40^{\circ}C$ (optimal temperature of $40^{\circ}C$) and maintained this activity at pH 5.0-10.0 (optimal pH of 9.0). Notably, relative activities of 30% and 45% were observed at $0^{\circ}C$ and $10^{\circ}C$, respectively, in comparison to the 100% activity observed at $40^{\circ}C$, and this enzyme showed a broad substrate range against synthetic peptides with a preference for proline in the cleavage reaction. Pro21717 activity was enhanced by $Cu^{2+}$ and remained stable in the presence of detergent surfactants (linear alkylbenzene sulfonate and sodium dodecyl sulfate) and other chemical components ($Na_2SO_4$ and metal ions, such as $Ba^{2+}$, $Mg^{2+}$, $Ca^{2+}$, $Zn^{2+}$, $Fe^{2+}$, $K^+$, and $Na^{2+}$), which are often included in commercial detergent formulations. These data indicate that the psychrophilic Pro21717 has properties comparable to the well-characterized mesophilic subtilisin Carlsberg, which is commercially produced by Novozymes as the trademark Alcalase. Thus it has the potential to be used as a new additive enzyme in laundry detergents that must work well in cold tap water below $15^{\circ}C$.

• The Korean Journal of Petroleum Geology
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• v.10 no.1_2 s.11
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• pp.23-33
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• 2004

The Lago Sofia conglomerate in southern Chile is a lenticular unit encased within mudstone-dominated, deep-sea successions (Cerro Toro Formation, upper Cretaceous), extending from north to south for more than $120{\cal}km$. The Lago Sofia conglomerate is a unique example of long, gravelly deep-sea channels, which are rare in the modern environments. In the northern part (areas of Lago Pehoe and Laguna Goic), the conglomerate unit consists of 3-5 conglomerate bodies intervened by mudstone sequences. Paleocurrent data from these bodies indicate sediment transport to the east, south, and southeart. The conglomerate bodies in the northern Part are interpreted as the tributary channels that drained down the Paleoslope and converged to form N-S-trending trunk channels. In the southern part (Lago Sofia section), the conglomerate unit comprises a thick (> 300 m) conglomerate body, which probably formed in axial trunk channels of the N-5-trending foredeep trough. The well-exposed Lago Sofia section allowed for detailed investigation of sedimentary facies and large-scale architecture of the deepsea channel conglomerate. The conglomerate in Lago Sofia section comprises stratified conglomerate, massive-to-graded conglomerate, and diamictite, which represent bedload deposition under turbidity currents, deposition by high-density turbidity currents, and muddy debris flows, respectively. Paleocurrent data suggest that the debris flows originated from the failure of nearby channel banks or slopes flanking the channel system, whereas the turbidity currents flowed parallel to the orientation of the overall channel system. Architectural elements produced by turbidity currents represent vertical stacking of gravel sheets, lateral accretion of gravel bars, migration of gravel dunes, and filling of channel thalwegs and scoured hollows, similar to those in terrestrial gravel-bed braided rivers. Observations of large-scale stratal pattern reveal that the channel bodies are offset stacked toward the east, suggestive of an eastward migration of the axial trunk channel. The eastward channel migration is probably due to tectonic tilting related to the uplift of the Andean protocordillera just west of the Lago Sofia deep-sea channel system.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of Animal Science and Technology
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• v.54 no.1
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• pp.1-8
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• 2012

This study aimed to investigate the single nucleotide polymorphisms (SNPs) of the porcine MC4R gene and validate the effect of the MC4R genotype for marker assisted selection (MAS). Six amplicons were produced to analyze the entire base sequences of the porcine MC4R gene and six SNPs were detected (c.-780C>G, c.-135C>T, c.175C>T-Leu59Leu, c.707A>G-Arg236His, c.892A>G-Asp298Asn, and c.*430A>T). Linkage disequilibrium (LD) of the six SNPs was analyzed by performing haploid analysis. There was a perfect linkage disequilibrium in c.-780C>G, c.-135C>T, c.175C>T-Leu59Leu, c.707A>G-Arg236His, and c.*430A>T. Only the c.892A>G (Asp298Asn) SNP showed a very low LD with an $r^2$ value of 0.028 and the D' value of 0.348. As a result, the two SNPs-c.707A>G (Arg236His) and c.892A>G (Asp298Asn)-were selected to extract the genotype frequencies from the 5 pig breeds by using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) genotype analysis method. The SNP frequency of c.707A>G (Arg236His) indicated the presence of the A (His) allele only in Yorkshire, while the G allele was fixed in the KNP, Landrace, Berkshire, and Duroc. Association analysis was carried out in 484 pigs with the c.707A>G (Arg236His) SNP and the meat quality traits of four different pig cross populations: a significant association was noted in crude fat, sirloin moisture, meat color, and the degree of red and yellow coloration. The frequency of the c.892A>G(Asp298Asn) SNP genotype varied among the breeds; while Duroc showed the highest frequency of the A (Asn) allele, KNP showed the highest frequency of the G (Asp) allele. Association analysis was carried out in 1126 pigs with the c.892A>G (Asp298Asn) SNP and the meat quality traits of four pig populations: a highly significant linkage was noted in the back-fat thickness (P<0.002). It was found that the back-fat thickness was higher in individuals with the AA genotype than in those with the AG or GG genotype. Thus, in this study, we verified that the c.892A>G (Asp298Asn) SNP in the pig MC4R gene has a sufficient effect as a gene marker for MAS in Korean pork industry.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.119-135
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• 2021

Korea has stepped up efforts to investigate and catalog its flora and fauna to conserve the biodiversity of the Korean Peninsula and secure biological resources since the ratification of the Convention on Biological Diversity (CBD) in 1992 and the Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits (ABS) in 2010. Thus, after its establishment in 2007, the National Institute of Biological Resources (NIBR) of the Ministry of Environment of Korea initiated a project called the Korean Indigenous Species Investigation Project to investigate indigenous species on the Korean Peninsula. For 15 years since its beginning in 2006, this project has been carried out in five phases, Phase 1 from 2006-2008, Phase 2 from 2009-2011, Phase 3 from 2012-2014, Phase 4 from 2015-2017, and Phase 5 from 2018-2020. Before this project, in 2006, the number of indigenous species surveyed was 29,916. The figure was cumulatively aggregated at the end of each phase as 33,253 species for Phase 1 (2008), 38,011 species for Phase 2 (2011), 42,756 species for Phase 3 (2014), 49,027 species for Phase 4 (2017), and 54,428 species for Phase 5(2020). The number of indigenous species surveyed grew rapidly, showing an approximately 1.8-fold increase as the project progressed. These statistics showed an annual average of 2,320 newly recorded species during the project period. Among the recorded species, a total of 5,242 new species were reported in scientific publications, a great scientific achievement. During this project period, newly recorded species on the Korean Peninsula were identified using the recent taxonomic classifications as follows: 4,440 insect species (including 988 new species), 4,333 invertebrate species except for insects (including 1,492 new species), 98 vertebrate species (fish) (including nine new species), 309 plant species (including 176 vascular plant species, 133 bryophyte species, and 39 new species), 1,916 algae species (including 178 new species), 1,716 fungi and lichen species(including 309 new species), and 4,812 prokaryotic species (including 2,226 new species). The number of collected biological specimens in each phase was aggregated as follows: 247,226 for Phase 1 (2008), 207,827 for Phase 2 (2011), 287,133 for Phase 3 (2014), 244,920 for Phase 4(2017), and 144,333 for Phase 5(2020). A total of 1,131,439 specimens were obtained with an annual average of 75,429. More specifically, 281,054 insect specimens, 194,667 invertebrate specimens (except for insects), 40,100 fish specimens, 378,251 plant specimens, 140,490 algae specimens, 61,695 fungi specimens, and 35,182 prokaryotic specimens were collected. The cumulative number of researchers, which were nearly all professional taxonomists and graduate students majoring in taxonomy across the country, involved in this project was around 5,000, with an annual average of 395. The number of researchers/assistant researchers or mainly graduate students participating in Phase 1 was 597/268; 522/191 in Phase 2; 939/292 in Phase 3; 575/852 in Phase 4; and 601/1,097 in Phase 5. During this project period, 3,488 papers were published in major scientific journals. Of these, 2,320 papers were published in domestic journals and 1,168 papers were published in Science Citation Index(SCI) journals. During the project period, a total of 83.3 billion won (annual average of 5.5 billion won) or approximately US $75 million (annual average of US $5 million) was invested in investigating indigenous species and collecting specimens. This project was a large-scale research study led by the Korean government. It is considered to be a successful example of Korea's compressed development as it attracted almost all of the taxonomists in Korea and made remarkable achievements with a massive budget in a short time. The results from this project led to the National List of Species of Korea, where all species were organized by taxonomic classification. Information regarding the National List of Species of Korea is available to experts, students, and the general public (https://species.nibr.go.kr/index.do). The information, including descriptions, DNA sequences, habitats, distributions, ecological aspects, images, and multimedia, has been digitized, making contributions to scientific advancement in research fields such as phylogenetics and evolution. The species information also serves as a basis for projects aimed at species distribution and biological monitoring such as climate-sensitive biological indicator species. Moreover, the species information helps bio-industries search for useful biological resources. The most meaningful achievement of this project can be in providing support for nurturing young taxonomists like graduate students. This project has continued for the past 15 years and is still ongoing. Efforts to address issues, including species misidentification and invalid synonyms, still have to be made to enhance taxonomic research. Research needs to be conducted to investigate another 50,000 species out of the estimated 100,000 indigenous species on the Korean Peninsula.