• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.81-81
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• 2012

Since the ultraviolet (UV) flux of an integrated population is a good tracer of recent star formation activities, UV observations provide an important constraint on star formation history (SFH) in galaxies. We present UV color-magnitude relations (CMRs) of early-type dwarf galaxies in the Virgo cluster, based on Galaxy Evolution Explorer (GALEX) UV data and the Extended Virgo Cluster Catalog (EVCC, Kim, S. in prep.). The EVCC covers an area 5.4 times larger (750 deg2) than the footprint of the classical Virgo cluster catalog by Binggeli and collaborators. We secure 1304 galaxies as members of the Virgo cluster and 526 galaxies of them are new objects not contained in the VCC. Morphological classification of galaxies in the EVCC is based on the optical image ("Primary Classification") and spectral feature ("Secondary Classification") of the SDSS data. We find that dwarf lenticular galaxies (dS0s) show a surprisingly distinct and tight locus separated from that of ordinary dwarf elliptical galaxies (dEs), which is not clearly seen in previous CMRs. The dS0s in UV CMRs follow a steeper sequence than dEs and show bluer UV-optical color at a given magnitude. Most early type dwarf galaxies with blue UV colors (FUV-r < 6 and NUV-r < 4) are identified as those showing spectroscopic hints of recent or ongoing star formation activities. We explore the observed CMRs with population models of a luminosity-dependent delayed exponential star formation history. The observed CMR of dS0s is well matched with models with relatively long delayed star formation. Our results suggest that dS0s are most likely transitional objects at the stage of subsequent transformation of late-type progenitors to ordinary red dEs in the cluster environment. In any case, UV photometry provides a powerful tool to disentangle the diverse subpopulations of early-type dwarf galaxies and uncover their evolutionary histories.

• Korean Journal of Breeding Science
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• v.43 no.5
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• pp.422-429
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• 2011

This study was conducted to investigate the genetic diversity and to develop a technique for cultivar identification using SSR markers in grapevine. Thirty Korean bred and introduced grapevine cultivars were evaluated by 28 SSR markers. A total of 143 alleles were produced ranging from 2 to 8 alleles with an average of 5.1 alleles per locus. Polymorphic information contents (PIC) were ranged from 0.666 (VVIp02) to 0.975 (VVIn33 and VVIn62) with an average of 0.882. UPGMA (unweighted pair-group method arithmetic average) clustering analysis based on genetic distances using 143 alleles classified 30 grapevine cultivars into 7 clusters by similarity index of 0.685. Similarity values among the tested grapevine cultivars ranged from 0.575 to 1.00, and the average similarity value was 0.661. The similarity index was the highest (1.00) between 'Jinok' and 'Campbell Early', and the lowest (0.575) between 'Alden' and 'Narsha'. The genetic relationships among the 30 studied grapevine cultivars were basically consistent with the known pedigree. The three SSR markers sets (VVIn61, VVIt60, and VVIu20) selected from 28 primers were differentiated all grapevine cultivars except for 'Jinok' and 'Campbell Early'. Five cultivars ('Narsha, 'Alden', 'Dutchess', 'Pione', and 'Muscat Hamburg') were identified by VVIn61 at the first step. Then 21 cultivars including 'Hongsodam' by VVIt60 at the second step and 2 cultivars ('Heukbosuck' and 'Suok') by VVIu20 at the third step were identified. These markers could be used as a reliable tool for the identification of Korean grapevine cultivars.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.