• ETRI Journal
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• v.38 no.1
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• pp.195-205
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• 2016

This paper presents a novel follicular unit (FU) classification method based on an angle variation of a boundary vector according to the number of hairs in several FU images. The recently developed robotic FU harvest system, ARTAS, classifies through digital imaging the FU type based on the number of hairs with defects in the contour and outline profile of the FU of interest. However, this method has a drawback in that the FU classification is inaccurate because it causes unintended defects in the outline profile of the FU. To overcome this drawback, the proposed method classifies the FU's type by the number of variation points that are calculated using an angle variation a boundary vector. The experimental results show that the proposed method is robust and accurate for various FU shapes, compared to the contour-outline profile FU classification method of the ARTAS system.

• BMB Reports
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• v.40 no.4
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• pp.595-603
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• 2007

To investigate whether phospholipase D (PLD, EC 3.1.4.4) plays a role in adaptive response of post-harvest fruit to environment, a PLD gene was firstly cloned from grape berry (Vitis Vinifera L. cv. Chardonnay) using RT-PCR and 3'- and 5'-RACE. The deduced amino acid sequence (809 residues) showed 84.7% identity with that of PLD from Ricinus communis. The secondary structures of this protein showed the characteristic C2 domain and two active sites of a phospholipid-metabolizing enzyme. The PLD activity and its expression in response to heat acclimation were then assayed. The results indicated PLD was significantly activated at enzyme activity, as well as accumulation of PLD mRNA and synthesis of new PLD protein during the early of heat acclimation, primary suggesting that the grape berry PLD may be involved in the heat response in post-harvest grape berry. This work offers an important basis for further investigating the mechanism of post-harvest fruit adaptation to environmental stresses.

• YAKHAK HOEJI
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• v.47 no.2
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• pp.57-68
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• 2003

Under the active search for biologically active novel agents for cancer prevention and treatment, some agents have been found from plants which are easily available. Our previous research on them revealed that C. annuum L. var. angulosum Mill have high antiproliferating effect on cancer cells. However, it has not been known whether the anticancer efficacy is different according to each part of C. annuum L. var. angulosum Mill or whether it can be changed by timing of harvest or solvent for extraction. Thus we compared the efficacy of each part of C. annuum L. var. angulosum Mill and assessed how much difference in the efficacy can be made according to the time of harvest or solvents for extraction. We observed the morphologic change and apoptosis 48 hr after treatment with the extract of each part of C. annuum L. var. angulosum Mill in MCF-7 mammary gland adenocarcinoma cells and human hepatoma cells. We also counted cancer cells by trypan blue method and MTT method to check the cytotoxicity. The leaf extract showed the highest anticancer effect among all the parts of C. annuum L. var. angulosum Mill; 50％ and 70％ reduction in the number of cancer cells was observed at 25 $\mu\textrm{g}$/mι and 50 $\mu\textrm{g}$/mι, respectively. It was more than 2 times as potent as 5-fluorouracil (5-FU). We found chromosomal fragmentation, clumping, and destuction by PI staining, and DNA fragmentation by electrophoresis. In conclusion, this study suggests that leaf extraction using water as solvent has the highest antiproliferative and apoptotic activity in cancer cells compared with other parts of extraction.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.7
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• pp.571-583
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• 2006

To secure high yield and good quality of rice, plant growth and nitrogen (N) nutrition status should be taken into account for managing panicle N topdressing (PN). This research aimed at investigating the rice yield response to PN under different plant growth and N nutrition status that was conditioned by different rates of basal and tillering N fertilizer (BTN). Stepwise multiple regression (SMR) was used for the analysis of yield response to (i) BTN and PN, and (ii) shoot N content at PIS (BTNup) and shoot N uptake from PIS to harvest (PNup). Rice yield increased significantly as BTN and PN Increased, but there was no significant interaction between BTN and PN. Yield increased almost linearly with the increasing BTN and PN up to $10{\sim}12$ and $6{\sim}7\;kgN/10a$, and with the increasing BTNup and PNup up to $6{\sim}7$ and $5{\sim}6\;kgN/10a$, respectively. But yield increment tended to decrease above those levels. These declines resulted from the decreased ripened grain ratio and 1000 grain weight even though spikelet number per unit area increased more at above those N levels. Spikelet number per unit area had the linear relationships with the shoot N uptake until heading, and with yield. Like most yield response curves, yield response in this experiment followed the diminishing return function with BTNup, PNup, and plant N uptake from seeding to harvest. Regardless of the degree of BTNup and PNup, yield had a quadratic relationship ($R^{2}$>0.88) with whole shoot N accumulation until harvest, suggesting that the yield determination was closely related with the whole shoot N uptake until harvest regardless of the differences in seasonal shoot N uptake.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.51 no.5
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• pp.386-395
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• 2006

The core questions for determining nitrogen topdress rate (Npi) at panicle initiation stage (PIS) are 'how much nitrogen accumulation during the reproductive stage (PNup) is required for the target rice yield or protein content depending on the growth and nitrogen nutrition status at PIS?' and 'how can we diagnose the growth and nitrogen nutrition status easily at real time basis?'. To address these questions, two years experiments from 2001 to 2002 were done under various rates of basal, tillering, and panicle nitrogen fertilizer by employing a rice cultivar, Hwaseongbyeo. The response of grain yield and milled-rice protein content was quantified in relation to RVIgreen (green ratio vegetation index) and SPAD reading measured around PIS as indirect estimators for growth and nitrogen nutrition status, the regression models were formulated to predict PNup based on the growth and nitrogen nutrition status and Npi at PIS. Grain yield showed quadratic response to PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict grain yield had a high determination coefficient of above 0.95. PNup for the maximum grain yield was estimated to be 9 to 13.5 kgN/10a within the range of RVIgreen around PIS of this experiment. decreasing with increasing RVIgreen and also to be 10 to 11 kgN/10a regardless of SPAD readings around PIS. At these PNup's the protein content of milled rice was estimated to rise above 9% that might degrade eating quality seriously Milled-rice protein content showed curve-linear increase with the increase of PNup, RVIgreen around PIS, and SPAD reading around PIS. The regression models to predict protein content had a high determination coefficient of above 0.91. PNup to control the milled-rice protein content below 7% was estimated as 6 to 8 kgN/10a within the range of RVIgreen and SPAD reading of this experiment, showing much lower values than those for the maximum grain yield. The recovery of the Npi applied at PIS ranged from 53 to 83%, increasing with the increased growth amount while decreasing with the increasing Npi. The natural nitrogen supply from PIS to harvest ranged from 2.5 to 4 kg/10a, showing quadratic relationship with the shoot dry weight or shoot nitrogen content at PIS. The regression models to estimate PNup was formulated using Npi and anyone of RVIgreen, shoot dry weight, and shoot nitrogen content at PIS as predictor variables. These models showed good fitness with determination coefficients of 0.86 to 0.95 The prescription method based on the above models predicting grain yield, protein content and PNup and its constraints were discussed.