• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2016.09a
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• pp.41-43
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• 2016

• Journal of Microbiology and Biotechnology
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• v.12 no.1
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• pp.31-38
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• 2002

Bacteria have evolved various types of resistance mechanism to toxic heavy metals, such as arsenic and antimony. An arsenical and antimonial resistant bacterium was isolated from a shallow creek draining a coal-mining area near Taebaek City, in Kangwon-Do, Korea. The isolated bacterium was identified and named as Pseudomonas sp. KM20 after biochemical and physiological studies were conducted. A plasmid was identified and its function was studied. Original cells harboring the plasmid were able to grow in the presence of 15 mM sodium arsenite, while the plasmid-cured (plasmidless) strain was sensitive to as little as 0.5 mM sodium arsenate. These results indicated that the plasmid of Pseudomonas sp. KM20 does indeed encode the arsenic resistance determinant. In growth experiments, prior exposure to 0.1 mM arsenate allowed immediate growth when they were challenged with 5 mM arsenate, 5 mM arsenite, or 0.1 mM antimonite. These results suggested that the arsenate, arsenite, and antimonite resistance determinants of Pseudomonas sp. KM20 plasmid were indeed inducible. When induced, plasmid-bearing resistance cells showed a decreased accumulation $of\;73^As$ and showed an enhanced efflux $of\;^73As$. These results suggested that plasmid encoded a transport system that extruded the toxic metalloids, resulting in the lowering of the intracellular concentration of toxic oxyanion. In a Southern blot study, hybridization with an E. coli R773 arsA-specific probe strongly suggested the absence of an arsA cistron in the plasmid-associated arsenical and antimonial resistance determinant of Pseudomonas sp. KM20.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.209-213
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• 2012

Purpose: This study was performed to develop a simple, disposable thin-film optical nitrate sensor. Methods: The sensor was fabricated by applying a nitrate-selective polymer membrane on the surface of a thin polyester film. The membrane was composed of polyvinylchloride (PVC), plasticizer, fluorescent dye, and nitrate-selective ionophore. Fluorescence intensity of the sensor increased on contact with a nitrate solution. The fluorescence response of the optical nitrate sensor was measured with a commercial fluorospectrometer. Results: The optical sensor exhibited linear response over four concentration decades. Conclusions: Nitrate ion concentrations in plant nutrient solutions can be determined by direct optical measurements without any conditioning before measurements.

• Korean Journal of Psychosomatic Medicine
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• v.19 no.2
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• pp.74-82
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• 2011

Objective:The aim of this study was to evaluate the relationship between sustained attention deficits in Attention-Deficit/Hyperactivity Disorder(ADHD) children and short-term Heart Rate Variability(HRV) parameters. In addition, we evaluate the relationship between The ADHD rating scale(ARS), the computerized ADHD diagnostic system(ADS) and Parenting stress index- short form(PSI-SF). Methods:This study was performed in the department of children and Adolescent psychiatry, Korea university Guro hospital from august 2008 to January 2009. We evaluated HRV parameters by short-term recordings of 5 minutes. K-ARS and ADS are used for screening and identifying ADHD children. Intelligence was measured using Korean educational Developmental Institute-wechsler Intelligence Scale for Children. The caregivers Complete Parenting Stress Index scale for evaluation parent stress. Results:The low frequency(LF) was significantly correlated with response variability of ADS. However, the other variables of ARS and ADS were not significantly correlated with LF. Hyperactivity subscale of ARS was significantly correlated with parental distress subscale and difficult child subscale of PSI-SF and inattention subscale of ARS was also significantly correlated with dysfunctional interaction and difficult child subscale of PSI-SF. Conclusion:The LF, 0.10-Hz component of HRV is known to measure effort allocation. This study shows that the LF component of HRV is significantly correlated with the response variability of ADS. This means that more severe symptoms of ADHD were correlated with the increase in the LF that means decreased effort allocation. These results also support the clinical usability of HRV in the assessment of ADHD. Furthermore, PSI-SF is correlated with hyperactivity and inattention variables of ARS.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Plant Breeding and Biotechnology
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• v.6 no.4
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• pp.381-390
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• 2018

Percentage of aerenchyma area has been closely linked with amounts of methane emitted by rice. A diversity panel of 39 global rice varieties were examined to determine genetic variation for root transverse section (RTS), aerenchyma area, and % aerenchyma. RTS and aerenchyma area showed a strong positive correlation while there existed no significant correlation between RTS area and % aerenchyma. Five varieties previously shown to differ in methane emissions under field conditions were found to encompass the variation found in the diversity panel for RTS and aerenchyma area. These five varieties were evaluated in a greenhouse study to determine the relationship of RTS, aerenchyma area, and % aerenchyma with methane emissions. Methane emissions at physiological maturity were the highest for 'Rondo', followed by 'Jupiter', while 'Sabine', 'Francis' and 'CLXL745' emitted the least. The same varietal rank, 'Rondo' being the largest and 'CLXL745' the smallest, was observed with RTS and aerenchyma areas. RTS and aerenchyma area were significantly correlated with methane emissions, r = 0.61 and r = 0.57, respectively (P < 0.001); however, there was no relationship with % aerenchyma. Our results demonstrated that varieties with a larger root area also developed a larger aerenchyma area, which serves as a gas conduit, and as a result, methane emissions were increased. This study suggests that root transverse section area could be used as a means of selecting germplasm with reduced $CH_4$ emissions.

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.04a
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• pp.162-163
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• 2006

• Korean Journal of Remote Sensing
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• v.20 no.3
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• pp.175-188
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• 2004

The objectives of this study were to estimate leaf area index (LAI) as a function of image-derived vegetation indices, and to compare measured and estimated LAI to the results of crop model simulation. Soil moisture, crop phenology, and LAI data were obtained several times during the 2001 growing season at monitoring sites established in two central Missouri experimental fields, one planted to com (Zea mays L.) and the other planted to soybean (Glycine max L.). Hyper- and multi-spectral images at varying spatial. and spectral resolutions were acquired from both airborne and satellite platforms, and data were extracted to calculate standard vegetative indices (normalized difference vegetative index, NDVI; ratio vegetative index, RVI; and soil-adjusted vegetative index, SAVI). When comparing these three indices, regressions for measured LAI were of similar quality $(r^2$ =0.59 to 0.61 for com; $r^2$ =0.66 to 0.68 for soybean) in this single-year dataset. CERES(Crop Environment Resource Synthesis)-Maize and CROPGRO-Soybean models were calibrated to measured soil moisture and yield data and used to simulate LAI over the growing season. The CERES-Maize model over-predicted LAI at all corn monitoring sites. Simulated LAI from CROPGRO-Soybean was similar to observed and image-estimated LA! for most soybean monitoring sites. These results suggest crop growth model predictions might be improved by incorporating image-estimated LAI. Greater improvements might be expected with com than with soybean.

• Korean Journal of Ecology and Environment
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• v.41 no.4
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• pp.512-518
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• 2008

This study were to evaluate trophic conditions, N : P ratios, and empirical relations of chlorophyll (CHL) systematically using TN, TP, and CHL values in agricultural reservoirs and drinking water supplying dams. During the study, nutrients and CHL varied depending on seasonal conditions and types of the reservoirs, but most reservoirs were diagnozed as eutrophic to hypertrophic. Mass ratios of TN : TP averaged 93.1 (range: $0.68{\sim}1342$) and about 96.6 % of the total observations (n=516) was > 17 in the N : P ratios. This result suggests that P was a potential factor limiting algal growth in the entire reservoir. Thus, TN : TP ratios were a function of phosphorus rather than nitrogen. Regression analysis of log-transformed N : P ratios against TP in DWDRs and ARs showed that ratios were linearly declined with an increase of TP ($R^2$>0.66; p<0.001). Seasonal mean CHL was minimum ($4.3{\mu}g\;L^{-1}$, range: $0.1{\sim}39.7{\mu}g\;L^{-1}$) in premonsoon, and was similar between the monsoon and postmonsoon. In contrast, one of the tremendous features was that values of CHL was greater in the ARs than DWDRs. Thus, the spatial and temporal patterns in CHL were similar to those of TP but not TN. Empirical models of CHL-TP showed that CHL variation could explain average 15.3% and 11.3% in DWDRs and ARs, respectively. Seasonal analysis of empirical models showed that CHL-TP relations were stronger in postmonsoon than those of premonsoon and monsoon.

• Journal of Biosystems Engineering
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• v.39 no.4
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• pp.377-388
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• 2014

Purpose: Determining the spatial structure of data is important in understanding within-field variability for site-specific crop management. An understanding of the spatial structures present in the data may help illuminate interrelationships that are important in subsequent explanatory analyses, especially when site variables are correlated or are a combined response to multiple causative factors. Methods: In this study, correlation, principal component analysis, and single and nested variogram models were applied to soil electrical conductivity and chemical property data of two fields in central Missouri, USA. Results: Some variables that were highly correlated, or were strongly expressed in the same principal component, exhibited similar spatial ranges when fitted with a single variogram model. However, single variogram results were dependent on the active lag distance used, with short distances (30 m) required to fit short-range variability. Longer active lag distances only revealed long-range spatial components. Nested models generally yielded a better fit than single models for sensor-based conductivity data, where multiple scales of spatial structure were apparent. Gaussian-spherical nested models fit well to the data at both short (30 m) and long (300 m) active lag distances, generally capturing both short-range and long-range spatial components. As soil conductivity relates strongly to profile texture, we hypothesize that the short-range components may relate to the scale of erosion processes, while the long-range components are indicative of the scale of landscape morphology. Conclusion: In this study, we investigated the effect of changing active lag distance on the calculation of the range parameter. Future work investigating scale effects on other variogram parameters, including nugget and sill variances, may lead to better model selection and interpretation. Once this is achieved, separation of nested spatial components by factorial kriging may help to better define the correlations existing between spatial datasets.