In 2004, total emissions of Greenhouse Gases(GHGs) in Korea was estimated to be about 590 million metric tons, which is the world's 10th largest emissions. Considering the much amount of nation's GHG emissions and growing nation's position in the world, GHG emissions in Korea should be reduced in near future. The CO
To determine the mechanism of the resistance to organophosphorus insecticide, chlorpyrifos, in diamondback-moth (Plutella xylostella L.), activities of esterases, glutathione-S-transferase (GST) and AChE insensitivity which were known for causing factor of resistance were measured. Also, the relationship between AChE insensitivity and the resistant ratio was investigated to inquiry the cross-resistance. The resistant ratio of chlorpyrifos-resistant strain (CRS) of diamondback-moth at the 6th generation was developed 160 fold compared to susceptible strain (SS) one. Activity of GST that are extracted from CRS was 1.7-fold higher than that from SS. However, activity of total esterases from CRS was similar to that from SS. In AChE insensitivity test, CRS was 11.8-fold less sensitive than that from SS. CRS was ranged from 17.6 to 33.6-fold less sensitive than SS to other insecticides having same target site with chlorpyrifos such as dichlorvos, dimethylvinphos and carbofuran. Insensitivity of AChE to phenthoate-oxon, however, was 1.7-fold. Resistance of CRS was 82-fold, 47-fold and 42-fold higher than SS to dichlorvos, dimethylvinphos and carbofuran, respectively, but 2.3-fold to phenthoate and then we could identify that the resistance development of insecticide might have a lot of difference among the chemicals with the same target site. The relationship between the AChE insensitivity and the resistant ratio was significantly correlated
Purpose : Empirical antimicrobial treatment is indicated before bacteriological results are available for young children with febrile UTI to minimize renal scarring. To ensure appropriate therapy, knowledge of the prevalence of causative organisms and their susceptibility patterns to antimicrobials is mandatory. We performed a retrospective analysis investigating the local prevalence and resistance patterns of uropathogens, primarily E. coli, isolated from community-acquired UTIs. Methods : A total of 103 positive urine cultures from children with febrile UTI collected at Bundang CHA General Hospital from February 2004 to February 2005 were analyzed. Inclusion criteria were fever higher than
1. The 'Kao Zheng Pai(考證派) comes from the 'Zhe Zhong Pai' and is a school that is influenced by the confucianism of the Qing dynasty. In Japan Inoue Kinga(井上金娥), Yoshida Koton(吉田篁墩) became central members, and the rise of the methodology of historical research(考證學) influenced the members of the 'Zhe Zhong Pai', and the trend of historical research changed from confucianism to medicine, making a school of medicine based on the study of texts and proving that the classics were right. 2. Based on the function of 'Nei Qu Li '(內驅力) the 'Kao Zheng Pai', in the spirit of 'use confucianism as the base', researched letters, meanings and historical origins. Because they were influenced by the methodology of historical research(考證學) of the Qing era, they valued the evidential research of classic texts, and there was even one branch that did only historical research, the 'Rue Xue Kao Zheng Pai'(儒學考證派). Also, the 'Yi Xue Kao Zheng Pai'(醫學考證派) appeared by the influence of Yoshida Kouton and Kariya Ekisai(狩谷掖齋). 3. In the 'Kao Zheng Pai(考證派)'s theories and views the 'Yi Xue Kao Zheng Pai' did not look at medical scriptures like the "Huang Di Nei Jing"("黃帝內經") and did not do research on 'medical' related areas like acupuncture, the meridian and medicinal herbs. Since they were doctors that used medicine, they naturally were based on 'formulas'(方劑) and since their thoughts were based on the historical ideologies, they valued the "Shang Han Ja Bing Lun" which was revered as the 'ancestor of all formulas'(衆方之祖). 4. The lives of the important doctors of the 'Kao Zheng Pai' Meguro Dotaku(目黑道琢) Yamada Seichin(山田正珍), Yamada Kyoko(山田業廣), Mori Ritsi(森立之) Kitamura Naohara(喜多村直寬) are as follows. 1) Meguro Dotaku(目黑道琢 1739
The genetic diagnosis methods by RT-PCR and Virion capture (VC)/RT-PCR against Rice stripe virus (RSV) were developed. Three diagnosis methods of seedling test, ELISA and RT-PCR were compared in virus detection sensitivity (VDS) for RSV. The VDS of ELISA for RSV viruliferous small brown plant hopper (SBPH) was higher with 40.5% than that of seedling test. The VDS of RT-PCR was higher with 21% than that of ELISA. The VDS of ELISA and VC/RT-PCR was same with 9.2% in average on the SBPH collected from fields at the areas of Gimpo, Pyungtaeg and Sihueng, Gyeonggi province in 2009. The specific primers of RSV for SBPH and rice plant were developed for the diagnosis by Real time PCR. The RQ value of Real time PCR for the viruliferous and non viruliferous SBPH was 1 for 50 heads of non viruliferous SBPH, 96.5 for 50 heads of viruliferous SBPH, 23.1 for 10 heads of viruliferous SBPH + 40 heads of non viruliferous SBPH, and 75.6 for 30 heads of viruliferous SBPH + 20 heads of non viruliferous SBPH. The RQ value was increased positively by the ratio of viruliferous SBPH. Full sequences of 4 genomes of RSV RNA1, RNA2, RNA3 and RNA4 were analysed for the 13 RSV isolates from rice plants collected from different areas. Genetic relationships among the RSV isolates of Korea, Japan and China were classified as China + Korea, and China + Korea + Japan by phylogenetic analysis for RSV RNA1 and RNA2. In case of RNA3 involved in pathogenicity, genetic relationship of RSV among the three countries was grouped into 3 as China, China + Korea, and Korea + Japan. According to the genetic relationships in RSV RNA4, RSV isolates were grouped into 4 as China, Korea, China + Korea + Japan, and Korea + Japan. Viruliferous insect rate (VIR) of RSV in average increased in each year from 2008 to 2010, and the rates were 4.3%, 6.1%, and 7.2%, respectively, at the 28 major rice production areas in 7 provinces including Gyeonggido. The highest VIR in each year was 11.3% of Gyeonggido in 2008, 20.1% of Jellanamdo in 2009 and 14.2% of Chungcheongbukdo in 2010. The highest VIR depending upon the investigated areas was 22.1% at Buan of Jellabukdo in 2008, 36% at Wando and Jindo of Jellanamdo in 2009, and 30.0% at Boeun of Chungcheongbukdo in 2010. Average population density (APD) of overwintered SBPH was 13.1 heads in 2008, 13.9 heads in 2009 and 5.6 heads in 2010. The highest APD was 39.1 and 60.4 heads at Buan of Jellabukdo in 2008 and 2009, respectively, and 14.0 heads at Pyungtaeg of Gyeonggido. The acreage of RSV occurred fields was 869 ha in the western and southern parts, mainly at Jindo and Wando areas, of Jellanamdo in 2008. In 2009, RSV occurred in the acreage of 21,541 ha covered whole country, especially, partial and whole plant death were occurred with infection rate of 55.2% at 3,025 plots in 53 Li, 39 Eup/Myun, 19 Si/Gun of Gyeonggido, Incheonsi, Chungcheongnamdo, Jeollabukdo and Jeollanamdo. Seasonal development of overwintered SBPH was investigated at Buan, Jeollabukdo, and Jindo, Jeollanamdo for 3 years from 2008. Most SBPH developed to the 3rd and 4th instar on the periods of May 20 to June 10, and they developed to the adult stage for the 1st generation on Mid and Late June. In 2009, all SBPH trapped by sky net trap were adult on May 31 to June 1 at Mid-western aeas of Taean, Seosan and Buan, and South-western areas of Sinan and Jindo. The population density of adult SBPH was 963 heads at Taean, 919 at Seocheon and 819 at Sinan area. The origin of these higher population of adult SBPH were verified from the population of non-overwintered SBPH but immigrant SBPH. From Mid May to Mid June in 2010, adult SBPH could not be counted as immigrant insects by sky net trap. The variation of RSV VIR was high with 2.1% to 9.5% for immigrant adult SBPH trapped by sky net trap at Hongsung of Chungcheongbukdo, Buan of Jeollabukdo and so forth in 2009. The highest VIR for the immigrant adult SBPH was 9.5% at Boryung of Chungcheongnamdo, followed by 7.9% at Hongsung of Chungcheongnamdo, 6.5% at Younggwang of Jeollanamdo, and 6.4% at Taean of Cheongcheongnamdo. The infection rate of RSV on rice plants induced by the immigrant adult SBPH cultivated near sky net trap after about 10 days from immigration on June 12 in 2009 was 84.6% at Taean, 65.4% at Buan and 92.9% at Jindo, and 81% in average through genetic diagnosis of RT-PCR. Barley known as a overwintering host plant of RSV had very low infection rate of 0.2% from 530 specimens collected at 10 areas covering whole country including Pyungtaeg of Gyeonggido. Twenty nine plant species were newly recorded as natural hosts of RSV. In winter annual plant species, 11 plants including Vulpia myuros showed RSV infection rate of 24.9%. The plant species in summer annual ecotype were 13 including Digitaria ciliaris with 44.9%, Echinochloa crusgalli var. echinata with 95.2% and Setaria faberi with 65.5% in infection rate of RSV. Five perennial plants including Miscanths sacchariflorus with infection rate of 33.3% were recorded as hosts of RSV. Rice cultivars, 8 susceptible cultivars including Donggin1 and 17 resistant ones including Samgwang, were screened in field conditions at 3 different areas of Buan, Iksan and Ginje in 2009. All the susceptible cultivars were showed typical symptom of mosaic and wilt. In 17 genetic resistant cultivar, 12 cultivars were susceptible, however, 5 cultivars were field-resistant plus genetic resistant to RSV as non symptom expression. When RSV was artificially inoculated at seedling stage to 4 cultivars known as genetic resistant and 3 cultivars known as genetic susceptible, the symptom expression in resistant cultivars was lower as 19.3% in average than that of 53.3% in susceptible ones. In comparison of symptom expression rate and viral infection rate using resistant Nampyung and susceptible Heugnam cultivars by artificial inoculation of RSV at seedling stage, the symptom expression of Heugnam was higher as 28% than 12% of Nampyung. However, virion infection of resistant Nampyung cultivar was higher as 12% reversely than 85% of susceptible Heugnam. Yield loss of rice was investigated by the artificial inoculation of RSV at the seedling stage of resistant cultivars of Nampyung and Onnuri, and susceptible cultivars of Donggin1 and Ungwang for 3 years from 2008. The average yield per plant was 7.8 g, 8.5 g and 13.8 g on rice plants inoculated at seedling stage, tillering stage and maximum tillering stage, respectively. The yield loss rate was increased by earlier infection of RSV with 51% at seedling stage, 46% at tillering stage and 13% at maximum tillering stage. In resistant rice cultivars, there was no statistically significant relation between infection time and yield loss. In natural fields on susceptible rice cultivar of Ungwang at Taean and Jindo areas in 2009, the yield loss rate was increased with same tendency to the infection hill rate having the corelation coefficient of 0.94 when the viral infection was over 23.4%.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.
1.The 'Kao Zheng Pai'(考證派) comes from the 'Zhe Zhong Pai(折衷派)' and is a school that is influenced by the confucianism of the Qing dynasty. In Japan Inoue Kinga(井上金峨), Yoshida Koton(古田篁墩
Experiment I: The objective of this study was to know variation in some selected agronomic characters of sweet sorghum when planted in several growing seasons. The 17 different sweet sorghum varieties having various maturities, and plant, syrup and sugar types were used in this study which had been carried out for the period of two years from 1968 to 1969 at Industrial Crops Division of Crop Experiment Station in Suwon. These varieties were planted at an interval of 20 days from April 5 to August 25 both in 1968 and 1969. The experimental results could be summarized as follows: 1. As planting was made early, the number of days from sowing to germination was getting prolonged while germination took place early when planted at the later date of which air temperature was relatively higher. However, such a tendency was not observed beyond the planting on August 25. In general, a significant negative correlation was found between the number of days from sowing to germination and the average daily temperature but a positive correlation was found between the former and the total accumulated average temperature during the growth period. 2. The period from sowing to heading was generally shortened as planting was getting delayed. The average varietal difference in number of days from sowing to heading was as much as 30.2 days. All the varieties were grouped into early-, medium and late-maturing groups based upon a difference of 10 days in heading. The average number of days from sowing to heading was 78.5