Eunho Ha

Leukotriene-related gene polymorphisms in patients with aspirin-intolerant urticaria and aspirin-intolerant asthma: differing contributions of ALOX5 polymorphism in Korean population

The pathogenesis of aspirin (acetylsalicylic acid, ASA)-intolerant urticaria (AIU) is still poorly understood but it has recently been suggested that it is associated with the overproduction of leukotriene (LT). This is supported by evidence that cyclooxygenase 2 inhibitor is given safely to patients with AIU. The present study was designed to investigate the role of genetic polymorphism of LT related genes in the pathogenesis of AIU via a case-control study. We screened single nucleotide polymorphisms (SNPs) in genes encoding enzymes involved in leukotriene synthesis in the Korean population with AIU (n=101), ASA-intolerant asthma (AIA, n=95) and normal healthy controls (n=123). Genotype was determined by primer extension reactions using the SNapShot ddNTP primer extension kit. Among 8 SNPs of four LT related genes, the polymorphism of ALOX5 at positions of -1708 G>A showed significant difference in genotype frequency between AIU and AIA (p=0.01). Furthermore, there were significant differences observed in the frequencies of two ALOX5 haplotypes between the AIU group and AIA group (p<0.05). However, there were no differences in allele, genotype, or haplotype frequencies of ALOX5 between the AIU group and the normal control group. These results suggested that ALOX5 has a differing contribution in two major clinical pathogenesis related to ASA-sensitivity.

Model vs. design sensitivity to the ground-truth problem of rainfall observation

Abstract In this study three multi-dimensional rainfall models, the Waymire–Gupta–Rodriguez-Iturbe multi-dimensional rainfall model (WGR model) [Water Resour. Res. 20 (10) (1984) 1453], the noise forced diffusive rainfall model (NFD model) [J. Atmos. Ocean Technol. 6 (1989) 985] and the Yoo–Valdes–North model (YVN model) [Water Resour. Res. 32 (7) (1996) 2175], are compared with their applications to the ground-truth problem to capture the sensor bias using multiple raingauges. All the model parameters used are those estimated tuned to the GATE by Valdes et al. [J. Geophys. Res. (Atmos.) 95 (D3) (1990) 2101], North and Nakamoto [J. Atmos. Ocean Technol. 6 (1989) 985] and Yoo et al. [Water Resour. Res. 32 (7) (1996) 2175], respectively, and the root mean square errors (RMSEs) for each model are estimated to compare. The difference among models can be seen easily from the comparison of their spectra, which, in turn, affects the RMSEs for the ground-truth problem. Two conclusions could be deduced from the results of the study: (1) The rainfall model is the more crucial factor for the ground-truth problem than the ground-truth design. That is, the design factors, such as the number of raingauges, the size of the field of view (FOV), and the distance between the first and the last raingauges, were found to be much less sensitive to the RMSEs than the model itself. For example, the RMSEs estimated for a model could be more than twice of another models, which could result in more than four times of satellite observations required to capture the sensor bias. However, twice the number of raingauges, twice the size of the FOV, or twice the length between the first and the last raingauges resulted in less than 20% difference of the RMSEs. (2) The model sensitivity is much higher than the parameter sensitivity to the RMSEs. For example, just about 25% difference of the RMSEs could be expected even when applying the NFD model parameters 100% bigger or smaller. Considering that the model parameters would be estimated to be within their reasonable ranges, the parameter sensitivity to the RMSEs must become much smaller than the model itself.

On the Use of Threshold for the Ground Validation of Satellite Rain Rate

Ground-truthing is a major problem in the satellite estimation of rain rate. This problem is that the measurement taken by the satellite sensor is fundamentally different from the one it is compared with on the ground. Additionally, since the satellite has the limited capability to measure the light rain rate exactly, the comparison should also consider the threshold value of satellite rain rate. This paper proposes a ground-truth design with threshold for the satellite rain rate. This ground-truth design is the generalization of the conventional ground-truth design which considered the only (zero, nonzero) and (nonzero, nonzero) measurement pairs. The mean-square error is used as an index of accuracy in estimating the ground measurement by satellite measurement. An application to the artificial random field shows that the proposed ground-truth design with threshold is valid as the design bias is zero. The same result is also derived in the application to the COMS (Communication, Ocean, and Meteorological Satellite) rain rate data in Korea.

Ground truth and climate model comparison for TRMM rainfall data

Five years of rainfall data from the Tropical Rainfall Measuring Mission are now available for use. This paper surveys comparisons of these rain rate data with buoy-based estimates over the Tropical Pacific and with simulations with the CCM3, a general circulation model of atmospheric circulation. We examine both the seasonal and diurnal cycles in the data. The TRMM data agree reasonably well with the buoy data for both the seasonal and diurnal cycles. While the data generally agree with the simulations, there are some discrepancies which can be attributed to model failures.

Multi-dimensional precipitation models and their application to the ground-truth problem: Multiple raingauge case

In this study three multi-dimensional precipitation models, the Waymire-Gupta-Rodriguez-Iturbe model (Waymireet al., 1984; hereafter the WGR model), the noise forced diffusive precipitation model (North and Nakamoto, 1989; hereafter the NFD model) and the Yoo-Valdes-North model (Yooet al., 1996; hereafter the YVN model), are applied to the ground-truth problem to capture the sensor bias using multiple raingauges. All the model parameters used are those estimated tuned to the GATE data by Valdeset al. (1990), North and Nakamoto (1989) and by Yooet al. (1996), respectively, and the RMSEs (root mean square errors) for each model are compared. As a result of the study, we found that the model structure is more sensitive to the RMSEs than the rainrate capturing design on the ground, and that there exist optimal numbers of raingauges for a ground-truth design. Even though the optimal number of raingauges for each model were estimated differently, whose sensitivity to the RMSEs was not so high as to lead a totally different ground-truth design. However, in some cases the RMSEs estimated for a model were more than twice of another models, which could result in more than 4 times of satellite observations required to capture the sensor bias. Thus, we can conclude that the rainrate model with realistic representation of observed rainrate field is more important than a ground-truth design for this kind of sampling related problems.