Soybean Rust Makes it to Iowa

Soybean rust, Phakopsora pachyrhizi, was first reported in the United States in November 2004 and survived winters on kudzu in the south. Soybean rust can reduce soybean yields and/or significantly increase the cost of soybean production when the disease occurs during the growing season with high incidence and severity. During the 2005 and 2006 growing seasons, soybean rust was not a threat for Iowa soybean growers. This year was a different story, as soybean rust was established fairly early in the season in Texas and Louisiana creating the potential for soybean rust to get to Iowa during the growing season. Thankfully, soybean rust was not found while soybean plants were in a vulnerable stage; however, soybean rust was found in a field in Dallas County, Iowa, on Tuesday, September 25, 2007. Since the initial find, soybean rust was confirmed in 13 additional counties throughout Iowa (Figure 1)

Update of Soybean Rust Research: What We Have Learned in the Last Two Seasons and How to Position for the Future in Soybean Rust Risk Management

In the last two seasons, soybean rust has shown limited dispersal during a growing season in the southern US. In summer, it mainly spread in the Gulf Coast states. Dry weather conditions in past two summers have been considered as a possible reason for the slow development of this disease in the US. The disease did not take off to the further northern states until September. This year it was found in three new states, Illinois, Indiana, and Virginia. So far, it was found 15 states in total242 counties (See the map on right). In Iowa, the disease was not found in sentinel plots by the time of frost come despite of the efforts using soybean rust test kits later in the season. Many people wonder why the disease is so devastating in South America but moves so slowly in North America, which is not consistent with anticipations concluded from the literature. New insights from recent research have shade light on this disease. Below is the review and summary what we have found

Effect of Statistical Fluctuation in Monte Carlo Based Photon Beam Dose Calculation on Gamma Index Evaluation

The gamma-index test has been commonly adopted to quantify the degree of agreement between a reference dose distribution and an evaluation dose distribution. Monte Carlo (MC) simulation has been widely used for the radiotherapy dose calculation for both clinical and research purposes. The goal of this work is to investigate both theoretically and experimentally the impact of the MC statistical fluctuation on the gamma-index test when the fluctuation exists in the reference, the evaluation, or both dose distributions. To the first order approximation, we theoretically demonstrated in a simplified model that the statistical fluctuation tends to overestimate gamma-index values when existing in the reference dose distribution and underestimate gamma-index values when existing in the evaluation dose distribution given the original gamma-index is relatively large for the statistical fluctuation. Our numerical experiments using clinical photon radiation therapy cases have shown that 1) when performing a gamma-index test between an MC reference dose and a non-MC evaluation dose, the average gamma-index is overestimated and the passing rate decreases with the increase of the noise level in the reference dose; 2) when performing a gamma-index test between a non-MC reference dose and an MC evaluation dose, the average gamma-index is underestimated when they are within the clinically relevant range and the passing rate increases with the increase of the noise level in the evaluation dose; 3) when performing a gamma-index test between an MC reference dose and an MC evaluation dose, the passing rate is overestimated due to the noise in the evaluation dose and underestimated due to the noise in the reference dose. We conclude that the gamma-index test should be used with caution when comparing dose distributions computed with Monte Carlo simulation

Antibunching photons in a cavity coupled to an optomechanical system

We study the photon statistics of a cavity linearly coupled to an optomechanical system via second order correlation functions. Our calculations show that the cavity can exhibit strong photon antibunching even when optomechanical interaction in the optomechanical system is weak. The cooperation between the weak optomechanical interaction and the destructive interference between different paths for two-photon excitation leads to the efficient antibunching effect. Compared with the standard optomechanical system, the coupling between a cavity and an optomechanical system provides a method to relax the constraints to obtain single photon by optomechanical interaction.Comment: 7 papes, 5 figure

The statistical properties of galaxy morphological types in compact groups of Main galaxies from the SDSS Data Release 4

In order to explore the statistical properties of galaxy morphological types in compact groups (CGs), we construct a random group sample which has the same distributions of redshift and number of member galaxies as those of the CG sample. It turns out that the proportion of early-type galaxies in different redshift bins for the CG sample is statistically higher than that for random group sample, and with growing redshift z this kind of difference becomes more significant. This may be due to the existence of interactions and mergers within a significant fraction of SDSS CGs. We also compare statistical results of CGs with those of more compact groups and pairs, but do not observe as large statistical difference as Hickson (1982)'results.Comment: 12 pages, 9 figure

Fast Monte Carlo Simulation for Patient-specific CT/CBCT Imaging Dose Calculation

Recently, X-ray imaging dose from computed tomography (CT) or cone beam CT (CBCT) scans has become a serious concern. Patient-specific imaging dose calculation has been proposed for the purpose of dose management. While Monte Carlo (MC) dose calculation can be quite accurate for this purpose, it suffers from low computational efficiency. In response to this problem, we have successfully developed a MC dose calculation package, gCTD, on GPU architecture under the NVIDIA CUDA platform for fast and accurate estimation of the x-ray imaging dose received by a patient during a CT or CBCT scan. Techniques have been developed particularly for the GPU architecture to achieve high computational efficiency. Dose calculations using CBCT scanning geometry in a homogeneous water phantom and a heterogeneous Zubal head phantom have shown good agreement between gCTD and EGSnrc, indicating the accuracy of our code. In terms of improved efficiency, it is found that gCTD attains a speed-up of ~400 times in the homogeneous water phantom and ~76.6 times in the Zubal phantom compared to EGSnrc. As for absolute computation time, imaging dose calculation for the Zubal phantom can be accomplished in ~17 sec with the average relative standard deviation of 0.4%. Though our gCTD code has been developed and tested in the context of CBCT scans, with simple modification of geometry it can be used for assessing imaging dose in CT scans as well.Comment: 18 pages, 7 figures, and 1 tabl