Planning and Research Division

Anderson, Oklahoma, United States

Planning and Research Division

Anderson, Oklahoma, United States

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Min Y.Y.,Tokyo University of Agriculture and Technology | Toyota K.,Tokyo University of Agriculture and Technology | Goto K.,Tokyo University of Agriculture and Technology | Sato E.,Tokyo University of Agriculture and Technology | And 5 more authors.
Nematology | Year: 2011

A real-time PCR (quantitative PCR: qPCR)-based detection method of the root-knot nematode Meloidogyne incognita was developed for sandy soils, the major soil type in sweet potato cultivated fields in Tokushima prefecture, Japan. Different numbers (5, 20, 80, 200 and 500) of second-stage juveniles (J2) were artificially added into 20 g of an air-dried sandy soil not containing M. incognita. To make homogenous samples, soil was homogenised by two different ways (ground with either a mortar and pestle or ball mill) and then 0.5 g of the soil was used for DNA extraction. There was a strong negative correlation in each homogenisation method between the cycle threshold number (Ct) and inoculated numbers of M. incognita J2. The Ct values were consistently lower and their variations among replicates were smaller in the samples ground with ball mill, suggesting that grinding with ball mill may be suitable for the preparation of soil for DNA extraction. Sandy soils were collected from sweet potato fields in Tokushima prefecture at the transplanting and harvesting times. Damage to sweet potato caused by M. incognita was also evaluated in some of the fields. At the transplanting time, no M. incognita was extracted in all the soils by the Baermann funnel method, while detection in the qPCR method ranged from zero to 4 210 000 J2 equivalent (20 g soil) -1. Heavy damage was observed in fields with more than 500 equivalent M. incognita J2 (20 g soil) -1. By contrast, very few galls were observed in fields with fewer than four individuals (20 g soil) -1. At harvest, zero to >1000 individuals of M. incognita was counted by the Baermann method and there was a significant correlation in estimated numbers of M. incognita between the two methods. However, the estimated numbers were 15 times higher in the qPCR method than in the Baermann method. These results indicate that direct quantification of M. incognita based on the qPCR method might enable a sensitive diagnosis to predict damage by the nematode. © Koninklijke Brill NV, Leiden, 2011.


Kalaa M.O.A.,University of Tulsa | Rajab S.,University of Tulsa | Refai H.H.,University of Tulsa | Johnson D.,Planning and Research Division
IEEE Intelligent Vehicles Symposium, Proceedings | Year: 2014

Vehicle classification is a vital measure used to ensure appropriate roadway design as it affects both capacity and pavement endurance. Given that, departments of transportation across the US collect vehicle miles travelled (VMT) for their highways using automatic vehicle classifiers (AVC), and then use these figures for future highway design. Accuracy assessment of AVCs is thus important to ensure proper VMT reporting. Studying the accuracy of AVC devices is therefore essential. Previous studies employed either manual counting or a 'play and pause' method of traffic video recording to verify the accuracy of AVC devices. This paper details a custom vision-aided software developed to aid in extracting accurate vehicle count and classification information used as ground truth data. Authors discuss the methodology used to study vehicle classification accuracy of AVC and weigh-in-motion sites tasked with vehicle classification. Several indicators introduced to investigate the accuracy of each site are highlighted. Results of a year-long 2013 study indicate a good performance of AVC devices and that the main source of error was the misclassification of class 2 and 3 vehicles as class 5. © 2014 IEEE.

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