In cells, the aptamers did not interfere with the structure or essential function of snR30, as the tagged RNA localized to the nucleolus and directed processing of ribosomal RNA in yeast. These factors included the clearance of the native unmodified snR30, the amount and length of dye incubation and the rinsing of cells. Multiple factors in cell preparation were vital for obtaining a good fluorescence signal. In snR30 a tandem repeat of the Broccoli aptamer produced the best signal in vitro. The ability to observe aptamer fluorescence in polyacrylamide gels stained with a fluorophore or with a microplate reader can ease preliminary screening of the aptamers in different RNA scaffolds. Here we describe how the Saccharomyces cerevisiae snoRNA, snR30, was tagged with the Spinach or the Broccoli aptamers and observed in live cells. Although used in mammalian and Escherichia coli cells, the use of these aptamers in yeast has been limited. These RNA aptamers interact with a fluorophore (DFHBI or DFHBI-1T) to produce a green fluorescence signal. Kang Tu, KeRen, Leiqing Pan and HongwenLi, “A Study of Broccoli Grading System Based on Machine Vision and Neural Networks”, Proceedings of the 2007 IEEE International Conference on Mechatronics and Automation August 5 - 8, 2007, Harbin, China, pp.2332 - 2336, 2007.The development of the RNA ‘vegetable’ aptamers, Spinach and Broccoli, has simplified RNA imaging, especially in live cells. Greater accuracy can be achieved by using larger number of training sets. So this network can be used for Broccoli grading with a pretty good accuracy. The RBF (fewer neurons) however has a very low error, much lesser than feed-forward back propagation network and is quiet accurate in itself. The RBF (exact fit) has a lesser error than RBF (fewer neurons).
Using feed-forward model withback-propagation algorithmĪ feed-forward back-propagation network using 10 neurons was trained and simulated using the 14 training sets shown in the previous slide.Įrror:
Grading thresholds of broccoli color and shape parameters Here, nntool in MATLAB is being used to perform the training and simulations. Then, the value of roundedness of the broccoli was calculated asĮ is between 0 and 1, and the higher value of E indicated better quality.įlow Chart of Image Processing and Analysisĭifferent artificial neural networks can be used to grade the broccoli on the basis of the following five parameters:ī – corresponding to yellowness of the broccoli Using the image processing software, the area S and length L of the broccoli were determined. Then, two kinds of analyses are performed:Ĭolorwas first captured in RGB (Red, Green, Blue) system and then determined by reflectance mode and expressed by L*(luminosity), a* (green-red) and b* (blue-yellow) parameters.īroccoli surface color can be expressed as H° (Hue angle, arctan(b*/a*)), and the color of the florets surface can be indicated by TCD (Total Color Difference). The first step in the process of grading broccoli is to measure the color of the broccoli using a computer vision system. Neural networks combined with image processing are being used to classify eggs, grade apples and other vegetables and even to classify animals. There are two stages involved in the grading of broccoli With the availability of technologies such as neural networks, it is possible to grade broccoli with machine vision. It has been discovered that there is a good relationship between instrumental color, sensory yellowness and chlorophyll content in cooked broccoli florets, and the chlorophyll content is a good index for evaluating the quality decay of broccoli florets during storage. The artificial grading standards of broccoli mainly include the weight, colorand external quality. The quality decay of broccoli is fast during post-harvest storage. That makes broccoli an important vegetable for exportation. The green florets used as the edible part of broccoli (Brassicaoleracea) with the tender texture and flavor, are high in antioxidants, but more importantly, in
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