The diversity of glycogen branching enzymes in microbes

Glycogen is the major carbon and energy reserve polymer in microorganisms and animals. Glycogen branching enzyme is one of the key enzymes in the glycogen synthesis. The glycogen branching enzymes were classified into GH13 and GH57 families. In this thesis, the two families GBEs were compared from crystal structures to their branched products. The GH13 GBEs are more active on starches than GH57 GBEs, while GH57 GBEs with high relative hydrolytic activity produce the products with more oligosaccharide. The two families GBEs in on bacteria shows that GH13 GBE is involved in glycogen synthesis, and the real function of GH57 GBE is not clear yet. The molecular weight of branched α-glucans increases with increasing amylose of the substrate for both families GBEs. The two families GBEs can be used to produce resistant starches with high amount of α-1,6-binds

Efficient Nonlinear Dimensionality Reduction for Pixel-wise Classification of Hyperspectral Imagery

Classification, target detection, and compression are all important tasks in analyzing hyperspectral imagery (HSI). Because of the high dimensionality of HSI, it is often useful to identify low-dimensional representations of HSI data that can be used to make analysis tasks tractable. Traditional linear dimensionality reduction (DR) methods are not adequate due to the nonlinear distribution of HSI data. Many nonlinear DR methods, which are successful in the general data processing domain, such as Local Linear Embedding (LLE) [1], Isometric Feature Mapping (ISOMAP) [2] and Kernel Principal Components Analysis (KPCA) [3], run very slowly and require large amounts of memory when applied to HSI. For example, applying KPCA to the 512×217 pixel, 204-band Salinas image using a modern desktop computer (AMD FX-6300 Six-Core Processor, 32 GB memory) requires more than 5 days of computing time and 28GB memory! In this thesis, we propose two different algorithms for significantly improving the computational efficiency of nonlinear DR without adversely affecting the performance of classification task: Simple Linear Iterative Clustering (SLIC) superpixels and semi-supervised deep autoencoder networks (SSDAN). SLIC is a very popular algorithm developed for computing superpixels in RGB images that can easily be extended to HSI. Each superpixel includes hundreds or thousands of pixels based on spatial and spectral similarities and is represented by the mean spectrum and spatial position of all of its component pixels. Since the number of superpixels is much smaller than the number of pixels in the image, they can be used as input for nonlinearDR, which significantly reduces the required computation time and memory versus providing all of the original pixels as input. After nonlinear DR is performed using superpixels as input, an interpolation step can be used to obtain the embedding of each original image pixel in the low dimensional space. To illustrate the power of using superpixels in an HSI classification pipeline,we conduct experiments on three widely used and publicly available hyperspectral images: Indian Pines, Salinas and Pavia. The experimental results for all three images demonstrate that for moderately sized superpixels, the overall accuracy of classification using superpixel-based nonlinear DR matches and sometimes exceeds the overall accuracy of classification using pixel-based nonlinear DR, with a computational speed that is two-three orders of magnitude faster. Even though superpixel-based nonlinear DR shows promise for HSI classification, it does have disadvantages. First, it is costly to perform out-of-sample extensions. Second, it does not generalize to handle other types of data that might not have spatial information. Third, the original input pixels cannot approximately be recovered, as is possible in many DR algorithms.In order to overcome these difficulties, a new autoencoder network - SSDAN is proposed. It is a fully-connected semi-supervised autoencoder network that performs nonlinear DR in a manner that enables class information to be integrated. Features learned from SSDAN will be similar to those computed via traditional nonlinear DR, and features from the same class will be close to each other. Once the network is trained well with training data, test data can be easily mapped to the low dimensional embedding. Any kind of data can be used to train a SSDAN,and the decoder portion of the SSDAN can easily recover the initial input with reasonable loss.Experimental results on pixel-based classification in the Indian Pines, Salinas and Pavia images show that SSDANs can approximate the overall accuracy of nonlinear DR while significantly improving computational efficiency. We also show that transfer learning can be use to finetune features of a trained SSDAN for a new HSI dataset. Finally, experimental results on HSI compression show a trade-off between Overall Accuracy (OA) of extracted features and PeakSignal to Noise Ratio (PSNR) of the reconstructed image

An Analysis on the Development Model of China’s County-level E-commerce

Based on the case studies of e-commerce activities in six counties of China’s eastern, middle and western regions respectively, this paper has probed into the characteristics of e-commerce development of each county, which are then classified into four development models of county-level e-commerce in China, featuring the integration and aggregation of resources endowment and production factors. The paper further analyzes the key factors contributing to the success of county-level e-commerce development, in a bid to provide reference and guidance for other counties in their e-commerce activities

High-order soliton evolution and pulse breaking-recovery in stretched ultrafast fiber lasers

We present a new pulse regime in a stretched ultrafast fiber laser based on numerical simulations. The pulse breaking due to high-order soliton evolution in the passive fiber is recovered to a smooth pulse in the gain fiber with normal dispersion. The new pulse regime formed by the two nonlinear processes makes the ultrafast fiber laser generate ultra-broadband, ultrashort duration, high energy and large breathing ratio pulses. Our work gives insights into the nonlinear dynamics in fiber lasers and has potential for a better design of the stretched fiber lasers

Use of dual-grating sensors formed by different types of fiber Bragg gratings for simultaneous temperature and strain measurements

We report on a systematic investigation of the dependence of both temperature and strain sensitivities on the fiber Bragg grating type, including the well-known Type I, Type IIA, and a new type that we have designated Type IA, using both hydrogen-free and hydrogenated B/Ge codoped fibres. We have identified distinct sensitivity characteristics for each grating type, and we have used them to implement a novel dual-grating, dual-parameter sensor device. Three dual-grating sensing schemes with different combinations of grating type have been constructed and compared, and that of a Type IA-Type IIA combination exhibits the best performance, which is also superior to that of previously reported grating-based structures. The characteristics of the measurement errors in such dual-grating sensor systems is also presented in detail. © 2004 Optical Society of America