Local Kikuchi band detection in electron backscatter diffraction patterns for enhanced pattern indexing.

Partially missing bands in the preset region for Hough transform might strongly affect the accuracy of indexing Kikuchi patterns and reduce the quality of electron backscatter diffraction (EBSD) maps. This paper proposes a novel local band detection method for such kind of low-quality patterns. The approach involves rotating bands to vertical direction, detecting the local line segments through calculating the largest horizontal average grey gradient with a constant interval of 400 pixels in vertical direction, and applying Hough transform as well as weighted averaging to these line clusters to unify the edges of Kikuchi band. Therefore, even if only part of a Kikuchi band is visible, the entire band can also be accurately extracted. The average interplanar angle error obtained by the proposed method is approximately 29.0% less than those obtained by Hough transform-based technique. Moreover, the comparison of mean angular deviation (MAD) is also discussed. The average MAD of this method is about 38.5% lower than that of Hough transform-based technique. Consequently, the local Kikuchi band detection method is expected to be used for post-processing and re-indexing the EBSD low-quality patterns.

Development and validation of multiple machine learning algorithms for the classification of G-protein-coupled receptors using molecular evolution model-based feature extraction strategy.

Machine learning is one of the most potential ways to realize the function prediction of the incremental large-scale G-protein-coupled receptors (GPCR). Prior research reveals that the key to determining the overall classification accuracy of GPCR is extracting valuable features and filtering out redundancy. To achieve a more efficient classification model, we put the feature synonym problem into consideration and create a new method based on functional word clustering and integration. Through evaluating the evolution correlation between features using the transition scores in mature molecular substitution matrices, candidate features are clustered into synonym groups. Each group of the clustered features is then integrated and represented by a unique key functional word. These retained key functional words are used to form a feature knowledge base. The original GPCR sequences are then transferred into feature vectors based on a feature re-extraction strategy according to the features in the knowledge base before the training and testing stage. We create multiple machine learning models based on Naïve Bayesian (NB), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) algorithms. The established model is applied to classify two public data sets containing 8354 and 12,731 GPCRs, respectively. These models achieve significant performance in almost all evaluation criteria in comparison with state-of-the art. This work demonstrated the potential of the novel feature extraction strategy and provided an effective theoretical design for the hierarchical classification of GPCRs.

Method for acquiring accurate coordinates of the source point in electron backscatter diffraction.

An electron backscatter diffraction device is an important accessory for a scanning electron microscope and can provide crystal structure orientation and phase content data through analysis of electron backscatter diffraction patterns. The acquisition of these data depends on pattern indexing, including interplanar angle calculation and crystal plane indexation. The coordinates of the source point are key points for interplanar angle calculation, and they vary with the movement of the incident beam. In this study, we first combined the grey gradient calculation with screen moving method to achieve accurate positioning of source point and obtained coordinates of source point with sub-pixel precision. The errors of three coordinates were 0.07%, 0.06% and 0.04%, respectively. By using this coordinate of source point to conduct interplanar angle calculation the maximum error was 0.53°, which was a good proof of the accuracy of source point positioning. Then we established the relationship between source point coordinates variation and incident beam movement. Coordinates can be given out based on the displacement of beam directly. And to illustrate the accuracy, interplanar angle calculation was performed and the maximum error was 0.81°. This means that the relationship between variation of source point coordinates and beam movement is highly accurate.

Isolation of a Novel Microcystin-Degrading Bacterium and the Evolutionary Origin of mlr Gene Cluster.

The mlr-dependent biodegradation plays an essential role in the natural attenuation of microcystins (MCs) in eutrophic freshwater ecosystems. However, their evolutionary origin is still unclear due to the lack of mlr gene cluster sequences. In this study, a Sphingopyxis sp. strain X20 with high MC-degrading ability was isolated, and the mlrA gene activity was verified by heterologous expression. The whole sequence of the mlr gene cluster in strain X20 was obtained through PCR and thermal asymmetric interlaced (TAIL)-PCR, and then used for evolutionary origin analyses together with the sequences available in GenBank. Phylogenetic analyses of mlr gene clusters suggested that the four mlr genes had the same origin and evolutionary history. Genomic island analyses showed that there is a genomic island on the genome of sphingomonads that is capable of degrading MCs, on which the mlr gene cluster anchors. The concentrated distribution of the mlr gene cluster in sphingomonads implied that these genes have likely been present in the sphingomonads gene pool for a considerable time. Therefore, the mlr gene cluster may have initially entered into the genome of sphingomonads together with the genomic island by a horizontal gene transfer event, and then become inherited by some sphingomonads. The species other than sphingomonads have likely acquired mlr genes from sphingomonads by recently horizontal gene transfer due to the sporadic distribution of MC-degrading species and the mlr genes in them. Our results shed new light on the evolutionary origin of the mlr cluster and thus facilitate the interpretation of characteristic distribution of the mlr gene in bacteria and the understanding of whole mlr pathway.

Effect of granular activated carbon on the aerobic granulation of sludge and its mechanism.

The granulation of activated sludge and effect of granular activated carbon (GAC) was investigated under the alternative anaerobic and aerobic conditions. The results showed that GAC accelerated the granulation, but had no obvious effect on the bacterial community structure of granules. The whole granulation process could be categorized into three phases, i.e. lag, granulation and granule maturation phase. During lag period GAC provided nuclei for sludge to attach, and thus enhanced the morphological regularization of sludge. During granulation period the granule size increased significantly due to the growth of bacteria in granules. GAC reduced the compression caused by the inter-particle collisions and thus accelerate the granulation. GAC has no negative effect on the performance of SBR, and thus efficient simultaneous removal of COD, nitrogen and phosphorus were obtained during most of the operating time.