Adaptive Clustering for Point Cloud.

The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.

Method to Solve Underwater Laser Weak Waves and Superimposed Waves.

With the rapid development of Lidar technology, the use of Lidar for underwater terrain detection has become feasible. There is still a challenge in the process of signal resolution: the underwater laser echo signal is different to propagating in the air, and it is easy to produce weak waves and superimposed waves. However, existing waveform decomposition methods are not effective in processing these waveform signals, and the underwater waveform signal cannot be correctly decomposed, resulting in subsequent data-processing errors. To address these issues, this study used a drone equipped with a 532 nm laser to detect a pond as the study background. This paper proposes an improved inflection point selection decomposition method to estimate the parameter. By comparing it with other decomposition methods, we found that the RMSE is 2.544 and R2 is 0.995975, which is more stable and accurate. After estimating the parameters, this study used oscillating particle swarm optimization (OPSO) and the Levenberg-Marquardt algorithm (LM) to optimize the estimated parameters; the final results show that the method in this paper is closer to the original waveform. In order to verify the processing effect of the method on complex waveform, this paper decomposes and optimizes the simulated complex waveforms; the final RMSE is 0.0016, R2 is 1, and the Gaussian component after decomposition can fully represent the original waveform. This method is better than other decomposition methods in complex waveform decomposition, especially regarding weak waves and superimposed waves.

High-level expression and characterization of a highly active hyaluronate lyase HylC with significant potential in hyaluronan oligosaccharide preparation.

Hyaluronate lyases (HA lyases) have been proved to distribute widely among microorganisms, with large potential in hyaluronan processing. Here, a highly active HA lyase HylC from Citrobacter freundii strain Cf1 is reported. HylC was expressed in Escherichia coli BL21(DE3) under the regulation of T7 promoter, and purified to electrophoretic homogeneity for enzymatic characterization, which suggested its suitable thermo- and pH stability under 45 °C and pH rang of 4-8, and high halotolerancy in 1.5 M NaCl. The enzyme exhibited the optimal activity under 37 °C and pH 5.5, and was activated by Ca2+, K+, Zn2+, Ni2+ and Li+. Analysis of degradation product proved it cleave HA in endolytic manner, releasing unsaturated disaccharides as final product. Then, through optimization of promoter and construction of dual promoter, expression level of HylC improved from 1.10 × 104 U/mL to 2.64 × 104 U/mL on shake-flask level. Finally, through batch fermentation, a highest activity of 2.65×105 U/mL was achieved in a 5-L fermenter. Taken together, this work demonstrates the potential of HylC and its recombinant strain in industrial applications. To our knowledge, the HA lyase production reported in this study was the highest level in literatures to date.

Genetic variation reveals the enhanced microbial hyaluronan biosynthesis via atmospheric and room temperature plasma.

This study reveals the genetic and biochemical changes underlying the enhanced hyaluronan (HA) biosynthesis in Streptococcus zooepidemicus. After multiple rounds of atmospheric and room temperature plasma (ARTP) mutagenesis combined with novel bovine serum albumin/cetyltrimethylammonium bromide coupled high-throughput screening assay, the HA yield of the mutant was increased by 42.9% and reached 0.813 g L-1 with a molecular weight of 0.54 × 106 Da within 18 h by shaking flask culture. HA production was increased to 4.56 g L-1 by batch culture in 5-L fermenter. Transcriptome sequencing exhibits that distinct mutants have similar genetic changes. Regulation in direction of metabolic flow into the HA biosynthesis, by enhancing genes responsible for the biosynthesis of HA including hasB, glmU and glmM, weaking downstream gene (nagA and nagB) of UDP-GlcNAc and significantly down-regulating transcription of wall-synthesizing genes, resulting in the accumulation of precursors (UDP-GlcA and UDP-GlcNAc) increased by 39.74% and 119.22%, respectively. These associated regulatory genes may provide control point for engineering of the efficient HA-producing cell factory.

Insights into the source, mechanism and biotechnological applications of hyaluronidases.

It has long been found that hyaluronidases exist in a variety of organisms, playing their roles in various biological processes including infection, envenomation and metabolic regulation through degrading hyaluronan. However, exploiting them as a bioresource for specific applications had not been extensively studied until the latest decades. In recent years, new application scenarios have been developed, which extended the field of application, and emphasized the research value of hyaluronidase. This critical review comprehensively summarizes existing studies on hyaluronidase from different source, particularly in their structures, action patterns, and biological functions in human and mammals. Furthermore, we give in-depth insight into the resource mining and protein engineering process of hyaluronidase, as well as strategies for their high-level production, indicating that mixed strategies should be adopted to obtain well-performing hyaluronidase with efficiency. In addition, advances in application of hyaluronidase were summarized and discussed. Finally, prospects for future researches are proposed, highlighting the importance of further investigation into the characteristics of hyaluronidases, and the necessity of investigating their products for the development of their application value.