Graph Regulation Network for Point Cloud Segmentation.

In point cloud, some regions typically exist nodes from multiple categories, i.e., these regions have both homophilic and heterophilic nodes. However, most existing methods ignore the heterophily of edges during the aggregation of the neighborhood node features, which inevitably mixes unnecessary information of heterophilic nodes and leads to blurred boundaries of segmentation. To address this problem, we model the point cloud as a homophilic-heterophilic graph and propose a graph regulation network (GRN) to produce finer segmentation boundaries. The proposed method can adaptively adjust the propagation mechanism with the degree of neighborhood homophily. Moreover, we build a prototype feature extraction module, which is utilised to mine the homophily features of nodes from the global prototype space. Theoretically, we prove that our convolution operation can constrain the similarity of representations between nodes based on their degree of homophily. Extensive experiments on fully and weakly supervised point cloud semantic segmentation tasks demonstrate that our method achieves satisfactory performance. Especially in the case of weak supervision, that is, each sample has only 1%-10% labeled points, the proposed method has a significant improvement in segmentation performance.

Long and Short-Range Dependency Graph Structure Learning Framework on Point Cloud.

Graph convolutional neural networks can effectively process geometric data and thus have been successfully used in point cloud data representation. However, existing graph-based methods usually adopt the K-nearest neighbor (KNN) algorithm to construct graphs, which may not be optimal for point cloud analysis tasks, owning to the solution of KNN is independent of network training. In this paper, we propose a novel graph structure learning convolutional neural network (GSLCN) for multiple point cloud analysis tasks. The fundamental concept is to propose a general graph structure learning architecture (GSL) that builds long-range and short-range dependency graphs. To learn optimal graphs that best serve to extract local features and investigate global contextual information, respectively, we integrated the GSL with the designed graph convolution operator under a unified framework. Furthermore, we design the graph structure losses with some prior knowledge to guide graph learning during network training. The main benefit is that given labels and prior knowledge are taken into account in GSLCN, providing useful supervised information to build graphs and thus facilitating the graph convolution operation for the point cloud. Experimental results on challenging benchmarks demonstrate that the proposed framework achieves excellent performance for point cloud classification, part segmentation, and semantic segmentation.

A Novel Local-Global Graph Convolutional Method for Point Cloud Semantic Segmentation.

Although convolutional neural networks (CNNs) have shown good performance on grid data, they are limited in the semantic segmentation of irregular point clouds. This article proposes a novel and effective graph CNN framework, referred to as the local-global graph convolutional method (LGGCM), which can achieve short- and long-range dependencies on point clouds. The key to this framework is the design of local spatial attention convolution (LSA-Conv). The design includes two parts: generating a weighted adjacency matrix of the local graph composed of neighborhood points, and updating and aggregating the features of nodes to obtain the spatial geometric features of the local point cloud. In addition, a smooth module for central points is incorporated into the process of LSA-Conv to enhance the robustness of the convolution against noise interference by adjusting the position coordinates of the points adaptively. The learned robust LSA-Conv features are then fed into a global spatial attention module with the gated unit to extract long-range contextual information and dynamically adjust the weights of features from different stages. The proposed framework, consisting of both encoding and decoding branches, is an end-to-end trainable network for semantic segmentation of 3-D point clouds. The theoretical analysis of the approximation capabilities of LSA-Conv is discussed to determine whether the features of the point cloud can be accurately represented. Experimental results on challenging benchmarks of the 3-D point cloud demonstrate that the proposed framework achieves excellent performance.

Engineering of Cry3Bb1 provides mechanistic insights toward countering western corn rootworm resistance.

The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is an economically important pest of corn (maize) in North America and Europe. Current management practices for WCR involve transgenic expression of insecticidal proteins to minimize larval feeding damage to corn roots. The evolution of resistant WCR populations to transgenic corn expressing insecticidal proteins (e.g. Cry3Bb1, Gpp34Ab1/Tpp35Ab1) necessitates efforts to discover and deploy new modes of action for WCR control. Here, we tested the hypothesis that the addition of short peptides selected for binding to the WCR gut would restore insecticidal activity of Cry3Bb1 to resistant insects. Phage display technology coupled with deep sequencing was used to identify peptides selected for binding to WCR brush border membrane vesicles and to recombinant putative receptors aminopeptidase and cadherin. The binding and specificity of selected peptides was confirmed by ELISA and pull-down assays, and candidate gut surface binding partners were identified. Although production of 284 novel Cry3Bb1 variants with these peptides did not restore activity against resistant WCR in artificial diet bioassays, 112 variants were active against susceptible insects. These results provided insights for the mechanism of Cry3Bb1 activity and toward engineering a new mode-of-action via receptor re-targeting in the context of protein structure and function.

Genome sequences of three agrobacterium biovars help elucidate the evolution of multichromosome genomes in bacteria.

The family Rhizobiaceae contains plant-associated bacteria with critical roles in ecology and agriculture. Within this family, many Rhizobium and Sinorhizobium strains are nitrogen-fixing plant mutualists, while many strains designated as Agrobacterium are plant pathogens. These contrasting lifestyles are primarily dependent on the transmissible plasmids each strain harbors. Members of the Rhizobiaceae also have diverse genome architectures that include single chromosomes, multiple chromosomes, and plasmids of various sizes. Agrobacterium strains have been divided into three biovars, based on physiological and biochemical properties. The genome of a biovar I strain, A. tumefaciens C58, has been previously sequenced. In this study, the genomes of the biovar II strain A. radiobacter K84, a commercially available biological control strain that inhibits certain pathogenic agrobacteria, and the biovar III strain A. vitis S4, a narrow-host-range strain that infects grapes and invokes a hypersensitive response on nonhost plants, were fully sequenced and annotated. Comparison with other sequenced members of the Alphaproteobacteria provides new data on the evolution of multipartite bacterial genomes. Primary chromosomes show extensive conservation of both gene content and order. In contrast, secondary chromosomes share smaller percentages of genes, and conserved gene order is restricted to short blocks. We propose that secondary chromosomes originated from an ancestral plasmid to which genes have been transferred from a progenitor primary chromosome. Similar patterns are observed in select Beta- and Gammaproteobacteria species. Together, these results define the evolution of chromosome architecture and gene content among the Rhizobiaceae and support a generalized mechanism for second-chromosome formation among bacteria.

Genome sequence of Azotobacter vinelandii, an obligate aerobe specialized to support diverse anaerobic metabolic processes.

Azotobacter vinelandii is a soil bacterium related to the Pseudomonas genus that fixes nitrogen under aerobic conditions while simultaneously protecting nitrogenase from oxygen damage. In response to carbon availability, this organism undergoes a simple differentiation process to form cysts that are resistant to drought and other physical and chemical agents. Here we report the complete genome sequence of A. vinelandii DJ, which has a single circular genome of 5,365,318 bp. In order to reconcile an obligate aerobic lifestyle with exquisitely oxygen-sensitive processes, A. vinelandii is specialized in terms of its complement of respiratory proteins. It is able to produce alginate, a polymer that further protects the organism from excess exogenous oxygen, and it has multiple duplications of alginate modification genes, which may alter alginate composition in response to oxygen availability. The genome analysis identified the chromosomal locations of the genes coding for the three known oxygen-sensitive nitrogenases, as well as genes coding for other oxygen-sensitive enzymes, such as carbon monoxide dehydrogenase and formate dehydrogenase. These findings offer new prospects for the wider application of A. vinelandii as a host for the production and characterization of oxygen-sensitive proteins.

Optical mapping as a routine tool for bacterial genome sequence finishing.

BACKGROUND: In sequencing the genomes of two Xenorhabdus species, we encountered a large number of sequence repeats and assembly anomalies that stalled finishing efforts. This included a stretch of about 12 Kb that is over 99.9% identical between the plasmid and chromosome of X. nematophila. RESULTS: Whole genome restriction maps of the sequenced strains were produced through optical mapping technology. These maps allowed rapid resolution of sequence assembly problems, permitted closing of the genome, and allowed correction of a large inversion in a genome assembly that we had considered finished. CONCLUSION: Our experience suggests that routine use of optical mapping in bacterial genome sequence finishing is warranted. When combined with data produced through 454 sequencing, an optical map can rapidly and inexpensively generate an ordered and oriented set of contigs to produce a nearly complete genome sequence assembly.

The entomopathogenic bacterial endosymbionts Xenorhabdus and Photorhabdus: convergent lifestyles from divergent genomes.

Members of the genus Xenorhabdus are entomopathogenic bacteria that associate with nematodes. The nematode-bacteria pair infects and kills insects, with both partners contributing to insect pathogenesis and the bacteria providing nutrition to the nematode from available insect-derived nutrients. The nematode provides the bacteria with protection from predators, access to nutrients, and a mechanism of dispersal. Members of the bacterial genus Photorhabdus also associate with nematodes to kill insects, and both genera of bacteria provide similar services to their different nematode hosts through unique physiological and metabolic mechanisms. We posited that these differences would be reflected in their respective genomes. To test this, we sequenced to completion the genomes of Xenorhabdus nematophila ATCC 19061 and Xenorhabdus bovienii SS-2004. As expected, both Xenorhabdus genomes encode many anti-insecticidal compounds, commensurate with their entomopathogenic lifestyle. Despite the similarities in lifestyle between Xenorhabdus and Photorhabdus bacteria, a comparative analysis of the Xenorhabdus, Photorhabdus luminescens, and P. asymbiotica genomes suggests genomic divergence. These findings indicate that evolutionary changes shaped by symbiotic interactions can follow different routes to achieve similar end points.