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Background: Manual measurement of endobronchial optical coherence tomography (EB-OCT) images means a heavy workload in the clinical practice, which can also introduce bias if the subjective opinions of doctors are involved. Objective: We aim to develop a convolutional neural network (CNN)-based EB-OCT image analysis algorithm to automatically identify and measure EB-OCT parameters of airway morphology. Methods: The ResUNet, MultiResUNet, and Siamese network were used for analyzing airway inner area (Ai), airway wall area (Aw), airway wall area percentage (Aw%), and airway bifurcate segmentation obtained from EB-OCT imaging, respectively. The accuracy of the automatic segmentations was verified by comparing with manual measurements. Results: Thirty-three patients who were diagnosed with asthma (n = 13), chronic obstructive pulmonary disease (COPD, n = 13), and normal airway (n = 7) were enrolled. EB-OCT was performed in RB9 segment (lateral basal segment of the right lower lobe), and a total of 17,820 OCT images were collected for CNN training, validation, and testing. After training, the Ai, Aw, and airway bifurcate were readily identified in both normal airway and airways of asthma and COPD. The ResUNet and the MultiResUNet resulted in a mean dice similarity coefficient of 0.97 and 0.95 for Ai and Aw segmentation. The accuracy Siamese network in identifying airway bifurcate was 96.6%. Bland-Altman analysis indicated there was a negligible bias between manual and CNN measurements for Ai (bias = −0.02 to 0.01, 95% CI = −0.12 to 0.14) and Aw% (bias = −0.06 to 0.12, 95% CI = −1.98 to 2.14). Conclusion: EB-OCT imaging in conjunction with ResUNet, MultiResUNet, and Siamese network could automatically measure normal and diseased airway structure with an accurate performance.

Morphological novelties, including formation of trait combinations, may result from de novo gene origination and/or co-option of existing genes into other developmental contexts. A variety of shape-color combinations of capitular florets occur in Chrysanthemum and its allies. We hypothesized that co-option of a carotenoid cleavage dioxygenase gene into the floral symmetry gene network would generate a white zygomorphic ray floret. We tested this hypothesis in an evolutionary context using species in Chrysanthemum sensu lato, a monophyletic group with diverse floral shape-color combinations, based on morphological investigation, interspecific crossing, molecular interaction and transgenic experiments. Our results showed that white color was significantly associated with floret zygomorphy. Specific expression of the carotenoid cleavage dioxygenase gene CCD4a in marginal florets resulted in white color. Crossing experiments between Chrysanthemum lavandulifolium and Ajania pacifica indicated that expression of CCD4a is trans-regulated. The floral symmetry regulator CYC2g can activate expression of CCD4a with a dependence on TEOSINTE BRANCHED1/CYCLOIDEA/PROLIFERATING (TCP) binding element 8 on the CCD4a promoter. Based on all experimental findings, we propose that gene co-option of carotenoid degradation into floral symmetry regulation, and the subsequent dysfunction or loss of either CYC2g or CCD4a, may have led to evolution of capitular shape-color patterning in Chrysanthemum sensu lato.

Background: With the continuous development of big data, artificial intelligence, the Internet of things, and blockchain technology, distributed interactive systems have gradually become the mainstream of computing technologies and applications with the help of real-time interaction platforms. The real-time interaction platforms provide a data communication bridge for distributed applications, not only meeting the timeliness and correctness of data interaction but also providing a unified interface for application development. It realizes the plug and play of different functional modules in the distributed system and improves development efficiency for system developers. Objective: According to the characteristics of the large scale, wide range, and complex running environment of distributed interaction systems, this paper analyzes the function and performance requirements of the distributed interactive platform and designs and implements a real-time interactive platform named NetDDS based on a publish-subscribe mechanism. Methods: Concerning the idea of a layered design, the real-time interactive platform is constructed from three aspects: distribution service layer, QoS guarantee layer, and data transmission layer. The platform provides a data synchronization function based on publish/ subscribe mechanism, a configurable QoS guarantee mechanism, and a multi-protocol data transmission mechanism based on Ethernet, ensuring the real-time correctness and reliability of data transmission. Results: Experimental results show that the interactive platform exhibits less delay in message update. Conclusion: This paper proposes a hierarchical architecture, designs and implements a real-time interaction platform based on publish/subscribe mechanism, and provides a topic- based publish/subscribe model to the application layer. The publisher and subscriber directly publish and subscribe with data as the center to meet the real-time requirements of distributed real-time applications.

The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analyzed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense advancements in the field urged researchers to devise new techniques and approaches, each sermonizing a different research analysis/question, that cope with upcoming issues and complex scenarios of Aspect-based Sentiment Analysis. Therefore, this survey emphasized on the issues and challenges that are related to extraction of different aspects and their relevant sentiments, relational mapping between aspects, interactions, dependencies and contextual-semantic relationships between different data objects for improved sentiment accuracy, and prediction of sentiment evolution dynamicity. A rigorous overview of the recent progress is summarized based on whether they contributed towards highlighting and mitigating the issue of Aspect Extraction, Aspect Sentiment Analysis or Sentiment Evolution. The reported performance for each scrutinized study of Aspect Extraction and Aspect Sentiment Analysis is also given, showing the quantitative evaluation of the proposed approach. Future research directions are proposed and discussed, by critically analysing the presented recent solutions, that will be helpful for researchers and beneficial for improving sentiment classification at aspect-level.

Photometric stereo is a widely used optical 3-D reconstruction method, which has advantages for high-resolution and well-detailed 3-D reconstruction even in weak texture regions. However, in underwater photometric stereo, the light refraction and attenuation using the camera and point light sources usually result in significant errors in shape recovery. In this article, we present a novel solution to address these challenges for improving the performance of underwater photometric stereo by combining it with underwater laser triangulation. First, we employ underwater laser triangulation as references to correct the global surface-shape distortion caused by nonuniform close-range illumination. Second, we propose to use an underwater camera refractive model to eliminate nonlinear refraction distortion. Third, we build a device implementing the proposed method for 3-D seabed reconstruction in on-site sea trials. Experimental results demonstrate that the proposed approach is able to produce accurate 3-D reconstruction results in the underwater environment.

Tenuous subGraph finding (TGF) problem has been proposed to identify a subgraph with few social interactions and weak relationships among nodes. In particular, it aims to find a subgraph consisting of k nodes in social network G, such that the subgraph can minimize the tenuity metric. Most of the proposed techniques suffer from enormous computation due to focusing on graph-structured data. Even worse, the design of these algorithms requires manually designing suitable search strategies for specific scenarios, which is hard to be understood and implemented. To overcome these issues, this paper proposes a novel model named graph neural network with reinforcement learning for tenuous subGraph finding (R TGF), an end-to-end model combining GNN module with the reinforcement learning module, which automatically obtains the optimal solution for the tenuous subgraph. We first introduce a variant of GNN that solves the tenuity reservation problem when encoding nodes. Then we design a reward function to evaluate the node influence on subgraph tenuity. Based on this model, we can construct the solution according to the tenuity evaluations efficiently, rather than spending a high cost to compute the shortest path and enumerate all the paths between nodes. Considering the error transfer of two modules, we introduce the joint optimization strategy. Experimental results on the real-world and synthetic datasets demonstrate our proposed method R TGF outperforms existing algorithms in efficiency and solution quality.