The Role of Occludin in Vascular Endothelial Protection

Endothelial tight junction proteins play an important role in maintaining the integrity of vascular endothelial structure and physiological function. In recent years, studies have found that alterations in the expression, distribution, and structure of endothelial tight junction proteins may lead to many related vascular diseases and pathologies (such as diabetes, atherosclerosis, neurodegenerative diseases, and hypertension). Therefore, related strategies to prevent and/or tight junction proteins dysfunction may be an important therapeutic target. Occludin, as the most representative one among tight junction proteins, is mainly responsible for sealing intercellular junctions, maintaining cell permeability and the integrity of vascular endothelium. Here, we review the published biological information of occludin. We highlight the relationship between occludin and vascular endothelial injury-related disease. At the same time, we show our current knowledge of how vascular endothelial occludin exerts the protective effect and possible clinical applications in the future

Adiponectin-Mediated Promotion of CD44 Suppresses Diabetic Vascular Inflammatory Effects

While adiponectin (APN) was known to significantly abolish the diabetic endothelial inflammatory response, the specific mechanisms have yet to be elucidated. Aortic vascular tissues from mice fed normal and high-fat diets (HFD) were analyzed by transcriptome analysis. GO functional annotation showed that APN inhibited vascular endothelial inflammation in an APPL1-dependent manner. We confirmed that activation of the Wnt/β-catenin signaling plays a key role in APN-mediated anti-inflammation. Mechanistically, APN promoted APPL1/reptin complex formation and β-catenin nuclear translocation. Simultaneously, we identified APN promoted the expression of CD44 by activating TCF/LEF in an APPL1-mediated manner. Clinically, the serum levels of APN and CD44 were decreased in diabetes; the levels of these two proteins were positively correlated. Functionally, treatment with CD44 C-terminal polypeptides protected diabetes-induced vascular endothelial inflammation in vivo. Collectively, we provided a roadmap for APN-inhibited vascular inflammatory effects and CD44 might represent potential targets against the diabetic endothelial inflammatory effect

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Luobuma Leaf Spot Disease Caused by <i>Alternaria tenuissima</i> in China

Luobuma (Apocynum venetum and Poacynum hendersonni) is widely cultivated for environmental conservation, medicinal purposes and the textile industry. In 2018, a severe leaf spot disease that attacked the leaves of Luobuma was observed in plants cultivated in Yuzhong County, Gansu Province, China. Symptoms of the disease appeared as white or off-white spots surrounded by brown margins on the leaves of A. venetum. The spots expanded and covered a large area of the leaf, presenting as “cankers” with progression of the disease, leading to leaf death. The initial symptoms of the disease on P. hendersonni were similar to the symptoms of A. venetum, with a larger disease spot than A. venetum, and the spot was black and thicker. The aim of this study was to identify the fungal species and evaluate the effectiveness of fungicides (hymexazol and zhongshengmycin) against the pathogen in vitro. The fungi species that caused the new disease was identified as Alternaria tenuissima based on the morphological characteristics, pathogenicity tests, and phylogenetic analysis of the internal transcribed spacer (ITS) region, glyceraldehyde 3-phosphate dehydrogenase (gpd), translation elongation factor 1-alpha (TEF) and the histone 3 (H3) gene sequences. The findings showed that hymexazol fungicide can be used to control leaf spot disease. This is the first report on Luobuma leaf spot disease caused by A. tenuissima in China

Self-Organized Patchy Target Searching and Collecting with Heterogeneous Swarm Robots Based on Density Interactions

The issue of searching and collecting targets with patchy distribution in an unknown environment is a challenging task for multiple or swarm robots because the targets are unevenly dispersed in space, which makes the traditional solutions based on the idea of path planning and full spatial coverage very inefficient and time consuming. In this paper, by employing a novel framework of spatial-density-field-based interactions, a collective searching and collecting algorithm for heterogeneous swarm robots is proposed to solve the challenging issue in a self-organized manner. In our robotic system, two types of swarm robots, i.e., the searching robots and the collecting robots, are included. To start with, the searching robots conduct an environment exploration by means of formation movement with Levy flights; when the targets are detected by the searching robots, they spontaneously form a ring-shaped envelope to estimate the spatial distribution of targets. Then, a single robot is selected from the group to enter the patch and locates at the patch’s center to act as a guiding beacon. Subsequently, the collecting robots are recruited by the guiding beacon to gather the patch targets; they first form a ring-shaped envelope around the target patch and then push the scattered targets inward by using a spiral shrinking strategy; in this way, all targets eventually are stacked near the center of the target patch. With the cooperation of the searching robots and the collecting robots, our heterogeneous robotic system can operate autonomously as a coordinated group to complete the task of collecting targets in an unknown environment. Numerical simulations and real swarm robot experiments (up to 20 robots are used) show that the proposed algorithm is feasible and effective, and it can be extended to search and collect different types of targets with patchy distribution

A Novel Chimp Optimization Algorithm with Refraction Learning and Its Engineering Applications

The Chimp Optimization Algorithm (ChOA) is a heuristic algorithm proposed in recent years. It models the cooperative hunting behaviour of chimpanzee populations in nature and can be used to solve numerical as well as practical engineering optimization problems. ChOA has the problems of slow convergence speed and easily falling into local optimum. In order to solve these problems, this paper proposes a novel chimp optimization algorithm with refraction learning (RL-ChOA). In RL-ChOA, the Tent chaotic map is used to initialize the population, which improves the population’s diversity and accelerates the algorithm’s convergence speed. Further, a refraction learning strategy based on the physical principle of light refraction is introduced in ChOA, which is essentially an Opposition-Based Learning, helping the population to jump out of the local optimum. Using 23 widely used benchmark test functions and two engineering design optimization problems proved that RL-ChOA has good optimization performance, fast convergence speed, and satisfactory engineering application optimization performance

Enhancement of Question Answering System Accuracy via Transfer Learning and BERT

Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA

Threshold Binary Grey Wolf Optimizer Based on Multi-Elite Interaction for Feature Selection

The traditional grey wolf algorithm is widely used for feature selection. However, within complex feature multi-dimensional problems, the grey wolf algorithm is prone to reach locally optimal solutions and premature convergence. In this paper, a threshold binary grey wolf optimizer based on multi-elite interaction for feature selection (MTBGWO) is proposed. Firstly, the multi-population topology is adopted to enhance the population&#x2019;s diversity for improving search space utilization. Secondly, an information interaction learning strategy is adopted for the update of sub-population elite wolf position (optimal position) via learning better position from other elite wolves; in order to improve the local exploitation ability of the sub-population. At the same time, the command of $\beta$ and $\delta$ wolves (second and third best positions) for population position updates is removed. Finally, a threshold approach is employed to convert the continuous position of grey wolf individuals into binary one to apply in the feature selection problem. Further, The MTBGWO algorithm proposed in this paper is compared with the traditional binary grey wolf algorithm (BGWO), binary whale algorithm (BWOA), as well as some recently developed novel algorithms to exhibit its superiority and robustness. Totally 16 classification datasets, from the UCI Machine Learning Repository, are chosen for comparison. The Wilcoxon&#x2019;s rank-sum non-parametric statistical test is carried out at 5&#x0025; significance level to evaluate whether the results of the proposed algorithms significantly differs from those of the other algorithms. In the experimental results for all datasets, the overall average accuracy of the MTBGWO algorithm is 94.7&#x0025;, while the highest of the other algorithms is 92.8&#x0025; and the selected feature subset is 25&#x0025; of the total dataset. The MTBGWO algorithm selects much smaller subset of features than other algorithms. In terms of computational efficiency, the overall processing time of MTBGWO is 24.2 seconds, whereas HSGW is 44.1 seconds. The results reveal that the MTBGWO has shown its superiority in solving the feature selection problem

Quantifying the Influences of Driving Factors on Land Surface Temperature during 2003–2018 in China Using Convergent Cross Mapping Method

International audienc

Enhancement of Question Answering System Accuracy via Transfer Learning and BERT

Entity linking and predicate matching are two core tasks in the Chinese Knowledge Base Question Answering (CKBQA). Compared with the English entity linking task, the Chinese entity linking is extremely complicated, making accurate Chinese entity linking difficult. Meanwhile, strengthening the correlation between entities and predicates is the key to the accuracy of the question answering system. Therefore, we put forward a Bidirectional Encoder Representation from Transformers and transfer learning Knowledge Base Question Answering (BAT-KBQA) framework, which is on the basis of feature-enhanced Bidirectional Encoder Representation from Transformers (BERT), and then perform a Named Entity Recognition (NER) task, which is appropriate for Chinese datasets using transfer learning and the Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model. We utilize a BERT-CNN (Convolutional Neural Network) model for entity disambiguation of the problem and candidate entities; based on the set of entities and predicates, a BERT-Softmax model with answer entity predicate features is introduced for predicate matching. The answer ultimately chooses to integrate entities and predicates scores to determine the definitive answer. The experimental results indicate that the model, which is developed by us, considerably enhances the overall performance of the Knowledge Base Question Answering (KBQA) and it has the potential to be generalizable. The model also has better performance on the dataset supplied by the NLPCC-ICCPOL2016 KBQA task with a mean F1 score of 87.74% compared to BB-KBQA