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Background Rubber tree powdery mildew is an important fungal leaf disease of rubber tree caused by obligate parasitic bacteria. It is one of the most important diseases in rubber planting areas around the world. The pathogen of rubber tree powdery mildew was previously named Oidium heveae, and its pathogen name is currently Erysiphe quercicola. Leaf-associated microbiomes are currently a new and hot research field for molecular ecology, and the biomass in plant ecosystems is also very large and the biological community is extremely rich. Results This study used high-throughput sequencing and other molecular techniques to analyze the structure and diversity of the dominant fungi and bacteria communities of rubber tree leaves in four representative rubber-producing areas in Hainan Province, China (BS, DZ, WN, WZS) after white powder disease occurred. It was found that the dominant bacterial phyla in the four regions were Cyanobacteria, Proteobacteria and Actinobacteria, and the dominant bacterial genus was Cyanobacteria. The dominant fungal phyla in the four regions were Ascomycota, Fungi_unclassified and Basidiomycota.There is significant difference in fungal community composition and relative abundance at genus levels between different areas. We explored the effect of rubber tree powdery mildew on its phyllosphere microbes found that in the same area, there is little difference in community structure between healthy leaves and grade 3 diseased leaves, which only affect the community abundance, while there is significant difference in community composition of different areas. This conclusion also confirms that in the harsh phyllosphere environment, rubber tree phyllosphere microbes need to maintain a good symbiotic relationship with hosts and pathogenic bacteria for surviving and evolving. Conclusions This study analyze the structure and diversity of the dominant fungi and bacteria communities of rubber tree leaves in four representative rubber-producing areas in Hainan Province, China after white powder disease occurred. This project the first time to study the influence of Erysiphe on leaf microorganisms of rubber trees. The conclusion confirms that in the harsh phyllosphere environment, rubber tree phyllosphere microbes need to maintain a good symbiotic relationship with hosts and pathogenic bacteria for surviving and evolving.

Background. Increasing attention has been paid to electroacupuncture (EA) for promoting postoperative rehabilitation, but the effectiveness of EA for rehabilitation after total knee arthroplasty (TKA) remains obscure. Objective. To examine the effect of EA on rehabilitation after TKA. Methods. Database searches on PubMed, CINAHL, Embase, and China National Knowledge Infrastructure (CNKI) were carried out to obtain articles, from inception to 15 October 2020. All identified articles were screened, and data from each included study were extracted independently by two investigators. Meta-analysis was conducted to assess the effects of acupuncture on pain, range of knee motion, and postoperative vomiting after TKA. Results. In the current study, a total of ten randomized clinical trials were included according to the inclusion and exclusion criteria. Compared to basic treatment, EA combined with basic treatment showed a significantly greater pain reduction on 3, 7, and 14 days postoperatively after TKA. However, we found that EA had no significant improvement in enhancing the range of knee motion and decreasing the percentage of vomiting. Subgroup analysis suggested that a combination of EA and rehabilitation training was superior to rehabilitation training in pain relief, while EA combined with celecoxib capsules showed no significant difference in improving pain compared to celecoxib capsules alone. Conclusions. In the postacute phase after TKA, EA, as a supplementary treatment, could reduce postoperative pain, but no evidence supported the benefits of EA for improving ROM of knee and decreasing the ratio of vomiting. Additional high-quality and large-scale RCTs are warranted.

BACKGROUND: The infrapatellar fat pad (IPFP) and synovium are reported to act as a functional unit, with emerging roles in the pathophysiology of knee osteoarthritis (KOA). Circular RNAs (circRNAs) are involved in the pathogenesis of KOA, especially in cartilage homeostasis regulation. Nevertheless, the regulatory mechanisms of circRNAs in the KOA IPFP/synovium unit remain to be elucidated. Therefore, the current study aimed to investigate alterations in the expression of circRNAs and predict their functions in the KOA IPFP/synovium unit using bioinformatics analysis. METHODS: In brief, 6 synovium and IPFP specimens were collected, in which 3 from patients with KOA and 3 from controls. Then, circRNA sequencing was conducted on 2 KOA synovium and IPFP specimens as well as 1 control to investigate the expression profiles of circRNAs. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome signaling pathway analyses were employed to predict the functions of the differentially expressed circRNAs. Based on the miRNA sponge theory, we constructed a circRNA-miRNA network to predict the molecular regulatory mechanism of these circRNAs. Venn analysis was performed to confirm the circRNAs and miRNAs expressed in both synovium and IPFP. Real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was also used to validate the expression levels of circRNAs that were co-expressed in both synovium and IPFP. RESULTS: A total of 65 and 72 circRNAs were differentially expressed in KOA synovium and IPFP, respectively (fold change ≥2, P

Recently, deep models have shown their superiority on the representation of the hyperspectral image. However, the limited number of training samples in hyperspectral image tasks makes it difficult to obtain well-trained deep model. Generally, deep belief network (DBN) which makes use of unsupervised learning can be introduced to partly solve the problem. To further improve the above-mentioned problem, this work feds DBN into deep ensemble. However, traditional deep ensemble usually produces models that tend to be very similar to the MAP solution and each other. To overcome this problem, this work introduces a novel strategy which divides the training samples into several subsets and thus the obtained multiple models would be diversified. Then a special information fusion method is proposed to obtain the final inference. Experiments are conducted over Pavia Unversity dataset to evaluate the effectiveness of the proposed method.

Recently, deep models have shown powerful ability for remote sensing scene representation. However, the training process of these deep methods requires large amount of labelled samples while usual remote sensing image datasets cannot provide enough training samples. Therefore, the learned model is usually suboptimal. To solve the problem, this work focuses on obtaining multiple choices by training multiple models simultaneously, and then the human oracle can choose a proper one from these choices. However, training several models separately usually makes the obtained results similar. This paper tries to diversify the obtained choices by encouraging the obtained choices to repulse from each other. Experiments are conducted on Ucmerced Land Use dataset to validate the effectiveness of the proposed method to provide multiple diversified choices.

Due to the high spectral resolution and the similarity of some spectrums between different classes, hyperspectral image classification turns out to be an important but challenging task. Researches show the powerful ability of deep learning for hyperspectral image classification. However, the lack of training samples makes it difficult to extract discriminative features and achieve performance as expected. To solve the problem, a multi-scale CNN which can extract multi-scale features is designed for hyperspectral image classification. Furthermore, D-DSML, a diversified metric, is proposed to further improve the representational ability of deep methods. In this paper, a D-DSML-MSCNN method, which jointly learns deep multi-scale features and diversified metrics for hyperspectral image classification, is proposed to take both advantages of D-DSML and MSCNN. Experiments are conducted on Pavia University data to show the effectiveness of our method for hyperspectral image classification. The results show the advantage of our method when compared with other recent results.

With the rapid growth in quantity and quality of remote sensing images, extracting the useful information in them effectively and efficiently becomes feasible but also challenging. Convolutional neural network (CNN) is a suitable method to deal with such challenge since it can effectively represent and extract the information. However, the CNN can release their potentials only when enough labelled data provided for the learning procedure. This is a very time-consuming task and even infeasible for the applications with non-cooperative objects or scenes. Unsupervised CNN learning methods, which relieve the need for the labels in the training data, is a feasible solution for the problem. In this work, we investigate a real-world motivated sparsity based unsupervised deep CNN learning method. At first, the method formulates a balanced data driven population and lifetime sparsity prior and thus construct the unsupervised learning method through a layerwise mean. Then we further perform the method on the deep model with multiple CNN layers. Finally, the method is used for the remote sensing image representation and scenes classification. The experimental results over the public UC-Merced Land-use dataset demonstrate that the developed algorithm obtained satisfactory results compared with the recent methods.