Discovery of Brassica yellows virus and porcine reproductive and respiratory syndrome virus in Diaphorina citri and changes in virome due to infection with ‘Ca. L. asiaticus’

Detection of new viruses or new virus hosts is essential for the protection of economically important agroecosystems and human health. Increasingly, metatranscriptomic data are being used to facilitate this process. Such data were obtained from adult Asian citrus psyllids (ACP) (Diaphorina citri Kuwayama) that fed solely on mandarin (Citrus _aurantium L.) plants grafted with buds infected with ?Candidatus Liberibacter asiaticus? (CLas), a phloem-limited bacterium associated with the severe Asian variant of huanglongbing (HLB), the most destructive disease of citrus. Brassica yellows virus (BrYV), the causative agent of yellowing or leafroll symptoms in brassicaceous plants, and its associated RNA (named as BrYVaRNA) were detected in ACP. In addition, the porcine reproductive and respiratory syndrome virus (PRRSV), which affects pigs and is economically important to pig production, was also found in ACP. These viruses were not detected in insects feeding on plants grafted with CLas-free buds. Changes in the concentrations of insect-specific viruses within the psyllid were caused by coinfection with CLas. IMPORTANCE The cross transmission of pathogenic viruses between different farming systems or plant communities is a major threat to plants and animals and, potentially, human health. The use of metagenomics is an effective approach to discover viruses and vectors. Here, we collected buds from the CLas-infected and CLas-free mandarin (Citrus aurantium L. [Rutaceae: Aurantioideae: Aurantieae]) trees from a commercial orchard and grafted them onto CLas-free mandarin plants under laboratory conditions. Through metatranscriptome sequencing, we first identified the Asian citrus psyllids feeding on plants grafted with CLas-infected buds carried the plant pathogen, brassica yellows virus and its associated RNA, and the swine pathogen, porcine reproductive and respiratory syndrome virus. These discoveries indicate that both viruses can be transmitted by grafting and acquired by ACP from CLas1 mandarin seedlings

Abundance changes of marsh plant species over 40 years are better explained by niche position water level than functional traits

Acknowledgements: This study was supported by the National Natural Science Foundation of China (grants No.41671109 and 41371107) and by the Natural Science Foundation of Jilin Province (grant No. 20190201281JC). We thank Xiaofeng Xu for the suggestions and Håkan Rydin for the comments on the manuscript.Peer reviewedPostprin

Predicting Transition Temperature of Superconductors with Graph Neural Networks

Predicting high temperature superconductors has long been a great challenge. The difficulty lies in how to predict the transition temperature (Tc) of superconductors. Although recent progress in material informatics has led to a number of machine learning models predicting Tc, prevailing models have not shown adequate generalization ability and physical rationality to find new high temperature superconductors, yet. In this work, a bond sensitive graph neural network (BSGNN) was developed to predict the Tc of various superconductors. In BSGNN, communicative message passing and graph attention methods were utilized to enhance the model's ability to process bonding and interaction information in the crystal lattice, which is crucial for the superconductivity. Consequently, our results revealed the relevance between chemical bond attributes and Tc. It indicates that shorter bond length is favored by high Tc. Meanwhile, some specific chemical elements that have relatively large van der Waals radius is favored by high Tc. It gives a convenient guidance for searching high temperature superconductors in materials database, by ruling out the materials that could never have high Tc

A Text-guided Protein Design Framework

Current AI-assisted protein design mainly utilizes protein sequential and structural information. Meanwhile, there exists tremendous knowledge curated by humans in the text format describing proteins' high-level functionalities. Yet, whether the incorporation of such text data can help protein design tasks has not been explored. To bridge this gap, we propose ProteinDT, a multi-modal framework that leverages textual descriptions for protein design. ProteinDT consists of three subsequent steps: ProteinCLAP which aligns the representation of two modalities, a facilitator that generates the protein representation from the text modality, and a decoder that creates the protein sequences from the representation. To train ProteinDT, we construct a large dataset, SwissProtCLAP, with 441K text and protein pairs. We quantitatively verify the effectiveness of ProteinDT on three challenging tasks: (1) over 90\% accuracy for text-guided protein generation; (2) best hit ratio on 10 zero-shot text-guided protein editing tasks; (3) superior performance on four out of six protein property prediction benchmarks

bifA Regulates Biofilm Development of Pseudomonas putida MnB1 as a Primary Response to H2O2 and Mn2+

Pseudomonas putida (P. putida) MnB1 is a widely used model strain in environment science and technology for determining microbial manganese oxidation. Numerous studies have demonstrated that the growth and metabolism of P. putida MnB1 are influenced by various environmental factors. In this study, we investigated the effects of hydrogen peroxide (H2O2) and manganese (Mn2+) on proliferation, Mn2+ acquisition, anti-oxidative system, and biofilm formation of P. putida MnB1. The related orthologs of 4 genes, mco, mntABC, sod, and bifA, were amplified from P. putida GB1 and their involvement were assayed, respectively. We found that P. putida MnB1 degraded H2O2, and quickly recovered for proliferation, but its intracellular oxidative stress state was maintained, with rapid biofilm formation after H2O2 depletion. The data from mco, mntABC, sod and bifA expression levels by qRT-PCR, elucidated a sensitivity toward bifA-mediated biofilm formation, in contrary to intracellular anti-oxidative system under H2O2 exposure. Meanwhile, Mn2+ ion supply inhibited biofilm formation of P. putida MnB1. The expression pattern of these genes showed that Mn2+ ion supply likely functioned to modulate biofilm formation rather than only acting as nutrient substrate for P. putida MnB1. Furthermore, blockade of BifA activity by GTP increased the formation and development of biofilms during H2O2 exposure, while converse response to Mn2+ ion supply was evident. These distinct cellular responses to H2O2 and Mn2+ provide insights on the common mechanism by which environmental microorganisms may be protected from exogenous factors. We postulate that BifA-mediated biofilm formation but not intracellular anti-oxidative system may be a primary protective strategy adopted by P. putida MnB1. These findings will highlight the understanding of microbial adaptation mechanisms to distinct environmental stresses

Inhibition of telomerase activity by HDV ribozyme in cancers

<p>Abstract</p> <p>Background</p> <p>Telomerase plays an important role in cell proliferation and carcinogenesis and is believed to be a good target for anti-cancer drugs. Elimination of template function of telomerase RNA may repress the telomerase activity.</p> <p>Methods</p> <p>A pseudo-knotted HDV ribozyme (g.RZ57) directed against the RNA component of human telomerase (hTR) was designed and synthesized. An in vitro transcription plasmid and a eukaryotic expression plasmid of ribozyme were constructed. The eukaryotic expression plasmid was induced into heptocellular carcinoma 7402 cells, colon cancer HCT116 cells and L02 hepatocytes respectively. Then we determine the cleavage activity of ribozyme against human telomerase RNA component (hTR) both in vitro and in vivo, and detect telomerase activity continuously.</p> <p>Results</p> <p>HDV ribozyme showed a specific cleavage activity against the telomerase RNA in vitro. The maximum cleavage ratio reached about 70.4%. Transfection of HDV ribozyme into 7402 cells and colon cancer cells HCT116 led to growth arrest and the spontaneous apoptosis of cells, and the telomerase activity dropped to 10% of that before.</p> <p>Conclussion</p> <p>HDV ribozyme (g.RZ57) is an effective strategy for gene therapy.</p

Hybrid Relative Attributes Based on Sparse Coding for Zero-Shot Image Classification

As a specific case of image recognition, zero-shot image classification is difficult to solve since its training set cannot cover all the categories of the testing set. From the view point of human vision recognition, the objects can be recognized through the visible and nameable description to the properties. To be the semantic description of the object property, attributes can be taken as a bridge between the seen and unseen categories, which are capable of using into zero-shot image classification. There are mainly binary attributes and relative attributes for zero-shot classification, where the relative attributes have the ability to catch more general sematic relationship than the binary ones. But relative attributes do not always work in zero-shot classification for those categories having similar relative strength attributes. Aiming at solving the defect of the relative attributes in describing the similar categories, we propose to construct the Hybrid Relative Attributes based on Sparse Coding (SC-HRA). First, sparse coding is implemented on low-level features to get nonsemantic relative attributes, which are the necessary complement to the existing relative attributes. After that, they are integrated with the relative attributes to form the hybrid relative attributes (HRA). HRA ranking functions are then learned by the relative attribute learning. Finally, the class label is obtained according to the predicted ranking results of HRA and the ranking relations of HRA among the categories. To verify the effectiveness of SC-HRA, the extensive experiments are conducted on the datasets of faces and natural scenes. The results show that SC-HRA acquires the higher classification accuracy and AUC value

Intrusion Detection System Based on Evolving Rules for Wireless Sensor Networks

Human care services, as one of the classical Internet of things applications, enable various kinds of things to connect with each other through wireless sensor networks (WSNs). Owing to the lack of physical defense devices, data exchanged through WSNs such as personal information is exposed to malicious attacks. Therefore, intrusion detection is urgently needed to actively defend against such attacks. Intrusion detection as a data mining procedure cannot control the size of rule sets and distinguish the similarity between normal and intrusion network behaviors. Therefore, in this paper, an evolving mechanism is introduced to extract the rules for intrusion detection. To extract diversified rules as well as control the quantity of rulesets, the extracted rules are examined according to the distance between the rules in the rule set of the same class and the rules in the rule set of different classes. Thereby, it alleviates the problem that the quantity of rules expands unexpectedly with the evolving genetic network programming. The simulations are conducted on a benchmark intrusion dataset, and the results show that the proposed method provides an effective solution to evolve the class association rules and improves the intrusion detection performance

Discriminative Fusion Correlation Learning for Visible and Infrared Tracking

Discriminative correlation filter- (DCF-) based trackers are computationally efficient and achieve excellent tracking in challenging applications. However, most of them suffer low accuracy and robustness due to the lack of diversity information extracted from a single type of spectral image (visible spectrum). Fusion of visible and infrared imaging sensors, one of the typical multisensor cooperation, provides complementarily useful features and consistently helps recognize the target from the background efficiently in visual tracking. Therefore, this paper proposes a discriminative fusion correlation learning model to improve DCF-based tracking performance by efficiently combining multiple features from visible and infrared images. Fusion learning filters are extracted via late fusion with early estimation, in which the performances of the filters are weighted to improve the flexibility of fusion. Moreover, the proposed discriminative filter selection model considers the surrounding background information in order to increase the discriminability of the template filters so as to improve model learning. Extensive experiments showed that the proposed method achieves superior performances in challenging visible and infrared tracking tasks