Trojan horselike T6SS effector TepC mediates both interference competition and exploitative competition.

The type VI secretion system (T6SS) is a bacterial weapon capable of delivering antibacterial effectors to kill competing cells for interference competition, as well as secreting metal ion scavenging effectors to acquire essential micronutrients for exploitation competition. However, no T6SS effectors that can mediate both interference competition and exploitation competition have been reported. In this study, we identified a unique T6SS-1 effector in Yersinia pseudotuberculosis named TepC, which plays versatile roles in microbial communities. First, secreted TepC acts as a proteinaceous siderophore that binds to iron and mediates exploitative competition. Additionally, we discovered that TepC has DNase activity, which gives it both contact-dependent and contact-independent interference competition abilities. In conditions where iron is limited, the iron-loaded TepC is taken up by target cells expressing the outer membrane receptor TdsR. For kin cells encoding the cognate immunity protein TipC, TepC facilitates iron acquisition, and its toxic effects are neutralized. On the other hand, nonkin cells lacking TipC are enticed to uptake TepC and are killed by its DNase activity. Therefore, we have uncovered a T6SS effector, TepC, that functions like a "Trojan horse" by binding to iron ions to provide a valuable resource to kin cells, whereas punishing cheaters that do not produce public goods. This lure-to-kill mechanism, mediated by a bifunctional T6SS effector, may offer new insights into the molecular mechanisms that maintain stability in microbial communities.

Salivary metabolomics of burning mouth syndrome: A cross-sectional study.

OBJECTIVE: There is a lack of systematic studies on salivary metabolomic profiles in burning mouth syndrome (BMS); metabolomics could help explore BMS pathogenesis. We aimed to explore the salivary metabolomic profile of patients with BMS using untargeted metabolomics techniques. DESIGN: A cross-sectional study was designed to analyze the characteristics of unstimulated whole salivary metabolomics of patients with BMS (n = 34) and healthy participants (n = 30). Ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry, principal component, orthogonal partial least-squares-discriminant, hierarchical clustering, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed to identify differentially expressed metabolites and metabolic pathways in which they were enriched. RESULTS: We identified 12,982 metabolite ions. Among them, 394 differentially expressed metabolites were identified with variable importance in projection scores of > 1 (P < 0.05) compared with those in the controls. Based on the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, 30 metabolites were identified, and 16 of them were enriched in 25 metabolic pathways. The levels of caffeine (log2-fold change = -2.91) and its metabolites, paraxanthine (-2.01) and theophylline (-2.03), were low, and the caffeine metabolism pathway was downregulated in the BMS group compared with those in the controls (P < 0.05). CONCLUSIONS: The salivary metabolomic profile of patients with BMS presented characteristics distinct from those of the controls. A low caffeine level may be associated with BMS. This study provides a novel insight for further exploration of the pathogenesis of and potential therapeutic approaches for BMS.

Semi-Supervised Multi-View Clustering with Weighted Anchor Graph Embedding.

A number of literature reports have shown that multi-view clustering can acquire a better performance on complete multi-view data. However, real-world data usually suffers from missing some samples in each view and has a small number of labeled samples. Additionally, almost all existing multi-view clustering models do not execute incomplete multi-view data well and fail to fully utilize the labeled samples to reduce computational complexity, which precludes them from practical application. In view of these problems, this paper proposes a novel framework called Semi-supervised Multi-View Clustering with Weighted Anchor Graph Embedding (SMVC_WAGE), which is conceptually simple and efficiently generates high-quality clustering results in practice. Specifically, we introduce a simple and effective anchor strategy. Based on selected anchor points, we can exploit the intrinsic and extrinsic view information to bridge all samples and capture more reliable nonlinear relations, which greatly enhances efficiency and improves stableness. Meanwhile, we construct the global fused graph compatibly across multiple views via a parameter-free graph fusion mechanism which directly coalesces the view-wise graphs. To this end, the proposed method can not only deal with complete multi-view clustering well but also be easily extended to incomplete multi-view cases. Experimental results clearly show that our algorithm surpasses some state-of-the-art competitors in clustering ability and time cost.

Unsupervised Trademark Retrieval Method Based on Attention Mechanism.

Aiming at the high cost of data labeling and ignoring the internal relevance of features in existing trademark retrieval methods, this paper proposes an unsupervised trademark retrieval method based on attention mechanism. In the proposed method, the instance discrimination framework is adopted and a lightweight attention mechanism is introduced to allocate a more reasonable learning weight to key features. With an unsupervised way, this proposed method can obtain good feature representation of trademarks and improve the performance of trademark retrieval. Extensive comparative experiments on the METU trademark dataset are conducted. The experimental results show that the proposed method is significantly better than traditional trademark retrieval methods and most existing supervised learning methods. The proposed method obtained a smaller value of NAR (Normalized Average Rank) at 0.051, which verifies the effectiveness of the proposed method in trademark retrieval.

A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19.

The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude (alpha), mean (mu), and standard deviation (sigma) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions.

Hybrid Low-Order and Higher-Order Graph Convolutional Networks.

With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of parameters and high computational complexity. Therefore, we propose a hybrid lower-order and higher-order graph convolutional network (HLHG) learning model, which uses a weight sharing mechanism to reduce the number of network parameters. To reduce the computational complexity, we propose a novel information fusion pooling layer to combine the high-order and low-order neighborhood matrix information. We theoretically compare the computational complexity and the number of parameters of the proposed model with those of the other state-of-the-art models. Experimentally, we verify the proposed model on large-scale text network datasets using supervised learning and on citation network datasets using semisupervised learning. The experimental results show that the proposed model achieves higher classification accuracy with a small set of trainable weight parameters.

The stringent response factor, RelA, positively regulates T6SS4 expression through the RovM/RovA pathway in Yersinia pseudotuberculosis.

The type VI secretion system (T6SS) is a versatile molecular machinery widely distributed in Gram-negative bacteria. The activity of the T6SS is tightly regulated by various mechanisms, including quorum sensing (QS), iron concentration, and transcriptional regulators. Here we demonstrated that the stringent response regulator, RelA, contributes to bacterial resistance to multiple environmental stresses in Yersinia pseudotuberculosis. We also revealed that the stress resistance function of stringent response (SR) was partially mediated by the general stress response T6SS4 system. RelA positively regulates the expression of T6SS4 to combat various stresses in response to nutrition starvation collectively mediated by the RovM and RovA regulators. These findings revealed not only the important role of T6SS4 in SR induced stress resistance, but also a new pathway to regulate T6SS4 expression in response to starvation stress.

RovM and CsrA Negatively Regulate Urease Expression in Yersinia pseudotuberculosis.

Urease acts as an important acid resistance system and virulence factor that is widespread among microorganisms. RovM is a global regulator that regulates a series of genes and pathways including acid survival systems in the enteric bacterium Yersinia pseudotuberculosis (Yptb). However, whether RovM regulates the urease activity in Yptb was still unknown. In this study, by using qualitative and quantitative urease assays, we show that the urease expression responds to nutrient conditions and the RovM protein represses urease expression by binding to its promoter. A previously reported positive regulator OmpR activates urease activity but RovM plays a dominant role in different nutrient conditions. In addition, carbon storage regulator system A (CsrA), the upstream regulator of RovM, dramatically down-regulates urease activity possibly by its binding to the Shine-Dalgarno (SD) sequence of the mRNA encoding the urease. In conclusion, this study demonstrates that urease activity is strictly controlled by nutrient conditions and is down-regulated by the CsrA-RovM pathway.

A starvation-induced regulator, RovM, acts as a switch for planktonic/biofilm state transition in Yersinia pseudotuberculosis.

The transition between the planktonic state and the biofilm-associated state is a key developmental decision for pathogenic bacteria. Biofilm formation by Yersinia pestis is regulated by hmsHFRS genes (ß-1, 6-N-acetyl-D-glucosamine synthesis operon) in its flea vector and in vitro. However, the mechanism of biofilm formation in Yersinia pseudotuberculosis remains elusive. In this study, we demonstrate that the LysR-type regulator RovM inversely regulates biofilm formation and motility in Y. pseudotuberculosis by acting as a transcriptional regulator of these two functions. RovM is strongly induced during growth in minimal media but strongly repressed in complex media. On one hand, RovM enhances bacterial motility by activating the expression of FlhDC, the master regulator of flagellar genes, via the recognition of an operator upstream of the flhDC promoter. On the other hand, RovM represses ß-GlcNAc production under nutrition-limited conditions, negatively regulating hmsHFRS expression by directly binding to the -35 element of its promoter. Compared to wild-type bacteria, the rovM mutant established denser biofilms and caused more extensive mortality in mice and silkworm larvae. These results indicate that RovM acts as a molecular switch to coordinate the expression of genes involved in biofilm formation and motility in response to the availability of nutrients.

Traffic-related heavy metals uptake by wild plants grow along two main highways in Hunan Province, China: effects of soil factors, accumulation ability, and biological indication potential.

This study was performed to investigate pollution of traffic-related heavy metals (HMs-Zn, Pb, Cu, Cr, and Cd) in roadside soils and their uptake by wild plants growing along highways in Hunan Province, China. For this, we analyzed the concentration and chemical fractionation of HMs in soils and plants. Soil samples were collected with different depths in the profile and different distances from highway edge. And leaves and barks of six high-frequency plants were collected. Results of the modified European Community Bureau of Reference (BCR) showed that the mobile fraction of these HMs was in the order of Cd > Pb > Zn > Cu > Cr. A high percentage of the mobile fraction indicates Cd, Pb, and Zn were labile and available for uptake by wild plants. The total concentration and values of risk assessment code (RAC) showed that Cd was the main risk factor, which were in the range high to very high risk. The accumulation ability of HMs in plants was evaluated by the biological accumulation factor (BAF) and the metal accumulation index (MAI), and the results showed that all those plant species have good phyto-extraction ability, while accumulation capacity for most HMs plants tissues was bark > leaf. The highest MAI value (5.99) in Cinnamomum camphora (L) Presl indicates the potential for bio-monitoring and a good choice for planting along highways where there is contamination with HMs.

In-vitro and in-vivo evaluation of ciprofloxacin liposomes for pulmonary administration.

OBJECTIVE: The aim of this study was to investigate the ciprofloxacin liposome of high encapsulation efficiency with optimal physical properties for pulmonary administration and to test its in-vivo potential in rats. METHODS: Ciprofloxacin-loaded liposome was prepared by gradient of ammonium sulfate method. The particle size and morphology were determined using a NANOPHOX particle size analyzer and a transmission electron microscope, respectively. Encapsulation efficiency was calculated by UV spectrophotometry. Ciprofloxacin liposome released in vitro was performed using simulated lung fluid. In-vivo studies, pharmacokinetics and pulmonary distribution, HPLC method was established to determine the concentration of ciprofloxacin in rat plasma and lung tissue. The pulmonary pathological section was used to observe the change of pulmonary pathology. RESULTS: The optimized ciprofloxacin liposome, which had a high encapsulation efficiency of 93.96%, and an average particle size of 349.6 nm with a span of 0.42, showed sustained in-vitro release. The optimized ciprofloxacin liposome was further examined in the in-vivo study in rats. The concentration of ciprofloxacin in lung and blood was simultaneously determined in each rat. The ratio of the AUClung value between ciprofloxacin liposome and ciprofloxacin solution was 288.33, whereas the relative bioavailability was 72.42%, and the drug targeting efficiency of ciprofloxacin liposome and ciprofloxacin solution by intratracheal administration were 799.71 and 2.01, respectively. CONCLUSION: Ciprofloxacin liposome for pulmonary administration offered an attractive alternative that was able to deliver high concentrations of antibiotic directly to the chosen target site while minimizing the local irritation.