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Antimicrobial resistance in Gram-negative bacteria has become one of the leading causes of morbidity and mortality and a serious worldwide public health concern due to the fact that Gram-negative bacteria have an additional outer membrane protecting them from an unwanted compound invading. It is still very difficult for antimicrobials to reach intracellular targets and very challenging to treat Gram-negative bacteria with the current strategies. Here, we found that (o-(bromomethyl)phenyl)boronic acid was incorporated into poly((2-N,N-diethyl)aminoethyl acrylate) (PDEA), forming a copolymer (poly(o-Bn-DEA)) having both phenylboronic acid (B) and ((2-N,N-diethyl)amino) (DEA) units. Poly(o-Bn-DEA) exhibits very strong intramolecular B-N coordination, which could highly promote the covalent binding of phenylboronic acid with lipopolysaccharide (LPS) on the outer membrane of E. coli and lodge poly(o-Bn-DEA) on the LPS layer on the surface of E. coli. Meanwhile, the strong electrostatic interaction between poly(o-Bn-DEA) and the negatively charged lipid preferred tugging the poly(o-Bn-DEA) into the lipid bilayer of E. coli. The combating interactions between covalent binding and electrostatic interaction form a tug-of-war action, which could trigger the lysis of the outer membrane, thereby killing Gram-negative E. coli effectively without detectable resistance.

This article applies quantile regression approaches to investigate the asymmetric effects of economic policy uncertainty (EPU) on G7 stock returns by dividing EPU changes into EPU increases and EPU...

PurposeThis study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.Design/methodology/approachMultiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.FindingsWith network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.Originality/valueMultiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

Angiogenesis is the process of growing endothelial capillary cells. Exosomes are extracellular vesicles that are rich in miRNAs. Studies have shown that exosomes can carry communication between cells and various tissues by delivering miRNAs to their target organs and cells. It has been repeatedly proven that miRNAs regulate the expression of growth factors and other proteins in endothelial cells through paracrine signalling and participate in the physiological and pathological processes of angiogenesis. In the diagnosis and treatment of diseases, exosome-derived microRNAs can play important roles as biomarkers and drug carriers. In this review, we introduce the characteristics of miRNAs and exosomes and their interactions. Then, we specifically summarize the exosome-derived miRNAs related to angiogenesis, and we discuss the potential uses of exosome-derived miRNAs for diagnosing and treating cardiovascular diseases. Graphical abstract.

The connectedness among financial institutions reflects potential channels for risk contagion and the amplification of risk to the financial system that can also propagate into the real economy. Th...

Black phosphorus (BP) has exhibited excellent biocompatibility and high photothermal conversion efficiency under near-infrared light, which makes it very promising for photothermal therapy. However, practical applications are highly hampered because it lacks a targeting property and rapidly degrades in cancer cells, especially in response to strong intracellular oxidative stress. Here, we reported that the mitochondrial targeting peptide functionalized black phosphorus nanosheets covered with an acid-labile polymer shell (doubly functionalized black phosphorus (DFBP) nanosheets) exhibited good stability. DFBP nanosheets not only have excellent ability of accumulating in tumor tissue via surface charge switching but also can target mitochondria. The doubly functionalized black phosphorus nanosheets resulted in robust cancer cell uptake but very poor normal cell accumulation.

In nature, the chemical energy and electrons stored in ATP and NADPH generated during irradiation can facilitate biochemical reactions under dark conditions. However, in artificial photoreaction systems, it is still very difficult to perform photoreactions under dark conditions due to the fact that the photogenerated charge pairs can recombine immediately upon ceasing the irradiation. Preventing the recombination of photogenerated charge pairs still constitutes a major challenge at present. Here, it is reported that functionalized carbon nitride nanomaterials having many heptazine rings with a positive charge distribution, which can tightly trap photogenerated electrons, efficiently prevent the recombination of photogenerated charges. These stored charges are exceedingly long-lived (up to months) and can drive photopolymerization without light irradiation, even after one month. The system introduced here demonstrates a new approach for storing light energy as long-lived radicals, enabling photoreactions under dark conditions.

Ti2C(OH)2 can relieve the hypoxia in tumors via generating O2 from endogenous H2O2 and inhibiting mitochondrial respiratory in cancer cell.

Pyroptosis is closely related to inflammation. However, the molecular mechanisms and pathologic contributions of pyroptotic epithelial cell are not yet fully understood.This study aimed to explore the function and molecular mechanisms of IL-17A on human nasal epithelial cell (hNEC) pyroptosis.The expression of pyroptosis-related biomarkers and IL-17A was assessed in sinonasal mucosa from control individuals, patients with chronic rhinosinusitis without nasal polyps, and patients with chronic rhinosinusitis with nasal polyps (CRSwNP) by using quantitative RT-PCR. Their localization was analyzed via immunohistochemistry and immunofluorescence. The ultrastructural characteristics of IL-17A-induced pyroptosis in hNECs were visualized by using electron microscopy. IL-17A functional assays were performed on hNECs and airway epithelial cell lines. Cytokine levels were quantified via ELISA. The signaling pathways involved in IL-17A-induced pyroptosis were studied via unbiased RNA sequencing and Western blotting.The expression of IL-17A and the pyroptotic biomarkers NOD-like receptor family, pyrin domain containing 3 (NLRP3), caspase-1, gasdermin D, and IL-1β was increased in nasal mucosa from patients with CRSwNP compared with in those with chronic rhinosinusitis without nasal polyps and the control subjects. IL-17A was positively correlated and colocalized with the pyroptotic biomarkers. IL-17A treatment induced pyroptosis in the hNECs and cell lines analyzed, primarily through the extracellular signal-regulated kinase (ERK)-NLRP3/caspase-1 signaling pathway, and increased IL-1β and IL-18 secretion in hNECs. Moreover, IL-17A-induced pyroptosis contributed to steroid resistance by affecting glucocorticoid receptor-α and glucocorticoid receptor-β expression, and the inhibition of pyroptotic proteins partially abolished IL-17A-induced steroid resistance in hNECs.Elevated IL-17A level promotes pyroptosis in hNECs through the ERK-NLRP3/caspase-1 signaling pathway and contributes to glucocorticoid resistance by affecting glucocorticoid receptor homeostasis in patients with CRSwNP.

The objective of this study is to analyze systemic importance of Chinese financial institutions and its influential factors based on return spillover network. We first investigate the return spillover effects among financial institutions and construct the return spillover networks by Granger causality in vector autoregressive (VAR) models. Then we calculate six network centralities (degree centrality, closeness centrality, betweenness centrality, modified Katz centrality, eccentricity centrality and information centrality) to measure systemic importance of financial institutions. Because different centrality measures are correlated with each other, we use the principal component analysis method to obtain comprehensive information about systemic importance of financial institutions. Finally, we identify the major factors, including market and accounting variables, which affect systemic importance of the financial institutions with panel data regression analysis. We find that financial institutions with larger tail risk of stock return, higher return on equity, lower turnover rate and lower assets growth rate tend to be associated with greater systemic importance.

Long noncoding RNAs (LncRNAs) are involved in biological processes and the pathology of diseases and represent an important biomarker or therapeutic target for disease. Emerging evidence has suggested that lncRNAs modulate angiogenesis by regulating the angiogenic cell process-including vascular endothelial cells (VECs); stem cells, particularly bone marrow-derived stem cells, endothelial progenitor cells (EPCs) and mesenchymal stem cells (MSCs); and vascular smooth muscle cells (VSMCs)-and participating in ischaemic heart disease (IHD). Therapeutic angiogenesis as an alternative therapy to promote coronary collateral circulation has been demonstrated to significantly improve the prognosis and quality of life of patients with IHD in past decades. Therefore, lncRNAs are likely to represent a novel therapeutic target for IHD through regulation of the angiogenesis process. This review summarizes the classification and functions of lncRNAs and their roles in regulating angiogenesis and in IHD, in the context of an overview of therapeutic angiogenesis in clinical trials.

From the perspective of volatility spillover, this paper investigates systemic importance and its influential factors of Chinese financial institutions by complex network modeling method. We first construct the volatility spillover networks by vector autoregressive models-multivariate generalized autoregressive conditional heteroscedastic models (VAR-MGARCH) in a BEKK form, and then construct a comprehensive network centrality index based on five network centralities (degree centrality, closeness centrality, betweenness centrality, modified Katz centrality and information centrality) to measure the financial institutions’ systemic importance. The results indicate that the larger comprehensive network centrality index is, the higher corresponding ranking for the node of networks is and the greater systemic importance of financial institution will be. Finally, we identify the major factors which affect systemic importance of the financial institutions with panel data regression analysis. We find that compared with the market factors, the accounting factors are more advantageous to identify important financial institutions. Specifically, financial institutions with lager size and higher assets growth rate tend to be associated with greater systemic importance.

Systemic risk is the risk that the defaults of one or more institutions trigger a collapse of the entire financial system. In this paper, we propose a measure for systemic risk, CoCVaR, the conditional value-at-risk (CVaR) of the financial system conditional on an institution being in financial distress. This measure is similar to Adrian and Brunnermeier’s CoVaR from 2008, but we change the systemic risk from VaR to CVaR. This measure considers severe losses of the financial system beyond VaR. CoCVaR is estimated using CVaR (superquantile) regression. We define the systemic risk contribution of an institution as the difference between CoCVaR conditional on the institution being under distress and the CoCVaR in the median state of the institution. We estimate the systemic risk contributions of the ten largest publicly traded banks in the United States for a sample period February 2000 to January 2015 and compare CoCVaR and CoVaR risk contributions for this period. We find that the new CoCVaR provides a unique perspective on the systemic risk contribution.

Several previously published trials comparing Zotarolimus Eluting Stents (ZES) with Sirolimus Eluting Stents (SES), Paclitaxel Eluting Stents (PES) or Everolimus Eluting Stents (EES) at a follow up period of 1 year, were continually being followed up in order to assess the long-term outcomes. In this meta-analysis, we aimed to compare the long-term (2–5 years) adverse clinical outcomes which were associated with ZES versus SES, PES and EES following Percutaneous Coronary Intervention (PCI). Risk Ratios (RR) with 95% Confidence Intervals (CIs) were generated and the analysis was carried out by the RevMan 5.3 software. In this analysis with a total number of 17,606 participants, ZES and EES were associated with similar adverse outcomes including Stent Thrombosis (ST), myocardial infarction (MI), major adverse cardiac events and repeated revascularization. When ZES were compared with SES and PES during the long-term, MI and definite or probable ST were significantly lower with ZES, with RR: 1.35, 95% CI: 1.17–1.56; P = 0.0001 and RR: 1.91, 95% CI: 1.33–2.75; P = 0.0004 respectively whereas the other adverse outcomes were similarly manifested. Future research should be able to confirm this hypothesis.

In this work, employing a moving window to scan through every stock price time series over a period from 2 January 1986 to 20 October 2015, we use cross-correlations to measure the interdependence between stock prices, and we construct a corresponding minimal spanning tree for 170 U.S. stocks in every given window. We show how the asset tree evolves over time and describe the dynamics of its normalized length, centrality measures, vertex degree and vertex strength distributions, and single- and multiple-step edge survival ratios. We find that the normalized tree length shows a tendency to decrease over the 30 years. The power-law of vertex degree or vertex strength distribution does not hold for all trees. The survival ratio analysis reveals an increased stability of the dependence structure of the stock market as time elapses. We then examine the relationship between tree structure variation and market phenomena, such as average, volatility and tail risk of stock (market) return. Our main observation is that the normalized tree length has a positive relationship with the level of stock market average return, and it responds negatively to the market return volatility and tail risk. Furthermore, the majority of stocks have their vertex degrees significantly positively correlated to their average return, and significantly negatively correlated to their return volatility and tail risk.

Background/Aims: This study aimed to identify the role of microRNA-22 (miR-22) in endothelial cell (EC) injury in coronary heart disease (CHD) by targeting NLRP3 through the inflammasome signaling pathway. Methods: A total of 24 healthy male Sprague-Dawley (SD) rats were divided into normal and atherosclerosis groups. The atherosclerosis rats were assigned into blank, negative control (NC), miR-22 mimic, miR-22 inhibitor and miR-22 inhibitor + siNLRP3 groups. A luciferase reporter gene assay was used to detect the relationship between miR-22 and NLRP3. MiR-22 expression as well as NLRP3 and caspase-1 mRNA and protein expression were measured using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. The activity and apoptosis of coronary arterial endothelial cells (CAECs) were determined by MTT and Hoechst 33258. CAEC lumen formation was detected by a lumen formation assay. An enzyme-linked immunosorbent assay (ELISA) was used to detect IL-1β, IL-6, IL-10 and IL-18 levels. Results: The results indicated that the atherosclerosis group significantly decreased miR-22 expression but increased NLRP3 and caspase-1 mRNA and protein expression. The cell survival rate was significantly increased in the miR-22 mimic group and significantly reduced in the miR-22 inhibitor group. The miR-22 mimic group displayed a lower apoptosis rate and more cells with obvious lumen walls and numerous tubular structures, while cells in the miR-22 inhibitor group were unable to form lumen walls and had a scattered distribution compared to the blank group. The ELISA showed that IL-1β, IL-6 and IL-18 levels were markedly decreased, while IL-10 was clearly increased in the miR-22 mimic group. In contrast, in the miR-22 inhibitor group, IL-1β, IL-6 and IL-18 levels were significantly increased, and IL-10 levels were decreased. Conclusion: Our findings indicated that miR-22 could lower the levels of pro-inflammatory cytokines by inhibiting the NLRP3 inflammasome pathway, which suppresses CAEC apoptosis and protects CAECs in rats with CHD.

Cyclic polymers have a number of unique physical properties compared with those of their linear counterparts. However, the methods for the synthesis of cyclic polymers are very limited, and some multicyclic polymers are still not accessible now. Here, we found that the five˗membered cyclic structure and electron withdrawing groups make methylene in rhodanine highly active to aldehyde via highly efficient Knoevenagel reaction. Also, rhodanine can act as an initiator for anionic ring-opening polymerization of thiirane to produce cyclic polythioethers. Therefore, rhodanine can serve as both an initiator for ring-opening polymerization and a monomer in Knoevenagel polymerization. Via rhodanine-based Knoevenagel reaction, we can easily incorporate rhodanine moieties in the backbone, side chain, branched chain, etc, and correspondingly could produce cyclic structures in the backbone, side chain, branched chain, etc, via rhodanine˗based anionic ring-opening polymerization. This rhodanine chemistry would provide easy access to a wide variety of complex multicyclic polymers., Cyclic polymers have a number of unique physical properties compared to their linear counterparts but their synthesis is very limited. Here, the authors show a method to form a wide variety of multicyclic polymers by a rhodanine˗based Knoevenagel reaction and ring-opening polymerization.

Polymeric micelles are promising vehicles for drug delivery these years. The stability of micelles in blood system is very important for the encapsulated therapeutic agents reaching the targeting area. However, generally, micelles are not stable in the presence of proteins and ions after being injected intravenously. The topology of polymer chain on the surface of micelles plays a key role on the stability of micelles. Here we have prepared flower micelles and the traditional micelles via self-assembly of triblock and diblock copolymer of polyethylene glycol (PEG) and poly(e-caprolactone) (PCL) in water. The special PEG shell of flower micelles prevents micelles from ions and proteins, which endows the micelles with the ability to resist aggregation. The experiment results show that flower micelles are much more stable in the presence of serum and salt than traditional micelles, which indicates these micelles would be promising in drug delivery in the future.

This study measures systemic importance of financial institutions based on network centralities and links them to institutions’ characteristics. We focus on the lower tail dependence networks constructed by combining Clayton copula model and planar maximally filtered graph method. Considering different centrality measures’ correlations, we obtain the comprehensive centrality index about systemic importance by principal component analysis. The centrality measures can capture cross-sectional differences and time-series variations of systemic importance. The financial institutions with higher leverage, lower price earning ratio, lower total assets turnover rate and lower return on equity tend to have higher systemic importance based on tail dependence.

This study considers the effects of the financial institutions’ local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial networks are constructed from dynamic conditional correlations (DCC) with graph filtering method of minimum spanning trees (MSTs). Then we investigate dynamics of systemic risk contributions of financial institution. Also we study dynamics of financial institution’s local topology structure in the financial network. Finally, we analyze the quantitative relationships between the local topology structure and systemic risk contribution with panel data regression analysis. We find that financial institutions with greater node strength, larger node betweenness centrality, larger node closeness centrality and larger node clustering coefficient tend to be associated with larger systemic risk contributions.

We investigate the impact of financial system on China's economic output from a financial institution tail-event driven networks (TENETs) perspective and forecast future economic growth by ARDL models. We assess five network topological measurements to reflect the change in the financial system, which detect tail risk spillover effects and reflect cross-sectional dimension of systemic risk. Through a study of relationship at different time lags, we find that except for total connectedness, all estimated network topological measurements have long-run positive impacts on economic growth and that test results present a highly accurate forecast for China's economic output using the network linkages.

Summary Among numerous iterative methods for solving the minimal nonnegative solution of an M-matrix algebraic Riccati equation, the structure-preserving doubling algorithm (SDA) stands out owing to its overall efficiency as well as accuracy. SDA is globally convergent and its convergence is quadratic, except for the critical case for which it converges linearly with the linear rate 1/2. In this paper, we first undertake a delineatory convergence analysis that reveals that the approximations by SDA can be decomposed into two components: the stable component that converges quadratically and the rank-one component that converges linearly with the linear rate 1/2. Our analysis also shows that as soon as the stable component is fully converged, the rank-one component can be accurately recovered. We then propose an efficient hybrid method, called the two-phase SDA, for which the SDA iteration is stopped as soon as it is determined that the stable component is fully converged. Therefore, this two-phase SDA saves those SDA iterative steps that previously have to have for the rank-one component to be computed accurately, and thus essentially, it can be regarded as a quadratically convergent method. Numerical results confirm our analysis and demonstrate the efficiency of the new two-phase SDA. Copyright © 2015 John Wiley & Sons, Ltd.

The eigenvalue problem of a graph Laplacian matrix L arising from a simple, connected and undirected graph has been given more attention due to its extensive applications, such as spectral clustering, community detection, complex network, image processing and so on. The associated matrix L is symmetric, positive semi-definite, and is usually large and sparse. It is often of interest for finding some smallest positive eigenvalues and corresponding eigenvectors. However, the singularity of L makes the classical eigensolvers inefficient since we need to factorize L for the purpose of solving large and sparse linear systems exactly. The next difficulty is that it is usually prohibitive to factorize L generated by real network problems from big data such as social media transactional databases, and sensor systems because there is in general not only local connections. In this paper, we propose a trimming to cure the singularity of L according to its special property: zero row/column sum. This remedy technique leads us to solve a positive definite linear system reduced in one dimension and then recover the result to get a suitable solution of the original system involved in an eigensolver. Besides, we apply a deflating approach to exclude the influence of converged eigenvalues. We show how to apply the idea of trimming to the graph Laplacian eigenvalue problem together with a deflated term and a target shift. Accordingly, based on the inexact residual Arnoldi (Lee, 2007; Lee and Stewart, 2007) method, we propose an integrated eigensolver for this kind of L combining with the implicit remedy of the singularity, an effective deflation for convergent eigenvalues and the shift–invert enhancement. Numerical experiments reveal that the integrated eigensolver outperforms the classical Arnoldi/Lanczos method for computing some smallest positive eigeninformation especially when the LU factorization is not available.

We investigated the role of mammalian target of rapamycin/nuclear factor-kappa B (mTOR/NF-κB) signaling pathway in high thoracic epidural anesthesia (HTEA) against myocardial ischemia-reperfusion (I/R) injury in rats. The rat model of myocardial I/R injury was established. Ninety rats were divided into the normal, sham, I/R, eHTEA, the PDTC, and HTEA + PDTC groups. ELISA was applied to detect cardiac function indexes. HE staining was conducted to observe histopathological changes of myocardial tissues, and TTC staining was performed to detect the myocardial infarction size. TUNEL staining was adopted to detect the cell apoptosis rate. The mRNA and protein levels of mTOR, NF-κB, Fasl, Bcl-2 and Bax, and LC3-I, LC3-II, BNIP3, and Atg5 were detected by RT-qPCR and Western blotting, respectively. The findings indicated that compared with the normal and sham groups, the I/R, PDTC, and HTEA groups showed the larger myocardial infarction size and increased cell apoptosis rate, while the results in the HTEA + PDTC group were opposite. Compared with the normal and sham groups, the I/R group showed reduced mRNA and protein levels of Bcl-2, LC3, BNIP3, and Atg5, and elevated mRNA and protein levels of mTOR, p50, p65, Bax, and Fasl, while the HTEA + PDTC group revealed the opposite results, and the PDTC and HTEA group revealed the increased mRNA and protein levels of Bcl-2, LC3, BNIP3, Atg5, mTOR, p50, p65, Bax, and Fasl. These results prove that the inhibition of mTOR/NF-κB signaling pathway potentiates HTEA against myocardial IR injury by autophagy and apoptosis in rats.