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Safety-critical systems, such as the railway signal system, are subject to potentially high costs from failures, including loss of life and property damage. The use of new technology, including communication-based train control (CBTC) systems with software and computers, has changed the types of accidents that occur. Software-related issues and dysfunctional interactions between system components controlled by the software are increasingly the cause of incidents. Developing a “safe” safety-critical system requires accurate and complete safety requirements, which are the foundation of system development. Traditional hazard analysis techniques are insufficient for identifying the causes of accidents in modern railway signaling systems. Systems-Theoretic Process Analysis (STPA) is a powerful new hazard analysis method designed to address these limitations. Building upon this foundation, a hierarchical approach to safety requirement development has been further developed. This approach combines STPA analysis with a hierarchical modeling approach to establish traceability links from safety requirements to specific architectures, refine and allocate system-level safety requirements to relevant subsystems, and abstract safety requirements at higher hierarchical levels to enable easy changes to lower-level implementations. This paper employs the aforementioned methodology within the context of the CBTC system, thereby enhancing risk management and hazard analysis, enabling early insights, and facilitating the generation of safety requirements of CBTC System.

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies.

The switch machine, an essential element of railway infrastructure, is crucial in maintaining the safety of railway operations. Traditional methods for fault diagnosis are constrained by their dependence on extensive labeled datasets. Semi-supervised learning (SSL), although a promising solution to the scarcity of samples, faces challenges such as the imbalance of pseudo-labels and inadequate data representation. In response, this paper presents the Semi-Supervised Adaptive Matrix Machine (SAMM) model, designed for the fault diagnosis of switch machine. SAMM amalgamates semi-supervised learning with adaptive technologies, leveraging adaptive low-rank regularizer to discern the fundamental links between the rows and columns of matrix data and applying adaptive penalty items to correct imbalances across sample categories. This model methodically enlarges its labeled dataset using probabilistic outputs and semi-supervised, automatically adjusting parameters to accommodate diverse data distributions and structural nuances. The SAMM model’s optimization process employs the alternating direction method of multipliers (ADMM) to identify solutions efficiently. Experimental evidence from a dataset containing current signals from switch machines indicates that SAMM outperforms existing baseline models, demonstrating its exceptional status diagnostic capabilities in situations where labeled samples are scarce. Consequently, SAMM offers an innovative and effective approach to semi-supervised classification tasks involving matrix data.

This article presents a novel parallel path detection algorithm for identifying suspicious fraudulent accounts in large-scale banking transaction graphs. The proposed algorithm is based on a three-step approach that involves constructing a directed graph, shrinking strongly connected components, and using a parallel depth-first search algorithm to mark potentially fraudulent accounts. The algorithm is designed to fully exploit CPU resources and handle large-scale graphs with exponential growth. The performance of the algorithm is evaluated on various datasets and compared with serial time baselines. The results demonstrate that our approach achieves high performance and scalability on multi-core processors, making it a promising solution for detecting suspicious accounts and preventing money laundering schemes in the banking industry. Overall, our work contributes to the ongoing efforts to combat financial fraud and promote financial stability in the banking sector.

Sphingolipids, as members of the large lipid family, are important components of plasma membrane. Sphingolipids participate in biological signal transduction to regulate various important physiological processes such as cell growth, apoptosis, senescence, and differentiation. Numerous studies have demonstrated that sphingolipids are strongly associated with glucose metabolism and insulin resistance. Insulin resistance, including peripheral insulin resistance and brain insulin resistance, is closely related to the occurrence and development of many metabolic diseases. In addition to metabolic diseases, like type 2 diabetes, brain insulin resistance is also involved in the progression of neurodegenerative diseases including Alzheimer’s disease and Parkinson’s disease. However, the specific mechanism of sphingolipids in brain insulin resistance has not been systematically summarized. This article reviews the involvement of sphingolipids in brain insulin resistance, highlighting the role and molecular biological mechanism of sphingolipid metabolism in cognitive dysfunctions and neuropathological abnormalities of the brain.

Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage. As engineering safety has always been a very important issue, this study investigated the problem of multi-crack identification in rock masses within the environment of track tunnels using transient waves. A tunnel rock was modeled using MIDAS GTS NX software (2019.v1.2) and a crack transient wave model in the frequency domain was obtained through data analysis and simulation. Then, this was combined with compressive sensing techniques to locate and detect multiple cracks in tunnel rock. The performance of the proposed approach was validated through experimental simulations, which included experiments on differences in the number of cracks, as well as spatial samples. The experimental results indicate that the technique performs well for single-crack localization in tunnel rock mass, where the average localization error is 4 m. Meanwhile, the localization error is larger in multi-crack localization, and the number of spatial sample points set using compressive sensing also has a large impact on the experimental results.

The intelligent maintenance of railway equipment plays a pivotal role in advancing the sustainability of transportation and manufacturing. Railway turnouts, being an essential component of railway infrastructure, often encounter various faults, which present operational challenges. Existing fault diagnosis methods for railway turnouts primarily utilize vectorized monitoring data, interpreted either through vector-based models or distance-based measurements. However, these methods exhibit limited interpretability or are heavily reliant on standard curves, which impairs their performance or restricts their generalizability. To address these limitations, a railway turnouts fault diagnosis method with monitoring signal images and support matrix machine is proposed herein. In addition, a pinball loss-based multiclass support matrix machine (PL-MSMM) is designed to address the noise sensitivity limitations of the multiclass support matrix machine (MSMM). First, the time-series monitoring signals in one dimension are transformed into images in two dimensions. Subsequently, the image-based feature matrix is constructed. Then, the PL-MSMM model is trained using the feature matrix to facilitate the fault diagnosis. The proposed method is evaluated using a real-world operational current dataset, achieving a fault identification accuracy rate of 98.67%. This method outperforms the existing method in terms of accuracy, precision, and F1-score, demonstrating its superiority.

In complex two-dimensional monitoring environments, how to enhance network efficiency and network lifespan while utilizing limited energy resources, and ensuring that wireless sensor networks achieve the required partial coverage of the monitoring area, are the challenges of optimizing coverage in wireless sensor networks.With the premise of ensuring connectivity in the target network area, an enhanced multi-objective salp swarm algorithm based on non-dominated sorting (EMSSA) is proposed in this paper, by jointly optimizing network coverage, node utilization, and network energy balance objectives. Firstly, the logistic chaotic mapping is used to maintain the diversity of the initial salp swarm population. Secondly, to balance global and local search capabilities, a new dynamic convergence factor is introduced. Finally, to escape local optima more effectively, a follower updating strategy is implemented to reduce the blind following of followers while retaining superior individual information. The effectiveness of the strategy is validated through comparative experiments on ZDT and DTLZ test functions, and the proposed algorithm is applied to coverage optimization in WSNs in complex environments. The results demonstrate that the algorithm can adjust coverage thresholds according to different application requirements, providing various effective coverage optimization configurations. With the same preset requirements for partial coverage achieved, both network efficiency and lifespan have been significantly improved.

Social media is a real-time social sensor to sense and collect diverse information, which can be combined with sentiment analysis to help IoT sensors provide user-demanded favorable data in smart systems. In the case of insufficient data labels, cross-domain sentiment analysis aims to transfer knowledge from the source domain with rich labels to the target domain that lacks labels. Most domain adaptation sentiment analysis methods achieve transfer learning by reducing the domain differences between the source and target domains, but little attention is paid to the negative transfer problem caused by invalid source domains. To address these problems, this paper proposes a cross-domain sentiment analysis method based on feature projection and multi-source attention (FPMA), which not only alleviates the effect of negative transfer through a multi-source selection strategy but also improves the classification performance in terms of feature representation. Specifically, two feature extractors and a domain discriminator are employed to extract shared and private features through adversarial training. The extracted features are optimized by orthogonal projection to help train the classification in multi-source domains. Finally, each text in the target domain is fed into the trained module. The sentiment tendency is predicted in the weighted form of the attention mechanism based on the classification results from the multi-source domains. The experimental results on two commonly used datasets showed that FPMA outperformed baseline models.

Zymomonas mobilis is a promising microorganism for industrial bioethanol production. However, ethanol produced during fermentation is toxic to Z. mobilis and affects its growth and bioethanol production. Although several reports demonstrated that the RNA-binding protein Hfq in Z. mobilis contributes to the tolerance against multiple lignocellulosic hydrolysate inhibitors, the role of Hfq on ethanol tolerance has not been investigated. In this study, hfq in Z. mobilis was either deleted or overexpressed and their effects on cell growth and ethanol tolerance were examined. Our results demonstrated that hfq overexpression improved ethanol tolerance of Z. mobilis, which is probably due to energy saving by downregulating flagellar biosynthesis and heat stress response proteins, as well as reducing the reactive oxygen species induced by ethanol stress via upregulating the sulfate assimilation and cysteine biosynthesis. To explore proteins potentially interacted with Hfq, the TEV protease mediated Yeast Endoplasmic Reticulum Sequestration Screening system (YESS) was established in Z. mobilis. YESS results suggested that Hfq may modulate the cytoplasmic heat shock response by interacting with the heat shock proteins DnaK and DnaJ to deal with the ethanol inhibition. This study thus not only revealed the underlying mechanism of enhanced ethanol tolerance by hfq overexpression, but also provided an alternative approach to investigate protein-protein interactions in Z. mobilis.

Wang et al. developed a fluorescence-assisted single-cell methodology (split protease-E. coli ClpXP (SPEC)) to characterise protein-protein interactions for both eukaryotic and prokaryotic species in E. coli cells. Their method can quantify these interactions with high sensitivity, easy manipulation, and broad temperature adaptability

Phenotypic evaluation and molecular biotechnology are both important in the identification and utilization of crop germplasm resources. In this study, the phenotypic variation and genetic diversity of 149 main potato cultivars in China were investigated with 12 phenotypic traits and 24 SSR markers. The coefficient of variation of 12 phenotypic traits ranged from 12.11% to 156.93%. The results of SSR markers exhibited a relatively high level of genetic variation (Na =5.458 ± 1.499, Ne =3.300 ± 1.087, I =1.397 ± 0.298, Ho =0.797 ± 0.178, He = 0.660 ± 0.117, and PIC=0.702 ± 0.087). Population structure and phylogenetic tree analysis divided the varieties into three subgroups. The results indicated that ninety percent of the molecular variance was attributed to within-group differences, and the remaining 10% was attributed to variation among groups. Consistent with previous report, alleles of the STI032 marker were significantly associated with tuber starch content and growth period traits in the population. The results of this study could facilitate the utilization of potato germplasm resources, molecular genetic breeding and improvement.

Epilepsy is a neurological disease and approximately 30% of patients have failed to respond to current anti-epilepsy drugs. The neuroinflammation mechanism has raised increasing concerns and been regarded as the novel treatment strategy in epilepsy, but the target molecules require further research. Pyruvate kinase isoform 2 (PKM2) is well studied in peripheral inflammation, but its role in epilepsy neuroinflammation remains unclear. We knocked down microglia PKM2 in the hippocampus using a stereotaxic adeno-associated virus (AAV) microinjection and established a pilocarpine-induced status epilepticus (PISE) model. Racine score was used to evaluate the seizure grade. Next, we used WB, Multiplex tyramide signal amplification (TSA) staining and other methods to determine neuroinflammation and the complement component 3 (C3)–C3aR interaction in primary microglia. Results showed that microglia PKM2 knockdown reduced epilepsy grade and rescued neuron loss. Mechanistically, PKM2 knockdown inhibited microglia activation and inflammation factor secretion through suppressing p65 expression and phosphorylation. The reduced microglia C1q, TNF-α, and IL-1α were responsible for the decreased astrocyte C3 expression and the following neuron damage caused by the C3–C3aR interaction. Our data suggest that microglia PKM2 inhibition ameliorates neuroinflammation and neuron loss through C3-C3aR interaction in epilepsy, which provides an attractive target for the intervention of damaged neuron-glia crosstalk in epilepsy.

As the pearl of China’s intangible cultural heritage, there are still many difficulties in the development of opera. This paper takes Meilin opera of Sanming City, Fujian Province, as an example, analyzes the current situation of the inheritance of Meilin opera in the context of “Internet+”, and compares it with the development of similar operas, and finally discusses the new strategies for the protection and development of intangible cultural heritage such as opera from the perspective of communication path and communication mechanism.

Acacia catechu (L.f.) Willd, a leguminous plant, is included in the 2020 edition of the Chinese Pharmacopoeia and is mainly used to treat eczema, mouth ulcers, diarrhea, bruising, and traumatic hemorrhage. However, there are imported and domestic Acacia catechu samples available in China, and their quality and price are very different, which seriously affects the safety and stability of their clinical application. Importantly, there is no simple and effective method for identifying or classifying grades of Acacia catechu. In this study, 47 batches of commercial Acacia catechu were used for identifying or classifying grades of Acacia catechu using high performance liquid chromatography (HPLC) combined with chemometric analysis. Firstly, gradient elution was adopted with 0.05% phosphoric acid water (A)-methanol (B) as the mobile phase to establish chromatographic conditions. The HPLC chromatograms of 47 batches of Acacia catechu samples were analyzed by the “Similarity Evaluation System for Chromatographic Fingerprint of TCM” software (version 2012A). The common peaks of Acacia catechu were identified to evaluate the similarity. Based on the determination results of fingerprint chromatographic peak area, the quality of the collected Acacia catechu was evaluated by chemometric methods such as CA, PCA, and OPLS-DA. The results showed that the collected Acacia catechu samples were significantly divided into three categories. The first-class samples were all imported Acacia catechu except S9 sample, which was domestic Acacia catechu; the second-class samples were partly domestic Acacia catechu and partly imported Acacia catechu; and the third-class samples were all domestic Acacia catechu. Moreover, OPLS-DA of 47 batches of samples showed that the contents of catechin and the total contents of catechin and epicatechin could be used as key indicators for assessing the quality of Acacia catechu. The developed HPLC fingerprint and quantitative analysis method of multi-indicator components can be used for classification and quality evaluation of market Acacia catechu, which has a significant reference value for developing Acacia catechu grade quality standards.

Systematic chemical and mineralogical analyses were conducted for the dust generated in the Shuikoushan (SKS) lead smelting process, based on the results, an alternative strategy of direct metallic Pb recovery was proposed using NaOH–carbon roasting process. The reduction thermodynamics of PbSO4 was discussed, and the influencing parameters, including NaOH/dust mass ratio, carbon/dust mass ratio, roasting temperature and roasting time, were investigated systematically by experiments. The results indicated that increasing in the operation conditions was beneficial for the improved Pb recovery rate, and the reaction mechanism in this study was presumed as PbSO4→ C PbS→ NaOH Pb. The optimal conditions were determined as NaOH/dust mass ratio of 1.0, carbon/dust mass ratio of 0.2, roasting temperature of 650 °C and roasting time of 180 min. Under these conditions, 87.33% of Pb in the dust was directly recovered as metal pellets, gathering 96.48% of Bi, and 97.06% of As was converted into the alkali solution, while Zn and Cd were retained in the leaching residue. This process is clean and environmentally friendly for recovery of secondary resources bearing high content of PbSO4.

Dopamine agonists increase brain glutathione levels. Here the authors identify noncanonical signalling by Nrf2 as a potential mechanism for the neuroprotective effects of dopamine D2 receptor activation.

In this study, the effects of the catalysis of heavy metals on the pyrolysis of waste polyester textiles (WPTs) and the adsorption behaviors of the pyrolysis products of WPTs for Cr(VI) were explored. TG−DTG analysis indicated that the metal ions catalyzed the pyrolysis process by reducing the temperature of the decomposition of WPTs. The surface morphology and pore structure of the carbons were analyzed using SEM and BET. The results demonstrated that Zn−AC possessed the largest specific surface area of 847.87 m2/g. The abundant acidic functional groups on the surface of the activated carbons were proved to be involved in the Cr(VI) adsorption process via FTIR analysis. Cr(VI) adsorption experiments indicated that the adsorption process was more favorable at low pH conditions, and the maximum adsorption capacities of Zn−AC, Fe−AC, and Cu−AC for Cr(VI) were 199.07, 136.25, and 84.47 mg/g, respectively. The FTIR and XPS analyses of the carbons after Cr(VI) adsorption, combined with the adsorption kinetics and isotherm simulations, demonstrated that the adsorption mechanism includes pore filling, an electrostatic effect, a reduction reaction, and complexation. This study showed that metal salts catalyze the pyrolysis processes of WPTs, and the activated carbons derived from waste polyester textiles are promising adsorbents for Cr(VI) removal.

With the improvement and growth of instructional informatisation, the contradiction between the open supply of academic resources, information expression, and mental property safety is turning into greater acute. Remedying the relationship between the two is very necessary for the overall performance of records expression of academic assets and the advent of true surroundings for mental property protection. The safety of mental property rights is to shield the rights and pursuits of know-how owners, defend the strength of information producers to produce knowledge, and defend the supply of academic sources sharing. The data expression and protection of intellectual property education resources based on machine learning is a kind of protection tool for the intellectual property of education resources developed using the characteristics of automation, real-time monitoring, and growth of machine learning. It can prevent web crawlers from harming e-commerce websites, prevent them from stealing the intellectual property of e-commerce websites, and analyse web crawlers that visit websites to prevent important website data from being stolen by them. From this point of view, based on the relationship between the fact expression of instructional sources and the safety of mental property rights, this paper advocates to promote the records expression and safety of mental property rights of academic sources from a couple of perspectives.

Human lysozyme (hLYZ), known for its bacteriolytic activity, is widely applied in the food and pharmaceutical industries as an antimicrobial agent. However, its extensive application was limited by its low large-scale production efficiency. In this study, a combinational method of integrating codon optimization, multiple gene copies, and ER molecular chaperone co-expression was developed to improve the heterologous production of hLYZ in Pichia pastoris GS115. Our results showed that increasing the copy number of the optimized hLYZ gene in P. pastoris could enhance its secretory production level up to 1.57-fold. The recombinant opt-hLYZ-6C strain that contains six copies of opt-hLYZ gene exhibited the highest mRNA transcription levels, giving the highest production of 0.22 ± 0.02 mg/mL of hLYZ in the medium supernatant with a bacteriolytic activity of 14,680 ± 300 U/mL against Micrococcus lysodeikticus in the shaking flask experiment. Moreover, co-overexpression of ER retention molecular chaperones, such as Pdi1 or Ero1, in the recombinant opt-hLYZ-6C strain both presented positive effects on the secretory production of hLYZ. Our further characterization indicated that tandem co-expression of Ero1 and Pdi1 together presented an added-up effect. The secretory production of hLYZ in the medium supernatant reached 0.34 ± 0.02 mg/mL of the recombinant opt-hLYZ-6C-EP strain in the shaking flask experiment, with a bacteriolytic activity of 21,200 ± 400 U/mL. Compared to the recombinant opt-hLYZ-1C strain, these final improvements were calculated as 2.43-fold and 2.30-fold on secretory protein levels and antibacterial activity, respectively. Finally, the recombinant opt-hLYZ-6C-EP strain was applied for high-density cultivation in 5 L of fermenter, in which the secretory yield of hLYZ reached 2.34 ± 0.02 mg/mL in the medium supernatant, with a bacteriolytic activity of 1.76 ± 0.02 × 105 U/mL against M. lysodeikticus. All these numbers presented the highest heterologous production levels of hLYZ in microbial systems.

The Boolean Satisfiability problem (SAT) is a prototypical NP-complete problem, which has been widely studied due to its significant importance in both theory and applications. Stochastic local search (SLS) algorithms are among the most efficient approximate methods available for solving certain types of SAT instances. The quantitative configuration checking (QCC) heuristic is an effective approach for improving SLS algorithms on solving the SAT problem, resulting in an efficient SLS solver for SAT named Swqcc. In this paper, we focus on combining the QCC heuristic with an aspiration mechanism, and then design a new heuristic called QCCA. On the top of Swqcc, we utilize the QCCA heuristic to develop a new SLS solver dubbed AspiSAT. Through extensive experiments, the results illustrate that, on random 3-SAT instances, the performance of AspiSAT is much better than that of Swqcc and Sparrow, which is an influential and efficient SLS solver for SAT. In addition, we further enhance the original clause weighting schemes employed in Swqcc and AspiSAT, and thus obtain two new SLS solvers called Ptwqcc and AspiPT, respectively. The eperimental results present that both Ptwqcc and AspiPT outperform Swqcc and AspiSAT on random 5-SAT instances, indicating that both QCC and QCCA heuristics are able to cooperate effectively with different clause weighting schemes.

For antibody discovery and engineering, yeast surface display (YSD) of antigen-binding fragments (Fabs) and coupled fluorescence activated cell sorting (FACS) provide intact paratopic conformations and quantitative analysis at the monoclonal level, and thus holding great promises for numerous applications. Using anti-TNFα mAbs Infliximab, Adalimumab, and its variants as model Fabs, this study systematically characterized complementary approaches for the optimization of Fab YSD. Results suggested that by using divergent promoter GAL1-GAL10 and endoplasmic reticulum (ER) signal peptides for co-expression of light chain and heavy chain-Aga2 fusion, assembled Fabs were functionally displayed on yeast cell surface with sigmoidal binding responses toward TNFα. Co-expression of a Hsp70 family molecular chaperone Kar2p and/or protein-disulfide isomerase (Pdi1p) significantly improved efficiency of functional display (defined as the ratio of cells displaying functional Fab over cells displaying assembled Fab). Moreover, fusing ER retention sequences (ERSs) with light chain also enhanced Fab display quality at the expense of display quantity, and the degree of improvements was correlated with the strength of ERSs and was more significant for Infliximab than Adalimumab. The feasibility of affinity maturation was further demonstrated by isolating a high affinity Fab clone from 1:103 or 1:105 spiked libraries.

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

A series of boron-doped diamond (BDD) films were deposited by using a hot cathode direct current plasma chemical vapor deposition(HCDC-PCVD) system with different ratios of CH4/H2/B(OCH3)3 (trimethylborate) gas mixture. The morphology, structure and quality of BDD films were controled by SEM, XRD and Raman measurements. The electrochemical properties of the BDD films were investigated by electrochemical methods. Cyclic voltammetric performances of the BDD films indicated that the main determinant in the electrochemical characteristics of BDD films was the boron doping amount. The threshold potential for oxygen evolution increased from 1 V to 2.5 V. Meanwhile, the electrochemical potential window of BDD films was enlarged from 2.2 V to 4.5 V when the B content was increased from 1.75 × 1019cm-3 to 2.4 × 1021 cm−3. The cyclic voltammograms of BDD films in K4Fe(CN)6 and K3Fe(CN)6 mixed solution indicated that the behavior of Fe(CN)6-3/-4 redox couple could be regarded as semi-reversible.DOI: http://dx.doi.org/10.5755/j01.ms.22.2.12926

Yersinia species are bacterial pathogens that can cause plague and intestinal diseases after invading into human cells through the Three Secre­tion System (TTSS). The effect of pathogenesis is mediated by Yersinia outer proteins (Yop) and manifested as down-regulation of the cytokine genes expression by inhibiting nuclear factor-κ-gene binding (NF-κB) and mitogen-activated protein kinase (MAPK) pathways. In addition, its pathogenesis can also manipulate the disorder of host innate immune system and cell death such as apoptosis, pyroptosis, and autophagy. Among the Yersinia effector proteins, YopB and YopD assist the injection of other virulence effectors into the host cytoplasm, while YopE, YopH, YopJ, YopO, and YopT target on disrupting host cell signaling pathways in the host cytosols. Many efforts have been applied to reveal that intracellular proteins such as Rho-GTPase, and transmembrane receptors such as Toll-like receptors (TLRs) both play critical roles in Yersinia pathogenesis, establishing a connection between the pathogenic process and the signaling response. This review will mainly focus on how the effector proteins of Yersinia modulate the intrinsic signals in host cells and disturb the innate immunity of hosts through TTSS.

Ice nucleation protein (INP) is frequently used as a surface anchor for protein display in gram-negative bacteria. Here, MalE and TorA signal peptides, and three charged polypeptides, 6×Lys, 6×Glu and 6×Asp, were anchored to the N-terminus of truncated INP (InaK-N) to improve its surface display efficiency for human Arginase1 (ARG1). Our results indicated that the TorA signal peptide increased the surface translocation of non-protein fused InaK-N and human ARG1 fused InaK-N (InaK-N/ARG1) by 80.7% and 122.4%, respectively. Comparably, the MalE signal peptide decreased the display efficiencies of both the non-protein fused InaK-N and InaK-N/ARG1. Our results also suggested that the 6×Lys polypeptide significantly increased the surface display efficiency of K6-InaK-N/ARG1 by almost 2-fold, while also practically abolishing the surface translocation of non-protein fused InaK-N, indicating the interesting roles of charged polypeptides in bacteria surface display systems. Cell surface-immobilized K6-InaK-N/ARG1 presented an arginase activity of 10.7 U/OD600 under the optimized conditions of 40°C, pH 10.0 and 1 mM Mn2+, which could convert more than 95% of L-Arginine (L-Arg) to L-Ornithine (L-Orn) in 16 hours. The engineered InaK-Ns expanded the INP surface display system, which aided in the surface immobilization of human ARG1 in E. coli cells.

We previously reported that oxidized low density lipoprotein (oxLDL) accelerated the calcification in aorta of rats and rat vascular smooth muscle cells (RVSMCs). However, the molecular mechanism underlying the acceleration remains poorly understood. The present study aimed to investigate the role of calpain-1, Ca2+-sensitive intracellular cysteine proteases, in the vascular calcification of rats treated with both high dose of vitamin D2 and high cholesterol diet. The results showed that calpain activity significantly increased in calcified aortic tissue of rats and RVSMCs treated with oxLDL. Specific calpain inhibitor I (CAI, 0.5mg/kg, intraperitoneal) inhibited the vascular calcification in rats with hypercholesterolemia accompanied by the increase in the level of extracellular inorganic pyrophosphate (PPi), the endogenous inhibitor of vascular calcification. In addition, CAI increased the content of adenosine triphosphate (ATP), decreased the activity, mRNA and protein expression of alkaline phosphatase (ALP) and reduced the production of superoxide anion in calcified aortic tissue. CAI also increased the activity of ATP synthase as well as protein expression of ATP5D, δ subunit of ATP synthase. In the in vitro study, suppression of calpain-1 using siRNA assay inhibited the calcium deposition, increased the levels of PPi and ATP, improved the activity of ATP synthase as well as protein expression of ATP5D in RVSMCs treated with oxLDL. Calpain-1 suppression also decreased the activity, mRNA and protein expression of ALP and reduced the mitochondrial ROS (Mito-ROS) production in RVSMCs. However, mito-TEMPO, the mitochondria-targeted ROS scavenger, reduced the calcium deposition, increased the PPi in culture medium, decreased the activity, mRNA and protein expression of ALP in RVSMCs treated with oxLDL. Taken together, the results suggested that calpain-1 activation plays critical role in vascular calcification caused by oxLDL, which might be mediated by PPi metabolism disorder. The results also implied that Mito-ROS might contribute to the PPi metabolism disorder through regulation of the activity and expression of ALP.

C15H12N2O2 monoclinic, P21/n (no. 14), a = 12.343(4) Å, b = 8.172(3) Å, c = 13.375(5) Å, β = 114.466(4)°, V = 1228.0 Å3, Z = 4, Rgt(F) = 0.048, wRref(F2) = 0.127, T = 296 K.