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Deeper understanding of the crosstalk between host cells and Mycobacterium tuberculosis (Mtb) provides crucial guidelines for the rational design of novel intervention strategies against tuberculosis (TB). Mycobacteria possess a unique complex cell wall with arabinogalactan (AG) as a critical component. AG has been identified as a virulence factor of Mtb which is recognized by host galectin-9. Here, we demonstrate that galectin-9 directly inhibited mycobacterial growth through AG-binding property of carbohydrate-recognition domain 2. Furthermore, IgG antibodies with AG specificity were detected in the serum of TB patients. Based on the interaction between galectin-9 and AG, we developed a monoclonal antibody (mAb) screening assay and identified AG-specific mAbs which profoundly inhibit Mtb growth. Mechanistically, proteomic profiling and morphological characterizations revealed that AG-specific mAbs regulate AG biosynthesis, thereby inducing cell wall swelling. Thus, direct AG-binding by galectin-9 or antibodies contributes to protection against TB. Our findings pave the way for the rational design of novel immunotherapeutic strategies for TB control.

Abstract Antimicrobial peptides (AMPs), ancient scavengers of bacteria, are very poorly induced in macrophages infected by Mycobacterium tuberculosis (M. tuberculosis), but the underlying mechanism remains unknown. Here, we report that L-alanine interacts with PRSS1 and unfreezes the inhibitory effect of PRSS1 on the activation of NF-κB pathway to induce the expression of AMPs, but mycobacterial alanine dehydrogenase (Ald) Rv2780 hydrolyzes L-alanine and reduces the level of L-alanine in macrophages, thereby suppressing the expression of AMPs to facilitate survival of mycobacteria. Mechanistically, PRSS1 associates with TAK1 and disruptes the formation of TAK1/TAB1 complex to inhibit TAK1-mediated activation of NF-κB pathway, but interaction of L-alanine with PRSS1, disables PRSS1-mediated impairment on TAK1/TAB1 complex formation, thereby triggering the activation of NF-κB pathway to induce expression of AMPs. Moreover, deletion of antimicrobial peptide gene β-defensin 4 (Defb4) impairs the virulence by Rv2780 during infection in mice. Both L-alanine and the Rv2780 inhibitor, GWP-042, exhibits excellent inhibitory activity against M. tuberculosis infection in vivo. Our findings identify a previously unrecognized mechanism that M. tuberculosis uses its own alanine dehydrogenase to suppress host immunity, and provide insights relevant to the development of effective immunomodulators that target M. tuberculosis.

Abstract Internal N6-methyladenosine (m6A) modifications are among the most abundant modifications of messenger RNA, playing a critical role in diverse biological and pathological processes. However, the functional role and regulatory mechanism of m6A modifications in the immune response to Mycobacterium tuberculosis infection remains unknown. Here, we report that methyltransferase-like 14 (METTL14)-dependent m6A methylation of NAPDH oxidase 2 (Nox2) mRNA was crucial for the host immune defense against M. tuberculosis infection and that M. tuberculosis-secreted antigen EsxB (Rv3874) inhibited METTL14-dependent m6A methylation of Nox2 mRNA. Mechanistically, EsxB interacted with p38 MAP kinase and disrupted the association of TAB1 with p38, thus inhibiting the TAB1-mediated autophosphorylation of p38. Interaction of EsxB with p38 also impeded the binding of p38 with METTL14, thereby inhibiting the p38-mediated phosphorylation of METTL14 at Thr72. Inhibition of p38 by EsxB restrained liquid–liquid phase separation (LLPS) of METTL14 and its subsequent interaction with METTL3, preventing the m6A modification of Nox2 mRNA and its association with the m6A-binding protein IGF2BP1 to destabilize Nox2 mRNA, reduce ROS levels, and increase intracellular survival of M. tuberculosis. Moreover, deletion or mutation of the phosphorylation site on METTL14 impaired the inhibition of ROS level by EsxB and increased bacterial burden or histological damage in the lungs during infection in mice. These findings identify a previously unknown mechanism that M. tuberculosis employs to suppress host immunity, providing insights that may empower the development of effective immunomodulators that target M. tuberculosis.

BackgroundAnemia leads to a lower cure rate and poor prognosis in tuberculosis patients. Effective predictors for the prognosis of tuberculosis with anemia (A-TB) are urgently needed. Monocyte has been proven to be a prognostic biomarker of many lung diseases. Whether monocyte that the predominant innate immune cell as early defense against tuberculosis can predict A-TB is not known.MethodsData for A-TB patients with initial treatment in Shanghai Pulmonary Hospital were retrospectively collected and analyzed. Logistics regression analysis was used to study the correlation between peripheral blood cells and treatment outcomes. The receiver operating characteristic (ROC) curve was used to determine the cut-off value. We estimated a 12-month prognosis using Kaplan–Meier techniques. The Cox proportional hazards model was used for the univariate and multivariate analyses to analyze the predictors of poor prognosis of A-TB.ResultsOf 181 patients analyzed, 94 were cured and 87 non-cured. Logistic regression analysis identified monocyte as an independent immune-related risk factor for the prognosis of A-TB (OR: 7.881, 95% CI: 1.675–37.075, P = 0.009). The ROC curve analysis proved that the most discriminative cut-off value of monocyte was 0.535 × 10^9/L. K–M analysis demonstrated that the cumulative cure rates of A-TB were significantly higher in A-TB with monocyte < 0.535 × 10^9/L (69.62%) than that in those with monocyte ≥ 0.535 × 10^9/L (38.24%) (Log-rank, χ2 = 16.530, P < 0.0001). On univariate and multivariable analysis, monocyte was an independent predictor of poor prognosis in A-TB. Similarly, monocyte was also an independent predictor of poor pulmonary cavity closure in A-TB (HR: 3.614, 95% CI: 1.335–9.787, P = 0.011).ConclusionIn A-TB patients, elevated monocyte was associated with poor prognosis and poor cavity pulmonary closure. Monocyte may provide a simple and inexpensive prognostic biomarker in A-TB.

BackgroundTuberculosis (TB) is a serious public health problem to human health, but the pathogenesis of TB remains elusive.MethodsTo identify novel candidate genes associated with TB susceptibility, we performed a population-based case control study to genotype 41SNPs spanning 21 genes in 435 pulmonary TB patients and 375 health donors from China.ResultsWe found Notch4 gene rs206018 and rs422951 polymorphisms were associated with susceptibility to pulmonary tuberculosis. The association was validated in another independent cohort including 790 TB patients and 1,190 healthy controls. Moreover, we identified that the rs206018 C allele was associated with higher level of Notch4 in PBMCs from pulmonary TB patients. Furthermore, Notch4 expression increased in TB patients and higher Notch4 expression correlated with the severer pulmonary TB. Finally, we explored the origin and signaling pathways involved in the regulation of Notch4 expression in response to Mycobacterium tuberculosis (Mtb) infection. We determine that Mtb induced Notch4 and its ligand Jagged1expression in macrophages, and Notch4 through TLR2/P38 signaling pathway and Jagged1 through TLR2/ERK signaling pathway.ConclusionOur work further strengthens that Notch4 underlay an increased risk of TB in humans and is involved in the occurrence and development of TB, which could serve as a novel target for the host-targeted therapy of TB.

Abstract Pathogenic mycobacteria induce the formation of hypoxic granulomas during latent tuberculosis (TB) infection, in which the immune system contains, but fails to eliminate the mycobacteria. Fatty acid metabolism-related genes are relatively overrepresented in the mycobacterial genome and mycobacteria favor host-derived fatty acids as nutrient sources. However, whether and how mycobacteria modulate host fatty acid metabolism to drive granuloma progression remains unknown. Here, we report that mycobacteria under hypoxia markedly secrete the protein Rv0859/MMAR_4677 (Fatty-acid degradation A, FadA), which is also enriched in tuberculous granulomas. FadA acts as an acetyltransferase that converts host acetyl-CoA to acetoacetyl-CoA. The reduced acetyl-CoA level suppresses H3K9Ac-mediated expression of the host proinflammatory cytokine Il6, thus promoting granuloma progression. Moreover, supplementation of acetate increases the level of acetyl-CoA and inhibits the formation of granulomas. Our findings suggest an unexpected mechanism of a hypoxia-induced mycobacterial protein suppressing host immunity via modulation of host fatty acid metabolism and raise the possibility of a novel therapeutic strategy for TB infection.

ABSTRACT Sputum smear microscopy for tuberculosis diagnosis has stood the test of time. However, due to its low sensitivity, the positive detection rate for tuberculosis in clinical specimens is not high. To improve the sensitivity of microscopic observation in Mycobacterium tuberculosis (MTB) detection, we developed the MTB-specific aptamer MA1. To further improve the binding reactivity of MA1 with MTB, we constructed a new derivative aptamer with a pocket-stem-loop-structure, MA1-39, and identified it to have high binding reactivity with the MTB reference strain. We developed an aptamer fluorescence microscopy test for MTB based on MA1-39 and evaluated its feasibility for diagnosing pulmonary tuberculosis. Among 56 tested strains, MA1-39 was proven to effectively discriminate MTB from the control strains, including 12 non-tuberculosis mycobacterial (NTM) reference strains, 6 NTM isolates, and 7 other bacteria. Next, this approach was applied to 169 clinical sputum samples from suspected tuberculosis patients and non-tuberculosis controls. Molecular tests together with both clinical and bacteriological identification were used as a protocol to evaluate the efficacy of aptamer detection. Compared with the traditional acid-fast staining light microscope, the aptamer fluorescence microscope showed a higher detection rate for MTB in clinical specimens (48.8% versus 32.6%), and the specificities of the two techniques had almost no significant difference (90.4% versus 94%). In addition, aptamer fluorescence microscopy showed the same positive predictive value (PPV) as staining (84% versus 84.9%), but a higher negative predictive value (NPV; 63% versus 57.3%). In conclusion, the newly established aptamer fluorescence microscopy approach is likely to be a feasible method for microbiological diagnosis of tuberculosis. IMPORTANCE We established an aptamer fluorescence microscopy approach for rapid detection of MTB in clinical sputum samples. The use of aptamer probes was proven to significantly increase the sensitivity of sputum smear microscopy. In resource-limited countries, microscopy is currently a fast, simple, and very common test method in many laboratories, and it will remain the primary means of microbiological diagnosis of tuberculosis in the foreseeable future. Improving detection techniques can further enhance the clinical application value of this ancient diagnostic method. Since aptamer fluorescence microscopy can provide rapid and sensitive results, it may be a feasible and useful method in resource-limited settings.

Policy gradient methods are effective means to solve the problems of mobile multimedia data transmission in Content Centric Networks. Current policy gradient algorithms impose high computational cost in processing high-dimensional data. Meanwhile, the issue of privacy disclosure has not been taken into account. However, privacy protection is important in data training. Therefore, we propose a randomized block policy gradient algorithm with differential privacy. In order to reduce computational complexity when processing high-dimensional data, we randomly select a block coordinate to update the gradients at each round. To solve the privacy protection problem, we add a differential privacy protection mechanism to the algorithm, and we prove that it preserves the ε -privacy level. We conduct extensive simulations in four environments, which are CartPole, Walker, HalfCheetah, and Hopper. Compared with the methods such as important-sampling momentum-based policy gradient, Hessian-Aided momentum-based policy gradient, REINFORCE, the experimental results of our algorithm show a faster convergence rate than others in the same environment.

Tuberculosis (TB) remains a highly contagious public health threat. Precise and prompt diagnosis and monitoring of treatment responses are urgently needed for clinics. To pursue novel and satisfied host blood-derived biomarkers, we streamlined a bioinformatic pipeline by integrating differentially expressed genes, a gene co-expression network, and short time-series analysis to mine the published transcriptomes derived from whole blood of TB patients in the GEO database, followed by validating the diagnostic performance of biomarkers in both independent datasets and blood samples of Chinese patients using quantitative real-time PCR (qRT-PCR). We found that four genes, namely UBE2L6 (Ubiquitin/ISG15-conjugating enzyme E2 L6), BATF2 (Basic leucine zipper transcriptional factor ATF-like), SERPING1 (Plasma protease C1 inhibitor), and VAMP5 (Vesicle-associated membrane protein 5), had high diagnostic value for active TB. The transcription levels of these four gene combinations can reach up to 88% sensitivity and 78% specificity (average) for the diagnosis of active TB; the highest sensitivity can achieve 100% by parallel of BATF2 and VAMP5, and the highest specificity can reach 89.5% through a combination of SERPIG1, UBE2L6, and VAMP5, which were significantly higher than 75.3% sensitivity and 69.1% specificity by T-SPOT.TB in the same patients. Quite unexpectedly, the gene set can assess the efficacy of anti-TB response and differentiate active TB from Latent TB infection. The data demonstrated these four biomarkers might have great potency and advantage over IGRAs in the diagnosis of TB.

In recent years, multi-classifier learning is of significant interest in industrial and economic fields. Moreover, neural network is a popular approach in multi-classifier learning. However, the accuracies of neural networks are often limited by their loss functions. For this reason, we design a novel cross entropy loss function, named MPCE, which based on the maximum probability in predictive results. In this paper, we first analyze the difference of gradients between MPCE and the cross entropy loss function. Then, we propose the gradient update algorithm based on MPCE. In the experimental part of this paper, we utilize four groups of experiments to verify the performance of the proposed algorithm on six public datasets. The first group of experimental results show that the proposed algorithm converge faster than the algorithms based on other loss functions. Moreover, the results of the second group show that the proposed algorithm obtains the highest training and test accuracy on the six datasets, and the proposed algorithm perform better than others when class number changing on the sensor dataset. Furthermore, we use the model of convolutional neural network to implement the compared methods on the mnist dataset in the fourth group of experiments. The results show that the proposed algorithm has the highest accuracy among all executed methods.

Abstract Background The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb), especially those that are multidrug resistant poses a serious threat to global tuberculosis control. However, the mechanism underlying the occurrence of drug resistance against more than one drug is poorly understood. Given that the Beijing/W strains are associated with outbreaks and multidrug resistance, they may harbor a genetic advantage and provide useful insight into the disease. One marker found in all Beijing/W Mtb strains is a deletion of RD105 region that results in a gene fusion, Rv0071/74, with a variable number (3–9 m) of VDP (V: Val, D: Asp; P: Pro) repeats (coded by gtggacccg repeat sequences) at the N-terminal. Here, we report that this variable number of VDP repeats in Rv0071/74 regulates the development of multidrug resistance. Results We collected and analyzed 1255 Beijing/W clinical strains. The results showed that the number of VDP repeats in Rv0071/74 was related to the development of multidrug resistance, and the deletion of Rv0071/74-9 m from Beijing/W clinical strain restored drug susceptibility. Rv0071/74-9 m also increased resistance to multiple drugs when transferred to different mycobacterial strains. Cell-free assays indicate that the domain carrying 4–9 VDP repeats (4–9 m) showed a variable binding affinity with peptidoglycan and Rv0071/74 cleaves peptidoglycan. Furthermore, Rv0071/74-9 m increased cell wall thickness and reduced the intracellular concentration of antibiotics. Conclusions These findings not only identify Rv0071/74 with VDP repeats as a newly identified multidrug resistance gene but also provide a new model for the development of multiple drug resistance.

β-Catenin is a key molecule of canonical Wnt/β-catenin pathway. Its roles and expression profiles in T cells of tuberculosis (TB) remain unclear. The aim of this study was to explore the role of β-catenin in CD4+ T cells and its expression characteristics in patients with pulmonary tuberculosis (PTB). In this study, CD4+ T cell-specific β-catenin conditional knockout mice (β-CAT-cKO mice) were aerosol infected with Mycobacteria tuberculosis (Mtb) H37RV with wild-type mice as controls. Four weeks after infection, the mRNA expression of IFN-γ, TNF-α, and TCF-7 in the lungs of mice was measured. CD4, CD8, β-catenin, IFN-γ, and TNF-α in mononuclear cells from the lungs and spleens were measured by flow cytometry, and the pathological changes of lungs were also observed. Patients with PTB were enrolled, with blood samples collected and PBMCs isolated. The expressions of β-catenin, IFN-γ, TNF-α, and PD-1 in CD4+ and CD8+ T cells were measured by flow cytometry. Results showed a decreased frequency of and reduced IFN-γ/TNF-α mRNA expression and secretion by CD4+ T cells in the lungs of infected β-CAT-cKO mice compared with infected wild-type controls, and only slightly more inflammatory changes were observed in the lungs. β-catenin expressions in CD4+ and CD8+ T cells were significantly decreased in blood cells of patients with severe PTB compared with those in mild PTB. The stimulation of peripheral blood mononuclear cells (PBMCs) with lithium chloride (LiCl), a stimulant of β-catenin, resulted in the increase in CD4+ T cell frequency, as well as their secretion of IFN-γ and TNF-α. β-Catenin demonstrated a moderately positive correlation with PD-1 in CD4+ T cells. β-Catenin along with PD-1 and IFN-γ in CD4+ T cells had a high correlation with those in CD8+ T cells. In conclusion, β-catenin may be involved in the regulation of Th1 response and CD4+ T cell frequency in TB.

The distributed online algorithms which are based on the Frank-Wolfe method can effectively deal with constrained optimisation problems. However, the calculation of the full (sub)gradient vector in those algorithms leads to a huge computational cost at each iteration. To reduce the computational cost of the algorithms mentioned above, the authors present a distributed online randomised block-coordinate Frank-Wolfe algorithm over networks. Each agent in the networks only needs to calculate a subset of the coordinates of its (sub)gradient vector in this algorithm. Furthermore, they make a detailed theoretical analysis of the regret bound of this algorithm. When all local objective functions satisfy the conditions of strongly convex functions, the authors’ algorithm attains the regret bound of [inline-formula], where T is the total number of iterations. Furthermore, the theorem results are verified via simulation experiments, which show that the algorithm is effective and efficient.

Genetic risk loci for tuberculosis (TB) have so far been identified in African and Russian populations. Here, the authors perform a three-stage GWAS for TB in Han Chinese populations and find two risk loci near ESRRB and TGM6 and further demonstrate that tgm6 protects mice from Mtb infection.

How Mycobacterium tuberculosis (Mtb) escapes autophagy-mediated clearance is poorly understood. Here, Liu et al. show that Mtb-induced MicroRNA-27a targets the ER-associated calcium transporter CACNA2D3, leading to suppression of antimicrobial autophagy and to enhanced intracellular survival of Mtb.

In this study, the authors consider the physical‐layer security for transmission in underlay cognitive radio networks, where consisting of a primary source–destination pair, a secondary transmitter–receiver pair and a common eavesdropper (E). To transmit data in a secure and reliable manner, they propose a transmission protocol for an underlay cognitive radio network. In particular, the secondary user is allowed to operate on the primary spectrum bandwidth when the interference threshold is satisfied. To protect the transmission confidentiality of primary system, PS node transmits a signal, which is linearly combined with the jamming signal (as artificial noise) and expectation signal, to interfere E. Furthermore, they propose a superimposed coding to combine with the artificial noise and expectation signal at PS node. We also analyse the performance of the proposed transmission protocol. We obtain the outage probability of secondary and primary systems. Simultaneously, they derive the closed‐form expression of intercept probability for primary system. In addition, they verify the performance of their proposed transmission protocol by numerical experiment. The numerical results show that their proposed transmission protocol not only has the better outage performance for secondary system but also has the better intercept performance for primary system.

With the development of cloud computing in the mobile communications industry, the credibility between users and the cloud has become an important obstacle to the development of mobile cloud services. Therefore, the user's abnormal behaviour detection is particularly important. The authors propose a selective clustering ensemble algorithm based on fractal dimension. In their proposed algorithm, they firstly use the sliding window to dynamically obtain data to improve the accuracy of user behaviour acquisition. Secondly, they use the initial clustering stage and incremental clustering stage to produce clustered members. Thirdly, they also use the Duun_index to select the clustered members, then the selection of high‐quality clustered members with the voting algorithm to get the final result. Finally, they use the average difference of change to determine whether the current behaviour is abnormal based on the clustering results. The experimental results show that the proposed scheme has a better performance than the traditional clustering algorithm in clustering accuracy. Moreover, their algorithm can improve the detection rate and accuracy rate, respectively.

Mobile cloud computing uses cloud computing to deliver applications to mobile devices. These applications can be delivered among different devices with different operating systems, computing tasks, and data storage capabilities, adopting D2D communication mode. However, because the application delivery process covers three entities, namely user-environmental-service, trusted problems are ubiquitous. Therefore, before cloud provides substantive services, how to identify the trusted degree of user identity and its behaviors for D2D communications is the core problem. First, from the perspective of user trustability, this paper proposed an analysis method to user abnormal behaviors for D2D communications in mobile cloud environment. In this method, user behavior is normalized to a “user sequence operation”identity fragment with the same length, offset, and amplitude. The hierarchical matching method and the blacklist mechanism are used to determine whether the user behavior for D2D communications is beyond the scope of trusted tolerance. Second, considering that the user sequence behavior step is a complex graph structure with continuous dynamic growth, this paper proposed a pattern growth method based on maximum and right-most path extension for autonomous optimization. At last, the experimental results showed that the classification accuracy under the KDD CUP99 data set and real network environment was 94.8% and 90.2%, respectively, which was 5.3% and 6.9% higher than the traditional methods. In addition, it can be seen from the experimental results that this scheme could significantly improve the recognition speed.

Wireless sensor networks are designed to perceive, gather, and process external environmental information and send it to the observer. However, the transmission of mass information is a challenge to the sensor nodes. To address this challenge, information fusion technologies are proposed to reduce mass redundant data. However, these techniques rarely consider the historical information, and thereby often encounter the difficulty of low prediction accuracy. In order to solve this difficulty, we propose a novel information fusion approach for the cluster heads. The proposed approach is based on time-recurrent neural network, called sparse long short-term memory, which is derived from the long short-term memory network. The sparse long short-term memory uses sparse matrix to reduce the dimension for a high-dimensional coefficient matrix. Therefore, the computational cost of the fusion algorithm is reduced in wireless sensor networks. The simulation results show that the sparse long short-term memory algorithm increases the survival number of sensor nodes in wireless sensor networks. Furthermore, the prediction accuracy of the sparse long short-term memory algorithm is almost the same as the other comparison algorithms.

We consider a distributed constrained optimization problem over graphs, where cost function of each agent is private. Moreover, we assume that the graphs are time-varying and directed. In order to address such problem, a fully decentralized stochastic subgradient projection algorithm is proposed over time-varying directed graphs. However, since the graphs are directed, the weight matrix may not be a doubly stochastic matrix. Therefore, we overcome this difficulty by using weight-balancing technique. By choosing appropriate step-sizes, we show that iterations of all agents asymptotically converge to some optimal solutions. Further, by our analysis, convergence rate of our proposed algorithm is O(ln Γ/Γ) under local strong convexity, where Γ is the number of iterations. In addition, under local convexity, we prove that our proposed algorithm can converge with rate O(ln Γ/Γ). In addition, we verify the theoretical results through simulations.

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

Mobile micro-learning has received extensive attention in the research of smart cities because it is a novel fusion service mode of the mobile Internet, cloud computing, and micro-learning. However, due to the explosively increased applications of the mobile micro-learning and the limited resources of mobile terminals, an effective energy saving approach for mobile micro-learning is urgently required. For this end, this article proposes an efficient task joint execution strategy to reduce energy consumption. First, a new matching method of time series is proposed to obtain the latest requested record, which can provide guidance for the selection of a future service mode. Second, a mapping-level service mode and a cloud-level service mode are proposed to achieve seamless switching. Finally, the genetic algorithm is used to find the optimal executive strategy. In addition, the experimental results show that the proposed method can effectively realize the target of energy saving by using real data set.

With the continuous development of cloud computing, cloud security has become one of the most important issues in cloud computing. For example, data stored in the cloud platform may be attacked, and its security is difficult to be guaranteed. Therefore, we must attach weight to the issue of how to protect the data stored in the cloud. To protect data, data monitoring is a necessary process. Based on autonomic computing, we develop a cloud data monitoring system on the cloud platform, monitoring whether the data is abnormal in the cycle and analyzing the security of the data according to the monitored results. In this paper, the feasibility of the scheme can be verified through simulation. The results show that the proposed method can adapt to the dynamic change of cloud platform load, and it can also accurately evaluate the degree of abnormal data. Meanwhile, by adjusting monitoring frequency automatically, it improves the accuracy and timeliness of monitoring. Furthermore, it can reduce the monitoring cost of the system in normal operation process.

We consider a stochastic continuous submodular huge-scale optimization problem, which arises naturally in many applications such as machine learning. Due to high-dimensional data, the computation of the whole gradient vector can become prohibitively expensive. To reduce the complexity and memory requirements, we propose a stochastic block-coordinate gradient projection algorithm for maximizing continuous submodular functions, which chooses a random subset of gradient vector and updates the estimates along the positive gradient direction. We prove that the estimates of all nodes generated by the algorithm converge to some stationary points with probability 1. Moreover, we show that the proposed algorithm achieves the tight (pmin/2F⁎-ϵ) approximation guarantee after O(1/ϵ2) iterations for DR-submodular functions by choosing appropriate step sizes. Furthermore, we also show that the algorithm achieves the tight (γ2/1+γ2pminF⁎-ϵ) approximation guarantee after O(1/ϵ2) iterations for weakly DR-submodular functions with parameter γ by choosing diminishing step sizes.

Interleukin-37 (IL-37), a novel member of the IL-1 family, plays fundamental immunosuppressive roles by broadly reducing both innate inflammation and acquired immunity, but whether it is involved in the pathogenesis of tuberculosis (TB) has not been clearly elucidated. In this study, single nucleotide polymorphism (SNP) analysis demonstrated an association of the genetic variant rs3811047 of IL-37 with TB susceptibility. In line with previous report, a significant elevated IL-37 abundance in the sera and increased expression of IL-37 protein in the peripheral blood mononuclear cells (PBMC) were observed in TB patients in comparison to healthy controls. Moreover, release of IL-37 were detected in either macrophages infected with Mycobacterium tuberculosis (Mtb) or the lung of BCG-infected mice, concurrent with reduced production of proinflammatory cytokines including IL-6 and TNF-α. Furthermore, in contrast to wild-type mice, BCG-infected IL-37-Tg mice manifested with reduced mycobacterial burden and tissue damage in the lung, accompanied by higher frequency of Th1 cell and less frequencies of regulatory T cells and Th17 cells in the spleen. Taken together, our findings demonstrated that IL-37 conferred resistance to Mtb infection possibly involving suppressing detrimental inflammation and modulating T cell responses. These findings implicated that IL-37 may be employed as a new molecular target for the therapy and diagnosis of TB.

Routing in wireless sensor networks (WSNs) is an extremely challenging issue due to the features of WSNs. Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, we establish a novel selecting next hop model (SNH). Based on this model, we present a novel Physarum-based routing scheme (P-bRS) for WSNs to balance routing efficiency and energy equilibrium. In P-bRS, a sensor node can choose the proper next hop by using SNH which comprehensively considers the distance, energy residue, and location of the next hop. The simulation results show how P-bRS can achieve the effective trade-off between routing efficiency and energy equilibrium compared to two famous algorithms.

The trust levels of cloud services should be evaluated to ensure their reliability. The effectiveness of these evaluations has major effects on user satisfaction, which is increasingly important. However, it is difficult to provide objective evaluations in open and dynamic environments because of the possibilities of malicious evaluations, individual preferences, and intentional praise. In this study, we propose a novel unfair rating filtering method for a reputation revision system. This method uses prior knowledge as the basis of similarity when calculating the average rating, which facilitates the recognition and filtering of unfair ratings. In addition, the overall performance is increased by a market mechanism that allows users and service providers to adjust their choice of services and service configuration in a timely manner. The experimental results showed that this method filtered unfair ratings in an effective manner, which greatly improved the precision of the reputation revision system.

There is a tradeoff between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum , this paper presents a novel Physarum -inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node can choose the proper next hop by using a proposed Physarum -inspired selecting next hop model (P-iSNH), which comprehensively considers the distance, energy residue, and location of the next hop. As a result, the P-iRP can get a rather low algorithm complexity of O ( n ) , which greatly reduces the processing delay and saves the energy of sensors. Moreover, by theoretical analysis, the P-iSNH always has an equilibrium solution for multiple next hop candidates, which is vital factor to the stability of routing protocol. Finally, simulation results show that P-iRP can perform better in many scenarios and achieve the effective tradeoff between routing efficiency and energy equilibrium compared to other famous algorithms.

BACKGROUND: The 10-kDa culture filtrate protein (CFP10) and 6-kDa early-secreted target antigen (ESAT-6) play important roles in mycobacterial virulence and pathogenesis through a 1:1 complex formation (CFP10/ESAT-6 protein, CE protein), which have been used in discriminating TB patients from BCG-vaccinated individuals. The B-cell epitopes of CFP10 and ESAT-6 separately have been analyzed before, however, the epitopes of the CE protein are unclear and the precise epitope in the positions 40 to 62 of ESAT-6 is still unknown. METHODS: In the present study, we searched for the B-cell epitopes of CE protein by using phage-display library biopanning with the anti-CE polyclonal antibodies. The epitopes were identified by sequence alignment, binding affinity and specificity detection, generation of polyclonal mouse sera and detection of TB patient sera. RESULTS: One linear B-cell epitope (KWDAT) consistent with the 162(nd)-166(th) sequence of CE and the 57(th)-61(st) sequence of ESAT-6 protein was selected and identified. Significantly higher titers of E5 peptide-binding antibodies were found in the sera of TB patients compared with those of healthy individuals. CONCLUSION: There was a B-cell epitope for CE and ESAT-6 protein in the position 40 to 62 of ESAT-6. E5 peptide may be useful in the serodiagnosis of tuberculosis, which need to be further confirmed by more sera samples.

BACKGROUND: Accurate and early diagnosis of tuberculosis (TB) is of major importance in the control of TB. One of the most important technical advances in diagnosis of tuberculosis is the development of nucleic acid amplification (NAA) tests. However, the choice of the target sequence remains controversial in NAA tests. Recently, interesting alternatives have been found in hypothetical protein coding sequences from mycobacterial genome. METHODOLOGY/PRINCIPAL FINDINGS: To obtain rational biomarker for TB diagnosis, the conservation of three hypothetical genes was firstly evaluated in 714 mycobacterial strains. The results showed that SCAR1 (Sequenced Characterized Amplified Region) based on Rv0264c coding gene showed the highest conservation (99.8%) and SCAR2 based on Rv1508c gene showed the secondary high conservation (99.7%) in M. tuberculosis (MTB) strains. SCAR3 based on Rv2135c gene (3.2%) and IS6110 (8%) showed relatively high deletion rate in MTB strains. Secondly, three SCAR markers were evaluated in 307 clinical sputum from patients in whom TB was suspected or patients with diseases other than TB. The amplification of IS6110 and 16SrRNA sequences together with both clinical and bacteriological identification was as a protocol to evaluate the efficacy of SCAR markers. The sensitivities and specificities, positive predictive value (PPV) and negative predictive value (NPV) of all NAA tests were higher than those of bacteriological detection. In four NAA tests, IS6110 and SCAR3 showed the highest PPV (100%) and low NPV (70% and 68.8%, respectively), and SCAR1 and SCAR2 showed the relatively high PPV and NPV (97% and 82.6%, 95.6% and 88.8%, respectively). CONCLUSIONS/SIGNIFICANCE: Our result indicated that SCAR1 and SCAR2 with a high degree of sequence conservation represent efficient and promising alternatives as NAA test targets in identification of MTB. Moreover, the targets developed from this study may provide more alternative targets for the development of a multisite system to effectively detect MTB in samples.

Pleural tuberculosis (TB), together with lymphatic TB, constitutes more than half of all extrapulmonary cases. Pleural effusions (PEs) in TB are representative of lymphocytic PEs which are dominated by T cells. However, the mechanism underlying T lymphocytes homing and accumulation in PEs is still incompletely understood. Here we performed a comparative analysis of cytokine abundance in PEs from TB patients and non-TB patients by protein array analysis and observed that MCP-2/CCL8 is highly expressed in the TB-PEs as compared to peripheral blood. Meanwhile, we observed that CCR5, the primary receptor used by MCP-2/CCL8, is mostly expressed on pleural CD4(+) T lymphocytes. Furthermore, we found that infection with either Mycobacterium bovis Bacillus Calmette-Guérin (BCG) or Mycobacterium tuberculosis H37Rv induced production of MCP-2/CCL8 at both transcriptional and protein level in Raw264.7 and THP-1 macrophage cells, mouse peritoneal macrophages as well as human PBMC monocyte-derived macrophages (MDMs). The induction of MCP-2/CCL8 by mycobacteria is dependent on the activation of TLR2/PI3K/Akt and p38 signaling pathway. We conclude that accumulation of MCP-2/CCL8 in TB-PEs may function as a biomarker for TB diagnosis.

BACKGROUND: Mycobacterial culture and identification provide a definitive diagnosis of TB. Culture on Löwenstein-Jensen (L-J) medium is invariably delayed because of the slow growth of M. tuberculosis on L-J slants. Automated liquid culture systems are expensive. A low-cost culturing medium capable of rapidly indicating the presence of mycobacteria is needed. The aim of this study was to develop and evaluate a novel biphasic culture medium for the recovery of mycobacteria from clinical sputum specimens from suspected pulmonary tuberculosis patients. METHODS AND FINDINGS: The biphasic medium consisted of 7 ml units of L-J slant medium, 3 ml units of liquid culture medium, growth indicator and a mixture of antimicrobial agents. The decontamination sediments of sputum specimens were incubated in the biphasic culture medium at 37°C. Mycobacterial growth was determined based on the appearance of red granule sediments and the examination using acid-fast bacilli (AFB). The clinical sputum specimens were cultured in the biphasic medium, on L-J slants and in the Bactec MGIT 960 culture system. Among smear-positive specimens, the mycobacteria recovery rate of the biphasic medium was higher than that of the L-J slants (P0.05). Among smear-negative specimens, the mycobacterial recovery rate of the biphasic medium was higher than that of L-J slants (P

OBJECTIVE: To evaluate the performance of phage amplified biologically assay (PhaB) for detecting tuberculosis (TB) in sputum in the pulmonary tuberculosis (PTB) patients. METHODS: Shanghai Tuberculosis Key Laboratory of Shanghai Pulmonary Hospital participated in the project in collaboration with the laboratories of six hospitals and a total of 1660 eligible participants (1351 PTB patients and 309 non-TB patients) were included in the study. The sputum samples from the participants were detected by smear microscopy, PhaB, and Löwenstein-Jensen (L-J) culture method, respectively. RESULTS: The overall sensitivity of PhaB were higher than that of L-J culture and smear microscopy (p