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Summary The selective functionalization of methane under ambient conditions remains a formidable challenge for chemists. While most studies have focused on transition metal complexes, much less attention has been devoted to molecular complexes based on non-metal elements, despite them being more sustainable and less environmentally impactful. Here, we report that an N-heterocyclic carbene-stabilized borenium complex can activate methane under relatively mild conditions. The resulting methylborenium complex can readily transfer the methyl group to catecholborane (HBcat), which can lead to a synthetic cycle for the conversion of methane to MeBcat. Both experimental and theoretical mechanistic studies suggest that the C–H bonds of methane are activated via a σ-bond metathesis pathway. Such direct aliphatic C–H borylation can be extended to other alkanes, such as ethane and octane. The formed alkylborenium complexes can react with terminal alkynes via 1,2-alkylboration to install both the alkyl and boryl functionalities onto organic scaffolds.

Subsurface damage (SSD) introduced from abrasive machining significantly degrades the reliability and lifetime of glass ceramics. In this work, nanoindentation tests were performed on the cross section near the lapped surface to determine the nanomechanical properties of subsurface layer of a lapped BK7. Remarkable differences were found between SSD layer and its bulk counterpart in terms of load-displacement characteristics and nanomechanical response. The nanomechanical parameters (hardness and elastic modulus) of subsurface layer follow an exponential descending trend with the shortening of distance from lapped surface, which is consistent with the increase of crack density. The ratio of hardness to elastic modulus decreases exponentially with increasing subsurface depth until it reaches bulk material, which suggests that SSD layer has lower plasticity. Furthermore, the crack distribution becomes a critical parameter for characterizing the hardness gradient in SSD layer.

Anomaly detection defines as a problem of finding those data samples, which do not follow the patterns of the majority of data points. Among the variety of methods and algorithms proposed to deal with this problem, boundary based methods include One-class support vector machine (OC-SVM) is considered as an effective and outstanding one. Nevertheless, extremely sensitivity to the presence of outliers and noises in the training set is considered as an important drawback of this group of classifiers. In this paper, we address this problem by developing a robust and sparse methodology for anomaly detection by introducing Ramp loss function to the original One-class SVM, called “Ramp-OCSVM”. The main objective of this research is to taking the advantages of non-convexity properties of the Ramp loss function to make robust and sparse semi-supervised algorithm. Furthermore, the Concave–Convex Procedure (CCCP) is utilized to solve the obtained model that is a non-differentiable non-convex optimization problem. We do comprehensive experiments and parameters sensitivity analysis on two artificial data sets and some chosen data sets from UCI repository, to show the superiority of our model in terms of detection power and sparsity. Moreover, some evaluations are done with NSL-KDD and UNSW-NB15 data sets as well-known and recently published intrusion detection data sets, respectively. The obtained results reveal the outperforming of our model in terms of robustness to outliers and superiority in the detection of anomalies.

A robust and sparse multi-class approach for Multi-Class classification is proposed.The proposed method is based on Ramp loss K-Support Vector Classification-Regression.The CCCP procedure is used to solve a non-differentiable non-convex optimization problem.ADMM is adopted to make our model well-adapted for the large-scale setting.The results of Ramp-KSVCR show superior generalization power and low computational cost. Network intrusion detection problem is an ongoing challenging research area because of a huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks, constantly changing the nature of new attacks and the attackers methods. Since the traditional network protection methods fail to adequately protect the computer networks, the need for some sophisticated methodologies has been felt. In this paper, we develop a precise, sparse and robust methodology for multi-class intrusion detection problem based on the Ramp Loss K-Support Vector Classification-Regression, named Ramp-KSVCR. The main objectives of this research are to address the following issues; 1) Highly imbalanced and skewed attacks distribution; hence, we utilized the K-SVCR model as a core of our model; 2) Sensitivity of SVM and its extensions to the presence of noises and outliers in the training sets, to cope with this problem, Ramp loss function is implemented to our model; 3) and since the proposed Ramp-KSVCR model is a non-differentiable non-convex optimization problem, we took ConcaveConvex Procedure (CCCP) to solve this model. Furthermore, we introduced Alternating Direction Method of Multipliers (ADMM) procedure to make our model well-adapted to be applicable in the large-scale setting and to reduce the training time. The performance of the proposed method has been evaluated by some artificial data and also by conducting some experiments with the NSL-KDD data set and UNSW-NB15 as a recently published intrusion detection data set. Experimental results not only demonstrate the superiority of the proposed method over the traditional approaches tested against it in terms of generalization power and sparsity but also saving a considerable amount of computational time.

Effective immobilization of enzymes on an electrode surface is of great importance for biosensor development, but it still remains challenging because enzymes tend to denaturation and/or form close-packed structures. In this work, a free-standing TiO2 hollow nanofibers (HNF-TiO2) was successfully prepared by a simple and scalable electrospun nanofiber film template-assisted sol-gel method, and was further explored for glucose oxidase (GOD) immobilization and biosensing. This porous and nanotubular HNF-TiO2 provides a well-defined hierarchical nanostructure for GOD loading, and the fine TiO2 nanocrystals facilitate direct electron transfer from GOD to the electrode, also the strong interaction between GOD and HNF-TiO2 greatly enhances the stability of the biosensor. The as-prepared glucose biosensors show good sensing performances both in O2-free and O2-containing conditions with good sensitivity, satisfactory selectivity, long-term stability and sound reliability. The novel textile formation, porous and hierarchically mesostructured nature of HNF-TiO2 with excellent analytical performances make it a superior platform for the construction of high-performance glucose biosensors.

Large-scale linear classification is widely used in many areas. Although SVM-based models for ordinal regression problem are proven to be powerful techniques, the performance with nonlinear kernels are often suffering from time consuming. Recently, linear SVC not only is shown to obtain competitive performance in most of the cases, but also it is considerably fast during the process of training and testing. However, few studies focused on linear SVM-based ordinal regression models. In this paper, we propose a new approach, called linear Nonparallel Support Vector Ordinal Regression (NPSVOR), which can deal with large-scale problems. An efficient algorithm based on Alternating Direction Method of Multipliers (ADMM) is designed to solve the proposed model. Our experiments are performed on large document data sets to demonstrate the effectiveness of the proposed method.

To evaluate the mechanical properties of structures in service, a portable micro-indentation device was developed and built. The basic design principle of the indentation device, i.e. measurement of the indentation depth and the indentation force with sufficient accuracy, was discussed. To avoid using load cell to decrease the machine compliance, a new method that using the current through voice coil motor to calculate the indentation force was proposed. After analyzing the equivalent mechanical model of the developed device, an eddy current displacement sensor was installed on the device to improve the measurement accuracy of indentation depth. Indentation tests were performed on polymethylmethacrylate (PMMA) and unplasticized polyvinyl chloride (UPVC) with the developed device and the commercial indentation device ZHU2.5 to demonstrate the validity and accuracy of the developed micro-indentation device.

Many organizations recognize the necessities of utilizing sophisticated tools and systems to protect their computer networks and reduce the risk of compromising their information. Although many machine-learning-based data classification algorithm has been proposed in network intrusion detection problem, each of them has its own strengths and weaknesses. In this paper, we propose an effective intrusion detection framework by using a new adaptive, robust, precise optimization method, namely, time-varying chaos particle swarm optimization (TVCPSO) to simultaneously do parameter setting and feature selection for multiple criteria linear programming (MCLP) and support vector machine (SVM). In the proposed methods, a weighted objective function is provided, which takes into account trade-off between the maximizing the detection rate and minimizing the false alarm rate, along with considering the number of features. Furthermore, to make the particle swarm optimization algorithm faster in searching the optimum and avoid the search being trapped in local optimum, chaotic concept is adopted in PSO and time varying inertia weight and time varying acceleration coefficient is introduced. The performance of proposed methods has been evaluated by conducting experiments with the NSL-KDD dataset, which is derived and modified from well-known KDD cup 99 data sets. The empirical results show that the proposed method performs better in terms of having a high detection rate and a low false alarm rate when compared with the obtained results using all features. Time-varying inertia weight and acceleration coefficients is introduced to CPSO.Feature selection and parameter setting applied simultaneously to MCLP and SVM.A weighted objective function is proposed to evaluate the proposed IDS's framework.Penalized MCLP is introduced to deal with unbalanced datasets.Proposed IDS's framework obtained low false alarm rate and high detection rate.

In this paper, a simple and highly selective electrochemical method for the simultaneous determination of catechol (CC) and hydroquinone (HQ) has been developed with a three-dimensional (3D) N-doped carbon nanotube (NCNT) film electrode. The 3D NCNT film was prepared by the combination of electrospinning and chemical vapor deposition procedure; dense and uniform NCNTs were firmly bonded onto the electrospun carbon nanofiber matrix (NCNT@CNFs). By directly dropping the flexible film onto the electrode surface without additional oxidant treatment, a dihydroxybenzene biosensor can be easily constructed. Differential pulse voltammetry (DPV) results showed that the isomers can be detected selectively at NCNT@CNF modified electrode with peak-to-peak separation about 115 mV. Under the optimized condition, the sensing platform showed wide linear responses from 0.08 to 350 μM and 0.1–425 μM with detection limits of 20 nM and 50 nM (S/N = 3) for CC and HQ, respectively. The proposed method was successfully applied to the simultaneous determination of CC and HQ in real samples with reliable recovery. The N-doping combining with abundant defective sites and favorable 3D network structure facilitate the electron transfer, which resulted in excellent electrocatalytic performance. The attractive electrochemical performances and facile preparation method made this novel electrode promising for the development of effective dihydroxybenzene sensor.

Support vector machine is a well-known and computationally powerful machine learning technique for pattern classification and regression problems, which has been successfully applied to solve many practical problems in a wide variety of fields. Nonparallel Support Vector Machine (NPSVM) which is an extension of Twin-SVMs, is proved to be theoretically and practically more flexible and superior than TWSVMs and also it overcomes several drawbacks of the existing typical SVMs in order to be applicable in large-scale data sets. However, one of the difficulties in successful implementation of NPSVM is its different parameters, which should be well adjusted during the training process. In fact, the generalization power, robustness and sparsity of NPSVM are extremely depended on well setting of its parameters. In this paper, we propose a hybrid approach for parameter determination of the NPSVM by Particle Swarm Optimization techniques. Furthermore, in order to increase the sparsity of NPSVM and to reduce the training time, we take into account the number of support vectors (SVs) along with classification accuracy as a weighted objective function. Our experiments on several public datasets show that the proposed method can achieve better classification accuracy compare to that of TWSVM and NPSVM with less computational time.

To investigate the effect of laser beam shaping on the determination of manganese (Mn) and chromium (Cr) elements in steel samples with different matrices using laser-induced breakdown spectroscopy (LIBS), a commercial beam shaper was used to convert the Gaussian laser into a top-hat laser. Owing to the uniform beam energy distribution and the less unwanted heating, the crater produced by the top-hat laser is much flatter and smoother than that produced by the Gaussian laser. The leave-one-out cross-validation (LOO-CV) method was used to evaluate the predictive capability of the calibration models. After laser beam shaping, the determination coefficient (R2) values of the calibration curves were slightly improved, and the root mean squared error of cross-validation (RMSECV), relative error (RE), relative standard deviation (RSD), and the limits of detection (LOD) values were all about 2 times reduction. The results indicate that the laser beam shaping method is an effective approach to reduce the matrix effect and improve the repeatability and accuracy of LIBS.

Herpes simplex virus type 1 H129 strain has been widely used as a useful anterograde neuronal circuit tracing tool. However, whether H129 is a rigorous anterograde tracer and undergoes anterograde-only spreading are questions of significant interest. In the present study, we evaluated the retrograde labeling efficiency of H129 using a TK and ICP34.5 dual deleted H129 recombinant (named as H306) which was replication-deficient in non-dividing postmitotic neurons. The novel tracer was tested in vitro and in vivo for evaluating its invasion properties and tracing capacities. The results demonstrated that H306 could efficiently label the neurons following intracerebral injection. Notably, H306 could also efficiently infect upstream innervating neurons through axon terminal uptake and displayed obvious retrograde labeling phenotype, regardless of 3 days or 10 days of tracing. The data implied that replication-competent, trans-multisynaptic H129 tracing results might be a mixed neural networks from two types of starter cells, because the retrogradely infected neurons would also replicate H129 and spread virus anterogradely through their axon collaterals (ectopic starter sites), as the local infected neurons in the injection site (true starter site). Therefore, the interpretation of the anterogradely tracing neural networks by current H129 tools at longer post-inoculation intervals need to be cautious, and effective modification strategies are needed to avoid or block the axon terminal invasion process of H129, which is important for rigorous anterograde H129 tracer.

Ca 9 Co 12 O 28 thermoelectric ceramics were fabricated by both sintering and hot-pressing methods. The hot pressed ceramic has a layered structure, and exhibits {0 l 0} preferred orientation and a high orientation degree of 0.84 calculated from the XRD patterns. Thermoelectric properties of the sintered and hot pressed samples were measured from 100 °C to 600 °C. For the hot pressed sample, the electrical conductivity in the direction perpendicular to the pressing direction is much higher than that in the direction parallel to the pressing direction. Thermal conductivity of the hot pressed sample is also anisotropic, and the lattice thermal conductivity is the main thermal transference. In the direction perpendicular to the pressing direction, the hot pressed sample shows a high ZT value of 0.18 at 600 °C.

Human cytomegalovirus virions contain three major glycoprotein complexes (gC I, II, III), all of which are required for CMV infectivity. These complexes also represent major antigenic targets for anti-viral immune responses. The gC II complex consists of two glycoproteins, gM and gN. In the current study, DNA vaccines expressing the murine cytomegalovirus (MCMV) homologs of the gM and gN proteins were evaluated for protection against lethal MCMV infection in a mouse model. Humoral and cellular immune responses, spleen viral titers, and mice survival and body-weight changes were examined. The results showed that immunization with gM or gN DNA vaccine alone was not able to offer good protection, whereas co-immunization with both gM and gN induced an effective neutralizing antibody response and cellular immune response, and provided mice with complete protection against a lethal MCMV challenge. This study provides the first in vivo evidence that the gC II (gM-gN) complex may be able to serve as a protective subunit antigen for future HCMV vaccine development.

Myocardial dysfunction is a common complication during sepsis and significantly contributes to the mortality of patients with septic shock. However, none of the available therapeutic strategies proven to be effective in patients with severe sepsis are designed specifically to target myocardial dysfunction. The purpose of the present study is to investigate the effect of rhynchophylline (Rhy) on LPS-induced myocardial dysfunction in mice. We found that pretreatment with Rhy significantly improved cardiac systolic dysfunction, increased stroke volume and cardiac output in mice challenged with LPS. LPS induced cardiac inhibitor-κBα (I-κBα) phosphorylation, tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) mRNA expression, and in turn increased cardiac TNF-α and IL-1β protein production, all of which were attenuated by pretreatment with Rhy. Immunohistochemistry revealed that TNF-α was found in infiltrated macrophages (F4/80(+)) and myocardium, and Rhy reduced TNF-α immunostaining in cardiac infiltrated macrophages in LPS-challenged mice. Furthermore, Rhy inhibited LPS-induced I-κBα phosphorylation and TNF-α production in cultured mouse peritoneal macrophages, but not in neonatal mouse cardiomyocytes. Pretreatment with Rhy significantly decreased the mortality of LPS-challenged mice. These results indicate that Rhy reduces cardiac dysfunction and improves survival via suppression of macrophage I-κBα phosphorylation in LPS-challenged mice, and suggest that Rhy may be a potential agent for the treatment of septic cardiac dysfunction.

Many members of the NOD-like receptor (NLR) family play important roles in pathogen recognition and inflammation. However, human PYNOD, an NLR-like protein consisting of a pyrin domain and a nucleotidebinding and oligomerization domain (NOD), has been reported to inhibit inflammatory signals. Using bioinformatics, we found a completely preserved canonical p53 binding site in the PYNOD core promoter (-228 to -237 bp) both in humans and in chimpanzees. In this study, we investigated the characterization and biologic function of this binding site in vitro. The results show that either deletion of the p53 binding elements within the PYNOD promoter or treatment with p53 inhibitor (PFT-α) could significantly reduce PYNOD promoter activity and PYNOD expression as detected by the enhanced green fluorescent protein (EGFP) reporter system, reverse transcription-polymerase chain reaction, and Western blot respectively. Furthermore, the chromatin immunoprecipitation (ChIP) method confirmed that p53 could bind to the PYNOD promoter. Our findings suggest that the p53 binding site plays a positive role in regulating PYNOD gene expression, which may maintain an efficient balance between defense and self-inflicted injury in respond to pathogen invasion.

Lipopolysaccharide (LPS) plays an important role in Gram-negative bacteria-induced sepsis and multiple organ dysfunction syndrome, which are still the leading cause of high mortality in intensive care units. Although paeoniflorin (Pae) has reportedly exhibited anti-inflammatory effect and protection against immunological liver injury in mice, it is not known whether Pae improve survival in endotoxemic mice. The purpose of this study was to determine the effect of Pae on the mortality, multiple organ dysfunction and cytokine production in lipopolysaccharide (LPS)-treated mice. We found that pretreatment with Pae decreased mortality, reduced lung and kidney injury, decreased serum creatinine level and improve systolic function of heart in mice challenged with LPS. Further experiments showed that Pae inhibited LPS-stimulated tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β) release and promoted LPS-induced interleukin-10 (IL-10) production. Our results indicate that Pae protects mice against lethal LPS challenge, at least in part, through inhibiting TNF-α and IL-1β production and accelerating IL-10 expression.