Acquisition and Spread of Antimicrobial Resistance: A tet(X) Case Study

Understanding the mechanisms leading to the rise and dissemination of antimicrobial resistance (AMR) is crucially important for the preservation of power of antimicrobials and controlling infectious diseases. Measures to monitor and detect AMR, however, have been significantly delayed and introduced much later after the beginning of industrial production and consumption of antimicrobials. However, monitoring and detection of AMR is largely focused on bacterial pathogens, thus missing multiple key events which take place before the emergence and spread of AMR among the pathogens. In this regard, careful analysis of AMR development towards recently introduced antimicrobials may serve as a valuable example for the better understanding of mechanisms driving AMR evolution. Here, the example of evolution of tet(X), which confers resistance to the next-generation tetracyclines, is summarised and discussed. Initial mechanisms of resistance to these antimicrobials among pathogens were mostly via chromosomal mutations leading to the overexpression of efflux pumps. High-level resistance was achieved only after the acquisition of flavin-dependent monooxygenase-encoding genes from the environmental microbiota. These genes confer resistance to all tetracyclines, including the next-generation tetracyclines, and thus were termed tet(X). ISCR2 and IS26, as well as a variety of conjugative and mobilizable plasmids of different incompatibility groups, played an essential role in the acquisition of tet(X) genes from natural reservoirs and in further dissemination among bacterial commensals and pathogens. This process, which took place within the last decade, demonstrates how rapidly AMR evolution may progress, taking away some drugs of last resort from our arsenal.     

Influence of substrate temperature on the microstructure of YSZ films and their application as the insulating layer of thin film sensors for harsh temperature environments

Thin film sensors are employed to monitor the health of hot-section components of aeroengine intelligence (for instance, blades), and electrical insulating layers are needed between the metal components and thin film sensors. For this purpose, the electrical insulation characteristics of an yttria-stabilized zirconia (YSZ)/Al₂O₃ multilayer insulating structure were investigated. First, YSZ thin films were deposited by DC reactive sputtering at various substrate temperatures, and the microstructural features were investigated by scanning electron microscopy and X-ray diffraction. The results indicate that the micromorphology of the YSZ thin film gradually became denser with increasing substrate temperature, and no new phases appeared. The compact and uniform topography of the YSZ thin film improved the insulation properties of the multilayer insulating structure and enhanced the adhesion of the thin film sensors. In addition, the electrical insulation properties of the YSZ/Al₂O₃ multilayer insulating structure were evaluated via insulation resistance tests from 25 to 800 °C, in which the YSZ thin film was deposited at 550 °C. The results show that the insulation resistance of the multilayer structure increased by an order of magnitude compared with that of the conventional Al₂O₃ insulating layer, reaching 135 kΩ (5.1 × 10^?6 S/m) at 800 °C. Notably, the insulation resistance was still greater than 75 kΩ after annealing at 800 °C for 5 h. Finally, the shunt effect of the YSZ/Al₂O₃ multilayer insulating structure was estimated using a PdCr thin film strain gauge. The relative resistance error was 0.24%, which demonstrates that the YSZ/Al₂O₃ multilayer insulating structure is suitable for thin film sensors.     

High-temperature electrical properties of polymer-derived ceramic SiBCN thin films fabricated by direct writing

The in situ temperature monitoring of hot components in harsh environments remains a challenging task. In this study, SiBCN thin-film resistance grids with thicknesses of 1.8 μm were fabricated on alumina substrates via direct writing. Owing to their dense microscopic morphology and extremely high graphitisation level, the produced SiBCN films exhibited large high-temperature oxidation resistance and electrical conductivity. The resistance–temperature, stability, and repeatability characteristics of these films were examined in an aerobic environment at temperatures up to 800 °C. The obtained results revealed that the thermistor resistance decreased monotonously with increasing temperature from room temperature to 800 °C. The SiBCN film resistance variations observed during repeated temperature cycling in the regions of 505–620 °C and 610–720 °C were 0.09% and 1.7%, respectively. The high cyclability and stability of the SiBCN thin film thermistor suggested its potential applicability for the in situ temperature monitoring of hot components in harsh environments.     

Growth mechanism and gas-sensing characteristics of organic-additive-free zinc oxide of various shapes

Zinc oxide is widely used in gas sensors, solar cells, and photocatalysts because of its wide bandgap and exciton binding energy of 60 meV in various metal oxides. To use ZnO as a gas sensor, it is necessary to synthesize it with surface defects and a large specific surface area. In this study, hydrothermal synthesis without surfactants was employed to obtain organic-additive-free ZnO. For morphology control, we varied the ratio of the hydroxide ion concentration to the zinc ion concentration. To confirm the growth mechanism of ZnO, we performed X-ray diffraction, scanning electron microscopy, and transmission electron microscopy analyses. Raman spectroscopy and photoluminescence measurements were performed to analyze the surface properties. The Brunauer–Emmett–Teller method and probe stations were used to measure the specific surface area and sensitivity of the gas sensor, respectively. The results confirmed that flower-shaped ZnO is the most suitable gas-sensing material.     

Coverage optimization algorithm based on VF-CS in mobile sensor network

A random placement of large-scale sensor network in the outdoor environment often causes low coverage.An area coverage optimization algorithm of mobile sensor network (MSN) based on virtual force perturbation and Cuckoo search (VF-CS) was proposed.Firstly,the virtual force of the sensor nodes within the Thiessen polygon was analyzed based on the partitioning of Voronoi diagram of the monitoring area.Secondly,the force of polygon vertices and neighbor nodes was taken as the perturbation factor for updating the node’s location of the Cuckoo search (CS).Finally,the VF-CS guided the node to move so as to achieve the optimal coverage.The simulation results demonstrate that the proposed algorithm has higher coverage and shorter average moving distance of nodes than the Voronoi diagram based algorithms in literatures.     

新型树启发式搜索算法的机器人路径规划

面对三维空间移动机器人从起始点到终止点的最短路径问题,提出一种新型的边缘点树启发式搜索(TreeEP)算法,该方法将地图空间进行密度可调的三维离散化处理,根据障碍安全距离筛选出障碍物的可靠边缘点信息,再利用树扩散架构选出最能引导搜索方向的潜力点进行扩散搜索,最终得出最短路径。提出局部调整策略,得到改进的Tree-EP算法。实验结果表明,在带障碍复杂地形最短路径搜索应用中,提出的Tree-EP算法与已有方法相比,能找到更短的移动路径。     

基于双声表面波器件的引信加速度传感器

In view of the shortcomings of silicon micro acceleration sensor based on piezoresistive effect and capacitance principle, such as temperature drift, low resolution and poor anti-interference ability, a fuze acceleration sensor based on dual SAW devices is proposed. The sensor adopts a dual saw device structure, one is coated with a sensitive film for measurement, the other is an uncoat- ed reference channel for compensation of environmental temperature, pressure, humidity and other factors. The experimental results show that the maximum linear error is only 1.6%, the sensitivity is 54.3Hz/g, and the maximum hysteresis error is less than 1%. Compared with piezoresistive accelerometer and capacitive accelerometer, the linear error of the accelerometer is small, the sensitivi- ty is high, and it has strong anti-interference ability.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Passive target detection and tracking from electromagnetic field measurements

This paper presents a novel approach to the localization of moving targets in a complex environment based on the measurement of the perturbations induced by the target presence on an independently‐generated time‐varying electromagnetic field. Field perturbations are measured via a set of sensors deployed over the domain of interest and used to detect and track a possible target by resorting to a particle Bernoulli filter (PBF). To comply with real‐time operation, the PBF works along with an artificial neural network (ANN) model of the environment trained offline via finite elements (FEs). The performance of the proposed algorithm is assessed via simulation experiments.     

基于改进CGANs的入侵检测方法研究

近年来,机器学习算法在入侵检测系统(IDS)中的应用获得越来越多的关注。然而,传统的机器学习算法更多的依赖于已知样本,因此需要尽可能多的数据样本来对模型进行训练。遗憾地是,随着越来越多未知攻击的出现,且用于训练的攻击样本具有不平衡性,传统的机器学习模型会遇到瓶颈。文章提出一种将改进后的条件生成对抗网络(CGANs)与深度神经网络(DNN)相结合的入侵检测模型(CGANs-DNN),通过解决样本不平衡性问题来提高检测模型对未知攻击类型或只有少数攻击样本类型的检测率。深度神经网络(DNN)具有表征数据潜在特征的能力,而经过改进后的条件CGANs,能够通过学习已知攻击样本潜在数据特征分布,来根据指定类型生成新的攻击样本。此外,与生成对抗网络(GANs)和变分自编码器(VAE)等无监督生成模型相比,CGANsDNN经过改进后加入梯度惩罚项,在训练的稳定性上有了很大地提升。通过NSL-KDD数据集对模型进行评估,与传统算法相比CGANs-DNN不仅在整体准确率、召回率和误报率等方面有更好的性能,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。