Droplet and Microchamber‐Based Digital Loop‐Mediated Isothermal Amplification (dLAMP)

Digital loop‐mediated isothermal amplification (dLAMP) refers to compartmentalizing nucleic acids and LAMP reagents into a large number of individual partitions, such as microchambers and droplets. This compartmentalization enables dLAMP to be an excellent platform to quantify the absolute number of the target nucleic acids. Owing to its low requirement for instrumentation complexity, high specificity, and strong tolerance to inhibitors in the nucleic acid samples, dLAMP has been recognized as a simple and accurate technique to quantify pathogenic nucleic acid. Herein, the general process of dLAMP techniques is summarized, the current dLAMP techniques are categorized, and a comprehensive discussion on different types of dLAMP techniques is presented. Also, the challenges of the current dLAMP are illustrated together with the possible strategies to address these challenges. In the end, the future directions of the dLAMP developments, including multitarget detection, multisample detection, and processing nucleic acid extraction are outlined. With recently significant advances in dLAMP, this technology has the potential to see more widespread use beyond the laboratory in the future.     

Magnetic Bead/Gold Nanoparticle Double‐Labeled Primers for Electrochemical Detection of Isothermal Amplified Leishmania DNA

A novel methodology for the isothermal amplification of Leishmania DNA using labeled primers combined with the advantages of magnetic purification/preconcentration and the use of gold nanoparticle (AuNP) tags for the sensitive electrochemical detection of such amplified DNA is developed. Primers labeled with AuNPs and magnetic beads (MBs) are used for the first time for the isothermal amplification reaction, being the amplified product ready for the electrochemical detection. The electrocatalytic activity of the AuNP tags toward the hydrogen evolution reaction allows the rapid quantification of the DNA on screen‐printed carbon electrodes. Amplified products from the blood of dogs with Leishmania (positive samples) are discriminated from those of healthy dogs (blank samples). Quantitative studies demonstrate that the optimized method allows us to detect less than one parasite per microliter of blood (8 × 10^?3 parasites in the isothermal amplification reaction). This pioneering approach is much more sensitive than traditional methods based on real‐time polymerase chain reaction (PCR), and is also more rapid, cheap, and user‐friendly.     

A Monitoring and Diagnostic Approach for Stochastic Textured Surfaces

We develop a supervised-learning-based approach for monitoring and diagnosing texture-related defects in manufactured products characterized by stochastic textured surfaces that satisfy the locality and stationarity properties of Markov random fields. Examples of stochastic textured surface data include images of woven textiles; image or surface metrology data for machined, cast, or formed metal parts; microscopy images of material microstructure samples; etc. To characterize the complex spatial statistical dependencies of in-control samples of the stochastic textured surface, we use rather generic supervised learning methods, which provide an implicit characterization of the joint distribution of the surface texture. We propose two spatial moving statistics, which are computed from residual errors of the fitted supervised learning model, for monitoring and diagnosing local aberrations in the general spatial statistical behavior of newly manufactured stochastic textured surface samples in a statistical process control context. We illustrate the approach using images of textile fabric samples and simulated two-dimensional stochastic processes, for which the algorithm successfully detects local defects of various natures. Supplemental discussions, results, data and computer codes are available online.     

One pot synthesis of monodispersed L-glutathione stabilized gold nanoparticles for the detection of Pb2+ ions

Direct mixture of Au³⁺ with glutathione (GSH), which act as both reduction agents and stabilizers, in aqueous solution gave rise to production of gold nanoparticles (Au NPs) with uniform sizes of around 21 nm. The GSH stabilizer Au NPs in solution show immediate aggregation after addition of 1 mol/L NaCl aqueous solution containing Pb²⁺ ions. The Pb²⁺-induced aggregation in Au NP solution is monitored by both colorimetric response and UV-vis spectroscopy. A rather broad linear range (from 0.1 to 30 μmol/L) and low detection limit (0.1 μmol/L) are explored for Au NP sensors used for detection of Pb²⁺ ions. Furthermore, the response of GSH-stabilized Au NPs toward Pb²⁺ ions is specific compared with other possible interferants (Hg²⁺, Mg²⁺, Zn²⁺, Ni²⁺, Cu²⁺, Co²⁺, Ca²⁺, Mn²⁺, Cd²⁺, and Ba²⁺).     

基于深度神经网络的水声FBMC通信信号检测方法

针对传统水声滤波器组多载波(Filter Bank Multi-Carrier,FBMC)通信接收端需经过信道估计和均衡才可恢复出发送符号,系统复杂度高且信道估计精度不佳等问题.文章将深度神经网络融入到水声多载波通信当中,提出一种基于深度神经网络的水声FBMC信号检测方法.在训练阶段通过大量的数据迭代、调试超参数和优化...     

一种基于改进PSO算法的结构损伤识别方法

根据“结构损伤发生在局部”这一信息特征,在粒子群算法中引入零变异率系数,提出一种改进的粒子群结构损伤识别方法。两层刚架单损伤数值仿真研究结果表明：与普通粒子群算法相比,本文所提出的方法具有收敛速度快、识别结果准确、抗噪能力较强及受种群数影响小的特点,这些优点对于将PSO应用于复杂结构的损伤诊断有着重要意义     

基于纹理特征的合成孔径声纳图像目标检测研究

随着成像声纳技术的发展,声纳图像的目标检测与识别逐渐成为数字图像处理领域的一个重要研究课题。合成孔径声纳图像含有丰富的纹理特征,而灰度共生矩阵具有丰富的特征参数,可以从不同的角度对纹理进行细致刻画。采用灰度共生矩阵可以描述合成孔径声纳图像纹理方面的特征,通过计算灰度共生矩阵在方位向和距离向的能量、相关性、对比度和熵值,并构造特征向量,可以对合成孔径声纳图像中的目标进行准确检测。从实验结果可以看出,基于纹理信息可以准确实现合成孔径声纳图像的目标检测。     

Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

Intrusion detection is a serious and complex problem. Undoubtedly due to a large number of attacks around the world, the concept of intrusion detection has become very important. This research proposes a multilayer bio-inspired feature selection model for intrusion detection using an optimized genetic algorithm. Furthermore, the proposed multilayer model consists of two layers (layers 1 and 2). At layer 1, three algorithms are used for the feature selection. The algorithms used are Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Firefly Optimization Algorithm (FFA). At the end of layer 1, a priority value will be assigned for each feature set. At layer 2 of the proposed model, the Optimized Genetic Algorithm (GA) is used to select one feature set based on the priority value. Modifications are done on standard GA to perform optimization and to fit the proposed model. The Optimized GA is used in the training phase to assign a priority value for each feature set. Also, the priority values are categorized into three categories: high, medium, and low. Besides, the Optimized GA is used in the testing phase to select a feature set based on its priority. The feature set with a high priority will be given a high priority to be selected. At the end of phase 2, an update for feature set priority may occur based on the selected features priority and the calculated F-Measures. The proposed model can learn and modify feature sets priority, which will be reflected in selecting features. For evaluation purposes, two well-known datasets are used in these experiments. The first dataset is UNSW-NB15, the other dataset is the NSL-KDD. Several evaluation criteria are used, such as precision, recall, and F-Measure. The experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.     

Rapid thermal annealed Al-doped ZnO film for a UV detector

A low-resistive Al-doped ZnO (AZO) film was achieved by rapid thermal annealing. A co-sputtering method was used in the initial growth of AZO films and a rapid annealing process was performed on the as-deposited AZO film under N₂ atmosphere for 3 min. An as-deposited AZO film had an optical transmittance of 84.78% at 550 nm and a resistivity of 7.8 × 10^− 3 Ω cm. A rapid annealing process significantly improved the optical transmittance and electrical resistivity of the AZO film to 99.67% and 1 × 10^− 3 Ω cm, respectively. The structural changes of the AZO films were investigated by X-ray diffraction and transmission electron microscopy. The high quality AZO film was used to fabricate a metal-semiconductor-metal (MSM) structure for a UV detector. The MSM device provided a stable current of 25 μA at a bias of 2 V in a dark condition. Under UV illumination, the MSM device was highly responsive to UV light uniformly and repeatedly, and it enhanced the current by 80% at 45 μA. This rapid thermal annealing process may provide a useful method to fabricate quality AZO films for photoelectric applications with a low thermal budget.     

基于FPGA的硅微机械谐振式陀螺仪数据采集系统设计

介绍了一种硅微机械谐振式陀螺仪的结构,给出了调制系数或最大频偏与输入角速度成正比的关系.通过对陀螺仪敏感质量块静电驱动力和双端音叉谐振器输出信号的分析,得出了双端音叉谐振器输出信号的相位差与调制系数之间的关系.提出了一种调频信号最大频偏的检测方法,通过此相位差的计算得出调制系数或最大频差,进而计算出输入角速度的值.设计了一种基于FPGA的数字化解调方案,给出了基于FPGA的数字化最大频差测量电路的测试结果.结果表明测量电路对最大频偏有很好的测量效果.     

Developments in nuclear techniques for hydrogen depth profiling

Hydrogen plays a crucial role in the properties of various materials, devices and in growth of hydrogenated amorphous materials. Therefore, its quantification and determination of concentration at various depths is of immense interest. Among various techniques of hydrogen depth profiling, the nuclear techniques being non-destructive in nature are widely used. Elastic recoil detection analysis (ERDA) with helium ions was first used for hydrogen depth profiling. Later on, the heavy ions were utilised due to certain advantages. It has been shown recently by us that H alongwith other elements in thin film up to Ca can be detected simultaneously with heavy ion ERDA, if the elements being detected are well separated in masses. It also has been shown that even the neighbouring elements such as C, N, O alongwith H can be detected provided a ΔE-E detector telescope is used in ERDA. The time of flight (TOF) with ERDA, nuclear reaction analysis (NRA), neutron TOF are other nuclear techniques such as hydrogen loss during the measurement, which must be taken into account for accurate and reliable results. A scenario of developments in nuclear techniques with suitable examples is given in this brief review.     

Virus meet metal-organic frameworks: A nanoporous solution to a world-sized problem?

Laboratory diagnosis of pathologies caused by virus plays a critical role in outbreak response efforts and establishing safe and expeditious testing strategies. Detection of pathogenic virus using commercial solutions require specific tools and laborious laboratory procedures. This makes the day-to-day on time detection of virus infections the limiting step in any outbreak. The need for new diagnostic tools easily available to poor and rural underdeveloped areas where health infrastructure and trained personnel are scarce is highly desirable. The widely known intrinsic properties of Metal-Organic Frameworks (MOFs) embody them with the potential to overcome some of the challenges inherent to virus detection. MOFs are already components of functional devices capable of perform an uninterrupted detection of molecular targets in real time. In this review, we summarise the few studies concerning the reported MOFs used as sensors for pathogenic virus. We emphasise the structural and physical properties of these materials which can open the possibility for their use in this type of sensors and conclude on how the field can progress to envisage the usage of MOFs by the pharmaceutical industry to develop new sensors for these sub-microscopic infectious agents.