Magnetic Graphitic Nanocapsules for Programmed DNA Fishing and Detection

Graphene nanomaterials are typically used in biosensing applications, and they have been demonstrated as good fluorescence quenchers. While many conventional amplification platforms are available, developing new nanomaterials and establishing simple, enzyme‐free and low‐cost strategies for high sensitivity biosensing is still challenging. Therefore, in this work, a core–shell magnetic graphitic nanocapsule (MGN) material is synthesized and its capabilities for the detection of biomolecules are investigated. MGN combines the unique properties of graphene and magnetic particles into one simple and sensitive biosensing platform, which quenches around 98% of the dye fluorescence within minutes. Based on a programmed multipurpose DNA capturing and releasing strategy, the MGN sensing platform demonstrates an outstanding capacity to fish, enrich, and detect DNA. Target DNA molecules as low as 50 pM could be detected, which is 3‐fold lower than the limit of detection commonly achieved by carbon nanotube and graphene‐based fluorescent biosensors. Moreover, the MGN platform exhibits good sensing specificity against DNA mismatch tests. Overall, therefore, these magnetic graphitic nanocapsules demonstrate a promising tool for molecular disease diagnosis and biomedicine. This simple fishing and enrichment strategy may also be extended to other biological and environmental applications and systems.     

芯片载带缺陷的机器视觉检测方法

目的 针对芯片包装载带在生产过程中经常出现的型腔底部和边缘变形、穿孔等缺陷的检测问题,提出一种机器视觉检测方法。方法 首先离线准备配准模板及标准模板图像,然后根据模板在生产过程中进行在线检测。在检测过程中由传感器触发采集待检测型腔图像,然后通过模板匹配方法配准模板图像和待检测图像,并进行异或运算检测两图像差异从而定位缺陷。结果 实验证明边缘变形检测最大错误率为0.45%,底部变形检测最大错误率为0.50%,穿孔检测最大错误率为0.35%,每帧图像检测平均耗时为0.22 s,满足用户错误率不超过1%和每帧耗时不超过0.5 s的要求。结论 该方法能够实时检测芯片载带边缘变形、穿孔等缺陷,有效地实现载带加工生产过程中的质量监控。     

A Nanostructured Microfluidic Immunoassay Platform for Highly Sensitive Infectious Pathogen Detection

Rapid and simultaneous detection of multiple potential pathogens by portable devices can facilitate early diagnosis of infectious diseases, and allow for rapid and effective implementation of disease prevention and treatment measures. The development of a ZnO nanorod integrated microdevice as a multiplex immunofluorescence platform for highly sensitive and selective detection of avian influenza virus (AIV) is described. The 3D morphology and unique optical property of the ZnO nanorods boost the detection limit of the H5N2 AIV to as low as 3.6 × 10³ EID₅₀ mL^?1 (EID₅₀: 50% embryo infectious dose), which is ≈22 times more sensitive than conventional enzyme‐linked immunosorbent assay. The entire virus capture and detection process could be completed within 1.5 h with excellent selectivity. Moreover, this microfluidic biosensor is capable of detecting multiple viruses simultaneously by spatial encoding of capture antibodies. One prominent feature of the device is that the captured H5N2 AIV can be released by simply dissolving ZnO nanorods under slightly acidic environment for subsequent off‐chip analyses. As a whole, this platform provides a powerful tool for rapid detection of multiple pathogens, which may extent to the other fields for low‐cost and convenient biomarker detection.     

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet. The interconnectivity of networks has brought various complexities in maintaining network availability, consistency, and discretion. Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities. An intrusion detection system controls the flow of network traffic with the help of computer systems. Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic. For this purpose, when the network traffic encounters known or unknown intrusions in the network, a machine-learning framework is needed to identify and/or verify network intrusion. The Intrusion detection scheme empowered with a fused machine learning technique (IDS-FMLT) is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks. The proposed IDS-FMLT system model obtained 95.18% validation accuracy and a 4.82% miss rate in intrusion detection.     

Programmable DNA Nanoindicator‐Based Platform for Large‐Scale Square Root Logic Biocomputing

The prospect of programming molecular computing systems to realize complex autonomous tasks has advanced the design of synthetic biochemical logic circuits. One way to implement digital and analog integrated circuits is to use noncovalent hybridization and strand displacement reactions in cell‐free and enzyme‐free nucleic acid systems. To date, DNA‐based circuits involving tens of logic gates capable of implementing basic and complex logic functions have been demonstrated experimentally. However, most of these circuits are still incapable of realizing complex mathematical operations, such as square root logic operations, which can only be carried out with 4 bit binary numbers. A high‐capacity DNA biocomputing system is demonstrated through the development of a 10 bit square root logic circuit. It can calculate the square root of a 10 bit binary number (within the decimal integer 900) by designing DNA sequences and programming DNA strand displacement reactions. The input signals are optimized through the output feedback to improve performance in more complex logical operations. This study provides a more universal approach for applications in biotechnology and bioengineering.     

Construction of a Fuzzy and Boolean Logic Gates Based on DNA

Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA‐based logic gates. These devices are important modules in molecular computing and biosensing. The ideal logic gate system should provide a wide selection of logical operations, and be integrable in multiple copies into more complex structures. Here we show the successful construction of a small DNA‐based logic gate complex that produces fluorescent outputs corresponding to the operation of the six Boolean logic gates AND, NAND, OR, NOR, XOR, and XNOR. The logic gate complex is shown to work also when implemented in a three‐dimensional DNA origami box structure, where it controlled the position of the lid in a closed or open position. Implementation of multiple microRNA sensitive DNA locks on one DNA origami box structure enabled fuzzy logical operation that allows biosensing of complex molecular signals. Integrating logic gates with DNA origami systems opens a vast avenue to applications in the fields of nanomedicine for diagnostics and therapeutics.     

喷墨数字印刷质量的闭环检测与色彩控制系统设计

以传统印刷质量检测与控制理论为基础,结合数字印刷技术特点,提出了基于CCD机器视觉技术的喷墨数字印刷质量检测与闭环控制系统及方法,采用色度颜色偏差理论与方法,分别从色差、灰平衡、网点扩大、均匀性及色域范围5个方面,对实验和数据进行了对比分析。结果表明,修正后的颜色偏差波动水平快速达到了目标状态,补偿后的印刷质量要明显优于未控制前,验证了系统方法的可行性和有效性,为高精度喷墨数字印刷质量自动在线检测与控制提供了解决方案。     

Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones, which generate synthetic examples randomly in selected line segments. In our work, we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method. In theory, our method covers a wider range. In experiment with real world data, our method performs better than the SMOTE and its meliorations.     

Ultraspecific and Highly Sensitive Nucleic Acid Detection by Integrating a DNA Catalytic Network with a Label‐Free Microcavity

Nucleic acid detection with label‐free biosensors circumvents costly fluorophore functionalization steps associated with conventional assays by utilizing transducers of impressive ultimate detection limits. Despite this technological prowess, molecular recognition at a surface limits the biosensors' sensitivity, specificity, and reusability. It is therefore imperative to integrate novel molecular approaches with existing label‐free transducers to overcome those limitations. Here, we demonstrate this concept by integrating a DNA strand displacement circuit with a micron‐scale whispering gallery mode (WGM) microsphere biosensor. The integrated biosensor exhibits at least 25‐fold improved nucleic acid sensitivity, and sets a new record for label‐free microcavity biosensors by detecting 80 pM (32 fmol) of a 22nt oligomer; this improvement results from the catalytic behavior of the circuit. Furthermore, the integrated sensor exhibits extremely high specificity; single nucleotide variants yield 40‐ to 100‐fold lower signal. Finally, the same physical sensor was demonstrated to alternatingly detect 2 different nucleic acid sequences through 5 cycles of detection, showcasing both its reusability and its versatility.     

基于机器视觉的药品包装生产线自动检测系统

目的提高包装药品效率,保证包装过程的正确率和安全性。方法在充分研究药品包装生产线现状的基础上,将机器视觉应用于药品包装生产线药品的自动检测,采用基于最大熵阈值,设计一种图像分割方法;同时采用自适应高斯引导图像滤波算法,设计一种图像去噪算法。结果通过实验验证,该系统可以实现药品包装生产线的自动检测,并能自动剔除不合格药品,保证生产安全。结论研究的药品包装生产线自动检测系统具有自动化程度高、效率高的优点,具有广阔的市场应用前景。     

基于支持向量机的油封缺陷图像检测方法

提出一种基于支持向量机分类的油封缺陷图像检测方法,把油封外观中的有无缺陷看作两种不同的类别模式,应用支持向量机对两类不同的样本采样学习,然后进行分类判断。采集油封各部位图像并进行预处理,利用算法切割出各个检测区域图像,根据油封主要部位的各类缺陷特点,选取不同特征参数。应用径向基核函数建立支持向量机识别模型,并通过实验实现核函数参数寻优。实验结果表明,该方法具有检测成本低、可靠性高、泛化能力强、容易在线实施等特点,具有实用推广价值。     

BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning

The number of botnet malware attacks on Internet devices has grown at an equivalent rate to the number of Internet devices that are connected to the Internet. Bot detection using machine learning (ML) with flow-based features has been extensively studied in the literature. Existing flow-based detection methods involve significant computational overhead that does not completely capture network communication patterns that might reveal other features of malicious hosts. Recently, Graph-Based Bot Detection methods using ML have gained attention to overcome these limitations, as graphs provide a real representation of network communications. The purpose of this study is to build a botnet malware detection system utilizing centrality measures for graph-based botnet detection and ML. We propose BotSward, a graph-based bot detection system that is based on ML. We apply the efficient centrality measures, which are Closeness Centrality (CC), Degree Centrality (CC), and PageRank (PR), and compare them with others used in the state-of-the-art. The efficiency of the proposed method is verified on the available Czech Technical University 13 dataset (CTU-13). The CTU-13 dataset contains 13 real botnet traffic scenarios that are connected to a command-and-control (C&C) channel and that cause malicious actions such as phishing, distributed denial-of-service (DDoS) attacks, spam attacks, etc. BotSward is robust to zero-day attacks, suitable for large-scale datasets, and is intended to produce better accuracy than state-of-the-art techniques. The proposed BotSward solution achieved 99% accuracy in botnet attack detection with a false positive rate as low as 0.0001%.     

基于 PLC 的裤袜自动包装机实时故障检测与处理

目的针对裤袜自动包装机包装过程中产生的故障问题研发一套故障检测系统。方法通过PLC采集输送带、电机、翻转折叠机构、折边铲、封袋铲、装袋铲等关键部位的传感信号,采用Visual Basic设计的上位机系统运用故障树原理,对所采集的信号进行实时故障诊断及显示。结果该故障检测系统可靠性高,能有效地提高裤袜自动包装机的故障检测与处理能力,从而提高了生产效率。结论该系统用户界面简洁、便于操作、运行稳定可靠、自动化程度高,降低了故障处理时间,对包装机的日常维护具有重要意义。