General Detection Method Research Articles

Ion Identification and Ultralow Concentration Sensing with Liquid Flexoelectricity.

The isolation and concentration of electrical charges at ionic-electronic interfaces are prevalent phenomena that impede effective communication between ionic and electronic systems. Detecting these concentrated charges at the interface is crucial for applications, such as signal transmission and ion detection. Current electrical detection approaches introduce additional ionic-electronic interfaces via metallic electrodes with an external stimulating voltage, which alters the initial ion distributions at the interfaces. In this work, we introduce the flexoelectricity of liquids to examine the electrical charge aggregation at ionic-electronic interfaces under cyclic mechanical loads. The measured electrical responses reflect the coupling phenomena between the flexoelectricity and the electric double layer. This proposed approach demonstrates the capability to quantify ion types and concentrations at interfaces. Furthermore, it can identify ion types in mixed solutions and offers high sensitivity at ultralow concentrations. This work promotes a nonchemical, general mechanical method for charge detection at ionic-electronic interfaces.

Rolling Circle Amplification-Enabled Ultrasensitive Point-of-Care Test Method for Aflatoxin B1 in the Environment and Food

Aflatoxin B1 (AFB1) contamination poses a fatal risk to human beings and urgently needs highly sensitive detection for environmental monitoring and food safety. However, the existing challenges are the unsatisfied sensitivity of the immunoassay methods and the complex matrix effect. Rolling circle amplification (RCA) is a promising method for nucleic acid isothermal amplification due to its high specificity and sensitivity. Herein, we constructed a general RCA-based point-of-care test method (RCA−POCT). With biotinylated antibodies, streptavidin, and biotinylated RCA primers, we realized the signal transduction and preliminary signal amplification. In this way, the fluorescent signal of the immunocomplex on the microwells was greatly enhanced. Under optimal conditions, we recorded sensitive detection limits for aflatoxin B1 (AFB1) of 1.94, 16.3, and 37.7 fg/mL (femtogram per microliter), and wide linear ranges with 5 × 10−6 to 5, 5 × 10−5 to 5, and 5 × 10−5 to 5 ng/mL in the irrigation water, field soil, and peanut samples, respectively. Satisfactory recovery, specificity, repeatability, and reproducibility were observed. The RCA−POCT was validated by comparing it to the HPLC method. This work provides a general RCA-assisted detection method for AFB1 in the environment and food.aflatoxin

Lighting and rapid detection of the Coronavirus S protein using computationally speculated ligand and its application in SARS-CoV-2

Detection of key proteins has enormous medical potential, particularly during the initial phase of an outbreak of infectious diseases. For this purpose, we develop a research paradigm based on computational speculation. With the rapid development of computers, it is possible to analyze the structure of key proteins and screen specific fluorescent ligands through computational tools. This process resembles the production of antibodies but is more effective, and the resultant product is significantly cheaper. A proof of concept was demonstrated by studying the S protein of SARS-CoV-2. The structural data of the S protein enabled automated docking with fluorescent molecules. After two rounds of conditional screening and verification, the most effective fluorescent ligand, Protoporphyrin IX (PPIX), was identified. PPIX illuminates the S protein, recovers fluorescence upon binding. The illuminated complex is enriched by a single microsphere, allowing for the quantitative analysis of the proteins through fluorescence intensity. Finally, less than 1000 copies S protein-bearing pseudovirus can be detected in 20 minutes without amplification. Overall, we established a highly sensitive general method for protein detection based on single microsphere enrichment with the aid of computerised simulation. We hope this integrated approach will provide new insights for rapid screening in epidemic populations.

Rapid discrimination of African swine fever virus nucleic acid and virions using BenzoNuclease

African swine fever (ASF) is an acute and severe infectious disease caused by the African Swine Fever Virus (ASFV). ASFV exhibits significant resistance and stability in the environment, which, coupled with its double-stranded DNA and large genome, predisposes it to contaminate laboratory samples. This characteristic can lead to false-positive results in swine farm settings even days after disinfection, as detectable through polymerase chain reaction (PCR) or real-time fluorescent quantitative PCR (qPCR) assays. To meet the demand for efficient clinical methods capable of discriminating between ASFV nucleic acid and ASFV virions, this study aims to ascertain the efficacy of the nuclease “BenzoNuclease” in distinguishing ASFV nucleic acid (ASFV-DNA) from ASFV virions. BenzoNuclease is a versatile nucleic acid enzyme with the capacity to degrade nearly all forms of DNA and RNA. Initially, this research established a highly sensitive general PCR detection method for ASFV. Subsequently, a positive control was constructed using the M13 bacteriophage to substitute for active ASFV, facilitating the development of an improved qPCR method. It is important to note that common disinfectants have the potential to deactivate BenzoNuclease. However, in an environment simulating actual production applications, residual disinfectants do not interfere with the enzymatic efficacy of BenzoNuclease, thus not affecting the detection capabilities of this method. Positive clinical samples from pig farms, upon testing with the improved method, revealed that three samples were positive, indicating the presence of viral particles, while the remaining samples were negative, indicating the presence of nucleic acids. This provides an additional new option for sample testing in pig farms.

A general method for trace substance detection based on cross-correlation theory

In order to improve the limit of detection (LOD) of trace substances, a general signal analysis and processing method based on cross-correlation theory is proposed, including correlation detection methods and multi-channel signal modified methods. In this paper, both methods were validated on Raman and GC-MS detection, trace substance in insulating oil (dibenzyl disulfide) is introduced as an example, and verification test of multi-substances detection is also conducted. More generally, the cross-correlation theory based method can be applied to all detection based on spectral peak areas. After applying this method, the LOD can be considerably improved. The traditional detection method based on linear fitting between characteristic peak area and trace substance concentration is implemented as a benchmark. The LOD of Raman detection in traditional way is 338.51 ppm, The LOD of GC-MS detection in traditional way is 317.26 ppb. The correlation detection results show that the LOD of Raman spectroscopy and GC-MS are respectively increased by 1.90/2.76 times compared with traditional way. And when the multi-channel signal modified method is introduced, the qualitative LOD of Raman detection reaches 9.8 ppm (number of channel is 100), the qualitative LOD of GC-MS detection reaches 39.1 ppb (number of channel is 10). Comparative study with conventional noise reduction methods have also been carried out, and the results show that correlation detection and multi-channel signal modified method are superior to them.

Unsupervised contaminated user profile identification against shilling attack in recommender system

A recommender system is susceptible to manipulation through the injection of carefully crafted profiles. Some recent profile identification methods only perform well in specific attack scenarios. A general attack detection method is usually complicated or requires label samples. Such methods are prone to overtraining easily, and the process of annotation incurs high expenses. This study proposes an unsupervised divide-and-conquer method aiming to identify attack profiles, utilizing a specifically designed model for each kind of shilling attack. Initially, our method categorizes the profile set into two attack types, namely Standard and Obfuscated Behavior Attacks. Subsequently, profiles are separated into clusters within the extracted feature space based on the identified attack type. The selection of attack profiles is then determined through target item analysis within the suspected cluster. Notably, our method offers the advantage of requiring no prior knowledge or annotation. Furthermore, the precision is heightened as the identification method is designed to a specific attack type, employing a less complicated model. The outstanding performance of our model, validated through experimental results on MovieLens-100K and Netflix under various attack settings, demonstrates superior accuracy and reduced running time compared to current detection methods in identifying Standard and Obfuscated Behavior Attacks.

Deep learning for enhancing cancer identification and diagnosis

One of the promising and significant fields of technology is the use of automated computer techniques, particularly machine learning, to facilitate and enhance medical analysis and diagnosis. In the area of artificial intelligence (AI), deep learning techniques using artificial neural networks (CNNs) so-called because they superficially resemble biological neural networks - are computational network models for discovering large, high-dimensional data sets (such as medical datasets) for complex structures and patterns. In this paper, the focus is on summarizing contemporary applications of various deep learning algorithms in the direction of cancer identification and diagnosis, and on re-implementing supervised learning for cancer detection after a new database based on the lung cancer detection project from the book Deep Learning with PyTorch. The automatic detection of lung malignancies from patient CT scans was re-implemented by deep learning. In this paper, the technique is applied to data provided by the Iraqi Oncology Teaching Hospital. The author demonstrates that the application of the new data will also maintain the accuracy of the identification, and thus promises to develop into a more comprehensive and general cancer detection and diagnosis method in the future as the algorithm and technology are further refined.

A Novel Model-Based Reinforcement Learning for Online Anomaly Detection in Smart Power Grid

Smart grids must detect cyber-attacks early to ensure their safety and reliability. There have been many outlier detection methods presented in the studies, varying from those requiring instance-by-instance decisions t the online diagnosing methods that require the use of accurate models of an attack. This study proposes a novel intelligent online anomaly or attack detection method based on the partially observable Markov decision procedure (POMDP). The proposed model may be categorized as a general detection method according to the reinforcement learning (RL) architecture for POMDP which can help the learning process based on the award concept. The performance of the proposed model is verified using the IEEE test system. Based on numerical results, the suggested RL-based algorithm shows to be very effective in detecting cyber-attacks against the smart grid quickly and accurately.

Plasmon-Exciton Coupling Effect in Nanostructured Arrays for Optical Signal Amplification and SARS-CoV-2 DNA Sensing.

A surface plasmon resonance (SPR)-enhanced optical signal using a nanoslit array and acridine orange (AO) dye system at a flexible poly(dimethylsiloxane) (PDMS) substrate was achieved in this work and demonstrated a simple sensing scheme to directly detect SARS-CoV-2 nucleic acid via DNA hybridization. A simple nanoimprinting pattern transfer technique was introduced to form uniform reproducible nanoslit arrays where the dimensions of the slit array were controlled by the thickness of the gold film. The plasmon-exciton coupling effect on the optical enhancement of different dye molecules, i.e., AO, propidium iodide (PI), or dihydroethidium (DHE) attached to the nanoslit surfaces, was examined thoroughly by measuring the surface reflection and fluorescence imaging. The results indicate that the best overlap of the plasmon resonance wavelength to the excitation spectrum of AO presented the largest optical enhancement (∼57×) compared to the signal at flat gold surfaces. Based on this finding, a sensitive assay for detecting DNA hybridization was generated using the interaction of the selected SARS-CoV-2 ssDNA and dsDNA with AO to trigger the metachromatic behavior of the dye at the nanoarray surfaces. We found strong optical signal amplification on the formation of acridine-ssDNA complexes and a quenched signal upon hybridization to the complementary target DNA (ct-DNA) along with a blue shift in the fluorescence of AO-dsDNAs. A quantitative evaluation of the ct-DNA concentration in a range of 100-0.08 nM using both the reflection and emission imaging signals demonstrated two linear regimes with a lowest detection limit of 0.21 nM. The sensing method showed high sensitivity and distinguished signals from 1-, 2-, and 3-base mismatched DNA targets, as well as high stability and reusability. This approach toward enhancing optical signal for DNA sensing offers promise in a general, rapid, and direct vision detection method for nucleic acid analytes.

Exploring the Trans-Cleavage Activity with Rolling Circle Amplification for Fast Detection of miRNA.

MicroRNAs (miRNAs) are a class of endogenous short noncoding RNA. They regulate gene expression and function, essential to biological processes. It is necessary to develop an efficient detection method to determine these valuable biomarkers for the diagnosis of cancers. In this paper, we proposed a general and rapid method for sensitive and quantitative detection of miRNA by combining CRISPR-Cas12a and rolling circle amplification (RCA) with the precircularized probe. Eventually, the detection of miRNA-21 could be completed in 70 min with a limit of detection of 8.1 pM with high specificity. The reaction time was reduced by almost 4 h from more than 5 h to 70 min, which makes detection more efficient. This design improves the efficiency of CRISPR-Cas and RCA-based sensing strategy and shows great potential in lab-based detection and point-of-care test.

Position Sensorless Control of IPMSM Using Adjustable Frequency Setting Square-Wave Voltage Injection

Position sensorless control of interior permanent magnet synchronous motors (IPMSMs) is of great importance for the sake of the cost reduction and robustness improvement. An improved position sensorless method based on adjustable high-frequency (HF) square-wave voltage injection has been proposed for the IPMSM drive under zero- and low-speed regions. The general detection method of position from positive- and negative-sequence HF current response has been developed by the simple algebraic operations of data at some adjacent sampling instants. In addition, the proposed method not only can retain the advantage of the removal of low-pass filters in current feedback but also can increase the voltage margin of inverter with adjustable frequency setting of HF signal compared with existing square-wave voltage injection methods. Comprehensive experimental results have been given to verify the proposed theoretical analysis.

Change-Point Detection and Regularization in Time Series Cross-Sectional Data Analysis

AbstractResearchers of time series cross-sectional data regularly face the change-point problem, which requires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high-dimensional data and present its application in the context of the fixed-effect model. Our proposed method, hidden Markov Bayesian bridge model, jointly estimates high-dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange’s (1991, American Political Science Review 85, 539–556) study of the relationship between government partisanship and economic growth and Allee and Scalera’s (2012, International Organization 66, 243–276) study of membership effects in international organizations. In both applications, we found that the proposed method successfully identify substantively meaningful temporal heterogeneity in parameters of regression models.

A deep learning-based method for deviation status detection in intelligent conveyor belt system

Belt deviation is one of the most common faults of belt conveyors . Its occurrence not only causes materials to be scattered and affect the environment but also results in abnormal wear of equipment and increased energy consumption, which severely affects the green production and sustainable development of enterprises. Therefore, the rapid and timely detection of the deviation state of conveyor belts is of great significance for ensuring the safe and efficient operation of transportation systems. In view of the disadvantages of the available technology in terms of detection speed, a novel conveyor belt deviation monitoring method based on deep learning is proposed in this paper, which is realized by improving the output results of a general target detection network, YOLOv5, such that the network is enhanced with the ability to detect straight lines instead of bounding box , which effectively solves the problem of rapid feature extraction and deviation judgment of the edges of the conveyor belt of a belt conveyor against a complex background. Experiments show that the proposed method balances detection accuracy and speed, with a detection accuracy of up to 90% and a detection speed of up to 67 frames per second (FPS), and shows good real-time performance. The method greatly simplifies the process of straight-line feature extraction in complex environments, helps realize the intellectualization of conveyors, and achieves unmanned operation and energy savings in coal mines to realize green, energy-saving, and sustainable development while ensuring safe and efficient transportation. • General target detection network-based straight line detection method. • Efficient conveyor belt edge detection under complex scenes. • New detection method of belt deviation to ensure safe and clean transportation.

Generalized durative event detection on social media.

Given the recent availability of large volumes of social media discussions, finding temporal unusual phenomena, which can be called events, from such data is of great interest. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from their usual behavior, for a sustained period. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect durative events in time series in a general sense. In addition, we also provide an incremental version of the algorithm for the purpose of real-time detection. We test our approaches on synthetic data and two real-world tasks. With the synthetic dataset, we compare the performance of retrospective and incremental versions of the algorithm. In the first real-world task, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. In the second real-world task, we use the event captured to help improve the accuracy of stock market movement prediction. We show that our event-based approach has a clear advantage compared to other ways of adding social media information.

Cross-Layer Distributed Attack Detection Model for the IoT

IoT is a huge network that contains many objects communicating with each other. It has a collection of sensitive data which is vulnerable to various threats at different layers. Due to the lack of infrastructure and the distributed control in IoT, there have been many security threats in all network layers. The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility and inefficiently of security solutions. There are many harmful attacks in IoT network such as DoS and DDoS attacks which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-Layer Distributed Attack Detection model (CLDAD) is proposed to enhance security solution for IoT environment. CLDAD presents a general detection method of DDoS in sensing layer, network layer and application layer. CLDAD is based on big data analytics techniques which enable the detection process to be performed in distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely, artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT and gives detection accuracy of 99.8% on average.

Artificial intelligence reinforced upconversion nanoparticle-based lateral flow assay via transfer learning

The combination of upconverting nanoparticles (UCNPs) and immunochromatography has become a widely used and promising new detection technique for point-of-care testing (POCT). However, their low luminescence efficiency, non-specific adsorption, and image noise have always limited their progress toward practical applications. Recently, artificial intelligence (AI) has demonstrated powerful representational learning and generalization capabilities in computer vision. We report for the first time a combination of AI and upconversion nanoparticle-based lateral flow assays (UCNP-LFAs) for the quantitative detection of commercial internet of things (IoT) devices. This universal UCNPs quantitative detection strategy combines high accuracy, sensitivity, and applicability in the field detection environment. By using transfer learning to train AI models in a small self-built database, we not only significantly improved the accuracy and robustness of quantitative detection, but also efficiently solved the actual problems of data scarcity and low computing power of POCT equipment. Then, the trained AI model was deployed in IoT devices, whereby the detection process does not require detailed data preprocessing to achieve real-time inference of quantitative results. We validated the quantitative detection of two detectors using eight transfer learning models on a small dataset. The AI quickly provided ultra-high accuracy prediction results (some models could reach 100% accuracy) even when strong noise was added. Simultaneously, the high flexibility of this strategy promises to be a general quantitative detection method for optical biosensors. We believe that this strategy and device have a scientific significance in revolutionizing the existing POCT technology landscape and providing excellent commercial value in the in vitro diagnostics (IVD) industry.

Self-assembled 1D nanostructures for direct nanoscale detection and biosensing

•Self-assembled 1D nanostructures allow for nanoscale detection and visualization •Enabled direct measurement of nanoscale objects at the single-particle level •Suitable for rapid and ultrasensitive sensing of virus samples, including SARS-CoV-2 Directly accurate detection of nanoscale objects remains a great challenge to many fields, including self-assembly, nanoparticle engineering, nanomaterial characterization, and biological diagnosis. Here, we report a general method for visualizable detection and measurement of many nanoscale objects by using one-dimensional (1D) nanostructures, including nanoparticles, defects, and pathogens. The large-scale 1D nanostructures are robustly prepared through the template-assisted self-assembly of colloidal nanoparticles, which exhibit wide spectral tunability in the visible region. In addition to the scalability, they can significantly amplify the weak optical signals from nanoscale objects, pushing the detection sensitivity down to the single-particle level. Then, we achieve the rapid and ultrasensitive testing of different clinical virus samples by using a simple optical microscope or a portable cell phone. This work demonstrates a label-free, simple, and high-efficiency optical detection platform at the nanoscale, which has a broad range of applications from material synthesis to in vitro diagnosis. Directly accurate detection of nanoscale objects remains a great challenge to many fields, including self-assembly, nanoparticle engineering, nanomaterial characterization, and biological diagnosis. Here, we report a general method for visualizable detection and measurement of many nanoscale objects by using one-dimensional (1D) nanostructures, including nanoparticles, defects, and pathogens. The large-scale 1D nanostructures are robustly prepared through the template-assisted self-assembly of colloidal nanoparticles, which exhibit wide spectral tunability in the visible region. In addition to the scalability, they can significantly amplify the weak optical signals from nanoscale objects, pushing the detection sensitivity down to the single-particle level. Then, we achieve the rapid and ultrasensitive testing of different clinical virus samples by using a simple optical microscope or a portable cell phone. This work demonstrates a label-free, simple, and high-efficiency optical detection platform at the nanoscale, which has a broad range of applications from material synthesis to in vitro diagnosis.

Cryptocurrency Mining Malware Detection Based on Behavior Pattern and Graph Neural Network

Miner malware has been steadily increasing in recent years as the value of cryptocurrency rises, which poses a considerable threat to users’ device security. Miner malware has obvious behavior patterns in order to participate in blockchain computing. However, most miner malware detection methods use raw bytes feature and sequential opcode as detection features. It is difficult for these methods to obtain better detection results due to not modeling robust features. In this paper, a miner malware identification method based on graph classification network is designed by analyzing the features of function call graph and control flow graph of miner malware, called MBGINet. MBGINet can model the behavior graph relationship of miner malware by extracting the connection features of critical nodes in the behavior graph. Finally, MBGINet transforms these node features into the feature vectors of the graph for miner malware identification. In the test experiments, datasets with different volumes are used for simulating real-world scenarios. The experimental results show that the MBGINet method achieves a leading and stable performance compared to the dedicated opcode detection method and obtains an accuracy improvement of 3.08% on the simulated in-the-wild dataset. Meanwhile, MBGINet gains an advantage over the general malware detection method Malconv. These experimental results demonstrate the superiority of the MBGINet method, which has excellent characteristics in adapting to realistic scenarios.

Cryptic Pockets Repository through Pocket Dynamics Tracking and Metadynamics on Essential Dynamics Space: Applications to Mcl-1.

Detection of cryptic pockets (hidden protein pockets) is a hot topic in structure-based drug discovery, especially for drugging the yet undruggable proteome. The experimental detection of cryptic pockets is still considered an expensive endeavor. Thus, computational methods, such as atomistic simulations, are used instead. These simulation methods can provide a perspective on protein dynamics that overpasses the experimental X-ray structures' static and average view. Nonetheless, unbiased molecular dynamics (MD) simulations fall short to detect transient and cryptic pockets requiring the crossing of high-energy barriers. Enhanced sampling methods, such as Metadynamics, provide a solution to overcome the time-scale problem faced by unbiased MD simulations. However, these methods are still limited by the availability of collective variable space to capture the intricate parameters, leading to the opening of cryptic pockets. Unfortunately, the design of such collective variables requires a priori knowledge of the binding site, information that is by definition lacking for cryptic pockets. In this work, we evaluated the use of the Metadynamics biasing scheme on essential coordinates space as a general method for cryptic pocket detection. This approach was applied to an antiapoptotic protein: Mcl-1 as a test model. In addition to providing a broader characterization of Mcl-1's conformational space, we show the effectiveness of this method in drawing the full repository of Mcl-1's known and novel cryptic pockets in an unsupervised manner.

In-situ determination of onset lithium plating for safe Li-ion batteries

Lithium plating in working batteries has attracted wide attention in the exploration of safe energy storage. Establishing an effective and rapid early-warning method is strongly considered but quite challenging since lithium plating behavior is determined by diverse factors. In this contribution, we present a non-destructive electrochemical detection method based on transient state analysis and three-electrode cell configuration. Through dividing the iR drop value by the current density, the as-obtained impedance quantity (Ri) can serve as a descriptor to describe the change of electrochemical reaction impedance on the graphite anode. The onset of lithium plating can be identified from the sharp drop of Ri. Once the dendritic plated lithium occurs, the extra electrochemical reactions at the lithium interfaces leads to growing active area and reduced surface resistance of the anode. We proposed a protocol to operate the batteries under the limited capacity, which renders the cell with 98.2% capacity retention after 1000 cycles without lithium plating. The early-warning method has also been validated in in-situ optical microscopy batteries and practical pouch cells, providing a general but effective method for online lithium plating detection towards safe batteries.