Powerful Method For Detection Research Articles

Highly Sensitive One-Pot Isothermal Assay Combining Rolling Circle Amplification and CRISPR/Cas12a for Aflatoxin B1 Detection.

Occurrences of mycotoxins in cereals are widespread throughout the world. However, the lack of efficient and ultrasensitive tests has largely impeded the identification of these substances in actual samples. Herein, a novel one-pot isothermal assay that integrates rolling-circle amplification (RCA) and CRISPR/Cas12a to detect aflatoxin B1 (AFB1) is reported. Upon addition of AFB1 to the magnetic bead functionalized with a duplex of the AFB1 aptamer and its complementary DNA (cDNA), the specific recognition of AFB1 by the aptamer causes the release of cDNA to activate the RCA reaction. Subsequently, the RCA amplicon initiates both trans-cleavage and cis-cleavage activities of the endonuclease Cas12a. The synergistic coupling of RCA and CRISPR/Cas12a enables exponential amplification of cDNA, which further promotes CRISPR/Cas12a to nonspecifically cleave the single-stranded DNA reporters with enhanced detection signals. Remarkably, the CRISPR/Cas12a-assisted one-pot isothermal assay can not only achieve ultrasensitive quantitative detection through fluorescence detection, but also achieve visual detection through a lateral flow strip, which improves accessibility to mycotoxin detection in resource-limited regions. The limit of detection was 0.016 and 0.408 ng/mL, respectively. The proposed assay successfully applies in real samples with satisfactory recoveries from 90 to 114%. This study presents a powerful and versatile method for reliable and ultrasensitive detection of mycotoxins in various applications.

A portable multifunctional biosensor based on a newly screened endogenous reference gene for pork adulteration detection

The adulteration of animal-derived food is a widespread problem with meat adulteration emerging as a global concern. To address the need for on-site rapid detection and visual analysis, a portable multifunction biosensor (PMB) was developed. This device integrates Recombinase Polymerase Amplification (RPA), CRISPR/Cas12a assay, and a custom-designed 3D-printed multifunction device, specifically for the visual and rapid detection of pork in food. Initially, a screening model was constructed using Perl bioinformatics based on chromosomal gene information, and the LOC110262058 gene was selected as a reliable endogenous reference gene for pigs. Subsequently, a one-tube RPA-CRISPR/Cas12a system was established to simplify the procedures, converting pork-specific signals into fluorescence while preventing cross-contamination. In addition, an independently designed 3D-printed multifunction device was developed to support the RPA-CRISPR/Cas12a system for one-tube detection, incorporating functions such as heating, centrifugation, and imaging. The reaction conditions of the PMB were optimized to reduce the reaction time to 20–30 min with a sensitivity of 0.01 g/100 g. The analysis of actual samples indicated that the PMB has high selectivity and practical effectiveness for pork detection. The proposed biosensor offers several detection advantages, including output, ease of use, stability, and independence from complex equipment, making it a powerful method for rapid pork detection in food.

IDS-DEC: A novel intrusion detection for CAN bus traffic based on deep embedded clustering

As the automotive industry advances towards greater automation, the proliferation of electronic control units (ECUs) has led to a substantial increase in the connectivity of in-vehicle networks with the external environment. However, the widely used Controller Area Network (CAN), which serves as the standard for in-vehicle networks, lacks robust security features, such as authentication or encrypted information transmission. This poses a significant challenge to the security of these networks. Despite the availability of powerful intrusion detection methods based on machine learning and deep learning, there are notable limitations in terms of stability and accuracy in the absence of a supervised learning process with labeled data. To address this issue, this paper introduces a novel in-vehicle intrusion detection system, termed IDS-DEC. This system combines a spatiotemporal self-coder employing LSTM and CNN (LCAE) with an entropy-based deep embedding clustering. Specifically, our approach involves encoding in-vehicle network traffic into windowed messages using a stream builder, designed to adapt to high-frequency traffic. These messages are then fed into the LCAE to extract a low-dimensional nonlinear spatiotemporal mapping from the initially high-dimensional data. The resulting low-dimensional mapping is subjected to a dual constraint in conjunction with our entropy-based pure deep embedding clustering module. This creates a bidirectional learning objective, addressing the optimization problem and facilitating an end-to-end training pattern for our model to adapt to diverse attack environments. The effectiveness of IDS-DEC is validated using both the benchmark Car Hacking dataset and the Car Hacking-Attack & Defense Challenge dataset. Experimental results demonstrate the model's high detection accuracy across various attacks, stabilizing at approximately 99% accuracy with a 0.5% false alarm rate. The F1 score also stabilizes at around 99%. In comparison with unsupervised methods based on deep stream clustering, LSTM-based self-encoder, and classification-based methods, IDS-DEC exhibits significant improvements across all performance metrics.

A highly specific colorimetric fluorescent probe for rapid detection of hypobromous acid and its application in the environment

The highly reactive hypobromous acid (HOBr), which is generated after chlorination process of tap water, acts as a precursor of toxic brominated disinfection by-products (Br-DBPs) and further reacts with organic matter. In addition, HOBr produced from the oxidation of Br− during the degradation of pollutants by peroxymonosulfate (PMS, HSO5−) can be considered as the cause of the expedited degradation of pollutants. Therefore, it is particularly important to detect HOBr level in the water environment. Resazurin was selected as a fluorescent probe for selective recognition of HOBr in the water environment. The probe exhibited excellent spectral performance and showed high sensitivity to HOBr (LOD = 515 nM). This method has a relatively ideal recovery rate for HOBr detection in environmental water samples. Furthermore, the HOBr production during the chlorination disinfection process was simulated and the HOBr generated from this process was detected by the probe. Importantly, the process of HOBr recognition by the probe is accompanied by the change of color. Based on this, the relationship between the change of color B/G value and HOBr concentration was successfully constructed. The probe was loaded on the filter paper to make a test strip, which was utilized to the detection of HOBr. Collectively, this work provided a promising and powerful method for HOBr detection in the environment.

Integrated CNN and Federated Learning for COVID-19 Detection on Chest X-Ray Images.

Currently, Coronavirus Disease 2019 (COVID-19) is still endangering world health and safety and deep learning (DL) is expected to be the most powerful method for efficient detection of COVID-19. However, patients' privacy concerns prohibit data sharing between medical institutions, leading to unexpected performance of deep neural network (DNN) models. Fortunately, federated learning (FL), as a novel paradigm, allows participating clients to collaboratively train models without exposing source data outside original location. Nevertheless, the current FL-based COVID-19 detection methods prefer optimizing secondary objectives including delay, energy consumption and privacy, while few works focus on improving the model accuracy and stability. In this paper, we propose a federated learning framework with dynamic focus for COVID-19 detection on CXR images, named FedFocus. Specifically, to improve the training efficiency and accuracy, the training loss of each model is taken as the basis for parameter aggregation weights. As training layer deepens, a constantly updated dynamic factor is designed to stabilize the aggregation process. In addition, to highly restore the real dataset, the training sets in our experiments are divided based on the population and the infection of three real cities. Extensive experiments conducted on the real-world CXR images dataset demonstrate that FedFocus outperforms the baselines in model training efficiency, accuracy and stability.

Community Detection Method Based on GNN and Improved K-means

As the scale of networks continues to expand, their structures become increasingly complex, and the diversity of node information within these networks intensifies. Traditional community detection methods are no longer capable of meeting the growing demands. Thus, the design of a powerful community detection method with low complexity and cost is an urgent issue to be addressed. This paper proposes a community detection method based on Graph Neural Networks (GNN) and an improved K-means algorithm. Initially, the original adjacency matrix is reconstructed based on the importance of nodes, resulting in a new node similarity matrix. Subsequently, a graph autoencoder is employed to extract structural information from the network graph. The decoder part utilizes the form of vector inner product to restore the similarity matrix using the structural information from the hidden layer. The features extracted by the graph autoencoder are then utilized by the improved K-means algorithm to ultimately achieve community division. Finally, this paper summarizes the shortcomings of community detection and provides a prospect for future research directions.

High sensitive setup of gold nanorods substrates for explosives detection using surface enhanced Raman spectroscopy

Surface enhanced Raman spectroscopy (SERS) is a sensitive analytical technique that has been widely utilised for detection of trace levels of organic species. The detection of trace levels of poor vapour pressure molecules such as explosives is a critical challenging due to the low concentration of adsorbed molecules on the surface sites of the SERS substrate. In this work, gold nanorods were fabricated with analyte samples and provided highly sensitivity of SERS detection along with low limit-of-detection (LOD) for explosives down to pM of 2,4- dinitrotoluene (DNT) and nM of both 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) with highly specificity. The sensitivity of the detection was developed by optimising the adsorption of the molecules on nanorods sites, where nanorods and analyte solution were mixed followed by rigorous stirring. This procedure allowed extra molecules to be adsorbed on the nanorods surface, thus, high SERS enhanced has been observed with high specific identification of target probes. This approach showed a powerful method for rapid detection of explosives based SERS technique especially for illicit samples, where the enhancement factor calculated for analytes was in the range 3.17×108 to 2.69×1011.

Advancements in precision oncology: Investigating the function of circulating DNA in the advancement of liquid biopsy technologies

While the traditional method is a biopsy of the potential tumor tissue, more non-invasive methods have been developed such as the liquid biopsy. It is an emerging, powerful cancer detection method that is used for molecular profiling of tumours apart from tissue-specific biopsy. It involves the analysis of free, circulating tumor genetic material and circulating tumor cells (CTCs) that are found in the fluids of the patient. This analysis helps to detect the abnormal genetic anomalies associated with the various forms of cancer as well as identify mutations corresponding to specific tumours. It is an upcoming method of diagnostics applied in the field of precision oncology that helps overcome the limitations of the traditional biopsy. It can interrogate and look through difficult-to-biopsy tumours and also aid in early cancer detection. Sequencing techniques help in building databases with different information on mutations in the genetic as well as epigenetic levels. In this review, we understand the role of liquid biopsy as not only a diagnostic tool but its role in therapeutics and its potential in the future.

A novel method for multiple phenotype association studies based on genotype and phenotype network.

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy.

An advanced fast method for the evaluation of multiple immunolabelling using gold nanoparticles based on low-energy STEM

We present a powerful method for the simultaneous detection of Au nanoparticles located on both sides of ultrathin sections. The method employs a high-resolution scanning electron microscope (HRSEM) operating in scanning transmission electron microscopy (STEM) mode in combination with the detection of backscattered electrons (BSE). The images are recorded simultaneously during STEM and BSE imaging at the precisely selected accelerating voltage. Under proper imaging conditions, the positions of Au nanoparticles on the top or bottom sides can be clearly differentiated, hence showing this method to be suitable for multiple immunolabelling using Au nanoparticles (NPs) as markers. The difference between the upper and lower Au NPs is so large that it is possible to apply common software tools (such as ImageJ) to enable their automatic differentiation. The effects of the section thickness, detector settings and accelerating voltage on the resulting image are shown. Our experimental results correspond to the results modelled in silico by Monte Carlo (MC) simulations.

In situ detection of dopamine in single living cells by molecularly imprinted polymer-functionalized nanoelectrodes

In situ detection of dopamine (DA) at single-cell level is critical for exploring neurotransmitter-related biological processes and diseases. However, the low content of DA and a variety of distractors with similar oxidation potentials as DA in cells brought great challenges. Here, a sensitive and specific electrochemical nanosensor was proposed for in situ detection of DA in single living cells based on nanodiamond (ND) and molecularly imprinted polymer (MIP)-functionalized carbon fiber nanoelectrode (ND/MIP/CFNE). Due to its excellent electrocatalytic property, ND was modified to the surface of CFNE based on amide bonding. Compared with bare CFNE, ND-modified CFNE can enhance oxidation currents of DA by about 4-fold, improving signal-to-noise ratio and detection sensitivity. MIP was further electropolymerized on the surface of nanoelectrodes to achieve specific capture and recognition of DA, which could avoid the interference of complex matrix and analogs in cells. Taking advantage of the precise positioning capability of a single-cell analyzer and micromanipulator, ND/MIP/CFNE could be precisely inserted into different locations of single cells and monitor oxidation signal of DA. The concentration of DA in the cytoplasm of single pheochromocytoma (PC12) cell was measured to be about 0.4 μM, providing a sensitive and powerful method for single-cell detection. Furthermore, the nanoelectrodes can monitor the fluctuation of intracellular DA under drug stimulation, providing new ideas and methods for new drug development and efficacy evaluation.

Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.

Fraud Prediction and Verification of Smart Credit Card using Machine Learning Techniques

This study unveils a powerful method for smart credit card fraud detection and verification. This system integrates data preprocessing, feature engineering, and real-time prediction using a hybrid model that incorporates supervised machine learning algorithms, an encoder, and LSTM networks. A supervised LSTM network sorts transactions, while an unsupervised Autoencoder finds outliers. Assessment criteria strike a balance between recall and accuracy. Alerts are sent by the system upon detection of fraud, and it runs in real-time. Compliance, scalability, and constant monitoring are key points. To close the gap between ease and safety in contemporary monetary transactions, this project offers a state-of-the-art method for strengthening the security of smart credit cards.

Ultrasensitive detection of tyrosinase with click reaction-combined dark-field imaging platform

Tyrosinase (TYR) is an essential oxidase that is responsible for the regulation of multiple physiological processes and diseases. Achieving the trace and reliable detection of TYR in complex biological samples is of great significance for the diagnosis of TYR-related diseases, but which faces a great challenge. In this study, we developed an ingenious and powerful method for the ultrasensitive detection of TYR by click reaction-combined dark-field microscopy. This method begins with the formation of cuprous ions (Cu+) based on the reduction of copper ions (Cu2+) by ascorbic acid (AA). Subsequently, the formed Cu+ can catalyze the crosslinking between azide- and alkyne-functionalized gold nanoparticles, causing a significant red-shift in the scattering spectrum. However, AA can chelate with TYR, which inhibits the generation of Cu+ and subsequent click reaction, thus achieving TYR-controlled scattering spectral shift. The proposed sensing platform shows a good linear detection range of 0.01–0.8 U/L with a low detection limit of 0.003 U/L, which is three orders of magnitude lower than the best performance of TYR sensing probes reported to date. Most importantly, the strategy has the ability to reliably and accurately detect TYR in serum sample, suggesting its potential clinical application in diagnosing TYR-related diseases. This visual sensing platform offers promising prospects for future research in enzymatic analysis and biomedical diagnostics.

Wind power anomaly data detection based on unsupervised methods

During the actual operation of the wind turbine, a large number of abnormal data will be generated due to environmental or human factors, which will have a great impact on the condition assessment and output prediction. In order to make wind energy a reliable source of energy, it is very important to establish an efficient and accurate wind power detection model. Therefore, it becomes essential to identify abnormal data for a more precise evaluation of wind turbine performance. Based on the data mining clustering technology K-means algorithm, this paper introduces an unsupervised abnormal wind power detection algorithm combining the variational autoencoders (VAE) model. The focus of this abnormal wind power detection method is primarily on assessing the reconstruction error, which, in turn, generates an abnormality score for wind power data. This score is then used to determine the presence of abnormal wind power data. Finally, the wind power data in 2021 is tested.

Sequential Double Immunoblotting with Peptide Antibodies.

Immunoblotting, also termed western blotting, is a powerful method for detection and characterization of proteins separated by various electrophoretic techniques. The combination of sodium dodecyl sulfate-poly acrylamide gel electrophoresis (SDS-PAGE), having high separating power, immunoblotting to synthetic membranes, and detection with highly specific peptide antibodies, is especially useful for studying individual proteins in relation to cellular processes, disease mechanisms, etc. Here, we describe a protocol for the sequential detection of various forms of an individual protein using peptide antibodies, exemplified by the characterization of antibody specificity for different forms of the protein calreticulin by double SDS-PAGE immunoblotting.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

Single-Cell Imaging of mRNA by Target RNA-Initiated RCA.

We present a powerful method for direct mRNA detection based on ligation-based recognition and in situ amplification, capable of single-cell imaging mRNA at single-nucleotide and single-molecule resolution. Attributed to the use of Splint R ligase that can ligate padlock probe with RNA as target template, this method can efficiently detect mRNA in the absence of reverse transcription. This method enables spatial localization and correlation analysis of gene expression in single cells, which helps us to elucidate gene function and regulatory mechanisms.

Real-Time Frequency Adaptive Tracking Control of the WPT System Based on Apparent Power Detection

In wireless power transfer (WPT) systems, inverters are used to achieve high-frequency conversion of DC/AC, and their conversion efficiency and working frequency are key factors affecting the system’s power transfer efficiency. In practical applications, many hardware issues, such as power transistor shutdown and loss, are the main reasons that affect the inverter conversion efficiency. On the other hand, the working frequency of WPT systems ranges from hundreds of kHz to a few MHz, and traditional voltage and current phasor estimation requires a very high sampling rate which is difficult to achieve. To overcome these limitations, this paper introduces a phase-shifting full bridge inverter using a zero-voltage switching (ZVS) soft switching technology to optimize the conversion efficiency of the inverter. Meanwhile, apparent power is introduced to detect the operating frequency and phase angle. Combined with an FPGA soft switching control strategy, this approach allows for the quick adjustment of the driving pulse of MOS transistors, as well as the voltage and current at the transmitting end, to a completely symmetrical state in real-time, effectively suppressing frequency offset and achieving efficient frequency tracking control and maximum efficiency tracking (MET) control of the WPT system. Through simulation and experiments, the ZVS soft switching technology has been achieved with the inverter control strategy, leading to improved conversion efficiency. The frequency offset that can be corrected can reach 0.1 Hz using the apparent power detection method, and the maximum transfer efficiency of the WPT system can reach 91%.

Perylene Diimide Based Fluorescent Sensors for Drug Simulant Detection: The Effect of Alkyl-Chain Branching on Film Morphology, Exciton Diffusion, Vapor Diffusion, and Sensing Response.

Luminescence-based sensing has been demonstrated to be a powerful method for rapid trace detection of chemical vapors (analytes). Analyte diffusion has been shown to be the critical factor for real-time luminescence-based detection of explosive analytes via photoinduced electron transfer in amorphous films of conjugated polymers and dendrimers. However, similar studies to determine the critical factors for sensing have not been performed on materials that employ photoinduced hole transfer (PHT) to detect low electron affinity analytes such as illicit drugs. Nor have such studies been performed on semicrystalline sensing films. We have developed a family of perylene diimide-based sensing materials capable of undergoing PHT with amine-group containing analytes. It was found that the choice of branched alkyl chain [1-hexylheptyl (PHH), 2-hexyloctyl (PHO), or 2,2-dihexyloctyl (PDHO)] attached to the nitrogen atoms of the imide moiety strongly affected the solution-processed film morphology. PHH and PHO were found to contain crystalline phases, whereas PDHO was essentially amorphous. The degree of crystallinity strongly influenced exciton diffusion, with PHH and PHO exhibiting exciton diffusion coefficients that were 20× and 10× greater than the value of the amorphous PDHO. The degree of film crystallinity was also found to be critical when the films were applied to detect N-methylphenethylamine (MPEA), a simulant of methamphetamine. While PHH had the largest exciton diffusion coefficient [(1.0 ± 0.2) × 10-2 cm2 s-1] and analyte uptake (12.3 ± 1.8 ng) it showed the smallest quenching efficiency (2.6% ng-1). In contrast, PHO, which sorbed the least analyte (6.1 ± 0.4 ng) of the three compounds, had the largest quenching efficiency (7.1% ng-1) due to its molecular packing and hence exciton diffusion coefficient [(4.5 ± 1.4) × 10-3 cm2 s-1] not being affected by sorption of the analyte. These results show that when applying fluorescent films in practical detection scenarios there is a potential trade-off between a high exciton diffusion constant and analyte diffusion for semicrystalline sensing materials and that a high exciton diffusion coefficient in an as-cast film does not necessarily translate into a more efficient fluorescent quenching. The results also show that sensing materials that form semicrystalline films, whose packing is not disrupted by analyte diffusion, provide a route for overcoming these effects and achieving high sensitivity.