Detection Method Research Articles

Dynamic interplay of reactive oxygen and nitrogen species (ROS and RNS) in plant resilience: unveiling the signaling pathways and metabolic responses to biotic and abiotic stresses.

Plants respond to environmental challenges by producing reactive species such as ROS and RNS, which play critical roles in signaling pathways that lead to adaptation and survival strategies. Understanding these pathways, as well as their detection methods and effects on plant development and metabolism, provides insight into increasing crop tolerance to combined stresses. Plants encounter various environmental stresses (abiotic and biotic) that affect plant growth and development. Plants sense biotic and abiotic stresses by producing different molecules, including reactive species, that act as signaling molecules and stimulate secondary messengers and subsequent gene transcription. Reactive oxygen and nitrogen species (ROS and RNS) are produced in both physiological and pathological conditions in the plasma membranes, chloroplasts, mitochondria, and endoplasmic reticulum. Various techniques, including spectroscopy, chromatography, and fluorescence methods, are used to detect highly reactive, short-half-life ROS and RNS either directly or indirectly. In this review, we highlight the roles of ROS and RNS in seed germination, root development, senescence, mineral nutrition, and post-harvest control. In addition, we provide information on the specialized metabolism involved in plant growth and development. Secondary metabolites, including alkaloids, flavonoids, and terpenoids, are produced in low concentrations in plants for signaling and metabolism. Strategies for improving crop performance under combined drought and pathogen stress conditions are discussed in this review.

Collaborative CRISPR-Cas System-Enabled Detection of Circulating Circular RNA for Reliable Monitoring of Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is one of the major causes of death worldwide, posing significant global health challenges. Circular RNA (circRNA) has recently emerged as a potential diagnostic biomarker for AMI, providing valuable information for timely medical care. In this work, a new electrochemical method for circRNA detection by engineering a collaborative CRISPR-Cas system is developed. This system integrates the unique circRNA-targeting ability with cascade trans-cleavage activities of Cas effectors, using an isothermal primer exchange reaction as the bridge. Using cZNF292, a circulating circRNA biomarker for AMI is identified by this group; as a model, the collaborative CRISPR-Cas system-based method exhibits excellent accuracy and sensitivity with a low detection limit of 2.13 × 10-15 m. Moreover, the method demonstrates a good diagnostic performance for AMI when analyzing whole blood samples. Therefore, the method may provide new insight into the detection of circRNA biomarkers and is expected to have great potential in AMI diagnosis in the future.

Epidermal Neural Crest Stem Cell Conditioned Medium Enhances Spinal Cord Injury Recovery via PI3K/AKT-Mediated Neuronal Apoptosis Suppression.

This study aimed to assess the impact of conditioned medium from epidermal neural crest stem cells (EPI-NCSCs-CM) on functional recovery following spinal cord injury (SCI), while also exploring the involvement of the PI3K-AKT signaling pathway in regulating neuronal apoptosis. EPI-NCSCs were isolated from 10-day-old Sprague-Dawley rats and cultured for 48h to obtain EPI-NCSC-CM. SHSY-5Y cells were subjected with H2O2 treatment to induce apoptosis. Cell viability and survival rates were evaluated using the CCK-8 assay and calcein-AM/PI staining. SCI contusion model was established in adult Sprague-Dawley rats to assess functional recovery, utilizing the Basso, Beattie and Bresnahan (BBB) scoring system, inclined test, and footprint observation. Neurological restoration after SCI was analyzed through electrophysiological recordings. Histological analysis included hematoxylin and eosin (H&E) staining and Nissl staining to evaluate tissue organization. Apoptosis and oxidative stress levels were assessed using TUNEL staining and ROS detection methods. Additionally, western blotting was performed to examine the expression of apoptotic markers and proteins related to the PI3K/AKT signaling pathway. EPI-NCSC-CM significantly facilitated functional and histological recovery in SCI rats by inhibiting neuronal apoptosis through modulation of the PI3K/AKT pathway. Administration of EPI-NCSCs-CM alleviated H2O2-induced neurotoxicity in SHSY-5Y cells in vitro. The use of LY294002, a PI3K inhibitor, underscored the crucial role of the PI3K/AKT signaling pathway in regulating neuronal apoptosis. This study contributes to the ongoing exploration of molecular pathways involved in spinal cord injury (SCI) repair, focusing on the therapeutic potential of EPI-NCSC-CM. The research findings indicate that EPI-NCSC-CM exerts a neuroprotective effect by suppressing neuronal apoptosis through activation of the PI3K/AKT pathway in SCI rats. These results highlight the promising role of EPI-NCSC-CM as a potential treatment strategy for SCI, emphasizing the significance of the PI3K/AKT pathway in mediating its beneficial effects.

Detection and severity assessment of obstructive sleep apnea according to deep learning of single-lead electrocardiogram signals.

Developing a convenient detection method is important for diagnosing and treating obstructive sleep apnea. Considering availability and medical reliability, we established a deep-learning model that uses single-lead electrocardiogram signals for obstructive sleep apnea detection and severity assessment. The detection model consisted of signal preprocessing, feature extraction, time-frequency domain information fusion, and classification segments. A total of 375 patients who underwent polysomnography were included. The single-lead electrocardiogram signals obtained by polysomnography were used to train, validate and test the model. Moreover, the proposed model performance on a public dataset was compared with the findings of previous studies. In the test set, the accuracy of per-segment and per-recording detection were 82.55% and 85.33%, respectively. The accuracy values for mild, moderate and severe obstructive sleep apnea were 69.33%, 74.67% and 85.33%, respectively. In the public dataset, the accuracy of per-segment detection was 91.66%. A Bland-Altman plot revealed the consistency of true apnea-hypopnea index and predicted apnea-hypopnea index. We confirmed the feasibility of single-lead electrocardiogram signals and deep-learning model for obstructive sleep apnea detection and severity evaluation in both hospital and public datasets. The detection performance is high for patients with obstructive sleep apnea, especially those with severe obstructive sleep apnea.

CRISPR/Cas12a Collateral Cleavage-Driven Transcription Amplification for Direct Nucleic Acid Detection.

The clustered regularly interspaced short palindromic repeat/Cas (CRISPR/Cas) system is a powerful tool for nucleic acid detection owing to specific recognition as well as cis- and trans-cleavage capabilities. However, the sensitivity of CRISPR/Cas-based diagnostic approaches is determined by nucleic acid preamplification, which has several limitations. Here, we present a method for direct nucleic acid detection without preamplification, by combining the CRISPR/Cas12a system with signal enhancement based on light-up RNA aptamer transcription. We first designed two DNA templates to transcribe the light-up RNA aptamer and kleptamer (Kb) RNA: the first DNA template encodes a Broccoli RNA aptamer for fluorescence signal generation, and the Kb DNA template comprises a dsDNA T7 promoter sequence and an ssDNA sequence that encodes an antisense strand for the Broccoli RNA aptamer. Hepatitis B virus (HBV) target recognition activates a CRISPR/Cas12a complex, leading to the catalytic cleavage of the ssDNA sequence. Transcription of the added Broccoli DNA template can then produce several Broccoli RNA aptamer transcripts for fluorescence enhancement. The proposed strategy exhibited excellent sensitivity and specificity with 22.4 fM detection limit, good accuracy, and stability for determining the target HBV dsDNA in human serum samples. Overall, this newly designed signal enhancement strategy can be employed as a universal sensing platform for ultrasensitive nucleic acid detection.

Reliability of impedance spectroscopy versus digital radiograph and ICDAS-II in occlusal caries detection: a prospective clinical trial

The traditional methods in early caries detection had many limitations. So, this study aimed to evaluate the clinical performance of alternating current impedance spectroscopy ACIST in comparison with digital radiograph and ICDAS-II in detection of occlusal carious lesions. Occlusal surfaces of molar and premolar teeth from 40 adult participants were examined by two observers using three diagnostic methods: (1) international caries detection and assessment system (ICDAS-II) (2) digital radiograph (DR) and (3) Cariescan Pro device (ACIST). Agreement analysis and the difference in sensitivities and specificities were evaluated. The results showed an excellent agreement in the different groups. The difference from the visual tactile against ACIST scoring for enamel caries detection, was statistically significant (p = 0.012) and the agreement was moderate (k = 0.509). For dentinal caries the difference was not statistically significant (p > 0.05) and the agreement was similarly moderate (k < 0.6). The difference from the digital radiograph against ACIST scoring, for enamel caries, digital radiography had significantly higher sensitivity and specificity than ACIST (p < 0.001) while for dentinal caries detection and overall, ACIST had higher sensitivity and digital radiography had higher specificity and the difference was statistically significant (p < 0.001). Visual-tactile examination is a considered as feasible and valid technique for occlusal caries detection, digital radiography is superior to ACIST in diagnosing enamel caries, but it could underestimate the caries depth, ACIST is a reliable tool for detecting occlusal caries in dentin.

An imbalanced learning method based on graph tran-smote for fraud detection

Fraud seriously threatens individual interests and social stability, so fraud detection has attracted much attention in recent years. In scenarios such as social media, fraudsters typically hide among numerous benign users, constituting only a small minority and often forming “small gangs”. Due to the scarcity of fraudsters, the conventional graph neural network might overlook or obscure critical fraud information, leading to insufficient representation of fraud characteristics. To address these issues, the tran-smote on graphs (GTS) method for fraud detection is proposed by this study. Structural features of each type of node are deeply mined using a subgraph neural network extractor, these features are integrated with attribute features using transformer technology, and the node’s information representation is enriched, thereby addressing the issue of inadequate feature representation. Additionally, this approach involves setting a feature embedding space to generate new nodes representing minority classes, and an edge generator is used to provide relevant connection information for these new nodes, alleviating the class imbalance problem. The results from experiments on two real datasets demonstrate that the proposed GTS, performs better than the current state-of-the-art baseline.

Vehicle mounted single-antenna GNSS spoofing detection method based on motion trajectory

Vehicle mounted single-antenna GNSS spoofing detection method based on motion trajectory

Revolution in malaria detection: unveiling current breakthroughs and tomorrow’s possibilities in biomarker innovation

The ongoing battle against malaria has seen significant advancements in diagnostic methodologies, particularly through the discovery and application of novel biomarkers. Traditional diagnostic techniques, such as microscopy and rapid diagnostic tests (RDTs), have their limitations in terms of sensitivity, specificity, and the ability to detect low-level infections. Recent breakthroughs in biomarker research promise to overcome these challenges, providing more accurate, rapid, and non-invasive detection methods. These advancements are critical in enhancing early detection, guiding effective treatment, and ultimately reducing the global malaria burden. Innovative approaches in biomarker detection are leveraging cutting-edge technologies like next-generation sequencing (NGS), proteomics, and metabolomics. These techniques have led to the identification of new biomarkers that can be detected in blood, saliva, or urine, offering less invasive and more scalable options for widespread screening. For instance, the discovery of specific volatile organic compounds (VOCs) in the breath of infected individuals presents a revolutionary non-invasive diagnostic tool. Additionally, the integration of machine learning algorithms with biomarker data is enhancing the precision and predictive power of malaria diagnostics, making it possible to distinguish between different stages of infection and identify drug-resistant strains. Looking ahead, the future of malaria detection lies in the continued exploration of multi-biomarker panels and the development of portable, point-of-care diagnostic devices. The incorporation of smartphone-based technologies and wearable biosensors promises to bring real-time monitoring and remote diagnostics to even the most resource-limited settings.

Toward Sensitive and Reliable Immunoassays of Marine Biotoxins: From Rational Design to Food Analysis.

Marine biotoxins are metabolites produced by algae that can accumulate in shellfish or fish and enter organisms through the food chain, posing a serious threat to biological health. Therefore, accurate and rapid detection is an urgent requirement for food safety. Although various detection methods, including the mouse bioassay, liquid chromatography-mass spectrometry, and cell detection methods, and protein phosphatase inhibition assays have been developed in the past decades, the current detection methods cannot fully meet these demands. Among these methods, the outstanding immunoassay virtues of high sensitivity, reliability, and low cost are highly advantageous for marine biotoxin detection in complex samples. In this work, we review the recent 5-year progress in marine biotoxin immunodetection technologies such as optical immunoassays, electrochemical immunoassays, and piezoelectric immunoassays. With the assistance of immunoassays, the detection of food-related marine biotoxins can be implemented for ensuring public health and preventing food poisoning. In addition, the immunodetection technique platforms including lateral flow chips and microfluidic chips are also discussed. We carefully investigate the advantages and disadvantages for each immunoassay, which are compared to demonstrate the guidance for selecting appropriate immunoassays and platforms for the detection of marine biotoxins. It is expected that this review will provide insights for the further development of immunoassays and promote the rapid progress and successful translation of advanced immunoassays with food safety detection.

Detection and mitigation of DDoS attacks based on multi-dimensional characteristics in SDN

Due to the large computational overhead, underutilization of features, and high bandwidth consumption in traditional SDN environments for DDoS attack detection and mitigation methods, this paper proposes a two-stage detection and mitigation method for DDoS attacks in SDN based on multi-dimensional characteristics. Firstly, an analysis of the traffic statistics from the SDN switch ports is performed, which aids in conducting a coarse-grained detection of DDoS attacks within the network. Subsequently, a Multi-Dimensional Deep Convolutional Classifier (MDDCC) is constructed using wavelet decomposition and convolutional neural networks to extract multi-dimensional characteristics from the traffic data passing through suspicious switches. Based on these extracted multi-dimensional characteristics, a simple classifier can be employed to accurately detect attack samples. Finally, by integrating graph theory with restrictive strategies, the source of attacks in SDN networks can be effectively traced and isolated. The experimental results indicate that the proposed method, which utilizes a minimal amount of statistical information, can quickly and accurately detect attacks within the SDN network. It demonstrates superior accuracy and generalization capabilities compared to traditional detection methods, especially when tested on both simulated and public datasets. Furthermore, by isolating the affected nodes, the method effectively mitigates the impact of the attacks, ensuring the normal transmission of legitimate traffic during network attacks. This approach not only enhances the detection capabilities but also provides a robust mechanism for containing the spread of cyber threats, thereby safeguarding the integrity and performance of the network.

Analysis of bacterial transcriptome and epitranscriptome using nanopore direct RNA sequencing.

Bacterial gene expression is a complex process involving extensive regulatory mechanisms. Along with growing interests in this field, Nanopore Direct RNA Sequencing (DRS) provides a promising platform for rapid and comprehensive characterization of bacterial RNA biology. However, the DRS of bacterial RNA is currently deficient in the yield of mRNA-mapping reads and has yet to be exploited for transcriptome-wide RNA modification mapping. Here, we showed that pre-processing of bacterial total RNA (size selection followed by ribosomal RNA depletion and polyadenylation) guaranteed high throughputs of sequencing data and considerably increased the amount of mRNA reads. This way, complex transcriptome architectures were reconstructed for Escherichia coliand Staphylococcus aureusand extended the boundaries of 225 known E. coli operons and 89 defined S. aureus operons. Utilizing unmodified in vitro-transcribed (IVT) RNA libraries as a negative control, several Nanopore-based computational tools globally detected putative modification sites in the E. coli and S. aureus transcriptomes. Combined with Next-Generation Sequencing-based N6-methyladenosine (m6A) detection methods, 75 high-confidence m6A candidates were identified in the E. coli protein-coding transcripts, while none were detected in S. aureus. Altogether, we demonstrated the potential of Nanopore DRS in systematic and convenient transcriptome and epitranscriptome analysis.

Microfluidic Chip Coupled with MALDI-TOF MS for Multitarget Detection of Allergens in Crucian Carp (Carassius auratus).

The goal of the present study was to establish a rapid, simple method for simultaneous allergy testing of sera from multiple fish-allergic patients. Sera from fish-allergic patients were pooled and used for capturing allergens in fish muscle of crucian carp (Carassius auratus), which was studied as a fish model. Sarcoplasmic proteins of crucian carp (Carassius auratus) were extracted for the analysis of allergens. Anti-human IgE antibody-functionalized magnetic beads were utilized to collect IgE antibodies from human pooled sera. The isolation of allergenic proteins was immunomagnetically performed in microfluidic channels, and the elution of the captured allergenic proteins was done with 5% (v/v) acetic acid aqueous solution. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and peptide mass fingerprinting were used for the analysis of tryptic digests of eluted proteins. Ten potential allergenic proteins were identified from crucian carp (Carassius auratus). The present protocol provides a rapid, efficient, and simple method for simultaneous detection of multiple allergens, based on multitargeted antibodies from pooled sera of allergic patients. The constructed multiple antibody-modified MBs can be applied for the deallergenicity of food matrices. The efficiency of allergen detection can be greatly improved, with promising application in allergen discovery and filtration for other muscle-based foods.

A new method for inspection and detection of typical defects of protective facilities in existing railways and its application

Due to the frequent occurrence of defects of slope protection facilities on existing lines causing the potential major geological hazards (e.g., landslides), it is necessary to promote the inspection and detection efficiency of various defects of slope protection facilities for carrying out early warning and evaluation, as well as, putting forward relevant control measures for slope disasters. Based on the case studies of hundreds of protective facilities on the existing railway line, the typical defect types, characteristics and specific causes of retaining wall, slope protection, anti-slide pile and anchorage engineering structure are firstly determined. Subsequently, a new comprehensive method combining unmanned air vehicle (UAV) and intelligent detection of portable radar (IDPR) are proposed. Among of them, UAV can effective and efficient identify the defect situations of slope protection facilities, and the water accumulation and cracks of retaining wall through performing a periodic inspection of the surrounding environment of protective facilities. In addition, IDPR can detect the filling situations behind the protective facilities to prevent the collapse danger. Through comparison with the drilling core results of tested areas, it is found that the detection results of the IDPR are more reliable and accurate, which is worthy of popularization and application in the field.

Neurofibroma detection using joint imaging genomics and ensemble learning

Accurate detection of neurofibromas is crucial for quantitative monitoring of tumor progression and surgical assessment. This paper proposes a method for neurofibroma detection in whole-body magnetic resonance imaging (WBMRI) using joint imaging genomics and ensemble learning. Firstly, we enhance texture features through a combination of image sharpening, filtering, brightness adjustment, and contrast enhancement. Then, we employ a weighted boxes fusion (WBF) technique based on test-time augmentation (TTA) under a single model and further integrate multiple models using the dual fusion approach of TTA and WBF. For segmentation, we utilize minimum bounding boxes based on segmentation masks for position calibration. Finally, false positive tumor regions are further eliminated through imaging genomics features. The experimental MRI data is obtained from collaboration between Harvard Medical School and domestic tertiary hospitals, comprising 158 cases with a total of 1380 tumors. Five-fold cross-validation is conducted with segmentation annotations completed by domain experts. Compared to the best results of single models, our proposed method achieves a 10.1% increase in average precision (AP), 7.8% increase in sensitivity, reduction of average false positives to 3.58, a decrease of 17.68, and an 8.5% improvement in competitive performance metric (CPM). This method effectively enhances the accuracy of neurofibroma detection and is applicable to detecting tumors and lesions in other medical imaging applications.

EA-DINO: Improved method for unmanned aerial vehicle detection in airspace based on DINO

In recent years, the increase in drone traffic and the potential for unauthorized surveillance has underscored the urgent need for technological advances in drone detection. Despite the rapid advancements in deep learning that have significantly improved object detection tasks, air-to-air unmanned aerial vehicle (UAV) detection continues to pose significant challenges. Challenges such as complex backgrounds, small size of UAVs in captured images, and variations in flight poses and angles pose significant difficulties for traditional deep learning approaches, mainly because of the inherent limitations of conventional convolutional neural network architectures in discriminating fine details against dynamically changing backdrops. To address these challenges, this study introduces EA-DINO, a new deep learning network based on enhanced aggregation (EA) and DINO. The network incorporates a series of improvements over DINO. First, the backbone is replaced with a Swin transformer, and agent attention is integrated. Second, an EA feature pyramid network is added to the network architecture. Experimental evaluations demonstrate that, in the context of air-to-air UAV detection complexities, the EA-DINO model achieves an mAP 50 of 96.6% on the Det-Fly dataset, representing an improvement of 8.3% over the baseline DINO model. This improvement is noteworthy compared with other mainstream models, illustrating the effectiveness of the proposed model in addressing the challenges of air-to-air UAV detection.

A composite manifold learning approach with traditional methods for gradient-based and patch-based adversarial attack detection

A composite manifold learning approach with traditional methods for gradient-based and patch-based adversarial attack detection

Models and methods of object detection in digital image processing

Object detection in digital images can face such problems that arise in the process of image registration as the presence of noise, low image quality/resolution, illumination heterogeneity, overlapping objects, and others. These problems complicate the process of object detection and lead to errors in image processing algorithms. To solve these problems, we study the advantages, disadvantages, and technical features of models and methods for detecting objects in digital images for their reasonable selection in practical applications. Figs. 2. Refs. 11 titles.

LHFFNet: A hybrid feature fusion method for lane detection

Lane line images have the essential attribute of large-scale variation and complex scene information, and the similarity between adjacent lane lines is high, which can easily cause classification errors. And remote lane lines are difficult to recognize due to visual angle changes in width. To address this issue, this paper proposes an effective lane detection framework, which is a hybrid feature fusion network that enhances multiple spatial features and distinguishes key features throughout the entire lane line segment. It enhances and fuses lane line features at multiscale to enhance the feature representation of lane line images, especially at the far end. Firstly, in order to enhance the correlation of multiscale lane features, a multi-head self attention is used to construct a multi-space attention enhancement module for feature enhancement in multispace. Secondly, a spatial separable convolutional branch is designed for the jumping layer structure connecting multiscale lane line features. While retaining feature information of different scales, important lane areas in multiscale feature information are emphasized through the allocation of spatial attention weights. Finally, considering that lane lines are elongated areas in the image, and the background information in the image is much more abundant than lane line information, the flexibility of traditional pooling operations in capturing widely existing anisotropic contexts in actual environments is limited. Therefore, before embedding feature output branches, strip pooling is introduced to refine the representation of lane line information and optimize model performance. The experimental results show that the accuracy on the TuSimple dataset reaches 96.84%, and the F1 score on the CULane dataset reaches 75.9%.

Fault Detection Methods for Electric Power Steering System Using Hardware in the Loop Simulation

The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers.