Visual Detection Method Research Articles

Development and evaluation of three multienzyme isothermal rapid amplification assays for fowl adenovirus serotype 4

Fowl adenovirus serotype 4 (FAdV-4) is the main causative agent of hydropericardium hepatitis syndrome (HHS), which has resulted in huge economic losses to the poultry industry in recent years. Hence, a rapid and simple visual detection method is needed for identification of FAdV-4. In this study, three multienzyme isothermal rapid amplification (MIRA) assays, basic MIRA, MIRA-qPCR and MIRA-LFD were developed for detection of FAdV-4. The amplification primers and reaction conditions were optimized, and the specificity and sensitivity of the assays were evaluated. The MIRA assays were specific for FAdV-4 with no cross-reaction with novel goose astrovirus, H9 subtype avian influenza virus, duck enteritis virus, Muscovy duck reovirus, or duck circovirus. The basic MIRA assay required only one primer pair and the reaction can be completed within 30 min at 36 °C. The MIRA-qPCR and MIRA-LFD assays were completed in 20 min with a minimum detection limit of 1 × 101 copies/μL and 1 × 102 copies/μL, respectively. The results of the MIRA-LFD assay can be observed directly with the naked eye, omitting the need for specialized instruments. The positive rate of three proposed MIRA assays were consistent with that of the conventional PCR assay. The MIRA assays are simple, rapid, and effective diagnostic tools for field detection of FAdV-4.

Development of a LAMP assay for the rapid visual detection of the emerging tick-borne Songling virus

BackgroundSongling virus (SGLV) within the genus Orthonairovirus, family Nairoviridae, is an emerging tick-borne virus associated with human febrile illness. However, no rapid detection method for SGLV has been established.MethodsIn this study, four primer sets targeting the nucleocapsid protein gene of SGLV were designed for use in the LAMP assay and evaluated to identify the optimal primer set. Recombinant plasmids were constructed and utilized for assessing the sensitivity of the assay. Tacheng tick virus 1 (TcTV-1)-, Beiji nairovirus (BJNV)-, Yezo virus (YEZV)-, severe fever with thrombocytopenia syndrome virus (SFTSV)-, and tick-borne encephalitis virus (TBEV)-positive tick samples were utilized to assess the specificity. Field-collected ticks were also evaluated as biological specimens to validate the assay.ResultsA SGLV-specific LAMP assay was established with a detection limit of 1 × 10–2 copies/μl and could be visually confirmed by a color change from purple to blue in SGLV-positive samples. No cross-reactivity was observed in the detection of TcTV-1, BJNV, YEZV, SFTSV, and TBEV using the LAMP assay. In addition to the detection of the same seven high-copy numbers of SGLV as the SYBR Green quantitative RT-PCR assay within a reduced timeframe, the developed LAMP method also effectively identified an additional sample with a low copy number in the field-collected tick samples.ConclusionsWe successfully developed a sensitive, specific, and cost-effective visual method for the rapid detection of SGLV using the LAMP assay, which can be applied in pathogenesis and epidemiological surveillance studies of SGLV.Graphical

Distance-Based Fluorescent Immunosensor for Point-of-Care Test of Illegal Additives through the Gas-Producing Nanozyme.

The colorimetric point-of-care test (POCT) offers a rapid and efficient method for detecting specific targets in real samples. However, traditional colorimetric methods often rely on complex signal amplification techniques or electronic devices to enhance detection sensitivity, which can inadvertently increase both cost and time, thus contradicting the fundamental goals of visual detection methods. Here, we presented a distance-based fluorescent immunosensor that utilized a gas-producing nanozyme for continuous gas production reaction as a signal. Specifically, the SOM-ZIF-8@Pt nanozyme catalyzed the production of O2 from H2O2 to cause an obvious increase in the pressure within a sealed chamber, thus driving the production of H2S to quench the fluorescence of CsPbBr3 on the walls of the capillaries. Based on the competitive immunoassay, the fluorescence quenching lengths were relative with the concentration of aminopyrine in the range from 0.2 to 20 ng/L; thus, the fluorescent POCT-based homemade device was realized through the amplification of distance-based signals facilitated by the continuous gas production reaction. This strategy provides an effective way to realize POCT assays in resource-limited areas by transforming pressure variations into directly observable signals. Furthermore, distinguished by its high sensitivity, ease of operation, and portability, it also represents a significant advancement in biomedical diagnostics, particularly within home healthcare and clinical POCT.

Repurposing expired metformin to fluorescent carbon quantum dots for ratiometric and color tonality visual detection of tetracycline with greenness evaluation

Repurposing expired drugs is highly significant as it prevents the accumulation of pharmaceutical wastes, optimizes resource usage, and reduces environmental harm. Metformin, used for type II diabetes, lowers blood sugar via decreasing liver glucose production and increasing muscle insulin sensitivity. However, the growing number of expired tablets annually necessitates a strategic approach for repurposing or reusing these tablets. Herein, we develop a simple hydrothermal approach for converting expired metformin to valuable fluorescent carbon quantum dots (m-CQDs) with sizes smaller than 10 nm. The m-CQDs displayed a blue emission at 420 nm when excited at 350 nm. Interestingly, when tetracycline (TC) was added, the blue emission diminished, and a greenish-yellow emission emerged at 530 nm. This emission tunning was the resulting of combination of inner filter effect and aggregation induced emission. Leveraging this dynamic emission tunning, we developed both ratiometric-based fluorescence and color tonality-based visual detection methods for detecting TC in pharmaceuticals and wastewater. More importantly, three assessment tools, ComplexMoGAPI, AGREE, and BAGI were used to evaluate the environmental sustainability of the proposed analytical method. The evaluation confirmed the sustainability of the method and the alignment with the greenness guidelines for analytical methods.

CRISPR/Cas13a-mediated visual detection: A rapid and robust method for early detection of Nosema bombycis in silkworms

The sericulture industry faces a significant threat from the Pebrine disease of silkworms, caused by Nosema bombycis. Nonetheless, the current microscopic diagnostic methods can be time-consuming, labor-intensive, and lacking sensitivity and accuracy. Therefore, it is crucial to develop a novel detection approach that is efficient, highly sensitive, and low-cost. In this regard, the CRISPR/Cas system has the potential to be a fast, accurate, and highly specific method of detection. Herein, using a microplate reader, a portable fluorescence detection device, and test strips as signal output tools respectively, we have efficiently developed three rapid and facile visual detection methods for N. bombycis using a CRISPR/Cas13a system with conjugation of Recombinase polymerase amplification (RPA). We evaluated the sensitivity of this combined technology by comparing it with the positive plasmid standard and the genome standard of N. bombycis. Remarkably, the sensitivity of the CRISPR/Cas13a system for N. bombycis positive plasmid standard based on the microplate reader, portable fluorescence detection device, and test strips was 1 copy/μL, 10 copies/μL, and 1 copy/μL, respectively, while for the N. bombycis genome standards, the detection sensitivity was 10 fg/μL, 10 fg/μL, and 1 fg/μL, respectively. In addition, extensive evaluations have demonstrated that the established technology can accurately detect N. bombycis without cross-reactivity with other pathogens, ensuring a specificity rate of 100%. In brief, this study will provide a practical, efficient, and affordable method for early and rapid detection of N. bombycis in various settings.

A high-precision visual detection method for rail profile based on Scheimpflug condition

Rail profile is an important indicator of rail service status, which should be always kept in good condition. How to efficiently collect rail profiles with high precision is a critical issue for their condition-based maintenance. This paper focuses on the method for accurate visual detection of rail full profile (RFP), including optical modeling, visual calibration, and error compensation. The proposed optical unit, featuring double line structured-light sensors, introduces an innovative method under the Scheimpflug condition to avoid the limitation of depth of field in RFP measurement. Subsequently, double visual sensors are calibrated together using the common reference target to extract the internal and external parameters respectively. The dynamic errors resulting from the impact of vehicle vibrations are ultimately investigated to rectify the distorted profiles to the normal. Both laboratory and field tests are performed to verify the effectiveness of the proposed system in terms of precision and efficiency.

Constructing a visual detection method for coagulation effect based on image feature machine learning

The coagulation process is influenced by several factors, including turbidity, temperature, pH, and hydraulic conditions. Consequently, optimizing the dosage of coagulants is often time-consuming and challenging. To quickly evaluate coagulation efficacy and optimize coagulant dosage, this study utilized a convolutional neural network (CNN) model to analyze the accuracy of effluent turbidity predictions from floc images at different time intervals. Python OpenCV was employed to develop a program that extracts macro features (e.g., texture) and micro features (e.g., particle size distribution) from the images, and these feature parameters were analyzed using a BP-ANN model. The results of the CNN analysis indicated that the middle to late stages of flocculation are the optimal periods for predicting effluent turbidity, with the highest prediction accuracy of 99.81 % achieved during the middle stage of flocculation. The BP-ANN analysis demonstrated that particle size distribution effectively represents the microscopic characteristics of flocs, achieving a maximum prediction accuracy of 96.94 % in the middle stage of flocculation. Furthermore, combining macroscopic and microscopic features yielded a prediction accuracy of 99.44 % at the end of flocculation using BP-ANN. These findings suggest that machine learning techniques applied to floc images can effectively predict effluent turbidity, offering valuable insights for future water quality prediction and the development of flocculation kinetics models.

Rapid and Ultrasensitive Detection of H. aduncum via the RPA-CRISPR/Cas12a Platform.

Hysterothylacium aduncum is one of six pathogens responsible for human anisakiasis. Infection with H. aduncum can cause acute abdominal symptoms and allergic reactions and is prone to misdiagnosis in clinical practice. This study aims to enhance the efficiency and accuracy of detecting H. aduncum in food ingredients. We targeted the internal transcribed spacer 1 (ITS 1) regions of Anisakis to develop a visual screening method for detecting H. aduncum using recombinase polymerase amplification (RPA) combined with the CRISPR/Cas12a system. By comparing the ITS 1 region sequences of eight nematode species, we designed specific primers and CRISPR RNA (crRNA). The specificity of RPA primers was screened and evaluated, and the CRISPR system was optimized. We assessed its specificity and sensitivity and performed testing on commercial samples. The results indicated that the alternative primer ADU 1 was the most effective. The final optimized concentrations were 250 nM for Cas12a, 500 nM for crRNA, and 500 nM for ssDNA. The complete test procedure was achievable within 45 min at 37 °C, with a limit of detection (LOD) of 1.27 pg/μL. The amplified product could be directly observed using a fluorescence microscope or ultraviolet lamp. Detection results for 15 Anisakis samples were entirely consistent with those obtained via Sanger sequencing, demonstrating the higher efficacy of this method for detecting and identifying H. aduncum. This visual detection method, characterized by simple operation, visual results, high sensitivity, and specificity, meets the requirements for food safety testing and enhances monitoring efficiency.

Visual Anomaly Detection via CNN-BiLSTM Network with Knit Feature Sequence for Floating-Yarn Stacking during the High-Speed Sweater Knitting Process

In order to meet the current expanding market demand for knitwear, high-speed automatic knitting machines with “one-line knit to shape” capability are widely used. However, the frequent emergence of floating-yarn stacking anomalies during the high-speed knitting process will seriously hinder the normal reciprocating motion of the needles and cause a catastrophic fracture of the whole machine needle plate, greatly affecting the efficiency of the knitting machines. To overcome the limitations of the existing physical-probe detection method, in this work, we propose a visual floating-yarn anomaly recognition framework based on a CNN-BiLSTM network with the knit feature sequence (CNN-BiLSTM-KFS), which is a unique sequence of knitting yarn positions depending on the knitting status. The sequence of knitting characteristics contains the head speed, the number of rows, and the head movements of the automatic knitting machine, enabling the model to achieve more accurate and efficient floating-yarn identification in complex knitting structures by utilizing contextual information from knitting programs. Compared to the traditional probe inspection method, the framework is highly versatile as it does not need to be adjusted to the specifics of the automatic knitting machine during the production process. The recognition model is trained at the design and sampling stages, and the resulting model can be applied to different automatic knitting machines to recognize floating yarns occurring in various knitting structures. The experimental results show that the improved network spends 75% less time than the probe-based detection, has a higher overall average detection accuracy of 93% compared to the original network, and responds faster to floating yarn anomalies. The as-proposed CNN-BiLSTM-KFS floating-yarn visual detection method not only enhances the reliability of floating-yarn anomaly detection, but also reduces the time and cost required for production adjustments. The results of this study will bring significant improvements in the field of automatic floating-yarn detection and have the potential to promote the application of smart technologies in the knitting industry.

RAA-CRISPR/Cas12a-Mediated Rapid, Sensitive, and Onsite Detection of Newcastle Disease in Pigeons.

Pigeon Newcastle disease, caused by pigeon paramyxovirus type 1 (PPMV-1), is a significant infectious disease in pigeons that can result in substantial mortality and poses a severe threat to the pigeon industry. The rapid and accurate onsite diagnosis of pigeon disease is crucial for timely diagnosis and the implementation of effective prevention and control measures. In this study, we established a rapid detection method for PPMV-1 based on recombinase-aided amplification (RAA) and CRISPR/Cas12a. The RAA primers target the conserved regions of the L gene for preamplification in clinical nucleic acid samples, followed by CRISPR/Cas12a detection of the target gene. Visualization could be achieved by combination with a lateral flow dipstick (LFD). This method demonstrated high specificity, showing no cross-reactivity with non-PPMV-1 samples. The sensitivity of the method assessed by fluorescence analysis reached 100 copies/µL, and when it was combined with an LFD, the sensitivity was 103 copies/µL. The constructed RAA-CRISPR/Cas12a-LFD visual detection method was applied to clinical sample testing and was found to enable the rapid and accurate detection of swab samples and tissue specimens. Its sensitivity was consistent with the current gold standard, quantitative real-time PCR results. The RAA-CRISPR/Cas12a-LFD detection method we developed provides a novel approach for the rapid, simple, precise, and specific onsite diagnosis of pigeon Newcastle disease.

A Surface Defect Detection Method for Cold-rolled Strip Steel Based on Improved YOLOv8

Cold-rolled strip steel has excellent mechanical properties, and it is widely used in the fields such as automotive manufacturing, construction industry, aircraft manufacturing and electronic product manufacturing. However, many different processing defects occur in the manufacturing process of cold-rolled strip steel, and this article proposes a visual detection method based on YOLOv8 to identify the surface defects on cold-rolled strip steel more accurately. Firstly, several common types of defects in cold-rolled strip steel were analyzed. Then, in order to enrich the dataset for subsequent detection, wavelet transform was used for filtering, denoising, and canny edge detection in the image processing stage for obtaining the clearer input model image with the more prominent graphics. Lastly, the CA attention mechanism was added into the YOLOv8 which is useful to improve the features recognition ability of the model, at the same time, the original standard convolution was replaced by DCS convolution and the loss function was optimized which is useful to get the lightweight models. The improved YOLOv8 model was validated on the NEU-DET dataset, the overall detection performance of the model such as the recall, the accuracy, and the average precision has significantly improved.

Rapid and Sensitive On-site Nucleic Acid Detection of Three Main Fusarium Pathogens of Maize Stalk Rot Based on RPA-CRISPR/Cas12a.

Maize stalk rot is a soil-borne disease that poses a serious threat to maize production worldwide, with the most significant cause being fungal stalk rot. The development of a visual and rapid detection method for the maize stalk rot pathogen is significant for its prompt and accurate identification, enhancing agricultural production efficiency, and implementing timely preventive measures. These measures will help safeguard the maize yield and quality, ultimately reducing agricultural losses. In this study, we aimed to develop an efficient method to detect maize stalk rot pathogens. We focused on three pathogenic fungi commonly found in maize-producing regions worldwide: Fusarium verticillioides, Fusarium proliferatum, and Fusarium graminearum. Based on TEF-1α, we developed a rapid detection technique using RPA-CRISPR/Cas12a, combined with test strips to develop an on-site rapid visual detection test for these pathogens. The method showed detection sensitivity for F. verticillioides, F. proliferatum, and F. graminearum within 20 min at concentrations of 7.8 pg/μL, 0.11 ng/μL, and 0.13 ng/μL, respectively. The sensitivity increased with increasing reaction time. Testing of field disease samples indicated that the method is effective in detecting nucleic acids obtained through crude extraction methods. In conclusion, we developed a visually rapid detection technology that does not rely on complex instruments and equipment for the on-site early detection of F. verticillioides, F. proliferatum, and F. graminearum in the field to implement effective control measures, ensuring stable and high maize yields.

Colorimetric and Fluorimetric Detection of Fe(III) Using a Rhodamine-Imidazole Hydrazone Based Chemosensor: Photophysical Properties, DFT, TGA, and DSC Studies.

Rhodamine-imidazole hydrazones (RIH-1 & RIH-2) based chemosensors have been synthesized. These are characterised and evaluated by FT-IR spectroscopy, 1H-NMR, 13C-NMR, LCMS, absorption and fluorescence spectroscopy. These chemosensors exhibit enhanced sensitivity and selectivity in detecting the biologically significant Fe3+ metal ion through both colorimetric and fluorescence changes. The optical properties have been investigated using binary acetonitrile-water (7:3 v/v) semi-aqueous solution. The probe RIH-1 can be deployed as a fluorescent and colorimetric probe for the detection of Fe3+ ion. It shows an absorption band at 559nm and an intensity band at 579nm increasing up to 50-fold with the increase in the concentration of Fe3+ with the detection limit as low as 11nM. In the visible light, RIH-1 helps in the detection of Fe3+ ion through the naked eye, while the addition of Fe3+ to the probe RIH-1 results in a colour change from colourless to pink. This is primarily due to the opening of the lactone ring in RIH-1. Notably, RIH-1 probe displays a high quantum yield of 0.51, after binding with Fe3+ ions. Indeed, it has been found that sensor RIH-1 is very effective in sensing Fe3+ ions through both fluorescence based and visual detection methods. Additionally, DFT studies of these chemosensors have been evaluated, TGA and DSC analysis showed good thermal stability.

Advancements in rapid diagnostics and genotyping of Piscirickettsia salmonis using Loop-mediated Isothermal Amplification.

Piscirickettsia salmonis, the causative agent of Piscirickettsiosis, poses a significant threat to the Chilean aquaculture industry, resulting in substantial economic losses annually. The pathogen, first identified as specie in 1992, this pathogen was divided into two genogroups: LF-89 and EM-90, associated with different phenotypic mortality and pathogenicity. Traditional genotyping methods, such as multiplex PCR, are effective but limited by their cost, equipment requirements, and the need for specialized expertise. This study validates Loop-mediated Isothermal Amplification (LAMP) as a rapid and specific alternative for diagnosing P. salmonis infections. We developed the first qPCR and LAMP assay targeting the species-conserved tonB receptor gene (tonB-r, WP_016210144.1) for the specific species-level identification of P. salmonis. Additionally, we designed two genotyping LAMP assays to differentiate between the LF-89 and EM-90 genogroups, utilizing the unique coding sequences Nitronate monooxygenase (WP_144420689.1) for LF-89 and Acid phosphatase (WP_016210154.1) for EM-90. The LAMP assays demonstrated sensitivity and specificity comparable to real-time PCR, with additional benefits including rapid results, lower costs, and simplified operation, making them particularly suitable for field use. Specificity was confirmed by testing against other salmonid pathogens, such as Renibacterium salmoninarum, Vibrio ordalii, Flavobacterium psychrophilum, Tenacibaculum maritimum, and Aeromonas salmonicida, with no cross-reactivity observed. The visual detection method and precise differentiation between genogroups underscore LAMP's potential as a robust diagnostic tool for aquaculture. This advancement in the specie detection (qPCR and LAMP) and genotyping of P. salmonis represents a significant step forward in disease management within the aquaculture industry. The implementation of LAMP promises enhanced disease surveillance, early detection, and improved management strategies, ultimately benefiting the salmonid aquaculture sector.

Monocular visual detection of coal flow rate in scraper conveyor based on template matching background differencing

Abstract The monitoring of coal flow is a crucial aspect of the intelligent regulation and control of comprehensive mining equipment. In recent years, machine vision technology has become a mainstream method for quickly and efficiently extracting coal flow information. However, the majority of research in this field has focused on belt conveyors, with relatively limited investigation into the use of this technology with scraper conveyors. In order to address the need for monitoring coal flow in scraper conveyors, a monocular visual detection method of coal flow rates based on template matching-background differencing is proposed. First, the region of interet in the images captured using a monocular camera mounted at a specific location is quickly identified using an enhanced template matching method. Second, the image motion region is segmented using interframe and background differencing. Finally, the coal flow rate is calculated on the basis of the number of pixel points in the segmented image. Experimental verification is performed using scraper conveyor test bench and real underground data. The results demonstrate that the proposed coal flow detection method is capable of achieving real-time detection of coal flow in scraper conveyor and provides a theoretical basis for the monitoring of coal flow of the scraper conveyor.

The development of RT-RPA and CRISPR-Cas12a based assay for sensitive detection of Hirame novirhabdovirus

Hirame novirhabdovirus (HIRRV) is a highly pathogenic fish virus that poses a significant threat to the farming of a variety of economic fish. Due to no commercial vaccines and effective drugs available, sensitive and rapid detection of HIRRV at latent and early stages is important and critical for the control of disease outbreaks. However, most of the current methods for HIRRV detection have a large dependence on instruments and operations. For better detection of HIRRV, we have established a detection technology based on the reverse transcription and recombinase polymerase amplification (RT-RPA) and CRISPR/Cas12a to detect the N gene of HIRRV in two steps. Following the screening of primer pairs, the reaction temperature and time for RPA were optimized to be 40 °C and 32min, respectively, and the CRISPR/Cas12a reaction was performed at 37 °C for 15min. The whole detection procedure including can be accomplished within 1 h, with a detection sensitivity of about 8.7 copies/μl. The detection method exhibited high specificity with no cross-reaction to the other Novirhabdoviruses IHNV and VHSV, allowing naked-eye color-based interpretation of the detection results through lateral flow (LF) strip or fluorescence under violet light. Furthermore, the proliferation dynamic of HIRRV in the spleen of flounder were comparatively detected by LF- and fluorescence-based RPA-CRISPR/Cas12a assay in comparison to qRT-PCR at the early infection stage, and the results showed that the viral positive signal could be firstly detected by the two RPA-CRISPR/Cas12a based methods at 6 hpi, and then by qRT-PCR at 12 hpi. Overall, our results demonstrated that the developed RPA-CRISPR/Cas12a method is a stable, specific, sensitive and more suitable in the field, which has a significant effect on the prevention of HIRRV. RT-RPA-Cas12a-mediated assay is a rapid, specific and sensitive detection method for visual and on-site detection of HIRRV, which shows a great application promise for the prevention of HIRRV infections.

Development of a visual detection method of porcine deltacoronavirus using loop-mediated isothermal amplification.

The emergence of porcine deltacoronavirus (PDCoV) presents a significant threat to both human and animal health due to its ability to cause highly contagious enteric diseases. This underscores the crucial need for timely and accurate diagnosis to facilitate effective epidemiological investigation and clinical management. This research aimed to establish a visual detection method based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) for PDCoV testing. In this study, six pairs of primers were designed according to the conserved sequences of PDCoV ORF1a/b genes. The primer sets and parameters that affect LAMP reaction were optimized. The visual RT-LAMP method was developed by incorporating methyl red into the optimized reaction system, it exclusively detected PDCoV without cross-reactivity with other viruses and the detection limits for PDCoV could reach 10 copies/μL. In comparison with RT-PCR for testing 132 clinical samples, the relative specificity and sensitivity of the visual RT-LAMP were found to be 99.2 and 100%, respectively, with a concordance rate of 99.2% and a kappa value of 0.959, indicating that the visual RT-LAMP is a reliable method for the application of PDCoV detection in clinical samples.

Facial detection of formaldehyde by using acidichromic carbon dots and the reaction between formaldehyde and ammonium chloride

Herein we report the facial detection of formaldehyde (FA) by using an interesting red acidichromic carbon dots (ACDs) which turns blue when pH gradually decreases. The color change was attributed to the conversion between the double bonds (C=N) and single bonds (C-N) on the surface of the ACDs. Inspired by the reaction between FA and ammonium chloride that produces H+ and methenamine and decrease the pH value of the solution, a fast and simple visual detection method for FA was found with a minimum discriminated concentration of 0.04 mol/L. A fluorescence detection method for FA was also found with LOD of 0.029 mol/L and FA in real sample, e.g., shredded squid was successfully analyzed. This work provides a new idea of developing fast visual detection method for daily monitor or in-site semiquantitative assessment on FA.

Detection of Encroachment in Civil Structure Using Dynamic Learning Techniques

Identifying cracks through visual inspection and automated surveys are both effective methods. While both approaches yield satisfactory distress analysis results, automated crack recognition technology stands out for its speed and cost-effectiveness compared to traditional human visual detection methods. This study introduces an innovative approach, namely "crack encroachment in concrete structures classified using DCNN" which employs a Deep Convolutional Neural Network. To enhance the precision of crack detection, the input image undergoes denoising through ADNLMF (Anisotropic Diffusion Non-Local Mean Filtering), preserving edges, textures, and features. Crack discrimination between crack and non-crack images was achieved using Yolo v3, and a deep convolutional neural network classifier was employed to identify specific crack types based on their widths, utilizing crack width transform. This method not only enhances accuracy but also reduces network complexity and processing time. A comprehensive performance comparison with existing crack identification techniques indicates that our proposed methodology for concrete structure crack recognition produces superior results.

Visual fluorescence detection of Listeria monocytogenes with CRISPR-Cas12a aptasensor.

Listeria monocytogenes (L. monocytogenes) is a prevalent food-borne pathogen that can cause listeriosis, which manifests as meningitis and other symptoms, potentially leading to fatal outcomes in severe cases. In this study, we developed an aptasensor utilizing carboxylated magnetic beads and Cas12a to detect L. monocytogenes. In the absence of L. monocytogenes, the aptamer maintains its spatial configuration, keeping the double-stranded DNA attached and preventing the release of a startup template and activation of Cas12a's trans-cleavage capability. Conversely, in the presence of L. monocytogenes, the aptamer undergoes a conformational change, releasing the double-stranded DNA to serve as a startup template, thereby activating the trans-cleavage capability of Cas12a. Consequently, as the concentration of L. monocytogenes increases, the observable brightness in a blue light gel cutter intensifies, leading to a rise in fluorescence intensity difference compared to the control. This Cas12a aptasensor demonstrates excellent sensitivity towards L. monocytogenes, with a lowest detection limit (LOD) of 57.15CFU/mL and a linear range of 4×102 to 4×107 CFU/mL (R2=0.9858). Notably, the proposed Cas12a aptasensor exhibited outstanding selectivity and recovery in beef samples, and could be employed for precise monitoring. This Cas12a aptasensor not only provides a novel fluorescent and visual rapid detection method for L. monocytogenes but also offers simplicity, speed, and stability compared to previous detection methods. Furthermore, it is suitable for on-site detection of beef samples.