Fluorescent Double Network Hydrogels with Ionic Responsiveness and High Mechanical Properties for Visual Detection

We developed a fluorescent double network hydrogel with ionic responsiveness and high mechanical properties for visual detection.The nanocomposite hydrogel of laponite and polyacrylamide serves as the first network,while the ionic cross-linked hydrogel of terbium ions and sodium alginate serves as the second network.The double-network structure,the introduction of nanoparticles and the reversible ionic crosslinked interactions confer high mechanical properties to the hydrogel.Terbium ions are not only used as the ionic cross-linked points,but also used as green emitters to endow hydrogels with fluorescent properties.On the basis of the “antenna effect” of terbium ions and the ion exchange interaction,the fluorescence of the hydrogels can make selective responses to various ions(such as organic acid radical ions,transition metal ions) in aqueous solutions,which enables a convenient strategy for visual detection toward ions.Consequently,the fluorescent double network hydrogel fabricated in this study is promising for use in the field of visual sensor detection.

Automatic Sensing and Detection for Subway Tunnel Pathologies

Subway tunnels often suffer from surface pathologies such as cracks,corrosion,fractures,peeling,water and sand infiltration,and sudden hazards caused by foreign object intrusions.Installing a mobile visual pathology sensing system at the front end of operating trains is a critical measure to ensure subway safety.Taking leakage as the typical pathology,a tunnel pathology automatic visual detection method based on Deeplabv3+(ASTPDS)was proposed to achieve automatic and high-precision detection and pixel-level morphology extraction of pathologies.Compared with similar methods,this approach showed significant advantages and achieved a detection accuracy of 93.12%,surpassing FCN and U-Net.Moreover,it also exceeded the recall rates for detecting leaks of FCN and U-Net by 8.33%and 8.19%,respectively.

Visualization Detection of Solid-Liquid Two-Phase Flow in Filling Pipeline by Electrical Capacitance Tomography Technology

During mine filling,the caking in the pipeline and the waste rock in the filling slurry may cause serious safety accidents such as pipe blocking or explosion.Therefore,the visualization of the innermine filling of the solid-liquid two-phase flow in the pipeline is important.This paper proposes a method based on capacitance tomography for the visualization of the solid-liquid distribution on the section of a filling pipe.A feedback network is used for electrical capacitance tomography reconstruction.This reconstruction method uses radial basis function neural network fitting to determine the relationship between the capacitance vector and medium distribution error.In the reconstruction process,the error in the linear back projection is removed;thus,the reconstruction problem becomes an accurate linear problem.The simulation results showthat the reconstruction accuracy of this algorithm is better than that of many traditional algorithms;furthermore,the reconstructed image artifacts are fewer,and the phase distribution boundary is clearer.This method can help determine the location and size of the caking and waste rock in the cross section of the pipeline more accurately and has great application prospects in the visualization of filling pipelines in mines.

Visual Detection of Vibrio parahaemolyticus using Combined CRISPR/Cas12a and Recombinase Polymerase Amplification

Objective To establish an ultra-sensitive,ultra-fast,visible detection method for Vibrio parahaemolyticus(VP).Methods We established a new method for detecting the tdh and trh genes of VP using clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 12a(CRISPR/Cas12a)combined with recombinase polymerase amplification and visual detection(CRISPR/Cas12a-VD).Results CRISPR/Cas12a-VD accurately detected target DNA at concentrations as low as 10^(-18)M(single molecule detection)within 30 min without cross-reactivity against other bacteria.When detecting pure cultures of VP,the consistency of results reached 100%compared with real-time PCR.The method accurately analysed pure cultures and spiked shrimp samples at concentrations as low as 10^(2)CFU/g.Conclusion The novel CRISPR/Cas12a-VD method for detecting VP performed better than traditional detection methods,such as real-time PCR,and has great potential for preventing the spread of pathogens.

A Cognitive Memory-Augmented Network for Visual Anomaly Detection

With the rapid development of automated visual analysis,visual analysis systems have become a popular research topic in the field of computer vision and automated analysis.Visual analysis systems can assist humans to detect anomalous events(e.g.,fighting,walking alone on the grass,etc).In general,the existing methods for visual anomaly detection are usually based on an autoencoder architecture,i.e.,reconstructing the current frame or predicting the future frame.Then,the reconstruction error is adopted as the evaluation metric to identify whether an input is abnormal or not.The flaws of the existing methods are that abnormal samples can also be reconstructed well.In this paper,inspired by the human memory ability,we propose a novel deep neural network(DNN)based model termed cognitive memory-augmented network(CMAN)for the visual anomaly detection problem.The proposed CMAN model assumes that the visual analysis system imitates humans to remember normal samples and then distinguishes abnormal events from the collected videos.Specifically,in the proposed CMAN model,we introduce a memory module that is able to simulate the memory capacity of humans and a density estimation network that can learn the data distribution.The reconstruction errors and the novelty scores are used to distinguish abnormal events from videos.In addition,we develop a two-step scheme to train the proposed model so that the proposed memory module and the density estimation network can cooperate to improve performance.Comprehensive experiments evaluated on various popular benchmarks show the superiority and effectiveness of the proposed CMAN model for visual anomaly detection comparing with the state-of-the-arts methods.The implementation code of our CMAN method can be accessed at https://github.com/CMANcode/CMAN_pytorch.

Sequential Pattern Technology for Visual Fire Detection

Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.

Vehicle Detection Based on Visual Saliency and Deep Sparse Convolution Hierarchical Model

Traditional vehicle detection algorithms use traverse search based vehicle candidate generation and hand crafted based classifier training for vehicle candidate verification.These types of methods generally have high processing times and low vehicle detection performance.To address this issue,a visual saliency and deep sparse convolution hierarchical model based vehicle detection algorithm is proposed.A visual saliency calculation is firstly used to generate a small vehicle candidate area.The vehicle candidate sub images are then loaded into a sparse deep convolution hierarchical model with an SVM-based classifier to perform the final detection.The experimental results demonstrate that the proposed method is with 94.81% correct rate and 0.78% false detection rate on the existing datasets and the real road pictures captured by our group,which outperforms the existing state-of-the-art algorithms.More importantly,high discriminative multi-scale features are generated by deep sparse convolution network which has broad application prospects in target recognition in the field of intelligent vehicle.

Biogenic amines detection in meat and meat products:the mechanisms,applications,and future trends

Biogenic amine is one of the main categories of hazardous compounds in meat and meat products,making its detection methods vital for the assurance of edible safety.In this sense,many biogenic amine detection techniques such as chromatographic,electrophoretic,and electrochemical methods have been developed,which play an irreplaceable role in ensuring the safety of meat and meat products.Due to the increasing demand for fast and on-site detection techniques,visual detection methods have been gradually developed compared with non-visual methods such as chromatography and electrophoresis.Herein,we comprehensively review the mechanism and the latest progress of these biogenic amine detection methods;besides,we put forward the prospects for the future development of biogenic amine detection techniques,with a view to providing support for the establishment of more accurate and efficient detection,prevention and control strategies of biogenic amines.

Paper-based biosensors based on multiple recognition modes for visual detection of microbially contaminated food

Microbially contaminated food can cause serious health hazards and economic losses,therefore sensitive,rapid,and highly specific visual detection is called for.Traditional detection of microorganisms is complex and time-consuming,which cannot meet current testing demands.The emergence of paper-based biosensors provided an effective method for efficient and visual detection of microorganisms,due to its high speed,all-in-one device,low cost,and convenience.This review focused on 5 biomarkers,namely nucleic acids,proteins,lipopolysaccharides.metabolites,and the whole microorganism of microorganisms.Besides,the recognition methods were summed up in 5 forms,including immunological recognition,aptamer recognition,nucleic acid amplification-mediated recognition.DNAzyme recognition and clustered regularly interspaced short palindromic repeats mediated recognition.In addition,we summarized the applications of paper-based biosensors in the detection of microorganisms thoroughly.Through the exploration of different biomarkers,identification methods,and applications,we hope to provide a reference for the development of paper-based biosensors and their application in safeguarding the food chain.

Fast Image Segmentation Algorithm Based on Salient Features Model and Spatial-frequency Domain Adaptive Kernel

A fast image segmentation algorithm based on salient features model and spatial-frequency domain adaptive kernel is proposed to solve the accurate discriminate objects problem of online visual detection in such scenes of variable sample morphological characteristics,low contrast and complex background texture.Firstly,by analyzing the spectral component distribution and spatial contour feature of the image,a salient feature model is established in spatial-frequency domain.Then,the salient object detection method based on Gaussian band-pass filter and the design criterion of adaptive convolution kernel are proposed to extract the salient contour feature of the target in spatial and frequency domain.Finally,the selection and growth rules of seed points are improved by integrating the gray level and contour features of the target,and the target is segmented by seeded region growing.Experiments have been performed on Berkeley Segmentation Data Set,as well as sample images of online detection,to verify the effectiveness of the algorithm.The experimental results show that the Jaccard Similarity Coefficient of the segmentation is more than 90%,which indicates that the proposed algorithm can availably extract the target feature information,suppress the background texture and resist noise interference.Besides,the Hausdorff Distance of the segmentation is less than 10,which infers that the proposed algorithm obtains a high evaluation on the target contour preservation.The experimental results also show that the proposed algorithm significantly improves the operation efficiency while obtaining comparable segmentation performance over other algorithms.

Establishment of RT-LAMP Assay for Infectious Pancreatic Necrosis Virus

The purpose of this study was to establish a method for the rapid detection of infectious pancreatic necrosis virus(IPNV,Jasper serotype)using reverse transcription loop-mediated isothermal amplification(RT-LAMP).Four groups of specific primers were designed,according to the genome sequence of a Chinese IPNV isolate ChRtm213.The results showed that primer set B2 had the best amplification effect.When the final concentration of Mg2+was 6 mmol·L-1,dNTPs were 1 mmol·L-1 and betaine was 0.4 mol·L-1,the reaction could be completed in a 63℃water bath within 60 min.This RT-LAMP assay for the detection of IPNV had no cross-reactivity with infectious hematopoietic necrosis virus,viral hemorrhagic septicemia virus,grass carp reovirus and spring viremia of carp virus.The detection limit was 3.2×10-12 ng·μL-1.The sensitivity of this method was 10-fold higher than that of a previously published RT-LAMP assay for detecting the Spajarup(Sp)serotype of IPNV.This method,aimed at detecting IPNV isolates that were currently prevalent in China,possessed the characteristics of strong specificity,high sensitivity and direct interpretation by the naked eyes.The IPNV RT-LAMP was successfully applied to determine the clinical samples,which indicated the IPNV RT-LAMP assay was suitable for the rapid and large-scale detections of IPNV in China.

Ship detection and extraction using visual saliency and histogram of oriented gradient

A novel unsupervised ship detection and extraction method is proposed. A combination model based on visual saliency is constructed for searching the ship target regions and suppressing the false alarms. The salient target regions are extracted and marked through segmentation. Radon transform is applied to confirm the suspected ship targets with symmetry profiles. Then, a new descriptor, improved histogram of oriented gradient(HOG), is introduced to discriminate the real ships. The experimental results on real optical remote sensing images demonstrate that plenty of ships can be extracted and located successfully, and the number of ships can be accurately acquired. Furthermore, the proposed method is superior to the contrastive methods in terms of both accuracy rate and false alarm rate.

An enhanced method for nucleic acid detection with CRISPR-Cas12a using phosphorothioate modified primers and optimized gold-nanopaticle strip

CRISPR-Cas12a system has been shown promising for nucleic acid diagnostics due to its rapid,portable and accurate features.However,cleavage of the amplicons and primers by the cis-and trans-activity of Cas12a hinders the attempts to integrate the amplification and detection into a single reaction.Through phosphorothioate modification of primers,we realized onepot detection with high sensitivity using plasmids of SARS-CoV-2,HPV16 and HPV18.We also identified the activated Cas12a has a much higher affinity to C nucleotide-rich reporter than others.By applying such reporters,the reaction time required for a lateral-flow readout was significantly reduced.Furthermore,to improve the specificity of the strip-based assay,we created a novel reporter and,when combined with a customized gold-nanopaticle strip,the readout was greatly enhanced owing to the elimination of the nonspecific signal.This established system,termed Targeting DNA by Cas12a-based Eye Sight Testing in an Onepot Reaction(TESTOR),was validated using clinical cervical scrape samples for human papillomaviruses(HPVs)detection.Our system represents a general approach to integrating the nucleic acid amplification and detection into a single reaction in CRISPR-Cas systems,highlighting its potential as a rapid,portable and accurate detection platform of nucleic acids.

A ratiometric fluorescent nanoprobe based on ZIF-8@AuNCs-Tb for visual detection of 2,6-pyridinedicarboxylic acid

The rapid and sensitive detection of 2,6-pyridinedicarboxylic acid(DPA),one of the main biomarkers of Bacillus anthracis,is of great significance for the screening and diagnosing of anthrax.Herein,a ratiometric fluorescent nanoprobe based on zeolite imidazolate framework-8(ZIF-8)@AuNCs-Tb was constructed by embedding both gold nanoclusters(AuNCs)and terbium ions(Tb^(3+))into ZIF-8 for highresolution visual detection of DPA.Due to the aggregation induced emission enhancement(AIE)effect,AuNCs embedded in ZIF-8 emit a strong orange fluorescence.When Tb^(3+)is coordinated with DPA added to the nanoprobe,it will emit a strong green fluorescence owing to the antenna effect.The results reveal that ZIF-8@AuNCs-Tb nanoprobe can detect DPA effectively with a good linear relationship in the range of 40-200 and 200-1000μmol/L,the limit of detection(LOD)is estimated at 1.8μmol/L(3σ/k).The proposed nanoprobe shows a remarkable selectivity for DPA and is quite easy to realize visualization based on the fluorescent color changing from orange to green,which has potential application in clinical diagnosis.The feasibility of this method was verified by standard addition recovery experiments simulating the release of DPA from spores.

Visual Detection of COVID‑19 from Materials Aspect

In the recent COVID-19 pandemic,World Health Organization emphasized that early detection is an effective strategy to reduce the spread of SARS-CoV-2 viruses.Several diagnostic methods,such as reverse transcription-polymerase chain reaction(RT-PCR)and lateral flow immunoassay(LFIA),have been applied based on the mechanism of specific recognition and binding of the probes to viruses or viral antigens.Although the remarkable progress,these methods still suffer from inadequate cellular materials or errors in the detection and sampling procedure of nasopharyngeal/oropharyngeal swab collection.Therefore,developing accurate,ultrafast,and visualized detection calls for more advanced materials and technology urgently to fight against the epidemic.In this review,we first summarize the current methodologies for SARS-CoV-2 diagnosis.Then,recent representative examples are introduced based on various output signals(e.g.,colorimetric,fluorometric,electronic,acoustic).Finally,we discuss the limitations of the methods and provide our perspectives on priorities for future test development.

Roundness measurement of cigarette based on visual information

Roundness is defined as the degree that the cross section of an object is close to a theoretical circle. In the cigarette production process, the quality and production efficiency of a cigarette are directly affected by the roundness of the un-cut cigarette. To improve the current measurement method using a charge-coupled device （CCD） sensor and measure the roundness of cigarettes in the production line, a visual detection system composed of an industrial camera and a structural light is developed. The system＇s roundness-calculation method is closer to the real environment of the cigarette roundness. In this visual system, the line-structure light shines on the cigarette with a fixed angle and height in a longitudinal section, forming a crescent-shaped spot when the industrial camera cannot capture the cigarette＇s end surface. Then, the spot is analyzed using image-processing techniques, such as a median filter and ellipse fitting, after the industrial camera captures the spot. The system with a non-contact measurement style can meet the requirements of on-line cigarette detection with stable results and high precision.