Approach For Rapid Detection Research Articles

A data-driven approach for rapid detection of aeroelastic modes from flutter flight test based on limited sensor measurements

Flutter flight test involves the evaluation of the airframe’s aeroelastic stability by applying artificial excitation on the aircraft lifting surfaces. The subsequent responses are captured and analyzed to extract the frequencies and damping characteristics of the system. However, noise contamination, turbulence, non-optimal excitation of modes, and sensor malfunction in one or more sensors make it time-consuming and corrupt the extraction process. In order to expedite the process of identifying and analyzing aeroelastic modes, this study implements a time-delay embedded Dynamic Mode Decomposition technique. This approach is complemented by Robust Principal Component Analysis methodology, and a sparsity promoting criterion which enables the automatic and optimal selection of sparse modes. The anonymized flutter flight test data, provided by the fifth author of this research paper, is utilized in this implementation. The methodology assumes no knowledge of the input excitation, only deals with the responses captured by accelerometer channels, and rapidly identifies the aeroelastic modes. By incorporating a compressed sensing algorithm, the methodology gains the ability to identify aeroelastic modes, even when the number of available sensors is limited. This augmentation greatly enhances the methodology’s robustness and effectiveness, making it an excellent choice for real-time implementation during flutter test campaigns.

Optimized Antibiotic Resistance Genes Monitoring Scenarios Promote Sustainability of Urban Water Cycle.

Dissemination of antibiotic resistance genes (ARGs) in urban water bodies has become a significant environmental and health concern. Many approaches based on real-time quantitative PCR (qPCR) have been developed to offer rapid and highly specific detection of ARGs in water environments, but the complicated and time-consuming procedures have hindered their widespread use. Herein, we developed a facile one-step approach for rapid detection of ARGs by leveraging the trans-cleavage activity of Cas12a and recombinase polymerase amplification (RPA). This efficient method matches the sensitivity and specificity of qPCR and requires no complex equipment. The results show a strong correlation between the prevalence of four ARG markers (ARGs: sul1, qnrA-1, mcr-1, and class 1 integrons: intl1) in tap water, human urine, farm wastewater, hospital wastewater, municipal wastewater treatment plants (WWTPs), and proximate natural aquatic ecosystems, indicating the circulation of ARGs within the urban water cycle. Through monitoring the ARG markers in 18 WWTPs in 9 cities across China during both peak and declining stages of the COVID epidemic, we found an increased detection frequency of mcr-1 and qnrA-1 in wastewater during peak periods. The ARG detection method developed in this work may offer a useful tool for promoting a sustainable urban water cycle.

Accelerated diagnosis: a crosslinking catalytic hairpin assembly system for rapid and sensitive SARS-CoV-2 RNA detection.

The COVID-19 pandemic has underscored the urgent need for rapid and reliable strategies for early detection of SARS-CoV-2. In this study, we propose a DNA nanosphere-based crosslinking catalytic hairpin assembly (CCHA) system for the rapid and sensitive SARS-CoV-2 RNA detection. The CCHA system employs two DNA nanospheres functionalized with catalytic hairpin assembly (CHA) hairpins. The presence of target SARS-CoV-2 RNA initiated the crosslinking of DNA nanospheres via CHA process, leading to the amplification of fluorescence signals. As a result, the speed of SARS-CoV-2 diagnosis was enhanced by significantly increasing the local concentration of the reagents in a crosslinked DNA product, leading to a detection limit of 363 fM within 5min. The robustness of this system has been validated in complex environments, such as fetal bovine serum and saliva. Hence, the proposed CCHA system offers an efficient and simple approach for rapid detection of SARS-CoV-2 RNA, holding substantial promise for enhancing COVID-19 diagnosis.

A Reliable Framework for Detection of Smart Contract Vulnerabilities for Enhancing Operability in Inter-Organizational Systems

Information and communication technology based inter-organizational systems enable companies to integrate information and conduct business electronically across different parts of the organization. For organizations embracing blockchain, smart contracts provide automation and operational efficiency for inter-organizational systems. Initially utilised for financial transactions, smart contract are extended beyond banking and deployed in wide number of organizations. Smart contracts are regarded as self-executing type of contract consisting of agreement’s terms embedded directly into the code which plays a vital role in operability for inter-organizational systems, however, smart contract vulnerabilities can arise due to programming errors, leading to security issues. The effects of smart contract vulnerabilities can be significant, including loss of funds, unauthorized access to sensitive information, manipulation of data, and loss of trust in the application leading to catastrophic financial losses followed by legal implications for an organization based on blockchain technology. The goal of smart contracts exploiting vulnerabilities is to discover and eliminate potential security vulnerabilities in smart contract code prior to it being deployed. Detecting vulnerabilities in a timely manner helps to prevent financial losses, unauthorized access, and data manipulation. In order to provide a robust solution to detect vulnerabilities in smart contracts, the proposed methodology presents a novel approach for rapid detection of vulnerabilities by integrating genetic algorithm with isolation forest. Furthermore, enhancing smart contract vulnerability identification with higher accuracy and false-positive rate provides a reliable gateway for organizations to adopt blockchain.

Rapid detection of six Oceanobacillus species in Daqu starter using single-cell Raman spectroscopy combined with machine learning.

Many traditional fermented foods and beverages industries around the world request the addition of multi-species starter cultures. However, the microbial community in starter cultures is subject to fluctuations due to their exposure to an open environment during fermentation. A rapid detection approach to identify the microbial composition of starter culture is essential to ensure the quality of the final products. Here, we applied single-cell Raman spectroscopy (SCRS) combined with machine learning to monitor Oceanobacillus species in Daqu starter, which plays crucial roles in the process of Chinese baijiu. First, a total of six Oceanobacillus species (O. caeni, O. kimchii, O. iheyensis, O. sojae, O. oncorhynchi subsp. Oncorhynchi and O. profundus) were detected in 44 Daqu samples by amplicon sequencing and isolated by pure culture. Then, we created a reference database of these Oceanobacillus strains which correlated their taxonomic data and single-cell Raman spectra (SCRS). Based on the SCRS dataset, five machine-learning algorithms were used to classify Oceanobacillus strains, among which support vector machine (SVM) showed the highest rate of accuracy. For validation of SVM-based model, we employed a synthetic microbial community composed of varying proportions of Oceanobacillus species and demonstrated a remarkable accuracy, with a mean error was less than 1% between the predicted result and the expected value. The relative abundance of six different Oceanobacillus species during Daqu fermentation was predicted within 60 min using this method, and the reliability of the method was proved by correlating the Raman spectrum with the amplicon sequencing profiles by partial least squares regression. Our study provides a rapid, non-destructive and label-free approach for rapid identification of Oceanobacillus species in Daqu starter culture, contributing to real-time monitoring of fermentation process and ensuring high-quality products.

Current trends in colorimetric biosensors using nanozymes for detecting biotoxins (bacterial food toxins, mycotoxins, and marine toxins).

Biotoxins, predominantly bacterial food toxins, mycotoxins, and marine toxins, have emerged as major threats in the fields of seafood, other foods, feeds, and medicine. They have potential teratogenic, mutagenic, and carcinogenic effects on humans, occasionally triggering high morbidity and mortality. One of the apparent concerns relates to the increasing consumption of fast food resulting in the demand for processed food without adequate consideration of the toxins they may contain. Therefore, developing improved methods for detecting biotoxins is of paramount significance. Nanozymes, a type of nanomaterials exhibiting enzyme-like activity, are increasingly being recognized as viable alternatives to natural enzymes owing to their benefits, such as customizable design, controlled catalytic performance, excellent biocompatibility, and superior stability. The remarkable catalytic activity of nanozymes has led to their broad utilization in the development of colorimetric biosensors. This has emerged as a potent and efficient approach for rapid detection, enabling the creation of innovative colorimetric sensing methodologies through the integration of nanozymes with colorimetric sensors. In this review, recent development in nanozyme research and their application in colorimetric biosensing of biotoxins are examined with an emphasis on their characteristics and performance. The study particularly focuses on the peroxidase (POD) activity, oxidase (OXD) activity, superoxide dismutase (SOD), and catalase (CAT) activity of nanozymes in colorimetric biosensors. Ultimately, the challenges and future prospects of these assays are explored.

Constructing graphene oxide/Au nanoparticle cellulose membranes for SERS detection of mixed pesticide residues in edible chrysanthemum.

Edible chrysanthemum is widely cultivated and used as an important ingredient of medicine, tea and multifunctional food. During the planting of chrysanthemum, pesticides are extensively used for preventing plant diseases and insect pests. To ensure the food safety of edible chrysanthemum, rapid detection methods are urgently needed for on-site inspection. In this study, a graphene oxide/Au nanoparticle (GO/Au NP) cellulose substrate was prepared through layer-by-layer assembly of GO and Au NPs on a mixed cellulose ester membrane. Surface-enhanced Raman spectroscopy (SERS) detection of four types of organophosphorus and organosulfur pesticides was achieved by filtering the extracting solution through the substrate and analysing SERS spectra. Qualitative and semi-quantitative detection of fenthion, phoxim, isocarbophos and thiram was accomplished with the detection limits of 38.01, 8.13, 48.97 and 8.74 ng mL-1, respectively. A spiking experiment further demonstrated the feasibility of this method for rapid and on-site detection of mixed pesticides in chrysanthemum. This study provides a new approach for rapid detection of multiple hazardous substances in flowering and herbal plants.

Toward the Rapid Seismic Assessment of Landslides in Coastal Alaska

Abstract As glaciers retreat, landslide-driven tsunamis pose mounting threats across the high latitudes. The recent landslide tsunamis in Alaska and Greenland have spurred efforts to forecast and monitor these events. We use nine large landslides spanning southern Alaska to test an approach for rapid detection and characterization. We use long-period seismograms recorded within three minutes of the start of a landslide to estimate the location and approximate volume. In the presence of good seismic network coverage, location errors are no more than a few kilometers, and detection limits are well below 1 Mm3. The combination of detection time, location, and size provides the ability to rapidly determine whether a landslide occurred close to open water and, if so, its tsunamigenic potential. Our approach is rapid enough to support National Oceanic and Atmospheric Administration (NOAA)’s five-minute tsunami warning goal. The historical analysis we present provides the foundation and parameter tuning for a prototype system that is now providing real-time detections.

A 2D carbon nitride-based electrochemical aptasensor with reverse amplification for highly sensitive detection of okadaic acid in shellfish.

Okadaic acid (OA) is one of the main virulence factors of diarrheal shellfish toxins (DSP). It is of great significance to detect OA with an accurate, specific and cost-effective technique in the fields of seafood safety and water quality control. In this work, an electrochemical aptasensor with reverse amplification was developed for the sensitive detection of OA. A two-dimensional graphite-phase nanomaterial (carbon nitride) modified with an anti-OA aptamer and thionine (Th) was immobilized onto the surface of the electrochemical electrode as the sensitive element to capture target OA molecules. ssDNA-modified carbon nitride was used as the reverse amplification element by hybridizing with non-OA linked aptamers. The preparation of the electrochemical aptasensor was well characterized by Scanning Electron Microscopy (SEM), zeta potential detection, UV-Vis absorption, Brunner-Emmet-Teller (BET) measurements, and electrochemical measurements. The quantitative assessment of OA was achieved by differential pulse voltammetry (DPV). Experimental results indicated that this aptasensor showed a concentration-dependent response to OA with a good detection performance including in terms of selectivity, repeatability, reproducibility, and stability. It exhibited 100-fold selectivity between OA and other toxins including dinophysistoxins (DTX), pectenotoxins (PTX), and yessotoxins (YTX). In addition, it showed a much wider quantification range, which is 10-13 M-10-10 M (0.080-80.50 pg mL-1). The detection limit was as low as 10-13 M (0.080 pg mL-1). The aptasensor also successfully achieved significant practicality on real shellfish samples contaminated by OA. All these results demonstrated that the reverse amplification strategy for marine toxin detection may provide a label-free and rapid detection approach for portable applications in the fields of environmental monitoring and food security.

A sensing platform for ascorbic acid based on the spatial confinement of covalent organic frameworks

Essential to many activities in our bodies, ascorbic acid is a small molecule essential to human health and physiological processes. In this study, a covalent organic framework called TpNda-COF was synthesized, which is composed of Tp (triformylephloroglucinol) and Nda (1, 5-napthalenediamine). This framework acts as a mimic enzyme and displays excellent oxidase-like activity when stimulated with purple light (at = 405 nm). It catalyzes the oxidation of 3,3′,5,5′-tetramethylbenzydine (TMB) by generating O2− free radicals in the presence of oxygen. The resulting oxTMB shows a characteristic absorption peak at 652 nm. The biomimetic catalysis efficiency is significantly improved due to spatial restriction. By introducing ascorbic acid (AA) in the system, the blue oxTMB is reduced to colorless TMB. The decrease in absorption peak intensity can be quantitatively measured using a UV–Vis spectrophotometer, enabling the detection of AA. The sensing platform demonstrates excellent selectivity and sensitivity. It has a wide linear detection range from 5 μM to 50 μM, with a low detection limit of 1.44 μM. Advantages such as the easy control of light, high stability and efficient oxidation are provided by the TpNda-COF mimic oxidase. This innovative method presents a promising and cost-effective approach for rapid detection of ascorbic acid, with potential applications across various fields.

Rapid Detection of Multi-QR Codes Based on Multistage Stepwise Discrimination and a Compressed MobileNet

Poor real-time performance in multi-QR codes detection has been a bottleneck in QR code decoding based Internet-of-Things (IoT) systems. To tackle this issue, we propose in this paper a rapid detection approach, which consists of Multistage Stepwise Discrimination (MSD) and a Compressed MobileNet. Inspired by the object category determination analysis, the preprocessed QR codes are extracted accurately on a small scale using the MSD. Guided by the small scale of the image and the end-to-end detection model, we obtain a lightweight Compressed MobileNet in a deep weight compression manner to realize rapid inference of multi-QR codes. The Average Detection Precision (ADP), Multiple Box Rate (MBR) and running time are used for quantitative evaluation of the efficacy and efficiency. Compared with a few state-of-the-art methods, our approach has higher detection performance in rapid and accurate extraction of all the QR codes. The approach is conducive to embedded implementation in edge devices along with a bit of overhead computation to further benefit a wide range of real-time IoT applications.

Proximity ligation initiated DNAzyme-powered catalytic hairpin assembly for sensitive and accurate microRNA analysis

MicroRNAs (miRNAs) play a significant role in regulating diverse physiological processes, and are regarded as novel diagnostic biomarkers. However, the sensitive and reliable miRNA detection remains a huge challenge. Herein, we propose a proximity ligated initiated magnesium ion (Mg2+)-dependent DNAzyme-powered signal cascade for sensitive, accurate and reliable detection of miRNAs. Three signal amplification processes are involved in this approach, including the target miRNA recycle, DNAzyme powered substrate cleavage, and catalytic hairpin reaction (CHA). Based on this, the approach shows a low limit of detection of 523 aM and a wide detection range of 7 orders of magnitudes, which is comparable or superior to most of the former miRNA detection methods. In addition, the approach also possesses a high selectivity to target miRNA, suggesting a potential promising future of the approach for rapid detection of miRNAs in the application of developing novel tools for skin cancer diagnosis, and recovery evaluation.

Rapid Visual LAMP Method for Detection of Genetically Modified Organisms.

We developed a novel loop-mediated isothermal amplification (LAMP) method using DNA captured on polyacrylamide microparticles (PAMMPs) as templates (PAMMPs@DNA-LAMP) for rapid qualitative detection of genetically modified organisms (GMOs). Here, DNA was extracted by a fast and cost-effective method using PAMMPs. Four LAMP primers were designed for the PAMMPs@DNA-LAMP method to detect the cauliflower mosaic virus 35S (CaMV35S) promotor in GMOs. We thus developed this method for rapid extraction of DNA (5-10 min) and fast amplification of DNA within ∼30 min at a constant temperature of 63 °C. Moreover, the DNA captured by PAMMPs (PAMMPs@DNA) could be effectively detected by both conventional and quantitative PCR (qPCR) and LAMP. The PAMMPs@DNA-LAMP method was validated with high specificity, sensitivity, and performance for practical sample analysis. This assay detected 0.01% target sequences, which had a high specificity like qPCR and better than the conventional PCR (cPCR). Furthermore, PAMMPs@DNA-LAMP was successfully used to extract and detect DNA from food samples of the major crops (soybean, maize, rice, etc.). In summary, a novel PAMMPs@DNA-LAMP assay has been developed, which has higher sensitivity and spends less time than the cPCR detection using the conventional DNA extracted process. This method offers a novel approach for rapid detection of GMOs in the field.

Rapid detection of thiabendazole residues in apple juice by surface-enhanced Raman scattering coupled with silver coated gold nanoparticles

In recent years, the excessive use of pesticides has posed significant hazards to the ecological environment and human health in the pursuit of high crop yields. In this work, we developed a simple, sensitive, and eco-friendly approach for rapid detection of thiabendazole in apple juice using surface-enhanced Raman scattering (SERS) coupled with silver-coated gold nanoparticles (Au@Ag NPs). The developed Au@Ag NPs exhibited excellent sensitivity, allowing for the detection of thiabendazole in standard solutions at a minimum concentration of 50 ng/mL. Furthermore, two sample preparation methods were compared for detecting thiabendazole in apple juice. As the direct detection method for SERS analysis failed to detect thiabendazole at levels below the maximum residue limit based on the Chinese standard (3000 ng/mL), the effects of main matrix components in apple juice on the detection of thiabendazole were further investigated. The results revealed that both sugars and organic acids in apple juice interfered with the SERS measurement to varying degrees. Consequently, we optimized the QuEChERS method for sample preparation and achieved a higher sensitivity with a minimum detectable concentration of 250 ng/mL, a limit of detection of 0.06 mg/L and the recoveries of spiked samples were ranged from 80.2 % to 108.6 %. This study demonstrated the feasibility of proposed SERS method for pesticide residue analysis, addressing the need for food safety monitoring.

A “turn-on” approach for rapid detection of tetracycline based on supramolecular aggregates of water soluble perylene diimide

Antibiotic residue in animal foods has aroused great attention. Herein, we report a “turn-on” approach via supramolecular aggregates of water soluble perylene diimide (PDI) for rapid detection of tetracycline (TC), which is very important to food quality and safety. The disassembly of PDI aggregate was promoted owing to the coordination of Eu3+ and TC, leading to a significant recovery of fluorescence intensity. The sensing mechanism and performance of this approach were studied by absorption and emission spectra. The limit of detection (LOD) and the operation range were 12.11 nM and 0.1–30 μM, respectively. This method features simple acquisition, free of organic solvents, rapid detection, high selectivity and sensitivity, ease of observation and operation. It could be applied to detecting TC in Chicken, Fish and Pork with good recovery. To our knowledge, this is the first report of TC based on PDI probe in aqueous media.

Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.

The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study's overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.

Imaging of Gastrointestinal Tract Ailments.

Gastrointestinal (GI) disorders comprise a diverse range of conditions that can significantly reduce the quality of life and can even be life-threatening in serious cases. The development of accurate and rapid detection approaches is of essential importance for early diagnosis and timely management of GI diseases. This review mainly focuses on the imaging of several representative gastrointestinal ailments, such as inflammatory bowel disease, tumors, appendicitis, Meckel's diverticulum, and others. Various imaging modalities commonly used for the gastrointestinal tract, including magnetic resonance imaging (MRI), positron emission tomography (PET) and single photon emission computed tomography (SPECT), and photoacoustic tomography (PAT) and multimodal imaging with mode overlap are summarized. These achievements in single and multimodal imaging provide useful guidance for improved diagnosis, staging, and treatment of the corresponding gastrointestinal diseases. The review evaluates the strengths and weaknesses of different imaging techniques and summarizes the development of imaging techniques used for diagnosing gastrointestinal ailments.

Detection of nitrification inhibitor dicyandiamide: A direct electrochemical approach.

A single run approach for rapid detection of nitrification inhibitor, dicyandiamide (DCD) using electrogenerated chlorine assisted polymerization through azo bond, under acidic conditions and at a preanodized screen printed carbon electrode (SPCE*) is presented. The role of chloride containing support electrolyte in acidic medium along with oxygen functionalities/edge sites are found to be crucial for the successful oxidative polymerization and subsequent adsorption of oxidized products on the electrode surface. The SEM, cyclic voltammetry and X-ray photoelectron spectroscopy studies were used to characterize the polymer film formation. The system exhibited a linear range between 20 and 170μM with a detection limit of 3μM (S/N=3). The method was successfully tested for the detection of DCD in dairy and water samples. Simultaneous detection of DCD in the presence of melamine has also been demonstrated.

Rapid detection of the adulterants in milk using gamma radiation

Milk and dairy adulteration practice nowadays is becoming a global concern. The present study discloses the role of gamma irradiation as a cost-effective and rapid-detection approach to detect adulterants in milk instead of using expensive and time-consuming chemical-based methods. In the present study, synthetic milk samples were prepared using different common adulterants (vegetable oil, urea, water) and a comparative analysis has been done with market milk samples along with fresh milk based upon the attenuation property. The obtained linear attenuation coefficient decreases significantly (p < 0.05) with the increasing concentration of adulterants in sample. The recorded attenuation coefficient for fresh milk was 0.379 ± 0.04 and that of adulterated samples found to be in the range of 0.296–0.188. Moreover, different protein coagulation patterns of the heat-processed milk samples were detected. The coagulated protein of adulterated milk shows a granular texture. The attenuation coefficient of milk samples decreases with the gradual increase of adulterants. The correlation between the attenuation coefficient and adulterants in milk indicated the possible use of gamma radiation as a significant adulterant-detection method.

A Real-Time PCR Approach for Rapid Detection of Viable Salmonella Enteritidis in Shell Eggs.

Rapid and robust detection assays for Salmonella Enteritidis (SE) in shell eggs are essential to enable a quick testing turnaround time (TAT) at the earliest checkpoint and to ensure effective food safety control. Real-time polymerase chain reaction (qPCR) assays provide a workaround for the protracted lead times associated with conventional Salmonella diagnostic testing. However, DNA-based analysis cannot reliably discriminate between signals from viable and dead bacteria. We developed a strategy based on an SE qPCR assay that can be integrated into system testing to accelerate the detection of viable SE in egg-enriched cultures and verify the yielded SE isolates. The specificity of the assay was evaluated against 89 Salmonella strains, and SE was accurately identified in every instance. To define the indicator for a viable bacteria readout, viable or heat-inactivated SE were spiked into shell egg contents to generate post-enriched, artificially contaminated cultures to establish the quantification cycle (Cq) for viable SE. Our study has demonstrated that this technique could potentially be applied to accurately identify viable SE during the screening stage of naturally contaminated shell eggs following enrichment to provide an early alert, and that it consistently identified the serotypes of SE isolates in a shorter time than conventional testing.