Rapid Target Detection Research Articles

A ratiometric fluorescence nanoprobe using CdTe QDs for fast detection of carbaryl insecticide in apple

In this study, a novel, simple and sensitive ratiometric fluorescence method is presented for the detection of very low quantities of the carbaryl in Iranian apple using cadmium telluride quantum dots (CdTe QDs) nanoprobe. The principle of the proposed strategy relies on the rapid hydrolysis of the carbaryl under an alkaline condition and production of the 1-naphthol with a blue emission at 470 nm. Besides, using the CdTe QDs with a yellow emission at 580 nm, as a reference, improves the visual tracking of carbaryl through changes in color tonality. The herein described methodology is applied for enzyme-free visual detection of carbaryl with satisfactory results in the presence of other common pesticides in Iranian apple sample. Additionally, the calculated limit of detection (LOD) of 0.12 ng mL−1 for carbaryl is much lower than the maximum residue limits of carbaryl warned value reported by the European Union and United States pesticides database, which is so promising for carbaryl sensing in the monitoring of fruits. Furthermore, the ability of the proposed method for the detection of carbaryl residues in Iranian apple was confirmed by the high-performance liquid chromatography (HPLC) method as a standard method through statistical analysis. This fast and highly sensitive naked-eye ratiometric sensor may hold great promise to provide the technical support for the rapid and valid detection of other targets in food safety fields.

Construction of electrochemical aptasensors with Ag(I) metal−organic frameworks toward high-efficient detection of ultra-trace penicillin

Two novel silver-based metal − organic frameworks (MOFs) have been successfully designed and synthesized by coordination assembly of tri(pyridin-4-yl)amine (TPA) and Ag(I) salts with different anions (SiF62− vs. CH3SO3−). Both MOFs show excellent physicochemical stability and superior electrochemical activity. It is the first time to implement Ag(I)-MOFs to fabricate high-efficient aptasensors to detect ultra-trace penicillin by electrochemical method. Remarkably, the Ag2SiF6-MOF-based biosensor shows ultra-high selectivity toward penicillin in the presence of other antibiotic interferences, which also exhibits the lowest detection limit of 0.849 pg mL−1 among all reported sensing materials, in the concentration range from 0.001 to 0.5 ng mL−1. Moreover, the fabricated aptasensor with good repeatability and high stability can effectively monitor penicillin in real milk sample. Interestingly, the isostructrual nature of two MOFs firstly illustrates the critical role of anion in constructing MOFs-based aptasensors. This work establishes a promising approach to design and modulate MOFs-based aptasensors for rapid and convenient detection of ultra-trace targets.

Modulation of oligonucleotide-binding dynamics on WS2 nanosheet interfaces for detection of Alzheimer's disease biomarkers

Non-covalent adsorption and desorption of oligonucleotides on two-dimensional nanosheets are widely employed to design nanobiosensors for the rapid optical detection of targets. A precise control over the weak interactions between nanosheet interfaces and oligonucleotides is crucial for a high-sensing performance. Herein, the interface of ultrathin WS2 nanosheets used as a fluorescence quencher was engineered by four different dextran polymers in an aqueous solution to control the adsorption kinetics and thermodynamics of the DNA probe. The WS2 nanosheets, functionalized by the carboxyl group-bearing dextran (CM-dex-WS2) or the trimethylammonium-modified dextran (TMA-dex-WS2), exhibited 3.6-fold faster adsorption rates of the fluorescein-labeled DNA probe (FAM-DNA), which led to the effective fluorescence quenching of FAM, compared to the nanosheets functionalized with pristine dextran (dex-WS2) or the hydrophobic phenoxy groups-bearing dextran (PhO-dex-WS2). Isothermal titration calorimetry measurements showed that the adsorption strength of FAM-DNA for CM-dex-WS2 was one order of magnitude greater than its hybridization energy for a target microRNA (miR-29a) that is well-known as an Alzheimer's disease (AD) biomarker, leading to the unfavorable desorption of the DNA probe from the surface. In contrast, TMA-dex-WS2 exhibited the proper adsorption strength of FAM-DNA, which was lower than its hybridization energy for miR-29a, leading to its favorable desorption from the nanosheet surface along with the noticeable restoration of the quenched fluorescence after its hybridization with miR-29a. Finally, the interface modulation of WS2 nanosheets allowed the selective and sensitive recognition of miR-29a against non-complementary RNA and single base-mismatched RNA in human serum via increases in target-specific fluorescence.

A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources

Water is a natural resource for agricultural irrigation. Recycling use of water is important in terms of resource conservation and is good for sustainable development of the ecological environment. The wastewater from daily living and industrial production contains various chemicals that are supposed as pollutants leading to the decline of water quality. For the demand of water protection and recycling, the assessment of water pollution level should be evaluated. An effective scientific technique is required for rapid detection of water pollution. Near-infrared (NIR) spectroscopy is a modern technology suitable for rapid detection of agricultural targets. For monitoring the agricultural water resource, the NIR modeling methods are required to be smart and artificially controlled to solve the issues when we confront a considerable number of data or a dynamic situation. In this study, an improved convolutional neural network (CNN) architecture was designed for a deep calibration on the NIR data. The architecture is shallow, simply constructed with one convolution layer and one pooling layer. The decision tree algorithm was employed in the pooling layer for extracting the informative features in a data driven manner. The CNN architecture was trained by combined tuning of multiple parameters in different layers. The convolution filters, the decision tree branches and the hidden neurons in the fully connected layer were automatically adaptive with fidelity to the measured data. A CNN calibration model for NIR quantitatively determination of water pollution level was then established and optimized in deep learning mode, and eventually improved the NIR prediction accuracy. Prospectively, the designed shallow CNN architecture is feasible to be used for establishing intelligent spectroscopic models for evaluating the level of water pollution, and is expected to provide smart technical support in dealing with the issues of water recycling and conservation for agricultural cultivation.

A rapid and convenient method for on-site detection of MON863 maize through real-time fluorescence recombinase polymerase amplification

As large-scale planting of genetically modified (GM) crops increases, the development of a rapid and convenient method for on-site detection of GM crops is important. We combined the advantages of recombinase polymerase amplification (RPA) and fluorescence detection to establish a rapid, sensitive, specific, and simple detection platform for on-site detection of MON863 maize. Test samples were added directly to the platform after simple pre-treatment with a DNA extraction-free method. Results were obtained through real-time monitoring with a portable instrument, which facilitated sample-in/answer-out on-site detection. The entire detection process, including sample preparation, RPA and identification of amplification results, was accomplished in approximately 10 min. Furthermore, the detection was achieved with a simple and inexpensive portable device. This method has high potential for application in other fields requiring rapid detection of DNA targets, such as in field research, resource-limited areas, and science education.

Underwater Hyperspectral Target Detection with Band Selection

Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, a direct use of hyperspectral images in detecting targets cannot meet the needs of rapid detection of underwater targets. To resolve this issue, a fast, hyperspectral underwater target detection approach using band selection (BS) is proposed. It first develops a constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset is constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm is used to detect the desired underwater targets. Experimental results demonstrate that the band subset selected by CTOIFBS is more effective in detecting underwater targets compared to the other three existing BS methods, uniform band selection (UBS), minimum variance band priority (MinV-BP), and minimum variance band priority with OIF (MinV-BP-OIF). In addition, the results also show that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS can be significantly improved over the original underwater hyperspectral image system without BS.

Thermo-Responsive Polymer Capsules in Real-Time One-Step RT-PCR for Highly Multiplex RNA Analysis.

Rapid and simple detection of RNA targets is in high demand due to the growing threat of pandemic viruses. One-step real-time, reverse transcription-polymerase chain reaction (One-step RT-qPCR) using a controlled release system of thermo-responsive materials is developed in this paper to enable high-fidelity RNA analysis as suppressing by-products. The nanocapsules, consisting of upper critical solution temperature (UCST) material and PCR primers, carry or release the primers depending upon the temperature. The UCST nanocapsules are introduced into hydrogel microparticles incorporated with RT primers and then the target RNA is selectively amplified in the microparticle through one-step RT-qPCR. Severe side products are sharply subdued by separating the PCR primers from the RT process by means of the microparticles with nanocapsules. Because the one-step assay is now implemented in a single microparticle, multiple target RNAs can be analyzed in a simple RT-qPCR of multiple particles. Reliable 18-plex one-step RT-qPCR is successfully conducted within 30 min using single-color fluorescent optics. This work also explains the facile fabrication processes used for the thermo-responsive nanocapsules and hydrogel microparticles by the blending polymerization method. Extensible multiplex analysis of influenza virus demonstrates the versatile uses of this one-step RT-qPCR platform.

A highly integrated system with rapid DNA extraction, recombinase polymerase amplification, and lateral flow biosensor for on-site detection of genetically modified crops

With the large-scale planting of genetically modified (GM) crops, the development of a rapid and convenient method for on-site monitoring GM crops is needed. In this study, a duplex recombinase polymerase amplification (DRPA)-based, quick and simple detection system is presented for on-site detection of GM crops. In this system, a rapid DNA extraction method, a DRPA, and a lateral flow biosensor (LFB) are integrated to allow for rapid DNA extraction and amplification, and fast visualization of the detection results. Using the rapid DNA extraction method, high-quality DNA suitable for RPA and polymerase chain reaction (PCR) was quickly isolated from various crops within 5 min. Utilizing the optimal DRPA assay, the universal screening sequences (Cauliflower mosaic virus 35S promoter [35S] and Agrobacterium tumefaciens NOS terminator [NOS]) were rapidly and simultaneously amplified with high selectivity and sensitivity. The sensitivity threshold of the DRPA assay was ∼10 copies for GM soybean genomic DNA and 100 ng DNA of 0.1% GM soybean. In combination with the LFB in an enclosed cassette, the entire detection process was performed in approximately 20–30 min and eliminated the carryover contamination. No special or expensive equipment was needed for the detection process. The system was successfully applied and validated for on-site detection of GM rice, demonstrating its suitability for on-site testing of GM crops and high potential for application to other fields, especially in low-resource regions that require rapid detection of DNA targets.

Gel-Based Methods for the Investigation of Signal Transduction Pathways in Trypanosoma brucei.

In the cell, reversible phosphorylation, controlled by protein phosphatases and protein kinases, initiates and regulates various signaling-dependent processes such as enzyme-substrate interactions, the cell cycle, differentiation, and immune responses. In addition to these processes, in unicellular parasites like Trypanosoma brucei, the causative agent of African sleeping sickness, additional signaling pathways have evolved to enable the survival of parasites in the changing environment of the vector and mammalian host. In this chapter, we describe two in vitro kinase assays and the use of the phosphoprotein chelator Phos-tag and show that these three polyacrylamide gel-based assays can be used for rapid target validation and detection of changes in phosphorylation.

Research on the Application of Deep Learning YOLOv3 in Aerial Patrol Inspection of Optical Cable Lines

Since the construction of engineering vehicles such as excavators in the vicinity of optical cable lines is the most important cause of the fault of optical cable lines, it is proposed to apply the deep learning YOLOv3 (You Only Look Once version 3) target detection algorithm to the engineering vehicle detection and warning of the aerial inspection image. Based on the Darknet deep learning framework, the rapid target detection of engineering vehicles is realized by making engineering vehicle datasets. The detection speed is 60ms per sheet, and the target recognition AP (Average Precision) value is 0.869, which exceeds the algorithms such as deep learning Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm and machine learning HOG+LBP+SVM (Histogram of Oriented Gradient + Local Binary Pattern + Support Vector Machine) algorithm. The research results can provide some reference for aerial patrol and inspection and early warning for optical cable lines.

Silver Nanoparticle-Based Surface-Enhanced Raman Spectroscopy for the Rapid and Selective Detection of Trace Tropane Alkaloids in Food

Recently, surface-enhanced Raman Spectroscopy (SERS) has been widely applied for rapid detection of trace targets in various fields. However, two key issues are still being explored: (1) how to for...

Detecting nucleic acid fragments in serum using a magnetically modulated sandwich assay

We present a novel assay for rapid and highly sensitive detection of specific nucleic acid fragments in human serum. In a magnetic modulation biosensing (MMB) system, magnetic beads and fluorescently labeled probes are attached to the target analyte and form a "sandwich" complex. An alternating external magnetic field gradient condenses the magnetic beads (and hence the target molecules with the fluorescently labeled probes) to the detection volume and sets them in a periodic motion, in and out of a laser beam. A synchronous detection enables the removal of background signal from the oscillating target signal without complicated sample preparation. The high sensitivity of the MMB system, combined with the specificity of a sandwich hybridization assay, enables detection of DNA fragments without enzymatic signal amplification. Here, we demonstrate the sensitivity of the assay by directly detecting the EML4-ALK oncogenic translocation sequence spiked in human serum. The calculated limit of detection is 1.4 pM, which is approximately 150 times better than a conventional plate reader. In general, the MMB-assisted SHA can be implemented in many other applications for which enzymatic amplification, such as PCR, is not applicable and where rapid detection of specific nucleic acid targets is required.

Solid-phase PCR based on thermostable, encoded magnetic microspheres for simple, highly sensitive and multiplexed nucleic acid detection

Multiplexed detection of nucleic acids is important for pathogen identification and disease diagnosis. Though real-time quantitative PCR allows for simultaneous amplification of different genes in a single test, the interferences between various primers inevitably cause difficulty in assay design, and pose limitation on multiplexing degree. Conventional solid-phase PCR (SP-PCR) on plenary microarrays can assess more targets than real-time quantitative PCR. However, they are suffering from low reaction efficiency, high background noise, and requiring special equipment and expertise for reliable analysis. Here, we addressed the challenges by performing multiplex PCR on thermostable, tailor-made host-guest encoded magnetic microspheres (Beads based PCR, BB-PCR), which were constructed by coupling magnetic host microspheres with fluorescent guest nanoparticles. As proof of concept, a 3-plex barcoded microbeads with high thermostability and low non-specific fluorescence absorption were selected as model BB-PCR carriers to amplify and detect three gene targets in Salmonella Enteritidis. After BB-PCR reaction, the barcoding and reporting signals were easily identified through flow cytometry. Attributed to the effective interactions between reagents and microspheres during PCR reaction, this method showed high sensitivity of 10 copies/reaction, which was comparable to that of liquid PCR. Thus, BB-PCR developed here allowed for simple, rapid, highly sensitive and high throughput detection of nucleic acid targets, which paves the way for routine multiplexed molecular diagnostics in clinical laboratories.

Subzymes: Regulating DNAzymes for point of care nucleic acid sensing

Rapid, sensitive and affordable nucleic acid sensors that can operate in a point of care (POC) setting are highly desired; however, their availability and implementation remain limited. We report the development of a strategy for building sensors which use novel catalytic nucleic acid structures, herein referred to as ‘Subzymes’. We examine the effectiveness of Subzymes to mediate signal amplification in a universal manner allowing faster rates of detection. We created Subzymes by manufacturing composite oligonucleotides that contain a catalytic nucleic acid component and a substrate component. We demonstrate that the activity of some catalytic DNAzyme components can be inhibited by attaching the Subzymes to micro-particles. Subsequent cleavage of a Subzyme’s internal substrate results in the release and activation of the surface-bound DNAzyme, thus providing a mechanism to control the catalytic activity of the DNAzyme. We demonstrate that released DNAzymes are capable of cleaving fluorescent-labelled reporter substrates to generate a signal, thus confirming the restoration of their catalytic activity. The addition of Subzymes to reactions where the detection of a target is achieved by multi-component nucleic acid enzymes, known as PlexZymes, showed improved target sensitivities in a shorter amount of time (10 pM of target detected in under 60 min), demonstrating rapid detection of nucleic acid targets without the use of protein-enzymes. Subzymes are constructed from low-cost materials and operate under isothermal reaction conditions which are advantageous for on-site diagnostic testing. Thus, Subzymes offer a universal, rapid and affordable tool for nucleic acid sensing, providing new avenues for POC testing.

Do target detection and target localization always go together? Extracting information from briefly presented displays.

The human visual system is capable of processing an enormous amount of information in a short time. Although rapid target detection has been explored extensively, less is known about target localization. Here we used natural scenes and explored the relationship between being able to detect a target (present vs. absent) and being able to localize it. Across four presentation durations (~ 33-199 ms), participants viewed scenes taken from two superordinate categories (natural and manmade), each containing exemplars from four basic scene categories. In a two-interval forced choice task, observers were asked to detect a Gabor target inserted in one of the two scenes. This was followed by one of two different localization tasks. Participants were asked either to discriminate whether the target was on the left or the right side of the display or to click on the exact location where they had seen the target. Targets could be detected and localized at our shortest exposure duration (~ 33 ms), with a predictable improvement in performance with increasing exposure duration. We saw some evidence at this shortest duration of detection without localization, but further analyses demonstrated that these trials typically reflected coarse or imprecise localization information, rather than its complete absence. Experiment 2 replicated our main findings while exploring the effect of the level of "openness" in the scene. Our results are consistent with the notion that when we are able to extract what objects are present in a scene, we also have information about where each object is, which provides crucial guidance for our goal-directed actions.

Eu-Doped Ceria Nanocrystals as Nanoenzyme Fluorescent Probes for Biosensing

Molecular nanoprobes with intrinsic enzyme-like activity represent a new wave of technology for rapid and sensitive detection of molecular targets. This work reports synthesis and characterization ...

A Probabilistic Target Search Algorithm Based on Hierarchical Collaboration for Improving Rapidity of Drones.

Finding a target quickly is one of the most important tasks in drone operations. In particular, rapid target detection is a critical issue for tasks such as finding rescue victims during the golden period, environmental monitoring, locating military facilities, and monitoring natural disasters. Therefore, in this study, an improved hierarchical probabilistic target search algorithm based on the collaboration of drones at different altitudes is proposed. This is a method for reducing the search time and search distance by improving the information transfer methods between high-altitude and low-altitude drones. Specifically, to improve the speed of target detection, a high-altitude drone first performs a search of a wide area. Then, when the probability of existence of the target is higher than a certain threshold, the search information is transmitted to a low-altitude drone which then performs a more detailed search in the identified area. This method takes full advantage of fast searching capabilities at high altitudes. In other words, it reduces the total time and travel distance required for searching by quickly searching a wide search area. Several drone collaboration scenarios that can be performed by two drones at different altitudes are described and compared to the proposed algorithm. Through simulations, the performances of the proposed algorithm and the cooperation scenarios are analyzed. It is demonstrated that methods utilizing hierarchical searches with drones are comparatively excellent and that the proposed algorithm is approximately 13% more effective than a previous method and much better compared to other scenarios.

Analysis of Importance of Search Altitude Control for Rapid Target Detection of Drones

Analysis of Importance of Search Altitude Control for Rapid Target Detection of Drones

TwistAmp® Liquid: a versatile amplification method to replace PCR

Here we introduce TwistAmp® Liquid, a new PCR replacement format that makes RPA technology more amenable to a wide range of research applications. In contrast to PCR amplification, RPA takes minutes, rather than hours, and can be run with little to no equipment. TwistAmp® Liquid Basic and Basic RT kits can be used for applications requiring good fidelity, gel electrophoresis or solid phase detection. TwistAmp® Liquid exo and exo RT allow rapid real-time amplification and detection of targets without sacrificing sensitivity or specificity.

Characterizing Bioconjugation and Electron Transfer at Coated Nanoparticles By Nano-Impact Electrochemistry

Nanoparticle collision electrochemistry provides extensive capabilities for detection and characterization of nanoparticles. This presentation will describe development of an electrochemical nano-impact technique for evaluating the fundamental surface properties, functionalization and reactivity of metal and metal oxide nanoparticles and its use for quantifying biomolecule-nanoparticle conjugation and biomolecular recognition with a high degree of sensitivity in the absence of exogenous reagents. The presentation will demonstrate the potential of this method for the: 1) assessment of surface reactivity and electron transfer properties of coated nanoparticles, 2) monitoring surface adsorption/desorption and biorecognition events at single particle surfaces, and 3) as a method enabling rapid label free detection of biomolecular recognition targets. We will demonstrate capabilities of this method as a novel tool for characterizing fundamental properties of nanoparticles, and illustrate novel applications of this method for assessing bioconjugation for biosensing design, analytical and diagnostic applications. Potential advantages and limitations of this approach as a method for the routine study of nanoparticles and nanoparticle systems will also be discussed.