Detection Of Multiple Targets Research Articles

CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s

To address the problem of the frequent occurrence of major casualties during construction, a lightweight multi-target detection model based on YOLOv5s, named CaSnLi-you only look once (YOLO), was proposed for the detection of multiple targets consisting of construction workers and various mechanical equipment at construction sites. In this study, the introduction of the coordinate attention mechanism along with DWConv and C3Ghost based on GhostNet, enhanced the expression and detection accuracy of the model, effectively reducing the number of parameters. Considering the environmental characteristics of construction sites, a detection box filtering strategy based on soft non-maximum suppression was employed, to effectively alleviate the issue of missed detections of occluded targets. Experimental results demonstrate the significant superiority of the proposed CaSnLi-YOLO over current mainstream detection models, such as faster region-based convolutional neural network and single-shot detector. The proposed CaSnLi-YOLO has a parameter number of 5.96 × 106, which is 15.2% less than that of the original YOLOv5s model, further improving precision, recall rate, mAP@0.5, and mAP@0.5:0.95 by 0.6%, 0.6 %, 0.2%, and 2.3%, respectively. The improved YOLOv5s model proposed in this study achieved significant improvements in multi-target detection at construction sites. The model demonstrated effective enhancements while significantly reducing parameter count and is expected to be deployed in small-edge devices for real-time security monitoring at construction sites.

Simultaneous multiple target detection platform based on vertical flow immunoassay

In general, vertical flow assay (VFA) has a disadvantage of requiring a complex analysis process that involves manually injecting various reagents (target analyte, washing buffer, detection conjugate, etc.) sequentially. However, in this study, we have developed an innovative paper-based VFA device that replaces the complex analysis process with one-step and enables the detection of multiple targets. The fabrication process of the multi-target detection VFA device is as follows: preparation and pre-treatment of the strip materials, design of strip cartridge, design of the multiple detection VFA device, optimization experiments for strip sample flow rates, determination of device analysis time, determination of device limit of detection (LOD), multiple target signal uniformity experiment, immunoglobulin G (IgG) and C-reactive protein (CRP) antigen-antibody multiple detection experiment, and data extraction and analysis method. The use of paper-based materials enables the device to be produced at cost-effective, and cartridge production allowed for uniform array formation. IgG and CRP are used to evaluate the performance of the device as common biomarkers. The device proposed in this study is currently under research. To validate multiple target detection capability of the VFA device proposed in this study, two types of antigens-antibodies (Human IgG and Human CRP) were employed. The detection limit is 0.15 μg/mL for IgG and 0.19 μg/mL for CRP in naked eye. Furthermore, it was confirmed that there is no cross-reactivity caused by the device structure through IgG and CRP antigens. In conclusion, the VFA device proposed in this study consists of a one-step analysis process, and it has been confirmed that it can detect multiple targets simultaneously.

Real-time PCR primers and probes for the detection of Shiga toxin genes, including novel subtypes

Shiga toxin-producing Escherichia coli (STEC) are food-borne enteric pathogens. STEC are differentiated from other E. coli by detection of Shiga toxin (Stx) or its gene (stx). The established nomenclature of Stx identifies ten subtypes (Stx1a, Stx1c, Stxd, Stx2a to Stx2 g). An additional nine subtypes have been reported and described (Stx1e, Stx2 h to Stx2o). Many PCR protocols only detect a subset of Stx subtypes which limits their inclusivity. Here we describe a real-time PCR assay inclusive of the DNA sequences of representatives of all currently described Stx subtypes.A multiplex real-time PCR assay for detection of stx was developed using nine primers and four probes. Since the identification of STEC does not require differentiation of stx subtypes, the probes use the same fluorescent reporter to enable detection of multiple possible targets in a single reaction. The PCR mixture includes an internal positive control to detect inhibition of the reaction. Thus, the protocol can be performed on a two-channel real-time PCR platform. To reduce the biosafety risk inherent in the use of STEC cultures as process controls, the protocol also includes the option of a non-pathogenic E. coli transformant carrying a plasmid encoding the targeted fragment of the stx2a sequence.The inclusivity of the PCR was assessed against colonies of 137 STEC strains and one strain of Shigella dysenteriae, including strains carrying single copies of stx representing fourteen subtypes (stx1 a, c, d; stx2 a-j and o). Five additional subtypes (stx1e, 2k, 2l, 2m and 2n) were represented by E. coli transformed with plasmids encoding toxoid (enzymatically inactive A subunit) sequences. The exclusivity panel consisted of 70 bacteria, including 21 stx-negative E. coli. Suitability for food analysis was assessed with artificially inoculated ground beef, spinach, cheese, and apple cider. The real-time PCR generated positive results for all 19 stx subtypes, represented by colonies of STEC, S. dysenteriae and E. coli transformants carrying stx toxoid plasmids. Tests of exclusivity panel colonies were all negative. The real-time PCR detected the presence of stx in all inoculated food enrichments tested, and the presence of STEC was confirmed by isolation.

Portable tri-color ratiometric fluorescence paper sensor for intelligent visual detection of dual-antibiotics and aluminium ion

The toxicological effect between co-existed antibiotics and metal ions was dangerous to the ecological environment and public health. However, the rapid quantification tools with convenience, accuracy and low cost for the detection of multiple targets were still challenging. Herein, a portable tri-color ratiometric fluorescence paper sensor was constructed by coupling of blue carbon dots and fluorescence imprinted polymer for down/up conversion simultaneous detection of tetracycline and sulfamethazine. Interestingly, the cascade detection of aluminum ion was also realized based on the individual detection system of tetracycline without the assistance of complex coupling reagents. The detection limits of smartphone method for the visual detection of tetracycline, sulfamethazine and aluminum ion were calculated as 0.014 μM, 0.004 μM and 0.019 μM, respectively. The portable fluorescence paper sensor was applied for the visual detection of tetracycline, sulfamethazine and aluminum ion in actual samples successfully with satisfactory recoveries. With the advantages of rapidness, low cost, and portability, the developed portable fluorescence paper sensor provided a new strategy for the visual real-time detection of multiple targets.

Color- and background-free Raman-encoded lateral flow immunoassay for simultaneous detection of carbendazim and imidacloprid in a single test line

Multiple detection based on lateral flow immunoassay (LFA) holds significant research importance across various fields. Recently, the use of multi-color surface-enhanced Raman scattering (SERS) nanotags has enabled simultaneous detection of multiple targets within a single test line (T-line) in LFA, greatly enhancing detection efficiency, reliability, and application flexibility. However, challenges regarding signal overlap in the Raman-fingerprint region (400–1800 cm−1) and the difficulty in determining specific targets based on typical color analysis have persisted. In this study, we developed two types of nanoprobes that encode both color and Raman information to address these challenges in simultaneous multiple detection on a single T-line in SERS-LFA. The nanoprobes were encoded with distinguishable blue and red colors, respectively, enabling colorimetric study in multiplex LFA for qualitative analysis. Additionally, they were encoded with distinctive and non-overlapping Raman signals in the Raman-silent region (1800–2800 cm−1), ensuring accurate and sensitive detection by effectively avoiding interference from each other as well as background signals in the sample matrix. Using carbendazim (CBZ) and imidacloprid (IMI) as model targets, the color- and Raman-encoded SERS-LFA demonstrated promising potential for the simultaneous detection of CBZ and IMI in both standard and real samples, whether in a colorimetric or a SERS mode.

Distributed Joint Detection, Tracking, and Classification via Labeled Multi-Bernoulli Filtering.

In this article, we propose a novel approach to distributed joint detection, tracking, and classification (D-JDTC) of multiple targets by means of a multisensor network. The proposed approach relies on labeled multi-Bernoulli (LMB) random finite set modeling of the multisensor state, and consists of two main tasks, that is, local filtering in each individual node and data fusion among multiple nodes. For local filtering, the LMB filter is extended to JDTC by augmenting the target state to incorporate class and mode information. Further, the well-known generalized covariance intersection and recently developed minimum information loss fusion paradigms are exploited for data fusion among sensors. The effectiveness of the resulting algorithm, called D-JDTC-LMB, is assessed via simulation experiments.

Capacitive immunosensing at gold nanoparticle-decorated reduced graphene oxide electrodes fabricated by one-step laser nanostructuration

Nanostructured electrochemical biosensors have ushered in a new era of diagnostic precision, offering enhanced sensitivity and specificity for clinical biomarker detection. Among them, capacitive biosensing enables ultrasensitive label-free detection of multiple molecular targets. However, the complexity and cost associated with conventional fabrication methods of nanostructured platforms hinder the widespread adoption of these devices. This study introduces a capacitive biosensor that leverages laser-engraved reduced graphene oxide (rGO) electrodes decorated with gold nanoparticles (AuNPs). The fabrication involves laser-scribed GO-Au3+ films, yielding rGO-AuNP electrodes, seamlessly transferred onto a PET substrate via a press-stamping methodology. These electrodes have a remarkable affinity for biomolecular recognition after being functionalized with specific bioreceptors. For example, initial studies with human IgG antibodies confirm the detection capabilities of the biosensor using electrochemical capacitance spectroscopy. Furthermore, the biosensor can quantify CA-19-9 glycoprotein, a clinical cancer biomarker. The biosensor exhibits a dynamic range from 0 to 300 U mL−1, with a limit of detection of 8.9 U mL−1. Rigorous testing with known concentrations of a pretreated CA-19-9 antigen from human fluids confirmed their accuracy and reliability in detecting the glycoprotein. This study signifies notable progress in capacitive biosensing for clinical biomarkers, potentially leading to more accessible and cost-effective point-of-care solutions.

Signal Design and Multiple Target Detection in Presence of Clutter in Pulsed FDA Radar

Signal Design and Multiple Target Detection in Presence of Clutter in Pulsed FDA Radar

Simultaneous detection and quantification of multiple pathogen targets in wastewater

Wastewater-based epidemiology has emerged as a critical tool for public health surveillance, building on decades of environmental surveillance work for pathogens such as poliovirus. Work to date has been limited to monitoring a single pathogen or small numbers of pathogens in targeted studies; however, few studies consider simultaneous quantitative analysis of a wide variety of pathogens, which could greatly increase the utility of wastewater surveillance. We developed a novel quantitative multi-pathogen surveillance approach (35 pathogen targets including bacteria, viruses, protozoa, and helminths) using TaqMan Array Cards (TAC) and applied the method on concentrated wastewater samples collected at four wastewater treatment plants in Atlanta, GA from February to October of 2020. From sewersheds serving approximately 2 million people, we detected a wide range of targets including many we expected to find in wastewater (e.g., enterotoxigenic E. coli and Giardia in 97% of 29 samples at stable concentrations) as well as unexpected targets including Strongyloides stercoralis (a human threadworm rarely observed in the USA). Other notable detections included SARS-CoV-2, but also several pathogen targets that are not commonly included in wastewater surveillance like Acanthamoeba spp., Balantidium coli, Entamoeba histolytica, astrovirus, norovirus, and sapovirus. Our data suggest broad utility in expanding the scope of enteric pathogen surveillance in wastewaters, with potential for application in a variety of settings where pathogen quantification in fecal waste streams can inform public health surveillance and selection of control measures to limit infections.

Target Detection in Challenging Environments: Photonic Radar with a Hybrid Multiplexing Scheme for 5G Autonomous Vehicles

The rapid deployment of 5G autonomous vehicles has placed a premium on low-latency communication and reliable sensor technologies for the real-time mapping of road conditions, aligning with sustainability objectives in transport. In response to this imperative, photonic-based radar systems have emerged as an increasingly attractive solution, characterized by their low power consumption and cost-effectiveness. This study delves into the application of linear frequency-modulated continuous wave (FMCW) techniques within photonic radar sensors for the precise detection of multiple targets. Our proposed system seamlessly integrates mode-division multiplexing (MDM) and polarization-division multiplexing (PDM) to achieve a robust target detection capability, contributing to sustainable traffic management. To assess its effectiveness, we rigorously evaluated the system’s performance under challenging conditions, marked by a high atmospheric attenuation of 75 dB/km and a low material reflectivity of 20%. Our results unequivocally demonstrate the efficacy of the MDM-PDM photonic radar in successfully detecting all four specified targets, underscoring its potential to enhance road safety in the realm of autonomous vehicles. The adoption of this technology supports sustainable mobility by mitigating human errors and optimizing the real-time mapping of road conditions.

Joint Aperture and Power Allocation in Collocated MIMO Radar for Multiple Targets Detection

Joint Aperture and Power Allocation in Collocated MIMO Radar for Multiple Targets Detection

Shared hairpin structure electrochemiluminescence biosensor based on Au@Ni-Co metal organic frameworks for simultaneous detection of Pb(II) and S.aureus

The excessive content of lead (Pb(II)) and Staphylococcus aureus (S.aureus) seriously harms the quality of aquatic products. In this paper, a highly sensitive electrochemiluminescence (ECL) biosensor was constructed using the synergistic effect of Au NPs@Nickel-Cobalt-Metal-organic frameworks (Au@Ni-Co-MOFs) and double potential resolution function of urchin-like Au@luminol and Cadmium sulfide quantum dots (CdS QDs) for synchronous detection of Pb(II) and S.aureus in aquatic products. Au@Ni-Co-MOFs as the base material, its cube structure can improve the surface active area and sensitivity of the sensor, providing more catalytic active sites for the two functional probes. Urchin-like Au@luminol binding aptamer DNA2 specifically recognizes Pb(II), CdS QDs binding aptamer DNA3 specifically recognizes S.aureus, which collaboratively catalyzed hydrogen peroxide reduction to produce two electrochemiluminescence signals. The shared hairpin structure DNA1 binds stably to Au@Ni-Co-MOFs via the Au-S bond, and the two functional probes are complementary paired with the DNA1 respectively to ensure the specificity of the aptamer. According to the ECL intensity changes of different potentials signal sources, the synchronous detection of Pb(II) and S.aureus with different concentrations is realized. The sensor realizes the detection of two targets in aquatic products and provides a new strategy for the simultaneous detection of multiple targets.

Multi-feature fusion sonar image target detection evaluation based on particle swarm optimization algorithm

Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particle swarm algorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimization algorithm, the sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field.

A smartphone-controlled portable multiplexed photoelectrochemical sensor based on dual UV intelligent response system-assisted signal resolution strategy

Portable multiplexed photoelectrochemical (PEC) sensor is essential for on-site detection of multiple targets in complex environment. However, the evolution of portable multiplexed PEC sensor is still constrained by specialized instruments and complex multi-interface modification steps, etc. In this work, we design a UV-assisted multiple signals resolution strategy and construct a smartphone-controlled portable multiplexed PEC sensor. Specifically, following the dual-target biorecognition, UV light triggers the cleavage of photo-responsive DNA so that the dual DNA signal labels can be separated. When the signal label (first target) attaches to the azobenzene-covalently bound DNA (Abz-DNA) and triggers HCR on the electrode surface, UV light illumination induces the structure change of azobenzene, resulting in the detachment of Abz-DNA/HCR complex from the electrode surface. After reincorporation of fresh Abz-DNA, the reconstituted photoelectrode can be reused for the second target analysis. The feasibility of the sensor is validated through the simultaneous detection of two co-existing mycotoxins in corn. Notably, the UV-meditated signal discrimination strategy and smartphone-displayed multi-photocurrent readout enable the portability of the PEC sensor, making it possible for in-situ monitoring of environmental safety and food quality.

COTiR: Molecular Communication Model for Synthetic Exosome-Based Tissue Regeneration.

Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.

Rapid and sensitive electrochemiluminescence detection using easily fabricated sensor with an integrated two-electrode system.

The electrochemiluminescence (ECL) behavior of a tri(2,2'-bipyridyl)ruthenium(ii) (Ru(bpy)32+)/tripropylamine (TPrA) system was investigated in sensor chips with two kinds of integrated two-electrode systems, which included screen-printed electrodes (SPE) and physical vapor deposition (PVD) electrodes. Firstly, under excitation with an optimal transient potential (TP) within 100 ms, the ECL assay could be carried out on the microchips using an Au & Au electrode system, emitting strong and stable light signal. Secondly, on the PVD chip, the ECL intensity initiated by optimal TP was eight times stronger than the peak light signal emitted by the linear sweep voltammetry model. Finally, the logarithmic ECL intensities exhibited a linear increase with the logarithmic concentrations of Ru(bpy)32+ in both the SPE and PVD chips without any reference electrode (RE). Typically, the integration of an interdigital two-electrode system in the microchip significantly enhanced the ECL sensitivity of Ru(bpy)32+ because the large relative area between the working electrode (WE) and counter electrode (CE) achieved a highly efficient mass transfer. This improvement enabled the establishment of a reliable linear relationship across a wide concentration range, spanning from 1 pM to 1 μM (R2 = 0.998). Therefore, the exceptional ECL response of the Ru(bpy)32+/TPrA system on microfluidic chips using a two-electrode system and the TP excitation model has been demonstrated. This suggests that ECL chips without a RE have broad potential for the rapid and sensitive detection of multiple targets.

High-Plex ELISPOT: FOLISPOT Based on Fluorescence Detection and DNA Complementary Pairing.

Enzyme-linked immunospot (ELISPOT) is one of the most important methods to measure the number of specific cells by detecting protein secretion at a single-cell level. However, traditional ELISPOT based on enzyme-substrate color development can only detect one target. Therefore, scientists developed multiple-target ELISPOT based on enzyme-substrate coloring. Besides, FluoroSPOT that can detect 2-4 fluorescent signals are developed. Nevertheless, the maximum detection targets of multiple-target ELISPOT and FluoroSPOT are around 4, and the signal amplification system can be further optimized. Fluorescence-based oligo-linked immunospot (FOLISPOT), which utilized DNA-barcoded antibodies to provide a highly multiplexed method with signal amplification, was developed to detect multiple targets simultaneously. In this method, multiple targets can be detected in one round and multiple rounds of detection can be conducted, and thus a large number of targets can be detected. Besides, signal amplification is achieved by DNA complementary pairing and modular orthogonal DNA concatemers, and thus cells secreting limited amounts of proteins can be detected. According to the studies, FOLISPOT can detect more spots than ELISPOT and can detect targets that are undetectable by ELISPOT. Furthermore, FOLISPOT can be utilized to detect more than 6 targets, by allowing sequential detection of multiple targets in one round and sequential detection in multiple rounds.

Photonic-Based Flexible Integrated Sensing and Communication With Multiple Targets Detection Capability for $W$-Band Fiber-Wireless Network

Photonic-Based Flexible Integrated Sensing and Communication With Multiple Targets Detection Capability for $W$-Band Fiber-Wireless Network

Joint design of transmit precoding and antenna selection for multi-user multi-target MIMO DFRC

Dual-function radar and communications (DFRC) systems based on multiple-input multiple-output (MIMO) arrays have received considerable attention in recent years due to their excellent ability to alleviate spectrum congestion. The MIMO DFRC systems enable high-resolution detection of multiple targets while communicating with multiple users simultaneously. However, MIMO arrays require numerous radio frequency (RF) units and suffer a strong mutual coupling among antennas, resulting in significant system overhead and performance degradation, respectively. In light of this, this paper investigates the joint optimization of transmit precoding and antenna selection for MIMO DFRC systems, aiming to improve the angular ambiguity function with a guaranteed communication quality of service (QoS) using a small number of antennas. To address the resultant non-convex optimization problem, both the indirect and direct precoding methods are proposed. In the former, the waveform covariance and antenna selection vector are first jointly optimized via a promoted sparsity along the covariance diagonal, followed by the precoding matrix indirectly derived from the optimal covariance. In the latter, the precoding matrix is directly optimized via an imposed group sparsity under the communication QoS and power constraints. Simulation results demonstrate that the proposed sparse MIMO DFRC system with fewer active antennas can achieve comparable dual-functional performance to that of the full array system.

3D walkable DNA gears for ultrasensitive detection of multiple microRNAs in lung cancer cell lysates

As a tumor biomarker with therapeutic application potential, microRNA (miRNA) was crucial for the accurate and sensitive detection of early-stage tumors. Herein, a unique three dimensional (3D) DNA nanomachine (DNM) was created, which was capable detecting lung cancer-related biomarkers miRNA-21, miRNA-205 and miRNA-125b in lung cancer cell lysates with extreme sensitivity. The 3D DNM was composed of DNA scissors and three flexible walkable DNA gears modified with various species of silver nanoclusters (AgNCs). Based on the flexibility of DNA scissors and the walkability of DNA gears, neighboring DNA gears closed the distance between different species of AgNCs by walking in the presence of targets, generating fluorescence resonance energy transfer (FRET) effect and emitting different kinds of fluorescence to complete the highly sensitive detection of single targets and multiple targets. The findings demonstrated that a linear model provided an excellent match for the association between fluorescence signal and target miRNAs. For miRNA-21, miRNA-205, and miRNA-125b, the limits of detection (LODs) (signal/noise = 3) were 4.2 pmol/L (pM), 6.3 pM, and 10.2 pM, respectively. Their recoveries in A549 cell lysate samples ranged from 95.3 to 108.8 % with relative standard deviations of 1.26 %–4.88 %. Satisfactorily, the 3D DNM displayed exceptional analytical performance with high sensitivity and stability, strong specificity and reproducibility, which was triumphantly employed to identify miRNAs in tumor cell lysates, providing a workable technique in creating adaptable nanostructure for dependable bioanalysis and clinical diagnosis of cancer biomarkers.