Ionically Imprinting-Based Copper(Ⅱ)Label-Free Detection for Preventing Hearing Loss

Copper is a microelement with important physiological functions in the body.However,the excess copper ion(Cu^(2+))may cause severe health problems,such as hair cell apoptosis and the resultant hearing loss.Therefore,the assay of Cu^(2+)is important.We integrate ionic imprinting technology(IIT)and structurally colored hydrogel beads to prepare chitosan-based ionically imprinted hydrogel beads(IIHBs)as a low-cost and high-specificity platform for Cu^(2+)detection.The IIHBs have a macroporous microstructure,uniform size,vivid structural color,and magnetic responsiveness.When incubated in solution,IIHBs recognize Cu^(2+)and exhibit a reflective peak change,thereby achieving label-free detection.In addition,benefiting from the IIT,the IIHBs display good specificity and selectivity and have an imprinting factor of 19.14 at 100μmol·L^(-1).These features indicated that the developed IIHBs are promising candidates for Cu^(2+)detection,particularly for the prevention of hearing loss.

Real-time and label-free detection of biomolecular interactions by oblique-incidence reflectivity difference method

We successfully conduct the label-free and real-time detection of the interactions between epoxy groups and rabbit IgG and 5＇ CTT CAG GTC ATG AGC CTG AT 3＇ oligonucleotide, and between the hybridization of 5＇ CTT CAG GTC ATG AGC CTG AT 3＇ and its complementary 3＇ GAA GTC CAC TAC TCG GAC TA 5＇ oligonucleotide, by the oblique-incidence reflectivity difference （OI-RD） method. The dynamic curves of OI-RD signals, corresponding to the kinetic processes of biomolecular combination or hybridization, are acquired. In our case, the combination of epoxy groups with rabbit IgG and 5＇ CTT CAC CTC ATG AGC CTG AT 3＇ oligonucleotide need almost one and a half hours and about two hundred seconds, respectively; and the hybridization of the two oligonucleotides needs about five hundred seconds. The experimental results show that the OI-RD is a promising method for the real-time and label-free detection of biomolecular interactions.

Theoretical study on the lasing plasmon of a split ring for label-free detection of single molecules and single nanoparticles

This paper reports the plasmonic lasing of a split ring filled with gain material in water. The lasing mode（1500 nm）is far from the pump mode（980 nm）, which can depress the detection noise from the pump light. The laser intensities of the two modes simultaneously increase by more than 10^3 in amplitude, which can intensify the absorption efficiency of the pumping light and enhance the plasmonic lasing. The plasmonic lasing is a sensitive sensor. When a single protein nanoparticle（n = 1.5, r = 1.25 nm） is trapped in the gap of the split ring, the lasing spectrum moves by 0.031 nm, which is much larger than the detection limit of 10^-5 nm. Moreover, the lasing intensity is also very sensitive to the trapped nanoparticle. It reduces to less than 1/600 when a protein nanoparticle（n = 1.5, r = 1.25 nm） is trapped in the gap.

A MXene-functionalized paper-based electrochemical immunosensor for label-free detection of cardiac troponin I

Convenient,rapid,and accurate detection of cardiac troponin I(cTnI)is crucial in early diagnosis of acute myocardial infarction(AMI).A paper-based electrochemical immunosensor is a promising choice in this field,because of the flexibility,porosity,and cost-efficacy of the paper.However,paper is poor in electronic conductivity and surface functionality.Herein,we report a paper-based electrochemical immunosensor for the label-free detection of cTnI with the working electrode modified by MXene(Ti_(3)C_(2))nanosheets.In order to immobilize the bio-receptor(anti-cTnI)on the MXene-modified working electrode,the MXene nanosheets were functionalized by aminosilane,and the functionalized MXene was immobilized onto the surface of the working electrode through Nafion.The large surface area of the MXene nanosheets facilitates the immobilization of antibodies,and the excellent conductivity facilitates the electron transfer between the electrochemical species and the underlying electrode surface.As a result,the paper-based immunosensor could detect cTnI within a wide range of 5-100 ng/mL with a detection limit of 0.58 ng/mL.The immunosensor also shows outstanding selectivity and good repeatability.Our MXene-modified paper-based electrochemical immunosensor enables fast and sensitive detection of cTnI,which may be used in real-time and cost-efficient monitoring of AMI diseases in clinics.

LABEL-FREE DETECTION OF PROTEIN MICROARRAY WITH HIGH THROUGHPUT SURFACE PLASMON RESONANCE IMAGING(SPRI)

A surface plasmon resonance imaging(SPRI)system was developed for the discrimination of proteins on a gold surface.As a label-free and high-throughput technique,SPRI enables simultaneously monitoring of the biomolecular interactions at low concentrations.We used SPRI as a label-free and parallel method to detect different proteins based on protein microarray.Bovine Serum Albumin(BSA),Casein and Immunoglobulin G(IgG)were immobilized onto the Au surface of a gold-coated glass chip as spots forming a 6×6 matrix.These proteins can be discriminated directly by changing the incident angle of light.Excellent reproducibility for label-free detection of protein molecules was achieved.This SPRI platform represents a simple and robust method for performing high-sensitivity detection of protein microarray.

Tb^(3+)-nucleic acid probe-based label-free and rapid detection of mercury pollution in food

Mercury is a threatening pollutant in food,herein,we developed a Tb^(3+)-nucleic acid probe-based label-free assay for mix-and-read,rapid detection of mercury pollution.The assay utilized the feature of light-up fluorescence of terbium ions(Tb^(3+))via binding with single-strand DNA.Mercury ion,Hg^(2+)induced thymine(T)-rich DNA strand to form a double-strand structure(T-Hg^(2+)-T),thus leading to fluorescence reduction.Based on the principle,Hg^(2+)can be quantified based on the fluorescence of Tb^(3+),the limit of detection was 0.0689μmol/L and the linear range was 0.1-6.0μmol/L.Due to the specificity of T-Hg^(2+)-T artificial base pair,the assay could distinguish Hg^(2+)from other metal ions.The recovery rate was ranged in 98.71%-101.34%for detecting mercury pollution in three food samples.The assay is low-cost,separation-free and mix-to-read,thus was a competitive tool for detection of mercury pollution to ensure food safety.

A vivid Au-porous anodic alumina composite film with the inverted taper structure for label-free detection

Localized surface plasmon resonance(LSPR)has been widely used in medical detection because of its time effectiveness,noninvasiveness,high sensitivity,and relatively simple fabrication process.Porous anodic alumina(PAA)can be regarded as a plasma substrate for label-free detection due to its unique two-dimensional structure.In this work,a vivid Au-PAA composite film with the inverted taper structure was developed by multi-step anodic oxidation and pore-widening processes followed by magnetron sputtering with Au nanoparticles(AuNPs).The highly saturated and bright structural color was generated by the synergistic effect of photonic and plasmonic modes.Interestingly,various Au-PAA composite films with structural colors altering from purple to red were obtained via adjusting the height/diameter ratio of PAA.Benefiting from the inverted taper structure,light trap characteristics were effectively enhanced by increasing the incident light and reducing the diffuse light.In addition,a finite difference time domain(FDTD)model was proposed to predict the relationship between the reflectance peak and the height of the composite film,and the simulated data were in good agreement with the experimental results.As a proof of concept,labelfree detections of various reagents(water,ethanol,glycol,glycerol,and glucose),the concentration of glucose(refractive index sensitivity of 376 nm/RIU,RIU:refractive index unit),and thrombin(detection limit of 0.1×10^(-7)mol/L)were realized by the Au-PAA composite film.This vivid Au-PAA composite film provides a very powerful tool for in-situ label-free bio-detection.

Label-free breast cancer detection and classification by convolutional neural network-based on exosomes surface-enhanced raman scattering

Because the breast cancer is an important factor that threatens women's lives and health,early diagnosis is helpful for disease screening and a good prognosis.Exosomes are nanovesicles,secreted from cells and other body fluids,which can reflect the genetic and phenotypic status of parental cells.Compared with other methods for early diagnosis of cancer(such as circulating tumor cells(CTCs)and circulating tumor DNA),exosomes have a richer number and stronger biological stability,and have great potential in early diagnosis.Thus,it has been proposed as promising biomarkers for diagnosis of early-stage cancer.However,distinguishing different exosomes remain is a major biomedical challenge.In this paper,we used predictive Convolutional Neural model to detect and analyze exosomes of normal and cancer cells with surface-enhanced Raman scattering(SERS).As a result,it can be seen from the SERS spectra that the exosomes of MCF-7,MDA-MB-231 and MCF-10A cells have similar peaks(939,1145 and 1380 cm^(-1)).Based on this dataset,the predictive model can achieve 95%accuracy.Compared with principal component analysis(PCA),the trained CNN can classify exosomes from different breast cancer cells with a superior performance.The results indicate that using the sensitivity of Raman detection and exosomes stable presence in the incubation period of cancer cells,SERS detection combined with CNN screening may be used for the early diagnosis of breast cancer in the future.

Structural characteristics and the label-free detection of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril)pseudorotaxane at single molecule level

A multi-technique approach to prove the preparation of poly(3,4-ethylenedioxythiophene/cucurbit[7]uril)pseudorotaxanes(PEDOT∙CB7-PPs)is reported.Molecular docking simulation and matrix-assisted laser desorption/ionization mass spectrometry(MALDI MS)validate the complexation ability of the CB7 molecule towards 3,4-ethylenedioxythiophene(EDOT),which leads to the EDOT∙CB7 inclusion complex.Oxidative polymerization of EDOT∙CB7 enabled the synthesis of PEDOT∙CB7-PPs.The water-soluble part of PEDOT∙CB7-PPs was selected,freeze-dried,and chemically characterized.Furthermore,dynamic light scattering(DLS)has been used to study the particle size and z-potential(ZP-ζ)of PEDOT∙CB7-PPs.The ZP-ζvalue(35 mV)evidenced that the PEDOT∙CB7-PPs formed stable water dispersion.By combining the emerging nanopore resistive pulse sensing technique(Np-RPS)and computational modeling,we identified strong interactions of PEDOT∙CB7-PPs with the aerolysin(Ael)nanopore.PEDOT∙CB7-PPs behave as positive charged species,and thus trans negative bias promotes its interactions with the Ael nanopore.The computational modeling results are fully consistent with the Np-RPS detection,which also reveals strong interactions between PEDOT∙CB7-PPs and the Ael nanopore.With this study,we hope to provide new insights and a better understanding of the interactions between supramolecular complexes based on CB7 and biological entities,which is instrumental for future applications in the field of nanobiotechnology.

Label-free detection of hybridization of oligonucleotides by oblique-incidence reflectivity difference method

The microarrays of 20-base oligonucleotide with different concentrations are detected before and after hybridization by the oblique-incidence reflectivity difference (OI-RD) method. The experimental results prove that OI-RD is a label-free method which can not only distinguish the concentration difference of oligonucleotides before and after the hybridization but also detect the hybridization of short oligonucleotides. At present the OI-RD method can detect 0.39 μmol/L 20-base oligonucleotide or less. These results suggest that the OI-RD method is a promising and potential technique for label-free detection of biological microarrays.

A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Label-free Detection for a DNA Methylation Assay Using Raman Spectroscopy

Background： DNA methylation has been suggested as a biomarker for early cancer detection and treatment. Varieties of technologies for detecting DNA methylation have been developed, but they are not sufficiently sensitive for use in diagnostic devices. The aim of this study was to determine the suitability of Raman spectroscopy for label-free detection of methylated DNA. Methods： The methylated promoter regions of cancer-related genes cadherin 1 （CDH1） and retinoic acid receptor beta （RARB） served as target DNA sequences. Based on bisulfite conversion, oligonucleotides ofmethylated or nonmethylated probes and targets were synthesized for the DNA methylation assay. Principal component analysis with linear discriminant analysis （PCA-DA） was used to discriminate the hybridization between probes and targets （methylated probe and methylated target or nonmethylated probe and nonmethylated target） of CDH! and RARB from nonhybridization between the probe and targets （methylated probe and nonmethylated target or nonmethylated probe and methylated target）. Results： This study revealed that the CDH1 and RARB oligo sets and their hybridization data could be classified using PCA-DA. The classification results for CDH1 methylated probe ＋ CDH1 methylated target versus CDH! methylated probe ＋ CDHI unmethylated target showed sensitivity, specificity, and error rates of 92%, 100%, and 8%, respectively. The classification results for the RARB methylated probe ＋ RARB methylated target versus RARB methylated probe ＋ RARB unmethylated target showed sensitivity, specificity, and error rates of 92%, 93%, and 11%, respectively. Conclusions： Label-free detection ofDNA methylation could be achieved using Raman spectroscopy with discriminant analysis.

Proteomic study of vitreous in proliferative diabetic retinopathy patients after treatment with aflibercept:a quantitative analysis based on 4D label-free technique

AIM:To identify different metabolites,proteins and related pathways to elucidate the causes of proliferative diabetic retinopathy(PDR)and resistance to anti-vascular endothelial growth factor(VEGF)drugs,and to provide biomarkers for the diagnosis and treatment of PDR.METHODS:Vitreous specimens from patients with diabetic retinopathy were collected and analyzed by Liquid Chromatography-Mass Spectrometry(LC-MS/MS)analyses based on 4D label-free technology.Statistically differentially expressed proteins(DEPs),Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway representation and protein interactions were analyzed.RESULTS:A total of 12 samples were analyzed.The proteomics results showed that a total of 58 proteins were identified as DEPs,of which 47 proteins were up-regulated and 11 proteins were down-regulated.We found that C1q and tumor necrosis factor related protein 5(C1QTNF5),Clusterin(CLU),tissue inhibitor of metal protease 1(TIMP1)and signal regulatory protein alpha(SIRPα)can all be specifically regulated after aflibercept treatment.GO functional analysis showed that some DEPs are related to changes in inflammatory regulatory pathways caused by PDR.In addition,protein-protein interaction(PPI)network evaluation revealed that TIMP1 plays a central role in neural regulation.In addition,CD47/SIRPαmay become a key target to resolve anti-VEGF drug resistance in PDR.CONCLUSION:Proteomic analysis is an approach of choice to explore the molecular mechanisms of PDR.Our data show that multiple proteins are differentially changed in PDR patients after intravitreal injection of aflibercept,among which C1QTNF5,CLU,TIMP1 and SIRPαmay become targets for future treatment of PDR and resolution of anti-VEGF resistance.

Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven state.In this way,the image quality and contrast will decrease.In this case,It is difficult to detect the target in the image by the universal detection network.Thus,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this paper.Firstly,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy scenes.In the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the model.Secondly,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous fog.Finally,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is redesigned.In this way,the feature extraction ability of the network for deep-level information can be enhanced.The experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection model.The DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

SDH-FCOS:An Efficient Neural Network for Defect Detection in Urban Underground Pipelines

Urban underground pipelines are an important infrastructure in cities,and timely investigation of problems in underground pipelines can help ensure the normal operation of cities.Owing to the growing demand for defect detection in urban underground pipelines,this study developed an improved defect detection method for urban underground pipelines based on fully convolutional one-stage object detector(FCOS),called spatial pyramid pooling-fast(SPPF)feature fusion and dual detection heads based on FCOS(SDH-FCOS)model.This study improved the feature fusion component of the model network based on FCOS,introduced an SPPF network structure behind the last output feature layer of the backbone network,fused the local and global features,added a top-down path to accelerate the circulation of shallowinformation,and enriched the semantic information acquired by shallow features.The ability of the model to detect objects with multiple morphologies was strengthened by introducing dual detection heads.The experimental results using an open dataset of underground pipes show that the proposed SDH-FCOS model can recognize underground pipe defects more accurately;the average accuracy was improved by 2.7% compared with the original FCOS model,reducing the leakage rate to a large extent and achieving real-time detection.Also,our model achieved a good trade-off between accuracy and speed compared with other mainstream methods.This proved the effectiveness of the proposed model.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Phenotypic Detection of Enterobacterales Strains Susceptible of Producing OXA-48 Carbapenemase

Background: Nowadays, emergence of Carbapenemase-Producing Enterobacterales (CPE) throughout the world has become a public health problem, especially in countries with limited resources. In recent years, CPE of type OXA-48 (Ambler class D) have been identified in Dakar. The aim of this study was to evaluate the phenotypic detection of OXA-48 CPE using a temocillin disc (30 μg). Methodology: A retrospective study was carried out at Medical Biology Laboratory of Pasteur Institute in Dakar on Ertapenem-Resistant Enterobacterales (ERE) strains isolated from 2015 to 2017. These strains were then tested with a 30 μg temocillin disc. Any strain resistant to temocillin (inhibition diameter Results: Forty-one ERE isolated during the study period were tested, of which 34 (82.9%) were OXA-48 based on phenotypic detection using temocillin disc and confirmed by PCR (100%). OXA-48 CPE strains detected were composed of Klebsiella pneumoniae (n = 14;41.2%), Enterobacter cloacae (n = 8;23.5%), Escherichia coli (n = 7, 20.5%), Citrobacter freundii (n = 3;8.8%), Cronobacter sakazakii (n = 1;3%) and Morganella morganii (n = 1;3%). Conclusion: Temocillin resistance has a good positive predictive value for detecting OXA-48 CPE by phenotypic method, confirmed by PCR. Temocillin is therefore a good marker for detection of OXA-48 CPE except Hafnia alvei.

Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security.This paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network attacks.We have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention mechanism.These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature subset.Moreover,we address class imbalance in the dataset using focal loss.Finally,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection performance.The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively.In binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of blasting.Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine learning.In this paper,we design a distributedmachine learning training application based on the AWS Lambda platform.Based on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively realized.It also encrypts the data set,effectively reducing the risk of data leakage.We rent a cloud server and a Lambda,and then we conduct experiments to evaluate our applications.Our results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.