Electrochemical-Fluorescent Bimodal Biosensor Based on Dual CRISPR-Cas12a Multiple Cascade Amplification for ctDNA Detection.

Circulating tumor DNA (ctDNA) is a critical biomarker for early tumor detection. However, accurately quantifying low-abundance ctDNA in human serum remains a significant challenge. To address this challenge, we introduce a bimodal biosensor tailored for detecting the epidermal growth factor receptor (EGFR) mutation L858R in specific nonsmall cell lung cancer (NSCLC) patients. This biosensor utilizes dual CRISPR-Cas12a systems to quantify the target via fluorescence and electrochemical signals. In our system, the EGFR L858R exhibits resistance to digestion by the restriction enzyme MscI, which activates the first CRISPR-Cas12a protein and inhibits the binding of magnetic beads with fluorescein (FAM)-labeled hybridization chain reaction (HCR) products, thereby reducing the fluorescence signal. This activation also inhibits the cleavage activity of the second CRISPR-Cas12a protein, allowing the electrode to sustain a higher electrochemical signal from nanomaterials. The wild-type EGFR (wt EGFR) produces the opposite effect. Consequently, the concentration of EGFR L858R can be accurately quantified and verified using both fluorescence and electrochemical signals. The biosensor offers a dynamic detection ranging from 10 fM to 1 µM, with a detection limit of 372 aM. It demonstrates excellent specificity, reproducibility, stability, and recovery rates. Moreover, the sensor's enhanced analytical sensitivity highlights its critical role in biosensing applications and early disease diagnosis.

Electrochemical detection of myoglobin using an ultrasensitive label-free sensor derived from cubic-ZIF67@Au-rGOF-NH2 composite of MOF and GOF.

Markers of myocardial injury, such as myoglobin (Mb), are substances swiftly released into the peripheral bloodstream upon myocardial cell injury or altered cardiac activity. During the onset of acute myocardial infarction, patients experience a significant surge in serum Mb levels. Given this, precise detection of Mb is essential, necessitating the development of innovative assays to optimize detection capabilities. This study introduces the synthesis of a three-dimensional hierarchical nanocomposite, Cubic-ZIF67@Au-rGOF-NH2, utilizing aminated reduced graphene oxide and zeolite imidazolium ester framework-67 (ZIF67) as foundational structures. Notably, this novel material, applied in a label-free electrochemical immunosensor, presents a groundbreaking approach for detecting myocardial injury markers. Experimental outcomes revealed ZIF67 and AuNPs exhibit enhanced affinity and growth on the 3D-rGOF-NH2 matrix, thus amplifying electrical conductivity while preserving the inherent electrochemical attributes of ZIF67. As a result, the Cubic-ZIF67@Au-rGOF-NH2 label-free electrochemical immunosensor exhibited a broad detection range and high sensitivity for Mb. The derived standard curve was ΔIp = 16.67552lgC+275.245 (R = 0.993) with a detection threshold of 3.47 fg/ml. Moreover, recoveries of standards spiked into samples ranged between 96.3% and 108.7%. Importantly, the devised immunosensor retained notable selectivity against non-target proteins, proving its potential clinical utility based on exemplary sample analysis performance.

One-step electrodeposition preparation of polyaniline/f-MWCNTs as electrochemical sensors for detection of 10-hydroxycamptothecine.

In our work, one-step electro-deposition method was adopted to produce polyaniline (PANI) and functional multiwalled carbon nanotubes (f-MWCNTs) films on glass carbon electrodes, and the modified electrodes were applied as an electrochemical sensor for determination of 10-hydroxycamptothecine (10-HCPT). The f-MWCNTs were handled by ultrasound processing in concentrated oxidizing acid solution, which can obtain a wonderful dissolution in water and attach new functional groups, such as -COOH and -OH. Then, aniline monomer could polymerize on the surface easily. The surface characterization was investigated using various techniques including scanning electron microscope, Fourier transform infrared spectroscopy, X-ray photoelectron spectroscopy, and X-ray diffraction, and electro-catalytic properties were characterized by cyclic voltammetry and electrochemical impedance spectroscopy. Under optimal conditions, the resulting of PANI/f-MWCNTs sensor showed a wide linear range (3 × 10-9 to 7 × 10-7 mol L-1 ) and a low detection limit (1 × 10-9 mol L-1 ), which is attributing to its large special surface area and good conductivity. Moreover, the modified electrodes are convenient to fabricate, which can be used to detect 10-HCPT in urine samples successfully.

A sandwich-configuration electrochemiluminescence immunoassay based on Cu2O@OMC-Ru nanocrystals and OMC-MoS2 nanocomposites for determination of alpha-fetoprotein.

A sandwich-format electrochemiluminescence (ECL) immunosensor has been developed for alpha-fetoprotein (AFP) detection based on the use of ordered mesoporous carbon-molybdenum disulfide (OMC-MoS2) as a sensor platform and cuprous oxide @ ordered mesoporous carbon-Ru(bpy)32+ (Cu2O@OMC-Ru) composites as signal tags. OMC alongside MoS2 plays a synergistic role in improving the electrochemical performance of the electrode in the electron transfer process. The uniform cubic-shaped Cu2O@OMC-Ru nanocrystals display excellent luminous efficiency, with a signal amplification strategy of OMC-MoS2 synergistic enhancement and Cu2O@OMC which is capable of immobilizing more Ru(bpy)32+ serving as a tracing tag to label antibodies. A detectable ECL emission at a Cu2O@OMC-Ru nanocrystals modified electrode is initiated at an applied voltage of +1.15 V (scanning range: 0-1.2 V), in the presence of the tripropylamine (TPA) as coreactant. With the increase in AFP concentration, the loading of Cu2O@OMC-Ru at the electrode increases. Afterward, the ECL detection of AFP shows a wide linear range from 0.1 pg/mL to 10 ng/mL with a correlation coefficient of 0.9964 and a detection limit of 0.011 pg/mL (S/N = 3) under the optimal experimental conditions. The recoveries were in the range 91.2-97.1% with RSD varying from 4.8 to 8.5%. Overall, the novel immunosensor has been successfully applied to the analysis of human serum samples, indicating a great potential for application in clinical diagnostics.

Anomalous Spin-Charge Separation in a Driven Hubbard System.

Spin-charge separation (SCS) is a striking manifestation of strong correlations in low-dimensional quantum systems, whereby a fermion splits into separate spin and charge excitations that travel at different speeds. Here, we demonstrate that periodic driving enables control over SCS in a Hubbard system near half filling. In one dimension, we predict analytically an exotic regime where charge travels slower than spin and can even become "frozen," in agreement with numerical calculations. In two dimensions, the driving slows both charge and spin and leads to complex interferences between single-particle and pair-hopping processes.

Photoinduced Electron Pairing in a Driven Cavity.

We demonstrate how virtual scattering of laser photons inside a cavity via two-photon processes can induce controllable long-range electron interactions in two-dimensional materials. We show that laser light that is red (blue) detuned from the cavity yields attractive (repulsive) interactions whose strength is proportional to the laser intensity. Furthermore, we find that the interactions are not screened effectively except at very low frequencies. For realistic cavity parameters, laser-induced heating of the electrons by inelastic photon scattering is suppressed and coherent electron interactions dominate. When the interactions are attractive, they cause an instability in the Cooper channel at a temperature proportional to the square root of the driving intensity. Our results provide a novel route for engineering electron interactions in a wide range of two-dimensional materials including AB-stacked bilayer graphene and the conducting interface between LaAlO_{3} and SrTiO_{3}.

Electrochemical immunoassay for the carcinoembryonic antigen based on Au NPs modified zeolitic imidazolate framework and ordered mesoporous carbon.

An electrochemical immunoassay for the carcinoembryonic antigen is described. It is based on the use of Au NPs modified zeolitic imidazolate framework (ZIF-8) and ordered mesoporous carbon (OMC). Au NPs@ZIF-8 was synthesized by reduction of chloroauric acid. It serves as immobilization support nanocarrier to increase antibody loading due to its large surface area. OMC was dropped on a glassy carbon electrode to improve electrochemical signals due to enhanced electrical conductivity. Differential pulse voltammetry was carried out to record electrochemical responses (best measured at 0.26 V vs. Ag/AgCl). The immunosensor demonstrated excellent electrochemical performance with a linear determination range of 5 pg mL-1 to 400 ng mL-1 and a determination limit of 1.3 pg mL-1 (S/N = 3). The sensor also exhibited high selectivity, good stability, and acceptable reproducibility. Graphical abstract Scheme 1 Schematic representation of fabrication of the immunosensor for CEA determination.

An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask R-CNN.

In this paper, we consider building extraction from high spatial resolution remote sensing images. At present, most building extraction methods are based on artificial features. However, the diversity and complexity of buildings mean that building extraction methods still face great challenges, so methods based on deep learning have recently been proposed. In this paper, a building extraction framework based on a convolution neural network and edge detection algorithm is proposed. The method is called Mask R-CNN Fusion Sobel. Because of the outstanding achievement of Mask R-CNN in the field of image segmentation, this paper improves it and then applies it in remote sensing image building extraction. Our method consists of three parts. First, the convolutional neural network is used for rough location and pixel level classification, and the problem of false and missed extraction is solved by automatically discovering semantic features. Second, Sobel edge detection algorithm is used to segment building edges accurately so as to solve the problem of edge extraction and the integrity of the object of deep convolutional neural networks in semantic segmentation. Third, buildings are extracted by the fusion algorithm. We utilize the proposed framework to extract the building in high-resolution remote sensing images from Chinese satellite GF-2, and the experiments show that the average value of IOU (intersection over union) of the proposed method was 88.7% and the average value of Kappa was 87.8%, respectively. Therefore, our method can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy.

A Hyperspectral Image Classification Method Based on Multi-Discriminator Generative Adversarial Networks.

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

Dimension Reduction for Hyperspectral Remote Sensor Data Based on Multi-Objective Particle Swarm Optimization Algorithm and Game Theory.

Information entropy and interclass separability are adopted as the evaluation criteria of dimension reduction for hyperspectral remote sensor data. However, it is rather single-faceted to simply use either information entropy or interclass separability as evaluation criteria, and will lead to a single-target problem. In this case, the chosen optimal band combination may be unfavorable for the improvement of follow-up classification accuracy. Thus, in this work, inter-band correlation is considered as the premise, and information entropy and interclass separability are synthesized as the evaluation criterion of dimension reduction. The multi-objective particle swarm optimization algorithm is easy to implement and characterized by rapid convergence. It is adopted to search for the optimal band combination. In addition, game theory is also introduced to dimension reduction to coordinate potential conflicts when both information entropy and interclass separability are used to search for the optimal band combination. Experimental results reveal that compared with the dimensionality reduction method, which only uses information entropy or Bhattacharyya distance as the evaluation criterion, and the method combining multiple criterions into one by weighting, the proposed method achieves global optimum more easily, and then obtains a better band combination and possess higher classification accuracy.

Deep Belief Network for Spectral⁻Spatial Classification of Hyperspectral Remote Sensor Data.

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

Domain Adaptation and Adaptive Information Fusion for Object Detection on Foggy Days.

Foggy days pose many difficulties for outdoor camera surveillance systems. On foggy days, the optical attenuation and scattering effects of the medium significantly distort and degenerate the scene radiation, making it noisy and indistinguishable. Aiming to solve this problem, in this paper we propose a novel object detection method that has the ability to exploit the information in the color and depth domains. To prevent the error propagation problem, we clean the depth information before the training process and remove false samples from the database. A domain adaptation strategy is employed to adaptively fuse the decisions obtained in the color and depth domains. In the experiments, we evaluate the contribution of the depth information for object detection on foggy days. Moreover, the advantages of the multiple-domain adaptation strategy are experimentally demonstrated via comparison with other methods.

Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors.

Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller.

Hyperspectral Remote Sensing Image Classification Based on Maximum Overlap Pooling Convolutional Neural Network.

In a traditional convolutional neural network structure, pooling layers generally use an average pooling method: a non-overlapping pooling. However, this condition results in similarities in the extracted image features, especially for the hyperspectral images of a continuous spectrum, which makes it more difficult to extract image features with differences, and image detail features are easily lost. This result seriously affects the accuracy of image classification. Thus, a new overlapping pooling method is proposed, where maximum pooling is used in an improved convolutional neural network to avoid the fuzziness of average pooling. The step size used is smaller than the size of the pooling kernel to achieve overlapping and coverage between the outputs of the pooling layer. The dataset selected for this experiment was the Indian Pines dataset, collected by the airborne visible/infrared imaging spectrometer (AVIRIS) sensor. Experimental results show that using the improved convolutional neural network for remote sensing image classification can effectively improve the details of the image and obtain a high classification accuracy.

Multi-class remote sensing object recognition based on discriminative sparse representation.

The automatic recognition of multi-class objects with various backgrounds is a big challenge in the field of remote sensing (RS) image analysis. In this paper, we propose a novel recognition framework for multi-class RS objects based on the discriminative sparse representation. In this framework, the recognition problem is implemented in two stages. In the first, or discriminative dictionary learning stage, considering the characterization of remote sensing objects, the scale-invariant feature transform descriptor is first combined with an improved bag-of-words model for multi-class objects feature extraction and representation. Then, information about each class of training samples is fused into the dictionary learning process; by using the K-singular value decomposition algorithm, a discriminative dictionary can be learned for sparse coding. In the second, or recognition, stage, to improve the computational efficiency, the phase spectrum of a quaternion Fourier transform model is applied to the test image to predict a small set of object candidate locations. Then, a multi-scale sliding window mechanism is utilized to scan the image over those candidate locations to obtain the object candidates (or objects of interest). Subsequently, the sparse coding coefficients of these candidates under the discriminative dictionary are mapped to the discriminative vectors that have a good ability to distinguish different classes of objects. Finally, multi-class object recognition can be accomplished by analyzing these vectors. The experimental results show that the proposed work outperforms a number of state-of-the-art methods for multi-class remote sensing object recognition.

Sandwich-type immunosensor based on aminated 3D-rGOF-NH2 and CMK-3-Fc-MgAl-LDH multilayer nanocomposites for detection of CA125.

Cancer antigen 125 (CA125)1 is the most important biological screening indicator used to monitor epithelial ovarian cancers, and it plays a vital role in distinguishing ovarian cancers from benign diseases. Biosensors show great potential in the analysis and detection of disease markers. In this study, we designed electrochemical sensors based on three-dimensional amino-functionalized reduced graphene oxide (3D-rGOF-NH2),2 MgAl layered double hydroxide nanocomposites containing ordered mesoporous carbon (CMK-3),3 and ferrocene carboxylic acids（Fc-COOH）4for the detection of CA125. 3D-rGOF-NH2 possesses good conductivity, a large surface area, and high porosity, enabling more immobilized nanoparticles to be deposited on its surface with excellent stability. CMK-3@Fc@MgAl-LDH nanocomposite was used as a carrier to enhance the immobilization of antibodies and the loading of Fc, conductors to enhance conductivity, and enhancers to gradually amplify the signal of Fc. The surface morphology, elemental composition, and surface groups of the materials were characterized using scanning electron microscopy (SEM),5 transmission electron microscopy (TEM),6 and X-ray diffraction (XRD)7 techniques. The response signal of the electrochemical sensor was measured by DPV. Under the optimal conditions, the electrochemical sensor obtained a linear detection range of 0.01 U/mL-100 U/mL with a detection limit of 0.00417 U/mL.

Ultrasensitive and label-free electrochemical immunosensor for the detection of the ovarian cancer biomarker CA125 based on CuCo-ONSs@AuNPs nanocomposites.

Cancer antigen 125 (CA125) is pivotal as a tumor marker in early ovarian cancer prevention and diagnosis. In this work, we introduced an ultrasensitive label-free electrochemical immunosensor tailored for CA125 detection, leveraging nanogold-functionalized copper-cobalt oxide nanosheets (CuCo-ONSs@AuNPs) as nanocomposites. For the inaugural application, copper-cobalt oxide nanosheets delivered the requisite DPV electrochemical response for the immunosensors. Their large specific surface area and commendable electrical conductivity amplify electron transfer and enable significant gold nanoparticle loading. Concurrently, AuNPs offer a plethora of active sites, facilitating easy immobilization of biomolecules via the bond between amino groups and AuNPs. We employed scanning electron microscopy, transmission electron microscopy, and x-ray photoelectron spectroscopy to characterize the nanomaterials' surface morphology and elemental composition. The electrochemical sensor response signals were ascertained using differential pulse voltammetry. Under optimal conditions, the immunosensor exhibited a linear detection range from 1×10-7 U/mL to 1×10-3 U/mL and a detection limit of 3.9×10-8 U/mL (S/N=3). The proposed label-free electrochemical immunosensor furnishes a straightforward, dependable, and sensitive approach for CA125 quantification and stands as a promising method for clinical detection of other tumor markers.

Dual electrochemical signal "signal-on-off" sensor based on CHA-Td-HCR and CRISPR-Cas12a for MUC1 detection.

Mucin 1 (MUC1) is frequently overexpressed in various cancers and is essential for early cancer detection. Current methods to detect MUC1 are expensive, time-consuming, and require skilled personnel. Therefore, developing a simple, sensitive, highly selective MUC1 detection sensor is necessary. In this study, we proposed a novel "signal-on-off" strategy that, in the presence of MUC1, synergistically integrates catalytic hairpin assembly (CHA) with DNA tetrahedron (Td)-based nonlinear hybridization chain reaction (HCR) to enhance the immobilization of electrochemically active methylene blue (MB) on magnetic nanoparticles (MNP), marking the MB signal "on". Concurrently, the activation of CRISPR-Cas12a by isothermal amplification products triggers the cleavage of single-stranded DNA (ssDNA) at the electrode surface, resulting in a reduction of MgAl-LDH@Fc-AuFe-MIL-101 (containing ferrocene, Fc) on the electrode, presenting the "signal-off" state. Both MB and MgAl-LDH@Fc-AuFe-MIL-101 electrochemical signals were measured and analyzed. Assay parameters were optimized, and sensitivity, stability, and linear range were assessed. Across a concentration spectrum of MUC1 spanning from 10 fg/mL to 100 ng/mL, the MB and MgAl-LDH@Fc-AuFe-MIL-101 signals were calibrated with each other, demonstrating a "signal-on-off" dual electrochemical signaling pattern. This allows for the precise and quantitative detection of MUC1 in clinical samples, offering significant potential for medical diagnosis.

Few-shot remote sensing scene classification based on multi subband deep feature fusion.

Recently, convolutional neural networks (CNNs) have performed well in object classification and object recognition. However, due to the particularity of geographic data, the labeled samples are seriously insufficient, which limits the practical application of CNN methods in remote sensing (RS) image processing. To address the problem of small sample RS image classification, a discrete wavelet-based multi-level deep feature fusion method is proposed. First, the deep features are extracted from the RS images using pre-trained deep CNNs and discrete wavelet transform (DWT) methods. Next, a modified discriminant correlation analysis (DCA) approach is proposed to distinguish easily confused categories effectively, which is based on the distance coefficient of between-class. The proposed approach can effectively integrate the deep feature information of various frequency bands. Thereby, the proposed method obtains the low-dimensional features with good discrimination, which is demonstrated through experiments on four benchmark datasets. Compared with several state-of-the-art methods, the proposed method achieves outstanding performance under limited training samples, especially one or two training samples per class.

Semi-supervised enhanced discriminative local constraint preserving projection for dimensionality reduction of medical hyperspectral images.

Microscopic hyperspectral images has the advantage of containing rich spatial and spectral information. However, the large number of spectral bands provides a significant amount of spectral features, but also leads to data redundancy and noise, which seriously affect the recognition and classification performance of the images, as well as increasing the requirements for computation and storage. To address this issue, we propose a dimensionality reduction algorithm named enhanced discriminant local constraint preserving projection (EDLCPP). Specifically, the global spectral attention mechanism focuses on important bands, the high discriminability sample selection module measures the discriminability of samples using a modified average neighborhood margin, the graph construction module preserves the local geometric relationship and discriminant information, and the graph embedding module embeds the constructed graphs into a low-dimensional space to obtain the projection matrices. Experimental results on eight cholangiocarcinoma (CCA) hyperspectral images, Bloodcell1-3, and Bloodcell2-2 datasets have demonstrated the effectiveness of the proposed method.