High Efficient Recognition Research Articles

Efficient DNA walker guided by ordered cruciform-shaped DNA track for ultrasensitive and rapid electrochemical detection of lead ion

The rational design of DNA tracks is an effective pathway to guide the autonomous movement and high-efficiency recognition in DNA walkers, showing outstanding advantages for the cascade signal amplification of electrochemical biosensors. However, the uncontrolled distance between two adjacent tracks on the electrode could increase the risk of derailment and interruption of the reaction. Hence, a novel four-way balanced cruciform-shaped DNA track (C-DNT) was designed as a structured pathway to improve the effectiveness and stability of the reaction in DNA walkers. In this work, two kinds of cruciform-shaped DNA were interconnected as a robust structure that could avoid the invalid movement of the designed DNA walker on the electrode. When hairpin H2 was introduced onto the electrode, the strand displacement reaction (SDR) effectively triggered movements of the DNA walker along the cruciform-shaped track while leaving ferrocene (Fc) on the electrode, leading to a significant enhancement of the electrochemical signal. This design enabled the walker to move in an excellent organized and controllable manner, thus enhancing the reaction speed and walking efficiency. Compared to other walkers moving on random tracks, the reaction time of the C-DNT-based DNA walker could be reduced to 20 min. Lead ion (Pb2+) was used as a model target to evaluate the analytical performance of this biosensor, which exhibited a low detection limit of 0.033 pM along with a wide detection ranging from 0.1 pM to 500 nM. This strategy presented a novel concept for designing a high-performance DNA walker-based sensing platform for the detection of contaminants.

Point cloud based hand gesture recognition using template matching

Hand gesture recognition is important in human-computer interaction with wide applications in many fields. Different from common hand gesture recognition based on 2D images acquired from RGB camera, the utilization of 3D images provides additional spatial information about the target and attracts more and more attention in hand gesture recognition. However, most 3D images for hand gesture recognition are based on depth maps, which only take the distance information as a channel of 2D images, without taking full use of the 3D information. Besides, greater data volume of 3D images brings challenges to the arithmetic facility of hand gesture recognition. Here, we proposed a point cloud based method for hand gesture recognition. To fully use the 3D information, plane points for template matching were extracted based on their normal distributions, which leads to the average recognition rate over 97%. Pre-classification was implemented to ensure a high-efficient recognition without additional requirements for the computer. The proposed method may provide approach for accurate and efficient hand gesture recognition based on 3D images.

Living Tree Moisture Content Detection Method Based on Intelligent UHF RFID Sensors and OS-PELM.

Moisture content (MC) detection plays a vital role in the monitoring and management of living trees. Its measurement accuracy is of great significance to the progress of the forestry informatization industry. Targeting the drawbacks of high energy consumption, low practicability, and poor sustainability in the current field of living tree MC detection, this work designs and implements an ultra-high-frequency radio frequency identification (UHF RFID) sensor system based on a deep learning model, with the main goals of non-destructive testing and high-efficiency recognition. The proposed MC diagnostic system includes two passive tags which should be mounted on the trunk and one remote data processing terminal. First, the UHF reader collects information from the living trees in the forest; then, an improved online sequential parallel extreme learning machine algorithm (OS-PELM) is proposed and trained to establish a specific MC prediction model. This mechanism could self-adjust its neuron network structure according to the features of the data input. The experimental results show that, for the entire living tree dataset, the MC prediction model based on the OS-PELM algorithm can identify the MC level with a root-mean-square error (RMSE) of no more than 0.055 within a measurement range of 1.2 m. Compared with the results predicted by other algorithms, the mean absolute error (MAE) and RMSE are 0.0225 and 0.0254, respectively, which are better than the ELM and OS-ELM algorithms. Comparisons also prove that the prediction model has the advantages of high precision, strong robustness, and broad applicability. Therefore, the designed MC detection system fully meets the demand of forestry Artificial Intelligence of Things.

Modulation Mode Recognition Method of Non-Cooperative Underwater Acoustic Communication Signal Based on Spectral Peak Feature Extraction and Random Forest

The modulation mode recognition of non-cooperative underwater acoustic (UWA) communication signal faces great challenges due to the influence of the UWA channel and the demand for efficient recognition. This work proposes a recognition method for UWA orthogonal frequency division multiplexing (OFDM), binary frequency shift keying (2FSK), four-frequency shift keying (4FSK), and eight-frequency shift keying (8FSK) by using spectral peak feature extraction combined with random forest (RF). First, a new spectral peak feature extraction method is proposed. In this method, pre-processing, waveform optimization, and feature extraction are used to ensure that the extracted feature maintains high robustness in the UWA channel. Then, we designed an RF classifier that can meet the demand for high-efficiency recognition and good performance. Finally, simulation and experimental results verified the feasibility of the recognition method.

Preparation of magnetic yolk-shell structured metal-organic framework material and its application in pharmacokinetics study of alkaloids.

In this study, a magnetic yolk-shell structured metal-organic framework material (Fe3O4@YS-UiO-66-NH2) is prepared by the directional etching of Co2+/peroxymonosulfate and in situ magnetization. The characteristic properties of the material were investigated by using field emission scanning electron microscopy, transmission electron microscopy, energy-dispersive X-ray spectroscopy, X-ray powder diffraction, Fourier transform infrared spectroscopy, vibrating sample magnetometer, Brunauer-Emmett-Teller, and contact angle test. The Fe3O4@YS-UiO-66-NH2 shows the advantages of large surface area, good magnetic property, and satisfactory stability, as well as giving high affinity to alkaloids (ALs) via hydrophilic interaction, hydrogen bonding, and π-π interaction. The results of static adsorption experiment indicate that the Fe3O4@YS-UiO-66-NH2 possesses high adsorption capacity towards ALs and the adsorption behaviors are fitted with Langmuir adsorption isotherm model. Furthermore, a magnetic solid-phase extraction using Fe3O4@YS-UiO-66-NH2 and HPLC method was developed for the analysis of ALs in spiked samples with the recovery of 89.6-100.8%. In addition, the proposed method was successfully applied in the pharmacokinetics study of berberine, coptisine, and palmatine in the rat. In short, the developed method might be used for high-efficient recognition and determination of ALs in plasma sample, which would also provide a new way to fabricate magnetic functionalized metal-organic framework in separation science.

Ultrasensitive Exosomal MicroRNA Detection with a Supercharged DNA Framework Nanolabel.

DNA self-assembly has created various nanostructured probes deployed in biosensors, whereas their direct charge contribution to sensitive bioassays remains elusive. Here, we report a supercharged tetrahedral DNA nanolabel-based electrochemical (eTDN) sensor for ultrasensitive detection of exosomal microRNAs (Exo-miRs). By using an "assembly before testing" strategy, there is high-efficiency recognition between the target Exo-miR and self-assembled TDN probe in a homogenous solution relative to surface-based hybridization. The TDN-miR complex can be further bridged specifically to form a stable sandwich construct with the surface-confined capture sequence via the base-stacking effect. The intrinsic supercharges of the TDN can adsorb numerous electroactive molecules in stoichiometry, which largely enhances the detection sensitivity, particularly by using electroneutral peptide nucleic acid instead of DNA probes to minimize the background signal. Using this approach, the eTDN sensor achieves a high sensitivity (34 aM), high specificity (against the single mismatch), and high selectivity (in serum). Furthermore, this ultrasensitive sensor provides a conjugation-free, non-enzymatic Exo-miR detection in blood and accurately distinguishes the breast cancer patients from normal individuals, showing to be a promising tool in the early diagnosis of malignant tumors.

High-efficiency recognition and detection of sulindac in sewage using hydrophilic imprinted resorcinol-formaldehyde resin magnetic nano-spheres as SPE adsorbents combined with HPLC

Hydrophilic molecularly imprinted polymer for effective removal of pollutants from environmental water has attracted much attention due to their high selectivity and matrix compatibility. Herein, resorcinol and formaldehyde have been first proposed as the hydrophilic monomers to form water-compatible molecularly imprinted resin magnetic nanomaterials (Fe3O4-RF-MIPs) by adopting a two-step template immobilization strategy of surface molecular imprinting technique. In the synthesis process, carboxyl-modified Fe3O4 nanoparticles and sulindac were adopted as carrier and template molecule, respectively. The physical and chemical properties, preparation process, and adsorption conditions of Fe3O4-RF-MIPs were investigated in detail. Fe3O4-RF-MIPs exhibit uniform morphology, high crystallinity, outstanding magnetic property, fast binding kinetics, high adsorption capacity, satisfactory selectivity, and excellent reusability. Meanwhile, combining with HPLC, Fe3O4-RF-MIPs as solid phase extraction adsorbents have been successfully applied to specifically enrich and detect sulindac in sewage water. Accordingly, Fe3O4-RF-MIPs with ordered and evenly distributed imprinted sites and excellent dispersibility in water would be potential in the field of water environmental pollution control.

Fabrication of inverse-opal lysozyme-imprinted polydopamine/polypyrrole microspheres with near-infrared-light-controlled release property

The combination of the molecular imprinting technology and porous materials is a promising way to obtain high-efficient selective adsorption and separation materials for bioactive macromolecules. In this work, we developed a novel approach to prepare near-infrared (NIR)-light-response inverse-opal lysozyme (Lyz)-imprinted polydopamine/polypyrrole (IO-PDA/PPy-MIP) composite microspheres using micron-sized SiO2 colloidal crystal microspheres as the sacrificed template. The pore size of the IO-PDA/PPy-MIP microspheres can be tuned from 200 to 800 nm by the size of silica nanoparticles which self-assemble to form the template SiO2 colloidal crystal microspheres. The IO-PDA/PPy-MIP microspheres show a rapid selective adsorption ability for Lyz due to the inverse-opal macroporous structure. The adsorption capacity exceeds 800 mg/g within 20 min, and the imprinting factor is as high as 24. The bound Lyz molecules can be released rapidly from IO-PDA/PPy-MIP microspheres triggered by the irradiation of NIR laser and remain enough bioactivity to decompose Escherichia coli efficiently. The prepared IO-PDA/PPy-MIP microspheres also exhibit excellent structure stability and good recyclability. The adsorption capacity can remain up to 90% of the initial value after 5 times recycle. This work provides not only a method to prepare novel NIR-light-response inverse-opal macroporous molecularly imprinted microspheres, but also a new perspective on the design of selectively separation materials for the fast, high-efficient recognition and separation of biomacromolecules.

Virus-Mimicking Cell Capture Using Heterovalency Magnetic DNA Nanoclaws.

Synergy represents a natural approach for high-efficiency recognition in biological systems. Inspired by the recognition mechanism of viral infection of mammalian cells, here we develop heterovalency magnetic DNA nanoclaws with octopus arms morphology for synergetic cell capture. We demonstrated that the rigid-flexible DNA nanoclaws can load multiple antibodies (Abs) targeting different epitopes for enhanced capture of cancer cells, especially significantly increasing the capture efficiency of MDA-MB-231 cells up to 82.3 ± 7.1%. We also employed DNA nanoclaws with the combined use of multiple Abs to capture circulating tumor cells from clinical samples with high efficiency and specificity. We expect that the DNA nanoclaws not only could play a key role in liquid biopsy, but also could be expanded, with more applications benefiting from their modularity and programmability to modify various functionalities in future.

Recognition of Angiographic Atherosclerotic Plaque Development Based on Deep Learning

In order to obtain accurate information on the degree of plaque development in patients’ blood vessels, and to assist clinicians in judging and recognizing atherosclerotic areas, a deep learning-based study of intravascular ultrasound atherosclerotic plaque development was performed (CPCA). First, different types of ROIs are extracted for plaque images. Secondly, according to different ROI regions, the size of the sliding neighborhood block is determined, and the central pixel traverses the plaque region to obtain a small image slice of the plaque developing region. Then, based on PCAnet based on principal component analysis vector as convolution kernel, a clustering PCA network is designed to cluster small image slices and calculate principal component vectors by category to generate multiple sets of convolution kernels. The multi-plaque visualization feature enables the input image to adaptively select the feature extractor to achieve classification recognition of the degree of plaque development. The result of manual labeling by doctors is taken as the standard true value. The experimental results show that the proposed algorithm can effectively extract the features of plaque developed images and achieve high-efficiency recognition of plaque development.

-Phosphorescent Mesoporous Surface Imprinting Microspheres: Preparation and Application for Transferrin Recognition from Biological Fluids.

The relationship between the thickness of surface molecularly imprinted polymers (MIPs) and specific recognition performance of transferrin (Trf) as well as the quantitative relation between the grafting amount of Mn-ZnS room-temperature phosphorescence (RTP) quantum dots (QDs) (short for PQDs) and RTP signals for recognition of Trf was analyzed in this study. Based on analysis results, RTP protein mesoporous imprinting microspheres (SiO2-PQDs-MIPs) with high specificity and strong interference resistance were developed using a mesoporous SiO2 nanomaterial that can create more three-dimensional precise recognition sites as the matrix and using PQDs with strong resistance to background fluorescence interference as the luminescent materials. A discriminatory analysis of Trf was realized by the phosphorescence quenching principle based on light quenching caused by the photoinduced electron transfer. The concentration range, limit of detection, relative standard deviation, and imprinting factor of Trf detection under pH 7.4 are 0.05-1.0 μM, 0.014 μM, 3.23%, and 3.09, respectively. Although the sensing signals of SiO2-PQDs-MIPs for proteins are based on the phosphorescence of PQDs, they are particularly suitable for specific recognition and accurate quantitative detection of proteins in biological fluids. Research conclusions are expected to realize high-efficiency recognition of target proteins in actual biological samples.

Two luminescent lanthanide(iii) metal-organic frameworks as chemosensors for high-efficiency recognition of Cr(vi) anions in aqueous solution.

Two new lanthanide(iii) metal-organic frameworks (MOFs) {[(CH3)2NH2]2[Ln4(FDA)7(DMF)2]·0.5DMF}n [Ln = Eu (1), and Tb (2)] based on furan-2,5-dicarboxylic acid (H2FDA) have been successfully assembled and well characterized in detail. These MOFs are isostructural and demonstrate 12-connected sqc15 topologies, which are rarely observed in MOF chemistry, especially in lanthanide(iii) MOFs. Moreover, these two MOFs could show a tolerance towards moisture and organic solvents and satisfactory chemical stabilities. More importantly, they exhibit sensitive and selective luminescence quenching response towards Cr2O72- and CrO42- anions in aqueous solution with the average quenching Ksv values of 1.25 × 104 L mol-1 (Cr2O72-) and 3.56 × 103 L mol-1 (CrO42-) for 1 and 1.46 × 104 L mol-1 (Cr2O72-) and 4.35 × 103 L mol-1 (CrO42-) for 2 and the detection limits of 1.14 × 10-4 mol L-1 (Cr2O72-) and 1.12 × 10-4 mol L-1 (CrO42-) for 1 and 7.42 × 10-5 mol L-1 (Cr2O72-) and 1.27 × 10-4 mol L-1 (CrO42-) for 2. The high quenching Ksv values and low detection limits make them more feasible in sensing Cr(vi) anions in aqueous solution. The possible detection mechanism has been discussed in detail.

Molecularly Imprinted Polymer Enables High-Efficiency Recognition and Trapping Lithium Polysulfides for Stable Lithium Sulfur Battery.

Using molecularly imprinted polymer to recognize various target molecules emerges as a fascinating research field. Herein, we applied this strategy for the first time to efficiently recognize and trap long-chain polysulfides (Li2Sx, x = 6-8) in lithium sulfur battery to minimize the polysulfide shuttling between anode and cathode, which enables us to achieve remarkable electrochemical performance including a high specific capacity of 1262 mAh g-1 at 0.2 C and superior capacity retention of over 82.5% after 400 cycles at 1 C. The outstanding performance is attributed to the significantly reduced concentration of long-chain polysulfides in electrolyte as evidenced by in situ UV/vis spectroscopy and Li2S nucleation tests, which were further confirmed by density functional theory calculations. The molecular imprinting is demonstrated as a promising approach to effectively prevent the free diffusion of long-chain polysulfides, providing a new avenue to efficiently recognize and trap lithium polysulfides for high-performance lithium sulfur battery with greatly suppressed shuttle effect.

Synthesis and evaluation of a molecularly imprinted polymer with high-efficiency recognition for dibutyl phthalate based on Mn-doped ZnS quantum dots

Molecularly imprinted polymers with Mn-doped ZnS quantum dots were prepared using dibutyl phthalate as the template molecule, 3-aminopropyltriethoxysilane as the functional monomer and the tetraethoxysilane as the cross-linker.

Potential Role of the Last Half Repeat in TAL Effectors Revealed by a Molecular Simulation Study.

TAL effectors (TALEs) contain a modular DNA-binding domain that is composed of tandem repeats. In all naturally occurring TALEs, the end of tandem repeats is invariantly a truncated half repeat. To investigate the potential role of the last half repeat in TALEs, we performed comparative molecular dynamics simulations for the crystal structure of DNA-bound TALE AvrBs3 lacking the last half repeat and its modeled structure having the last half repeat. The structural stability analysis indicates that the modeled system is more stable than the nonmodeled system. Based on the principle component analysis, it is found that the AvrBs3 increases its structural compactness in the presence of the last half repeat. The comparison of DNA groove parameters of the two systems implies that the last half repeat also causes the change of DNA major groove binding efficiency. The following calculation of hydrogen bond reveals that, by stabilizing the phosphate binding with DNA at the C-terminus, the last half repeat helps to adopt a compact conformation at the protein-DNA interface. It further mediates more contacts between TAL repeats and DNA nucleotide bases. Finally, we suggest that the last half repeat is required for the high-efficient recognition of DNA by TALE.

High-Efficiency Recognition and Identification of Disulfide Bonded Peptides in Rat Neuropeptidome Using Targeted Electron Transfer Dissociation Tandem Mass Spectrometry.

The main goal of the present study is to develop a method to recognize and identify endogenous intrachain disulfide bonded peptide, which are rarely sequenced in current peptidomics studies. In order to achieve highly efficient detection of these peptides in a neuropeptidome analysis, we alkylated the peptides, mined the raw mass spectrometry data, and then recognized the candidates of untreated disulfide bonded peptides from unalkylated peptide extracts. After removing more than 90% features, targeted electron transfer dissociation fragmentation was performed for detecting and fragmenting disulfide bonded peptides, and even most of them were present in low abundance in the original sample. Diverse endogenous disulfide bonded peptides were then detected and sequenced, opening up new perspectives for comprehensively understanding the response of a neuropeptidome.

Face Recognition Based on Maximum Spanning Tree Kernel and Gray Kernel

Face Recognition has been recognized as a major research field in pattern recognition and computer vision. This technique is widely adopted, because of its unique convenience, economy and high accuracy compared to other biological recognition techniques. An interesting and important challenge is thus to investigate high-efficient recognition algorithm. The introduction of kernel methods in pattern recognition has been received significant attentions in the recent several years, and gray kernel and graph kernel are two popular approaches. The paper proposes maximum spanning tree kernel and region histogram intersection kernel; moreover, experiments demonstrate that higher face recognition accuracy can be achieved by multiple kernels which are the combination of them.