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Image quality assessment (IQA) aims to build a quality prediction model to assess image quality automatically rather than artificially. Due to a lack of reference images, blind image quality assessment (BIQA) has become an attractive yet challenging research topic. Inspired by the hierarchical perception mechanism in the human visual system, some existing BIQA methods aggregate multi-stage features of a convolutional neural network (CNN). However, they are regardless of the latent dependencies. To solve this problem, we propose a novel BIQA method based on hierarchical dependency learning and quality aggregation (HDLaQA). The proposed method includes multi-stage feature extraction, hierarchical dependency learning, and quality aggregation. In multi-stage feature extraction, a CNN is used as the feature extractor and multi-stage features are output for further learning. In hierarchical dependency learning, spatial and channel dependencies among the multi-stage features are modeled. To this end, a dual-head spatial dependency (DSD) module is designed to harvest the spatial dependencies between the adjacent-stage features and deliver these dependencies to the next stage. Moreover, exponential bilinear pooling (EBP) is presented to learn the channel dependencies, which is more stable than commonly used BP. In quality aggregation, multiple quality scores are predicted based on the learned dependencies, and multiple learnable weights are used to measure the importance of the predicted scores for final quality evaluation. Experimental results on seven IQA databases demonstrate the competitiveness of the proposed method on both synthetic and authentic distortions. [ABSTRACT FROM AUTHOR]

LiFePO/C and xLiFePO· yLiV(PO) composites were successfully prepared by the high-temperature carbon thermal reduction method using LiOH·HO, FeO, NHHPO, five different compounds (citric acid, polyaniline, carboxymethyl cellulose, E-44 epoxy resin, and tartaric acid), and VO as raw materials. X-ray diffraction (XRD) analysis, scanning electron microscopy (SEM), laser particle size analysis, specific surface area measurements, and electrochemical performance testing were used to study their structure, morphology, and electrochemical properties. The results showed that the LiFePO/C materials exhibited significantly different polymerization degree, XRD spectra, specific surface area, and particle size distribution. Finally, 9LiFePO·LiV(PO) and LiFePO/C coated using tartaric acid were shown to exhibit improved electrochemical performance. [ABSTRACT FROM AUTHOR]

Recently, electrochemistry- and photoelectrochemistry-based biosensors have been regarded as powerful tools for trace monitoring of carcinoembryonic antigen (CEA) due to the fact of their intrinsic advantages (e.g., high sensitivity, excellent selectivity, small background, and low cost), which play an important role in early cancer screening and diagnosis and benefit people's increasing demands for medical and health services. Thus, this mini-review will introduce the current trends in electrochemical and photoelectrochemical biosensors for CEA assay and classify them into two main categories according to the interactions between target and biorecognition elements: immunosensors and aptasensors. Some recent illustrative examples are summarized for interested readers, accompanied by simple descriptions of the related signaling strategies, advanced materials, and detection modes. Finally, the development prospects and challenges of future electrochemical and photoelectrochemical biosensors are considered. [ABSTRACT FROM AUTHOR]

Polarimetric synthetic aperture radar (PolSAR) image classification plays an important role in various PolSAR image application. And many pixel-wise, region-based classification methods have been proposed for PolSAR images. However, most of the pixel-wise methods can not model local spatial relationship of pixels due to negative effects of speckle noise, and most of the region-based methods fail to figure out the regions with the similar polarimetric features. Considering that color features can provide good visual expression and perform well for image interpretation, in this work, based on the PolSAR pseudo-color image over Pauli decomposition, we propose a supervised PolSAR image classification approach combining learned superpixels and quaternion convolutional neural network (QCNN). First, the PolSAR RGB pseudo-color image is formed under Pauli decomposition. Second, we train QCNN with quaternion PolSAR data converted by RGB channels to extract deep color features and obtain pixel-wise classification map. QCNN treats color channels as a quaternion matrix excavating the relationship among the color channels effectively and avoiding information loss. Third, pixel affinity network (PAN) is utilized to generate the learned superpixels of PolSAR pseudo-color image. The learned superpixels allow the local information exploitation available in the presence of speckle noise. Finally, we fuse the pixel-wise classification result and superpixels to acquire the ultimate pixel-wise PolSAR image classification map. Experiments on three real PolSAR data sets show that the proposed approach can obtain 96.56%, 95.59%, and 92.55% accuracy for Flevoland, San Francisco and Oberpfaffenhofen data set, respectively. And compared with state-of-the-art PolSAR image classification methods, the proposed algorithm can obtained competitive classification results. [ABSTRACT FROM AUTHOR]

• A ratiometric electrochemical biosensor for sensitive miRNA detection was developed. • The combination of Mg2+-dependent DNAzyme and competitive host-guest recognition enhanced detection sensitivity. • Satisfactory monitoring of miRNA in human serum samples was achieved. A novel ratiometric electrochemical biosensor was designed for sensitive miRNA-21 detection by combining Mg2+-dependent DNAzyme-cleavage cycling with competitive host-guest inclusion. The dual ferrocene-labeled hairpin DNA (2Fc-DNA)-assembled triple-helix magnetic probe (THMP) and methylene blue (MB)-immobilized reduced graphene oxide/β-cyclodextrin nanocomposites-modified glassy carbon electrode (RGO/β-CD/GCE) were served as target recognition and signal transformation units, respectively. Upon the hybridization reaction between target miRNA-21 and two auxiliary probes, the active DNAzyme was liberated from the caged structure, which bound with 2Fc-DNA to catalyze its cleavage in the presence of Mg2+, and triggered continuous cleavage reaction to obtain a large number of Fc-labeled DNA fragments. After magnetic separation, Fc-labeled DNA fragments could replace MB by the competitive host-guest recognition reaction and accumulate on the electrode surface, resulting in the gradually enhanced Fc and weakened MB signals, respectively. Under the optimal conditions, the constructed biosensor with ratiometric detection mode showed wide concentration range (10 fM−0.1 nM), low detection limit (1.8 fM), high sensitivity, and good selectivity, as well as satisfactory reproducibility and stability. And the developed sensing strategy may provide a new pathway in molecular diagnosis and bioanalysis. [ABSTRACT FROM AUTHOR]