Conformal sulfidation of HKUST-1 for constructing porous Cu2S/CuO octahedrons realizing highly sensitive non-enzymatic glucose detection.

Metal-organic frameworks (MOFs) are believed to be promising precursors for constructing novel and efficient catalysts for glucose sensing. Herein, HKUST-1 precursors are first fabricated using a one-pot hydrothermal approach, and then HKUST-1 is converted into porous Cu2S/CuO octahedrons through conformal sulfidation with the help of OH-ions. The as-obtained Cu2S/CuO composite can provide rich electrochemical active sites and promoted electric transfer kinetics. Benefiting from these combined merits, the as-fabricated Cu2S/CuO composite is confirmed to be a high-performance catalyst, with high sensitivities of 8269.45 and 4140.82µA mM-1cm-2in the corresponding ranges of 0.05 ∼ 0.6 mM and 0.6 ∼ 1.2 mM, respectively. Moreover, the as-prepared electrode materials possess good anti-interference ability, reproducibility and long-term stability. This work opens up new avenues for the design and preparation of transition metal sulfide composites.

Quasi-aligned Cu2S/Cu(OH)2nanorod arrays anchored on Cu foam as self-supported electrode for non-enzymatic glucose detection.

Self-supported Cu2S/Cu(OH)2composite nanorods for highly sensitive non-enzymatic glucose sensing werein situgrown on Cu foam by simple hydrothermal treatment of aligned Cu(OH)2nanorods. The physicochemical and electrochemical properties of the as-fabricated Cu2S/Cu(OH)2composite nanorods were characterized by scanning electron microscopy, transmission electron microscopy, x-ray diffraction, Raman spectroscope, x-ray photoelectron spectroscope, cyclic voltammetry, electrochemical impedance spectroscopy, amperometrici-tmeasurements. The mechanism of the composite nanorods produced on conductive substrates was also explored. The electrode exhibits a sensitivity of 9626.88µA mM-1cm-2towards glucose with good anti-interference ability, indicating it a promising electrode material for the enhanced non-enzymatic glucose detection.

Facile synthesis of Cu/Co-ZIF nanoarrays for non-enzymatic glucose detection.

Cu/Co-ZIF nanoflake arrays on carbon cloth are fabricated by controlling the introducing of Cu2+ions during the growth of Co-ZIF. The Cu/Co-ZIF-20 electrode prepared with 20 mM Cu2+possesses large electrochemically active surface area and bimetallic active sites, which can be revealed by cyclic voltammetry tests. The amperometrici-tmeasurements demonstrate that the Cu/Co-ZIF-20 electrode displays a wide linear range from 0.05 mM to 6.0 mM, and a high sensitivity of 1.03 mA mM-1cm-2. Good selectivity, repeatability and practical applicability indicate its promising application in enzyme-free glucose sensing.

Hierarchically porous CuO spindle-like nanosheets grown on a carbon cloth for sensitive non-enzymatic glucose sensoring.

Herein, porous CuO spindle-like nanosheets were fabricated on a carbon cloth using a facile hydrothermal method, and surface morphology, microstructure, and glucose sensing performance were studied. The porous spindle-like nanosheets are constructed by nanoparticles and slit-like pores, exhibiting a hierarchical structure. When used for non-enzymatic glucose sensoring, the obtained CuO nanosheet electrode exhibits a wide linear range from 0.05 to 3.30 mM, a high sensitivity of 785.2 µA mM-1 cm-2 and a low detection limit of 0.22 µM (S/N = 3). Besides, good selectivity, stability, and reproducibility for glucose detection indicate a promising application of CuO nanosheet electrodes as non-enzymatic glucose sensors.

Co(OH)2 nanosheets decorated Cu(OH)2 nanorods for highly sensitive nonenzymatic detection of glucose.

Co(OH)2 nanosheets/Cu(OH)2 nanorods composite electrodes for non-enzymatic glucose detection were fabricated by electrodepositing Co(OH)2 nanosheets on Cu(OH)2 nanorods substrate grown directly on the copper sheet via a simple one-step reaction. The Co(OH)2 nanosheets/Cu(OH)2 nanorods composite electrode was characterized by scanning electron microscopy, energy dispersive x-ray spectroscopy, x-ray diffraction and x-ray photoelectron spectroscopy. The glucose sensing performance of the composite electrode was investigated by cyclic voltammetry and chronoamperometry. The composite electrode shows high sensitivity of 2366 µA mM-1 cm-2 up to 2 mM with a lower detection limit of 0.17 mM (S/N = 3). The composite electrode is highly selective to glucose in the presence of various substances that always co-exist with glucose in real blood samples. The response of the composite towards human blood serum was in good agreement with that of commercially available glucose sensors, suggesting that a promising electrode material for highly sensitive and selective non enzymatic detection of glucose can be envisioned.

A topical VAEGAN-IHMM approach for automatic story segmentation.

Feature representations with rich topic information can greatly improve the performance of story segmentation tasks. VAEGAN offers distinct advantages in feature learning by combining variational autoencoder (VAE) and generative adversarial network (GAN), which not only captures intricate data representations through VAE's probabilistic encoding and decoding mechanism but also enhances feature diversity and quality via GAN's adversarial training. To better learn topical domain representation, we used a topical classifier to supervise the training process of VAEGAN. Based on the learned feature, a segmentor splits the document into shorter ones with different topics. Hidden Markov model (HMM) is a popular approach for story segmentation, in which stories are viewed as instances of topics (hidden states). The number of states has to be set manually but it is often unknown in real scenarios. To solve this problem, we proposed an infinite HMM (IHMM) approach which utilized an HDP prior on transition matrices over countably infinite state spaces to automatically infer the state's number from the data. Given a running text, a Blocked Gibbis sampler labeled the states with topic classes. The position where the topic changes was a story boundary. Experimental results on the TDT2 corpus demonstrated that the proposed topical VAEGAN-IHMM approach was significantly better than the traditional HMM method in story segmentation tasks and achieved state-of-the-art performance.

Grade-Skewed Domain Adaptation via Asymmetric Bi-Classifier Discrepancy Minimization for Diabetic Retinopathy Grading.

Diabetic retinopathy (DR) is a leading cause of preventable blindness worldwide. Deep learning has exhibited promising performance in the grading of DR. Certain deep learning strategies have facilitated convenient regular eye check-ups, which are crucial for managing DR and preventing severe visual impairment. However, the generalization performance on cross-center, cross-vendor, and cross-user test datasets is compromised due to domain shift. Furthermore, the presence of small lesions and the imbalanced grade distribution, resulting from the characteristics of DR grading (e.g., the progressive nature of DR disease and the design of grading standards), complicates image-level domain adaptation for DR grading. The general predictions of the models trained on grade-skewed source domains will be significantly biased toward the majority grades, which further increases the adaptation difficulty. We formulate this problem as a grade-skewed domain adaptation challenge. Under the grade-skewed domain adaptation problem, we propose a novel method for image-level supervised DR grading via Asymmetric Bi-Classifier Discrepancy Minimization (ABiD). First, we propose optimizing the feature extractor by minimizing the discrepancy between the predictions of the asymmetric bi-classifier based on two classification criteria to encourage the exploration of crucial features in adjacent grades and stretch the distribution of adjacent grades in the latent space. Moreover, the classifier difference is maximized by using the forward and inverse distribution compensation mechanism to locate easily confused instances, which avoids pseudolabel bias on the target domain. The experimental results on two public DR datasets and one private DR dataset demonstrate that our method outperforms state-of-the-art methods significantly.

Remote sensing traffic scene retrieval based on learning control algorithm for robot multimodal sensing information fusion and human-machine interaction and collaboration.

In light of advancing socio-economic development and urban infrastructure, urban traffic congestion and accidents have become pressing issues. High-resolution remote sensing images are crucial for supporting urban geographic information systems (GIS), road planning, and vehicle navigation. Additionally, the emergence of robotics presents new possibilities for traffic management and road safety. This study introduces an innovative approach that combines attention mechanisms and robotic multimodal information fusion for retrieving traffic scenes from remote sensing images. Attention mechanisms focus on specific road and traffic features, reducing computation and enhancing detail capture. Graph neural algorithms improve scene retrieval accuracy. To achieve efficient traffic scene retrieval, a robot equipped with advanced sensing technology autonomously navigates urban environments, capturing high-accuracy, wide-coverage images. This facilitates comprehensive traffic databases and real-time traffic information retrieval for precise traffic management. Extensive experiments on large-scale remote sensing datasets demonstrate the feasibility and effectiveness of this approach. The integration of attention mechanisms, graph neural algorithms, and robotic multimodal information fusion enhances traffic scene retrieval, promising improved information extraction accuracy for more effective traffic management, road safety, and intelligent transportation systems. In conclusion, this interdisciplinary approach, combining attention mechanisms, graph neural algorithms, and robotic technology, represents significant progress in traffic scene retrieval from remote sensing images, with potential applications in traffic management, road safety, and urban planning.

Arrhythmia classification detection based on multiple electrocardiograms databases.

According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.

microRNA-mRNA Analysis Reveals Tissue-Specific Regulation of microRNA in Mangrove Clam (Geloina erosa).

Geloina erosa is an important benthic animal in the mangrove, serving as an indicator organism for coastal environmental pollution. This study aimed to investigate the tissue-specific expression of miRNAs and their regulatory roles in predicted targets in G. erosa. Through miRNA sequencing and co-expression network analysis, we extensively studied the miRNA expression in three tissues: gills, hepatopancreas, and muscle. The results revealed a total of 1412 miRNAs, comprising 1047 known miRNAs, and 365 newly predicted miRNAs. These miRNAs exhibited distinct tissue-specific expression patterns. In the miRNA target gene prediction, a total of 7404 potential predicted targets were identified, representing approximately 33% of all unique transcripts associated with miRNAs. Further co-expression network analysis revealed nine modules, each showing a positive correlation with specific tissues (gills, hepatopancreas, or muscle). The blue module showed a significant correlation with gills (r = 0.83, p-value = 0.006), the black module was significantly related to the hepatopancreas (r = 0.78, p-value = 0.01), and the purple module was significantly correlated with muscle (r = 0.83, p-value = 0.006). Within these modules, related miRNAs tended to cluster together, while their correlations with other modules were relatively weak. Functional enrichment analysis was performed on miRNAs and their predicted targets in each tissue. In the gills, miRNAs primarily regulate immune-related genes, substance transport, and cytoskeletal organization. In the hepatopancreas, miRNAs suppressed genes involved in shell formation and played a role in cellular motor activity and metabolism. In muscle, miRNAs participate in metabolism and photoreceptive processes, as well as immune regulation. In summary, this study provides valuable insights into the tissue-specific regulation of miRNAs in G. erosa, highlighting their potential roles in immune response, metabolism, and environmental adaptation. These findings offer important clues for understanding the molecular mechanisms and biological processes in G. erosa, laying the foundation for further validation and elucidation of these regulatory relationships.

Efficient Oxygen Vacancy Defect Engineering for Enhancing Visible-Light Photocatalytic Performance over SnO2-x Ultrafine Nanocrystals.

Regardless of its good electron-transfer ability and chemical stability, pure Zn2SnO4 (ZSO) still has intrinsic deficiencies of a narrow spectral response region, poor absorption ability, and high photo-activated carrier recombination rate. Aiming to overcome the deficiencies above-mentioned, we designed a facile hydrothermal route for etching ZSO nanoparticles in a dilute acetic acid solution, through which efficient oxygen vacancy defect engineering was accomplished and SnO2-x nanocrystals were obtained with an ultrafine particle size. In comparison with the untreated ZSO nanoparticles, the specific surface area of SnO2-x nanocrystals was substantially enlarged, subsequently leading to the notable augmentation of active sites for the photo-degradation reaction. Aside from the above, it is worth noting that SnO2-x nanocrystals were endowed with a broad spectral response, enhancing light absorption capacity and the photo-activated carrier transfer rate with the aid of oxygen vacancy defect engineering. Accordingly, SnO2-x nanocrystals exhibited significantly enhanced photoactivity toward the degradation of the organic dye rhodamine B (RhB), which could be imputed to the synergistic effect of increasing active sites, intensified visible-light harvesting, and the separation rate of the photo-activated charge carrier caused by the oxygen vacancy defect engineering. In addition, these findings will inspire us to open up a novel pathway to design and prepare oxide compound photocatalysts modified by oxygen vacancy defects in pursuing excellent visible-light photoactivity.

Facile synthesis of a Bi2MoO6/TiO2 nanotube arrays composite by the solvothermal method and its application for high-performance supercapacitor.

In this study, bismuth molybdate/titania nanotube arrays (Bi2MoO6/TNTs) as a binder-free electrode for supercapacitors were fabricated via a facile solvothermal method. The effects of precursor amounts, solvothermal time and temperature on the microstructure and electrochemical properties of the composite were analyzed. The surface morphology, microstructure, chemical composition and chemical states of the composite electrode material were analyzed using scanning electron microscopy, X-ray diffraction, energy dispersive X-ray spectroscopy, and X-ray photoelectron spectroscopy. Cyclic voltammetry tests, galvanostatic charge-discharge measurements, and electrochemical impedance spectroscopy were employed to analyze the electrochemical behavior of the composite. A specific capacitance of ∼330 mF cm-2 has been achieved for this Bi2MoO6 nanosheets/TNTs composite electrode at the current density of 1 mA cm-2. Galvanostatic charge-discharge experiments suggest a moderate cycling stability together with 76.7% capacitance retention after 1000 cycles of continuous charge-discharge operation. These results indicate that the Bi2MoO6/TNTs composite is a promising electrode material for supercapacitors.