A critical bottleneck limiting the performance of rechargeable zinc–air batteries lies in the inefficient bifunctional electrocatalysts for the oxygen reduction and evolution reactions at the air ...electrodes. Hybridizing transition‐metal oxides with functional graphene materials has shown great advantages due to their catalytic synergism. However, both the mediocre catalytic activity of metal oxides and the restricted 2D mass/charge transfer of graphene render these hybrid catalysts inefficient. Here, an effective strategy combining anion substitution, defect engineering, and the dopant effect to address the above two critical issues is shown. This strategy is demonstrated on a hybrid catalyst consisting of sulfur‐deficient cobalt oxysulfide single crystals and nitrogen‐doped graphene nanomeshes (CoO0.87S0.13/GN). The defect chemistries of both oxygen‐vacancy‐rich, nonstoichiometric cobalt oxysulfides and edge‐nitrogen‐rich graphene nanomeshes lead to a remarkable improvement in electrocatalytic performance, where CoO0.87S0.13/GN exhibits strongly comparable catalytic activity to and much better stability than the best‐known benchmark noble‐metal catalysts. In application to quasi‐solid‐state zinc–air batteries, CoO0.87S0.13/GN as a freestanding catalyst assembly benefits from both structural integrity and enhanced charge transfer to achieve efficient and very stable cycling operation over 300 cycles with a low discharge–charge voltage gap of 0.77 V at 20 mA cm−2 under ambient conditions.
A high‐temperature ammonolysis is proven effective for improving both the mediocre catalytic activity of metal oxides and the restricted 2D mass/charge transfer of graphene. The defect chemistries of both oxygen‐vacancy‐rich cobalt oxysulfides and edge‐nitrogen‐rich graphene nanomeshes of the hybrid catalyst lead to a remarkable improvement in electrocatalytic performance for oxygen reduction and evolution reactions.
The desert pioneer plant Stipagrostis pennata plays an important role in sand fixation, wind prevention, and desert ecosystem recovery. An absence of reference genes greatly limits investigations ...into the regulatory mechanism by which S. pennata adapts to adverse desert environments at the molecular and genetic levels. In this study, eight candidate reference genes were identified from rhizosheath development transcriptome data from S. pennata, and their expression stability in the rhizosheaths at different development stages, in a variety of plant tissues, and under drought stress was evaluated using four procedures, including geNorm, NormFinder, BestKeeper, and RefFinder. The results showed that GAPDH and elF were the most stable reference genes under drought stress and in rhizosheath development, and ARP6 and ALDH were relatively stable in all plant tissues. In addition, elF was the most suitable reference gene for all treatments. Analysis of the consistency between the reverse transcription-quantitative PCR (RT-qPCR) and RNA sequencing data showed that the identified elF and GAPDH reference genes were stable during rhizosheath development. These results provide reliable reference genes for assuring the accuracy of RT-qPCR and offer a foundation for further investigations into the genetic responses of S. pennata to abiotic stress.
Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods ...require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.
MLL3 and MLL4 are two closely related members of the SET1/MLL family of histone H3K4 methyltransferases and are responsible for monomethylating histone H3K4 on enhancers, which are essential in ...regulating cell-type-specific gene expression. Mutations of MLL3 or MLL4 have been reported in different types of cancer. Recently, the PHD domains of MLL3/4 have been reported to recruit the MLL3/4 complexes to their target genes by binding to histone H4 during the NT2/D1 stem cell differentiation. Here we show that an extended PHD domain (ePHD
) involving the sixth PHD domain and its preceding zinc finger in MLL3 and MLL4 specifically recognizes an H4H18-containing histone H4 fragment and that modifications of residues surrounding H4H18 modulate H4 binding to MLL3/4. Our in vitro methyltransferase assays and cellular experiments further reveal that the interaction between ePHD
of MLL3/4 and histone H4 is required for their nucleosomal methylation activity and MLL4-mediated neuronal differentiation of NT2/D1 cells.
Spectral clustering has been an attractive topic in the field of computer vision due to the extensive growth of applications, such as image segmentation, clustering and representation. In this ...problem, the construction of the similarity matrix is a vital element affecting clustering performance. In this paper, we propose a multi-view joint learning (MVJL) framework to achieve both a reliable similarity matrix and a latent low-dimensional embedding. Specifically, the similarity matrix to be learned is represented as a convex hull of similarity matrices from different views, where the nuclear norm is imposed to capture the principal information of multiple views and improve robustness against noise/outliers. Moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. With these techniques, we formulate the MVJL as a joint optimization problem and derive its mathematical solution with the alternating direction method of multipliers strategy and the proximal gradient descent method. The solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or K-means for multi-view clustering. Extensive experimental results on real-world datasets demonstrate that MVJL achieves superior clustering performance over other state-of-the-art methods.
Nitrate (NO3−) pollution is a serious problem worldwide. Identification of NO3− sources and transformation processes in aquifers is a key step in effectively controlling and mitigating NO3− ...contamination. In this study, hydrochemical, microbial, and dual isotopic approaches were integrated to elucidate the sources and processes influencing NO3− contamination in the Pearl River Delta, China. The results showed a severe NO3− contamination, with 75% of the samples having NO3−-N concentrations above the WHO standard of 10 mg L−1. The δ15NNO3- and δ18ONO3- values and a multivariate statistical analysis of hydrochemical data both revealed that manure and sewage were mainly responsible for NO3− contamination. Biological indicators further demonstrated that, manure and sewage had greater impacts on groundwater quality during the rainy season than during the dry season. Based on the significant relationships of δ15NNO3- and δ18ONO3- with the logarithmic NO3− concentration (Ln(NO3−)), denitrification was confirmed to occur in the discharge zone during the rainy season. Proteobacteria, Bacteroidetes, and Planctomycetes were identified as the dominant phyla, and Dechloromonas, Flavobacterium, and Nitrospira were dominant among the denitrifying bacteria in groundwater. The abundance of denitrifying bacteria had significant positive correlations with δ15NNO3- and NO2−-N during the rainy season, further confirming the occurrence of denitrification during the rainy season. This study showed that dual isotope techniques combined with microbial data can be a powerful tool for identifying the sources and microbial processes affecting NO3− in groundwater. Moreover, the results can provide useful insights for environmental managers to verify groundwater pollution and better apply remediation solutions.
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•Microbial and isotopic approaches were combined to identify the sources and transformation of NO3−•Manure and sewage were the main sources of NO3− and had a greater effect on groundwater during the rainy season.•Denitrification occurred in the discharge zone during the rainy season.•These findings can assist in controlling and mitigating groundwater NO3− contamination.
Drought is one of the important abiotic factors that adversely affects plant growth and production. The WRKY transcription factor plays a pivotal role in plant growth and development, as well as in ...the elevation of many abiotic stresses. Among three major groups of the WRKY family, the group IIe WRKY has been the least studied in floral crops. Here, we report functional aspects of group IIe WRKY member, i.e., CmWRKY10 in chrysanthemum involved in drought tolerance. The transactivation assay showed that CmWRKY10 had transcriptional activity in yeast cells and subcellular localization demonstrated that it was localized in nucleus. Our previous study showed that CmWRKY10 could be induced by drought in chrysanthemum. Moreover, the overexpression of CmWRKY10 in transgenic chrysanthemum plants improved tolerance to drought stress compared to wild-type (WT). High expression of DREB1A, DREB2A, CuZnSOD, NCED3A, and NCED3B transcripts in overexpressed plants provided strong evidence that drought tolerance mechanism was associated with abscisic acid (ABA) pathway. In addition, lower accumulation of reactive oxygen species (ROS) and higher enzymatic activity of peroxidase, superoxide dismutase and catalase in CmWRKY10 overexpressed lines than that of WT demonstrates its role in drought tolerance. Together, these findings reveal that CmWRKY10 works as a positive regulator in drought stress by regulating stress-related genes.
The steady-state visual evoked potential (SSVEP) has been widely used in building multi-target brain-computer interfaces (BCIs) based on electroencephalogram (EEG). However, methods for high-accuracy ...SSVEP systems require training data for each target, which needs significant calibration time. This study aimed to use the data of only part of the targets for training while achieving high classification accuracy on all targets. In this work, we proposed a generalized zero-shot learning (GZSL) scheme for SSVEP classification. We divided the target classes into seen and unseen classes and trained the classifier only using the seen classes. During the test time, the search space contained both seen classes and unseen classes. In the proposed scheme, the EEG data and the sine waves are embedded into the same latent space using convolutional neural networks (CNN). We use the correlation coefficient of the two outputs in the latent space for classification. Our method was tested on two public datasets and reached 89.9% of the classification accuracy of the state-of-the-art (SOTA) data-driven method, which needs the training data of all targets. Compared to the SOTA training-free method, our method achieved a multifold improvement. This work shows that it is promising to build an SSVEP classification system that does not need the training data of all targets.
In this study, magnetic graphene oxide (MGO) nanomaterials were synthesized based on covalent binding of amino Fe3O4 nanoparticles onto the graphene oxide (GO), and the prepared MGO was successfully ...applied as support for the immobilization of laccase. The MGO-laccase was characterized by transmission electron microscopy (TEM) and a vibrating sample magnetometer (VSM). Compared with free laccase, the MGO-laccase exhibited better pH and thermal stabilities. The optimum pH and temperature were confirmed as pH 3.0 and 35 °C. Moreover, the MGO-laccase exhibited sufficient magnetic response and satisfied reusability after being retained by magnetic separation. The MGO-laccase maintained 59.8% activity after ten uses. MGO-laccase were finally utilized in the decolorization of dye solutions and the decolorization rate of crystal violet (CV), malachite green (MG), and brilliant green (BG) reached 94.7% of CV, 95.6% of MG, and 91.4% of BG respectively. The experimental results indicated the MGO-laccase nanomaterials had a good catalysis ability to decolorize dyes in aqueous solution. Compared with the free enzyme, the employment of MGO as enzyme immobilization support could efficiently enhance the availability and facilitate the application of laccase.
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