Mitogen‐activated protein kinase (MAPK) cascades are key signaling modules downstream of receptors/sensors that perceive either endogenously produced stimuli such as peptide ligands and ...damage‐associated molecular patterns (DAMPs) or exogenously originated stimuli such as pathogen/microbe‐associated molecular patterns (P/MAMPs), pathogen‐derived effectors, and environmental factors. In this review, we provide a historic view of plant MAPK research and summarize recent advances in the establishment of MAPK cascades as essential components in plant immunity, response to environmental stresses, and normal growth and development. Each tier of the MAPK cascades is encoded by a small gene family, and multiple members can function redundantly in an MAPK cascade. Yet, they carry out a diverse array of biological functions in plants. How the signaling specificity is achieved has become an interesting topic of MAPK research. Future investigations into the molecular mechanism(s) underlying the regulation of MAPK activation including the activation kinetics and magnitude in response to a stimulus, the spatiotemporal expression patterns of all the components in the signaling pathway, and functional characterization of novel MAPK substrates are central to our understanding of MAPK functions and signaling specificity in plants.
Mitogen‐activated protein kinase (MAPK) cascades are key signaling modules downstream of receptors/sensors in eukaryotes. This review provides a historic view of plant MAPK research and summarizes recent advances in the establishment of MAPK cascades as essential signaling components in plant immunity, response to environmental stresses, and growth/development.
Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a ...classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.
Deep learning methods have been widely used in hyperspectral image classification and have achieved state-of-the-art performance. Nonetheless, the existing deep learning methods are restricted by a ...limited receptive field, inflexibility, and difficult generalization problems in hyperspectral image classification. To solve these problems, we propose HSI-BERT, where BERT stands for bidirectional encoder representations from transformers and HSI stands for hyperspectral imagery. The proposed HSI-BERT has a global receptive field that captures the global dependence among pixels regardless of their spatial distance. HSI-BERT is very flexible and enables the flexible and dynamic input regions. Furthermore, HSI-BERT has good generalization ability because the jointly trained HSI-BERT can be generalized from regions with different shapes without retraining. HSI-BERT is primarily built on a multihead self-attention (MHSA) mechanism in an MHSA layer. Moreover, several attentions are learned by different heads, and each head of the MHSA layer encodes the semantic context-aware representation to obtain discriminative features. Because all head-encoded features are merged, the resulting features exhibit spatial-spectral information that is essential for accurate pixel-level classification. Quantitative and qualitative results demonstrate that HSI-BERT outperforms any other CNN-based model in terms of both classification accuracy and computational time and achieves state-of-the-art performance on three widely used hyperspectral image data sets.
The traditional approach to e-commerce marketing encounters challenges in effectively extracting and utilizing user data, as well as analyzing and targeting specific user segments. This manuscript ...aims to address these limitations by proposing the establishment of a consumer behavior analysis system based on an Internet of Things (IoT) platform. The system harnesses the potential of radio frequency identification devices (RFID) technology for product identification encoding, thus facilitating the monitoring of product sales processes. To categorize consumers, the system incorporates a k-means algorithm within its architectural framework. Furthermore, a similarity metric is employed to evaluate the gathered consumption information and refine the selection strategy for initial clustering centers. The proposed methodology is subjected to rigorous testing, revealing its effectiveness in resolving the issue of insufficient differentiation between customer categories after clustering. Across varying values of k, the average false recognition rate experiences a notable reduction of 20.6%. The system consistently demonstrates rapid throughput and minimal overall latency, boasting an impressive processing time of merely 2 ms, thereby signifying its exceptional concurrent processing capability. Through the implementation of the proposed system, the opportunity for further target market segmentation arises, enabling the establishment of core market positioning and the formulation of distinct and precise marketing strategies tailored to diverse consumer cohorts. This pioneering approach introduces an innovative and efficient methodology that e-commerce enterprises can embrace to amplify their marketing endeavors.
Multisensor fusion is of great importance in Earth observation related applications. For instance, hyperspectral images (HSIs) provide wealthy spectral information while light detection and ranging ...(LiDAR) data provide elevation information, and using HSI and LiDAR data together can achieve better classification performance. In this paper, an unsupervised feature extraction framework, named as patch-to-patch convolutional neural network (PToP CNN), is proposed for collaborative classification of hyperspectral and LiDAR data. More specific, a three-tower PToP mapping is first developed to seek an accurate representation from HSI to LiDAR data, aiming at merging multiscale features between two different sources. Then, by integrating hidden layers of the designed PToP CNN, extracted features are expected to possess deeply fused characteristics. Accordingly, features from different hidden layers are concatenated into a stacked vector and fed into three fully connected layers. To verify the effectiveness of the proposed classification framework, experiments are executed on two benchmark remote sensing data sets. The experimental results demonstrate that the proposed method provides superior performance when compared with some state-of-the-art classifiers, such as two-branch CNN and context CNN.
Necroptosis and ferroptosis are two distinct necrotic cell death modalities with no known common molecular mechanisms. Necroptosis is activated by ligands of death receptors such as tumor necrosis ...factor-α (TNF-α) under caspase-deficient conditions, whereas ferroptosis is mediated by the accumulation of lipid peroxides upon the depletion/or inhibition of glutathione peroxidase 4 (GPX4). The molecular mechanism that mediates the execution of ferroptosis remains unclear. In this study, we identified 2-amino-5-chloro-N,3-dimethylbenzamide (CDDO), a compound known to inhibit heat shock protein 90 (HSP90), as an inhibitor of necroptosis that could also inhibit ferroptosis. We found that HSP90 defined a common regulatory nodal between necroptosis and ferroptosis. We showed that inhibition of HSP90 by CDDO blocked necroptosis by inhibiting the activation of RIPK1 kinase. Furthermore, we showed that the activation of ferroptosis by erastin increased the levels of lysosome-associated membrane protein 2a to promote chaperone-mediated autophagy (CMA), which, in turn, promoted the degradation of GPX4. Importantly, inhibition of CMA stabilized GPX4 and reduced ferroptosis. Our results suggest that activation of CMA is involved in the execution of ferroptosis.
Cu2O is a typical photoelectrocatalyst for sustainable hydrogen production, while the fast charge recombination hinders its further development. Herein, Ni2+ cations have been doped into a Cu2O ...lattice (named as Ni‐Cu2O) by a simple hydrothermal method and act as electron traps. Theoretical results predict that the Ni dopants produce an acceptor impurity level and lower the energy barrier of hydrogen evolution. Photoelectrochemical (PEC) measurements demonstrate that Ni‐Cu2O exhibits a photocurrent density of 0.83 mA cm−2, which is 1.34 times higher than that of Cu2O. And the photostability has been enhanced by 7.81 times. Moreover, characterizations confirm the enhanced light‐harvesting, facilitated charge separation and transfer, prolonged charge lifetime, and increased carrier concentration of Ni‐Cu2O. This work provides deep insight into how acceptor‐doping modifies the electronic structure and optimizes the PEC process.
Taking charge: A nickel (Ni) electron acceptor has been successfully doped into a Cu2O lattice without forming impurity phases. Both theoretical and experimental results demonstrate that Ni dopants attract electrons from neighboring atoms, therefore facilitating charge separation and transfer. Consequently, the obtained Ni‐Cu2O exhibits a significantly enhanced photoelectrochemical performance.
Abstract
Improving the accessibility of ions in the electrodes of electrochemical energy storage devices is vital for charge storage and rate performance. In particular, the kinetics of ion transport ...in organic electrolytes is slow, especially at low operating temperatures. Herein, we report a new type of MXene-carbon nanotube (CNT) composite electrode that maximizes ion accessibility resulting in exceptional rate performance at low temperatures. The improved ion transport at low temperatures is made possible by breaking the conventional horizontal alignment of the two-dimensional layers of the MXene Ti
3
C
2
by using specially designed knotted CNTs. The large, knot-like structures in the knotted CNTs prevent the usual restacking of the Ti
3
C
2
flakes and create fast ion transport pathways. The MXene-knotted CNT composite electrodes achieve high capacitance (up to 130 F g
−1
(276 F cm
−3
)) in organic electrolytes with high capacitance retention over a wide scan rate range of 10 mV s
−1
to 10 V s
−1
. This study is also the first report utilizing MXene-based supercapacitors at low temperatures (down to −60 °C).
A composite coating containing hexagonal boron nitride（hBN） particles and titanium oxide（TiO_2） was formed on the surface of Ti-6Al-4V alloy via micro-arc oxidation（MAO）. The effect of quantity of ...the hBN-particles added into electrolyte on microstructure, composition, and wear behavior of the resulting composite coatings was investigated. Microstructure, phase composition, and tribological behavior of the resulting MAO coatings were evaluated via scanning electron microscopy, X-ray diffraction, and ball-on-disc abrasive tests. The results reveal that the TiO_2/hBN composite coating consisting of rutile TiO_2, anatase TiO_2, and an hBN phase was less porous than particle-free coating. Furthermore, the presence of hBN particles in the MAO coating produced an improved anti-friction property. The composite coating produced in the electrolyte containing 2 g/L of hBN particles exhibited the best wear resistance.The outer loose layer of the MAO coatings was removed by a mechanical polishing process, which led to a significant improvement in the wear resistance and anti-friction properties of the MAO coatings and highlighted an essential lubricating role of hBN particles in the composite coatings. However, wear mechanism of the MAO coatings was not relevant to the presence of hBN particles, where fatigue wear dominated the anti-fraction properties of the MAO coatings with and without hBN particles.
Cancer is a major factor threatening human health and life safety, and there is a lack of safe and effective therapeutic drugs. Intervention and prevention in premalignant process are effective ways ...to reverse carcinogenesis and prevent cancer from occurring. Plant natural products are rich in sources and are a promising source for cancer chemoprevention. This article reviews the chemopreventive effects of natural products, especially focused on polyphenols, flavonoids, monoterpene and triterpenoids, sulfur compounds, and cellulose. Meanwhile, the main mechanisms include induction of apoptosis, antiproliferation and inhibition of metastasis are briefly summarized. In conclusion, this article provides evidence for natural products remaining a prominent source of cancer chemoprevention.
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