Abscisic acid (ABA) is an important phytohormone regulating plant growth, development, and stress responses. It has an essential role in multiple physiological processes of plants, such as stomatal ...closure, cuticular wax accumulation, leaf senescence, bud dormancy, seed germination, osmotic regulation, and growth inhibition among many others. Abscisic acid controls downstream responses to abiotic and biotic environmental changes through both transcriptional and posttranscriptional mechanisms. During the past 20 years, ABA biosynthesis and many of its signaling pathways have been well characterized. Here we review the dynamics of ABA metabolic pools and signaling that affects many of its physiological functions.
Abscisic acid (ABA) is the major stress hormone that coordinates plant growth, development and abiotic stress responses. In this review, we summarized the recent progresses on its metabolism, transport and signaling, and discussed the open questions about ABA dynamics and functions.
Hashing has attracted increasing research attention in recent years due to its high efficiency of computation and storage in image retrieval. Recent works have demonstrated the superiority of ...simultaneous feature representations and hash functions learning with deep neural networks. However, most existing deep hashing methods directly learn the hash functions by encoding the global semantic information, while ignoring the local spatial information of images. The loss of local spatial structure makes the performance bottleneck of hash functions, therefore limiting its application for accurate similarity retrieval. In this paper, we propose a novel deep ordinal hashing (DOH) method, which learns ordinal representations to generate ranking-based hash codes by leveraging the ranking structure of feature space from both local and global views. In particular, to effectively build the ranking structure, we propose to learn the rank correlation space by exploiting the local spatial information from fully convolutional network and the global semantic information from the convolutional neural network simultaneously. More specifically, an effective spatial attention model is designed to capture the local spatial information by selectively learning well-specified locations closely related to target objects. In such hashing framework, the local spatial and global semantic nature of images is captured in an end-to-end ranking-to-hashing manner. Experimental results conducted on three widely used datasets demonstrate that the proposed DOH method significantly outperforms the state-of-the-art hashing methods.
High-entropy pyrochlore-type structures based on rare-earth zirconates are successfully produced by conventional solid-state reaction method. Six rare-earth oxides (La
2
O
3
, Nd
2
O
3
, Sm
2
O
3
, ...Eu
2
O
3
, Gd
2
O
3
, and Y
2
O
3
) and ZrO
2
are used as the raw powders. Five out of the six rare-earth oxides with equimolar ratio and ZrO
2
are mixed and sintered at different temperatures for investigating the reaction process. The results demonstrate that the high-entropy pyrochlores (5RE
1/5
)
2
Zr
2
O
7
have been formed after heated at 1000°C. The (5RE
1/5
)
2
Zr
2
O
7
are highly sintering resistant and possess excellent thermal stability. The thermal conductivities of the (5RE
1/5
)
2
Zr
2
O
7
high-entropy ceramics are below 1 W·m
–1
·K
–1
in the temperature range of 300–1200°C. The (5RE
1/5
)
2
Zr
2
O
7
can be potential thermal barrier coating materials.
Social image tag refinement, which aims to improve tag quality by automatically completing the missing tags and rectifying the noise-corrupted ones, is an essential component for social image search. ...Conventional approaches mainly focus on exploring the visual and tag information, without considering the user information, which often reveals important hints on the (in)correct tags of social images. Towards this end, we propose a novel tri-clustered tensor completion framework to collaboratively explore these three kinds of information to improve the performance of social image tag refinement. Specifically, the inter-relations among users, images and tags are modeled by a tensor, and the intra-relations between users, images and tags are explored by three regularizations respectively. To address the challenges of the super-sparse and large-scale tensor factorization that demands expensive computing and memory cost, we propose a novel tri-clustering method to divide the tensor into a certain number of sub-tensors by simultaneously clustering users, images and tags into a bunch of tri-clusters. And then we investigate two strategies to complete these sub-tensors by considering (in)dependence between the sub-tensors. Experimental results on a real-world social image database demonstrate the superiority of the proposed method compared with the state-of-the-art methods.
Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by joint training with facial priors. ...However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://github.com/IVIPLab/CTCNet.
The introduction of oxygen vacancies (Ov) has been regarded as an effective method to enhance the catalytic performance of photoanodes in oxygen evolution reaction (OER). However, their stability ...under highly oxidizing environment is questionable but was rarely studied. Herein, NiFe‐metal–organic framework (NiFe‐MOFs) was conformally coated on oxygen‐vacancy‐rich BiVO4 (Ov‐BiVO4) as the protective layer and cocatalyst, forming a core–shell structure with caffeic acid as bridging agent. The as‐synthesized Ov‐BiVO4@NiFe‐MOFs exhibits enhanced stability and a remarkable photocurrent density of 5.3±0.15 mA cm−2 at 1.23 V (vs. RHE). The reduced coordination number of Ni(Fe)‐O and elevated valence state of Ni(Fe) in NiFe‐MOFs layer greatly bolster OER, and the shifting of oxygen evolution sites from Ov‐BiVO4 to NiFe‐MOFs promotes Ov stabilization. Ovs can be effectively preserved by the coating of a thin NiFe‐MOFs layer, leading to a photoanode of enhanced photocurrent and stability.
A core–shell Ov‐BiVO4@NiFe‐MOFs photoanode was constructed via a coordination‐assisted self‐assembly method. A NiFe‐MOFs thin layer acts as protective layer and cocatalyst to shift active sites from oxygen vacancies to NiFe‐MOFs, leading to improved stability and activity for OER. This molecular‐based approach tailors the coordination and electronic structure of active sites and provides mechanistic insights for rational design of photocatalysts.
Since an outbreak of 2019 novel coronavirus (COVID-19) in Wuhan and related regions in Hubei province, an increasing number of exported cases have been confirmed in other provinces in China and in ...multiple countries around the world with substantial morbidity and mortality 1–4. The WHO has declared a public health emergency of international concern considering rapid increases in numbers of confirmed cases in China and additional countries. As of February 22, 2020, a total of 12 938 patients had been confirmed outside of Wuhan and related regions in Hubei province of China 1. However, there is limited information about COVID-19 outside of Wuhan 5, and no study has reported the time to RT-PCR conversion and radiological changes after treatment.
The novel coronavirus can be transmitted from person to person with infection ranging from mild disease to severe pneumonia and radiological abnormalities on chest CT for most patients improved after RT-PCR conversion.
Solid‐state lithium‐sulfur batteries have shown prospects as safe, high‐energy electrochemical storage technology for powering regional electrified transportation. Owing to limited ion mobility in ...crystalline polymer electrolytes, the battery is incapable of operating at subzero temperature. Addition of liquid plasticizer into the polymer electrolyte improves the Li‐ion conductivity yet sacrifices the mechanical strength and interfacial stability with both electrodes. In this work, we showed that by introducing a spherical hyperbranched solid polymer plasticizer into a Li+‐conductive linear polymer matrix, an integrated dynamic cross‐linked polymer network was built to maintain fully amorphous in a wide temperature range down to subzero. A quasi‐solid polymer electrolyte with a solid mass content >90 % was prepared from the cross‐linked polymer network, and demonstrated fast Li+ conduction at a low temperature, high mechanical strength, and stable interfacial chemistry. As a result, solid‐state lithium‐sulfur batteries employing the new electrolyte delivered high reversible capacity and long cycle life at 25 °C, 0 °C and −10 °C to serve energy storage at complex environmental conditions.
We demonstrate a fully amorphous quasi‐solid polymer electrolyte with a dynamic cross‐linked network composed of a star‐shaped plasticising polymer and a linear poly‐1,3‐dioxolane. The electrolyte achieves high Li+ conductivity (2.96×10−4 S cm−1) and high tLi+ (0.81), and the as‐prepared solid‐state lithium‐sulfur batteries exhibit high reversible capacity and long cycle life when operating at subzero temperature conditions.
A three‐component synthesis of 2‐substituted benzothiazoles from aryl amines, elemental sulfur and styrenes or aryl acetylenes in 1‐methylpyrrolidin‐2‐one (NMP) is described. Two C−S and one C−N ...bonds were selectively formed in one‐pot under metal‐free conditions.
Summary
Aims
Anesthesia and surgery can cause delirium‐like symptoms postoperatively. Increasing evidence suggests that gut microbiota is a physiological regulator of the brain. Herein, we ...investigated whether gut microbiota plays a role in postoperative delirium (POD).
Methods
Mice were separated into non‐POD and POD phenotypes after abdominal surgery by applying hierarchical clustering analysis to behavioral tests. Fecal samples were collected, and 16S ribosomal RNA gene sequencing was performed to detect differences in gut microbiota composition among sham, non‐POD, and POD mice. Fecal bacteria from non‐POD and POD mice were transplanted into antibiotics‐induced pseudo‐germ‐free mice to investigate the effects on behaviors.
Results
α‐diversity and β‐diversity indicated differences in gut microbiota composition between the non‐POD and POD mice. At the phylum level, the non‐POD mice had significantly higher levels of Tenericutes, which were not detected in the POD mice. At the class level, levels of Gammaproteobacteria were higher in the POD mice, whereas the non‐POD mice had significantly higher levels of Mollicutes, which were not detected in the POD mice. A total of 20 gut bacteria differed significantly between the POD and non‐POD mice. Interestingly, the pseudo‐germ‐free mice showed abnormal behaviors prior to transplant. The pseudo‐germ‐free mice that received fecal bacteria transplants from non‐POD mice but not from POD mice showed improvements in behaviors.
Conclusions
Abnormal gut microbiota composition after abdominal surgery may contribute to the development of POD. A therapeutic strategy that targets gut microbiota could provide a novel alterative for POD treatment.
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