96 research outputs found

    Joint Correcting and Refinement for Balanced Low-Light Image Enhancement

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    Low-light image enhancement tasks demand an appropriate balance among brightness, color, and illumination. While existing methods often focus on one aspect of the image without considering how to pay attention to this balance, which will cause problems of color distortion and overexposure etc. This seriously affects both human visual perception and the performance of high-level visual models. In this work, a novel synergistic structure is proposed which can balance brightness, color, and illumination more effectively. Specifically, the proposed method, so-called Joint Correcting and Refinement Network (JCRNet), which mainly consists of three stages to balance brightness, color, and illumination of enhancement. Stage 1: we utilize a basic encoder-decoder and local supervision mechanism to extract local information and more comprehensive details for enhancement. Stage 2: cross-stage feature transmission and spatial feature transformation further facilitate color correction and feature refinement. Stage 3: we employ a dynamic illumination adjustment approach to embed residuals between predicted and ground truth images into the model, adaptively adjusting illumination balance. Extensive experiments demonstrate that the proposed method exhibits comprehensive performance advantages over 21 state-of-the-art methods on 9 benchmark datasets. Furthermore, a more persuasive experiment has been conducted to validate our approach the effectiveness in downstream visual tasks (e.g., saliency detection). Compared to several enhancement models, the proposed method effectively improves the segmentation results and quantitative metrics of saliency detection. The source code will be available at https://github.com/woshiyll/JCRNet

    Surface Immunoproteomics Reveals Potential Biomarkers in Alicyclobacillus acidoterrestris

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    Alicyclobacillus acidoterrestris is a major putrefying bacterium that can cause pecuniary losses in the global juice industry. Current detection approaches are time-consuming and exhibit reduced specificity and sensitivity. In this study, an immunoproteomic approach was utilized to identify specific biomarkers from A. acidoterrestris for the development of new detection methods. Cell surface-associated proteins were extracted and separated by 2-D (two-dimensional) gel electrophoresis. Immunogenic proteins were detected by Western blot analysis using antisera against A. acidoterrestris. Twenty-two protein spots exhibiting immunogenicity were excised and eighteen of the associated spots were successfully identified by matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF MS). These proteins were observed to be involved in energy and carbohydrate metabolism, transmembrane transport, response to oxidative stress, polypeptide biosynthesis, and molecule binding activity. This is the first report detailing the identification of cell surface-associated antigens of A. acidoterrestris. The identified immunogenic proteins could serve as potential targets for the development of novel detection methods

    Ultra-bright, ultra-broadband hard x-ray driven by laser-produced energetic electron beams

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    We propose a new method of obtaining a compact ultra-bright, ultra-broadband hard X-ray source. This X-ray source has a high peak brightness in the order of 1022 photons/(s mm2 mrad2 0.1\%BW), an ultrashort duration (10 fs), and a broadband spectrum (flat distribution from 0.1 MeV to 4 MeV), and thus has wide-ranging potential applications, such as in ultrafast Laue diffraction experiments. In our scheme, laser-plasma accelerators (LPAs) provide driven electron beams. A foil target is placed oblique to the beam direction so that the target normal sheath field (TNSF) is used to provide a bending force. Using this TNSF-kick scheme, we can fully utilize the advantages of current LPAs, including their high charge, high energy, and low emittance

    Scheme for proton-driven plasma-wakefield acceleration of positively charged particles in a hollow plasma channel

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    A new scheme for accelerating positively charged particles in a plasma wakefield accelerator is proposed. If the proton drive beam propagates in a hollow plasma channel, and the beam radius is of order of the channel width, the space charge force of the driver causes charge separation at the channel wall, which helps to focus the positively charged witness bunch propagating along the beam axis. In the channel, the acceleration buckets for positively charged particles are much larger than in the blowout regime of the uniform plasma, and stable acceleration over long distances is possible. In addition, phasing of the witness with respect to the wave can be tuned by changing the radius of the channel to ensure the acceleration is optimal. Two dimensional simulations suggest that, for proton drivers likely available in future, positively charged particles can be stably accelerated over 1 km with the average acceleration gradient of 1.3 GeV/m.Comment: 16 pages, 4 figures, 25 reference

    CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition

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    EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1—Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems

    Biological control of Fusarium crown rot of wheat with Chaetomium globosum 12XP1-2-3 and its effects on rhizosphere microorganisms

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    Chaetomium globosum is a common plant endophytic fungi that exhibits great biocontrol potential in plant disease. Fusarium crown rot (FCR) is an important disease in wheat that seriously threatens wheat production worldwide. The control effect of C. globosum against wheat FCR remains unclear. In this study, we introduced an identified C. globosum 12XP1-2-3 and tested its biological control potential against wheat FCR. The hypha and fermentation broth exhibited an antagonistic effect against Fusarium pseudograminearum. Results from indoor experiments showed that C. globosum 12XP1-2-3 might delay the onset of symptoms of brown stem base and significantly reduced the disease index (37.3%). Field trials showed that wheat seeds coated with a spore suspension of 12XP1-2-3 grew better than the control seeds, had control effects of 25.9–73.1% on FCR disease, and increased wheat yield by 3.2–11.9%. Analysis of rhizosphere microorganisms revealed that seeds coated with C. globosum (‘Cg’ treatment) had a greater effect on fungal rather than on bacterial alpha diversity and may improve the health state of rhizosphere microorganisms, as reflected by the significantly increased fungal Shannon index at Feekes 11 and the increased complexity of the bacterial co-occurrence network but decreased complexity of the fungal network. Moreover, the accumulation of beneficial bacteria such as Bacillus and Rhizobium at Feekes 3, and Sphingomonas at Feekes 7 in the ‘Cg’ treatment may be the important contributions to healthier wheat growth state, significantly reduced relative abundance of Fusarium at Feekes 11, and reduced occurrence of FCR disease. These results provide a basis for further research on the mechanism of action of C. globosum and its application in the biological control of FCR in the field

    Triptolide Inhibits the Proliferation of Prostate Cancer Cells and Down-Regulates SUMO-Specific Protease 1 Expression

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    Recently, traditional Chinese medicine and medicinal herbs have attracted more attentions worldwide for its anti-tumor efficacy. Celastrol and Triptolide, two active components extracted from the Chinese herb Tripterygium wilfordii Hook F (known as Lei Gong Teng or Thunder of God Vine), have shown anti-tumor effects. Celastrol was identified as a natural 26 s proteasome inhibitor which promotes cell apoptosis and inhibits tumor growth. The effect and mechanism of Triptolide on prostate cancer (PCa) is not well studied. Here we demonstrated that Triptolide, more potent than Celastrol, inhibited cell growth and induced cell death in LNCaP and PC-3 cell lines. Triptolide also significantly inhibited the xenografted PC-3 tumor growth in nude mice. Moreover, Triptolide induced PCa cell apoptosis through caspases activation and PARP cleavage. Unbalance between SUMOylation and deSUMOylation was reported to play an important role in PCa progression. SUMO-specific protease 1 (SENP1) was thought to be a potential marker and therapeutical target of PCa. Importantly, we observed that Triptolide down-regulated SENP1 expression in both mRNA and protein levels in dose-dependent and time-dependent manners, resulting in an enhanced cellular SUMOylation in PCa cells. Meanwhile, Triptolide decreased AR and c-Jun expression at similar manners, and suppressed AR and c-Jun transcription activity. Furthermore, knockdown or ectopic SENP1, c-Jun and AR expression in PCa cells inhibited the Triptolide anti-PCa effects. Taken together, our data suggest that Triptolide is a natural compound with potential therapeutic value for PCa. Its anti-tumor activity may be attributed to mechanisms involving down-regulation of SENP1 that restores SUMOylation and deSUMOyaltion balance and negative regulation of AR and c-Jun expression that inhibits the AR and c-Jun mediated transcription in PCa
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