60 research outputs found

    Study on genetic engineering of Acremonium chrysogenum, the cephalosporin C producer

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    AbstractAcremonium chrysogenum is an important filamentous fungus which produces cephalosporin C in industry. This review summarized the study on genetic engineering of Acremonium chrysogenum, including biosynthesis and regulation for fermentation of cephalosporin C, molecular techniques, molecular breeding and transcriptomics of Acremonium chrysogenum. We believe with all the techniques available and full genomic sequence, the industrial strain of Acremonium chrysogenum can be genetically modified to better serve the pharmaceutical industry

    SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

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    Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving and the generalization of learning-based algorithms on different environmental effects is still an open problem. Although monocular depth prediction has been well studied recently, there is few work focusing on the robust learning-based depth prediction across different environments, e.g. changing illumination and seasons, owing to the lack of such a multi-environment real-world dataset and benchmark. To this end, the first cross-season monocular depth prediction dataset and benchmark SeasonDepth is built based on CMU Visual Localization dataset. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset, the influence of multiple environments on performance and robustness is analyzed qualitatively and quantitatively, showing that the long-term monocular depth prediction is still challenging even with fine-tuning. We further give promising avenues that self-supervised training and stereo geometry constraint help to enhance the robustness to changing environments. The dataset is available on https://seasondepth.github.io, and benchmark toolkit is available on https://github.com/SeasonDepth/SeasonDepth.Comment: 19 pages, 13 figure

    Monoindole Alkaloids from a Marine Sponge Spongosorites sp.

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    Seven (1–7) monoindole derivatives were isolated from the MeOH extract of a marine sponge Spongosorites sp. by bioactivity-guided fractionation. The planar structures were established on the basis of NMR and MS spectroscopic analyses. Compounds 1–5 are unique indole pyruvic acid derivatives. Compounds 1–2 and 4–6 are isolated for the first time from a natural source although they were previously reported as synthetic intermediates. Compound 3 was defined as a new compound. Co-occurring bisindoles such as hamacanthins and topsentins might be biosynthesized by condensation of two units of these compounds. The compounds were tested for cytotoxicity against a panel of five human solid tumor cell lines, and compound 7 displayed weak activity

    Ship Tracking Based on Underwater Electric Potential

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    Underwater Electric Potential is an important signal characteristic of a ship. The signal contains location information which can be used to track the ship. This research tries to study the possibility of ship tracking using Underwater Electric Potential. Aiming at the problems existing in the traditional Kalman filters under large initial errors, a new nonlinear filter is proposed. State space model of ship tracking is established; the problem existing in the ordinary Kalman filters is analyzed from the perspective of Kullbeck-Leibler Divergence; the new algorithm is proposed based on progressive Bayesian; simulations are designed. Simulation results show: it is feasible to use underwater electric potential to track the ship; the new method can effectively improve filter performance under large initial error and can effectively track the ship with preferable precision and convergence, which has great practical value

    An Integrated Bearing Fault Diagnosis Method Based on Multibranch SKNet and Enhanced Inception-ResNet-v2

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    Deep learning has recently received extensive attention in the field of rolling-bearing fault diagnosis owing to its powerful feature expression capability. With the help of deep learning, we can fully extract the deep features hidden in the data, significantly improving the accuracy and efficiency of fault diagnosis. Despite this progress, deep learning still faces two outstanding problems. (1) Each layer uses the same convolution kernel to extract features, making it difficult to adaptively select convolution kernels based on the features of the input image, which limits the network’s adaptability to different input features and leads to weak feature extraction. (2) Large number of parameters and long training time. To solve the above problems, this paper proposes an integrated deep neural network that combines an improved selective kernel network (SKNet) with an enhanced Inception-ResNet-v2, named SIR-CNN. First, based on the SKNet, a new three-branch SKNet was designed. Second, the new SKNet is embedded into a depthwise separable convolution network such that the model can adaptively select convolution kernels of different sizes during training. Furthermore, the convolution structure in the Inception-ResNet-v2 network was replaced by the improved depthwise separable convolution network to achieve effective feature extraction. Finally, the time-frequency maps of the raw vibration signals are obtained through short-time Fourier transform (STFT) and then sent to the proposed SIR-CNN network for experiments. The experimental results show that the proposed SIR-CNN achieves superior performance compared to other methods

    A New Fault Diagnosis Method for Unbalanced Data Based on 1DCNN and L2-SVM

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    In general, the measured health condition data from rolling bearings usually exhibit imbalanced distribution. However, traditional intelligent fault diagnosis methods usually assume that the data categories are balanced. To improve the diagnosis accuracy of unbalanced datasets, a new fault diagnosis method for unbalanced data based on 1DCNN and L2-SVM is proposed in this paper. Firstly, to prevent the minority class samples from being heavily suppressed by the rectified linear unit (ReLU) activation function in the traditional convolutional neural network (CNN), ReLU is improved by linear and scaled exponential linear units (SELUs). Secondly, to solve the problem where the cross-entropy loss treats all input samples equally, it is replaced by the L2-support vector machine (L2-SVM) loss. Furthermore, a dynamic adjustment parameter is introduced to assign less misclassification cost to the majority of class samples. Finally, we add a new modulation factor that reduces the weight of more distinguishable samples to generate more focus on training indiscernible samples. The proposed method is carried out on two kinds of bearing datasets. The experimental results illustrate a significant improvement in recognition accuracy and the higher diagnosis performance of the model when dealing with unbalanced data compared with other intelligent methods

    Linearization of power amplifiers with mismatched output impedance using on-line digital pre-distortion structure

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    Ilomastat contributes to the survival of mouse after irradiation via promoting the recovery of hematopoietic system.

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    Ilomastat, a broad-spectrum inhibitor of matrix metalloproteinases (MMPs), has drawn attentions for its function in alleviating radiation damage. However, the detailed mechanisms of Ilomastat's protection from animal model remain not fully clear. In this study, the C57BL/6 mice were pre-administrated with Ilomastat or vihicle for 2 h, and then total body of mice were exposed to 6 Gy of γ-rays. The protective effect of Ilomastat on the hematopoietic system in the irradiated mice were investigated. We found that pretreatment with Ilomastat significantly reduced the level of TGF-β1 and TNF-α, and elevated the number of bone marrow (BM) mononuclear cells in the irradiated mice. Ilomastat pretreatment also increased the fraction of BM hematopoietic progenitor cells (HPCs) and hematopoietic stem cells (HSCs) at day 30 after irradiation, and protected the spleen of mouse from irradiation. These results suggest that Ilomastat promotes the recovery of hematopoietic injury in the irradiated mice, and thus contributes to the survival of mouse after irradiation

    Tunnel Construction Workers’ Cognitive Biases and Unsafe Behaviors: The Mediating Effects of Risk Perceptions

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    The poor safety performance of the construction industry is a global concern. Workers’ unsafe behaviors (WUBs) are viewed as critical causes of construction accidents. Thus, it has been a leading research topic to identify antecedents of WUBs and establish reliable models to explain WUBs. Cognitive biases (CBs) are salient antecedents of WUBs. However, the relationships between CBs and WUBs are not further explored in construction safety management. As such, this study selected three prevalent CBs, i.e., availability bias (AB), confirmation bias (FB), and overconfidence, and investigated the relationships between the three CBs and WUBs and the functions of RPs in these relationships among tunnel construction workers. We established six multiple regression models. Research results show that CBs can directly influence RPs and WUBs, RPs are antecedents of WUBs, and the mediating effect of RPs on the relationships between CBs and WUBs is salient. In detail, AB and FB explain more ERP and overconfidence explains more BRP. AB and FB indirectly influence more WUBs through ERP, while overconfidence indirectly affects more WUBs through BRP. The study provides a novel theoretical paradigm for conceptualizing CBs and their relationships with RPs and WUBs. The research results can also guide managers to take debiasing measures to decrease workers’ unsafe behaviors on the construction site

    Protocol for efficient and self-healing near-infrared perovskite light-emitting diodes.

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    Preparation of highly efficient and stable perovskite light-emitting diodes (PeLEDs) with reproducible device performance is challenging. This protocol describes steps for fabrication of high-performance and self-healing PeLEDs. These include instructions for synthesis of charge-transporting zinc oxide (ZnO) nanocrystals, step-by-step device fabrication, and control over self-healing of the degraded devices. For complete details on the use and execution of this protocol, please refer to Teng et al. (2021).Funding agencies: RC Starting Grant (no. 717026), the Swedish Energy AgencyEnergimyndigheten (no. 48758-1), and the Swedish Government Strategic Research Area in Mate-rials Science on Functional Materials Linko ̈ ping University (Faculty Grant SFO-Mat-LiU no. 2009-00971). Y.Z. and B.S. also thank the support from Macau SAR (file no. 0044/2021/A)</p
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