5,927 research outputs found

    Modelling the Self-similarity in Complex Networks Based on Coulomb's Law

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    Recently, self-similarity of complex networks have attracted much attention. Fractal dimension of complex network is an open issue. Hub repulsion plays an important role in fractal topologies. This paper models the repulsion among the nodes in the complex networks in calculation of the fractal dimension of the networks. The Coulomb's law is adopted to represent the repulse between two nodes of the network quantitatively. A new method to calculate the fractal dimension of complex networks is proposed. The Sierpinski triangle network and some real complex networks are investigated. The results are illustrated to show that the new model of self-similarity of complex networks is reasonable and efficient.Comment: 25 pages, 11 figure

    Up-regulation of p21 and TNF-Ī± is mediated in lycorine-induced death of HL-60 cells

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    <p>Abstract</p> <p>Background</p> <p>Leukemia is one of the most life-threatening cancers today, and acute promyelogenous leukemia (APL) is a common type of leukemia. Many natural compounds have already been found to exhibit significant anti-tumor effects. Lycorine, a natural alkaloid extracted from Amaryllidaceae, exhibited anti-leukemia effects in vitro and in vivo. The survival rate of HL-60 cells exposed to lycorine was decreased, cell growth was slowed down, and cell regeneration potential was inhibited. HL-60 cells exhibited typical apoptotic characteristic. Lycorine can suppress leukemia growth and reduce cell survival and inducing apoptosis of tumor cells. The purpose of this work is to elucidate the mechanism by which lycorine induces APL cells.</p> <p>Results</p> <p>When HL-60 cells were treated with different concentration of lycorine, the expression of p21 and TNF-Ī± was up-regulated in a concentration-dependent manner as shown by real-time quantitative reverse transcriptase-polymerase chain reaction and Western blotting. Lycorine also down-regulated p21-related gene expression, including Cdc2, Cyclin B, Cdk2 and Cyclin E, promoted Bid truncation, decreased IĪŗB phosphorylation and blocked NF-ĪŗB nuclear import. Cytochrome c was released from mitochondria as observed with confocal laser microscopy.</p> <p>Conclusions</p> <p>The TNF-Ī± signal transduction pathway and p21-mediated cell-cycle inhibition were involved in the apoptosis of HL-60 cells induced by lycorine. These results contribute to the development of new lycorine-based anti-leukemia drugs.</p

    Learning-based Intelligent Surface Configuration, User Selection, Channel Allocation, and Modulation Adaptation for Jamming-resisting Multiuser OFDMA Systems

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    Reconfigurable intelligent surfaces (RISs) can potentially combat jamming attacks by diffusing jamming signals. This paper jointly optimizes user selection, channel allocation, modulation-coding, and RIS configuration in a multiuser OFDMA system under a jamming attack. This problem is non-trivial and has never been addressed, because of its mixed-integer programming nature and difficulties in acquiring channel state information (CSI) involving the RIS and jammer. We propose a new deep reinforcement learning (DRL)-based approach, which learns only through changes in the received data rates of the users to reject the jamming signals and maximize the sum rate of the system. The key idea is that we decouple the discrete selection of users, channels, and modulation-coding from the continuous RIS configuration, hence facilitating the RIS configuration with the latest twin delayed deep deterministic policy gradient (TD3) model. Another important aspect is that we show a winner-takes-all strategy is almost surely optimal for selecting the users, channels, and modulation-coding, given a learned RIS configuration. Simulations show that the new approach converges fast to fulfill the benefit of the RIS, due to its substantially small state and action spaces. Without the need of the CSI, the approach is promising and offers practical value.Comment: accepted by IEEE TCOM in Jan. 202

    Impact of inclusive leadership on employee innovative behavior : Perceived organizational support as a mediator

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    This research was financially supported by the National Social Science Foundation (14BGL073), Ministry of Education Humanities and Social Sciences Research Planning Fund Project (19YJA0056), Shandong Social Science Planning Fund Program (17CLYJ26), Major Program of Humanities and Social Sciences of Shandong University (17RWZD21), Bing Liu as the funding recipients.Peer reviewedPublisher PD

    DeSAM: Decoupling Segment Anything Model for Generalizable Medical Image Segmentation

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    Deep learning based automatic medical image segmentation models often suffer from domain shift, where the models trained on a source domain do not generalize well to other unseen domains. As a vision foundation model with powerful generalization capabilities, Segment Anything Model (SAM) shows potential for improving the cross-domain robustness of medical image segmentation. However, SAM and its fine-tuned models performed significantly worse in fully automatic mode compared to when given manual prompts. Upon further investigation, we discovered that the degradation in performance was related to the coupling effect of poor prompts and mask segmentation. In fully automatic mode, the presence of inevitable poor prompts (such as points outside the mask or boxes significantly larger than the mask) can significantly mislead mask generation. To address the coupling effect, we propose the decoupling SAM (DeSAM). DeSAM modifies SAM's mask decoder to decouple mask generation and prompt embeddings while leveraging pre-trained weights. We conducted experiments on publicly available prostate cross-site datasets. The results show that DeSAM improves dice score by an average of 8.96% (from 70.06% to 79.02%) compared to previous state-of-the-art domain generalization method. Moreover, DeSAM can be trained on personal devices with entry-level GPU since our approach does not rely on tuning the heavyweight image encoder. The code is publicly available at https://github.com/yifangao112/DeSAM.Comment: 12 pages. The code is available at https://github.com/yifangao112/DeSA
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