Extracellular vesicles (EVs) derived from mesenchymal stem cells (MSCs) have emerged as an appealing alternative to cell therapy in regenerative medicine. Unlike bone marrow MSCs (BMSCs) cultured in vitro with normoxia, bone marrow in vivo is exposed to a hypoxic environment. To date, it remains unclear whether hypoxia preconditioning can improve the function of BMSC-derived EVs and be more conducive to bone repair. Herein, it is found that hypoxia preconditioned BMSCs secrete more biglycan (Bgn)-rich EVs via proteomics analysis, and these hypoxic EVs (Hypo-EVs) significantly promote osteoblast proliferation, migration, differentiation, and mineralization by activating the phosphatidylinositide 3-kinase/protein kinase B pathway. Subsequently, an injectable bioactive hydrogel composed of poly(ethylene glycol)/polypeptide copolymers is developed to improve the stability and retention of Hypo-EVs in vivo. The Hypo-EVs-laden hydrogel shows continuous liberation of Hypo-EVs for 3 weeks and substantially accelerates bone regeneration in 5-mm rat cranial defects. Finally, it is confirmed that Bgn in EVs is a pivotal protein regulating osteoblast differentiation and mineralization and exerts its effects through paracrine mechanisms. Therefore, this study shows that hypoxia stimulation is an effective approach to optimize the therapeutic effects of BMSC-derived EVs and that injectable hydrogel-based EVs delivery is a promising strategy for tissue regeneration. 相似文献
Phosphorus exhibits high capacity and low redox potential, making it a promising anode material for future sodium-ion batteries. However, its practical applications are confined by poor durability and sluggish kinetics. Herein, an innovative in-situ electrochemically self-driven strategy is presented to embed phosphorus nanocrystal (≈10 nm) into a Fe-N-C-rich 3D carbon framework (P/Fe-N-C). This strategy enables rapid and high-capacity sodium ion storage. Through a combination of experimental assistance and theoretical calculations, a novel synergistic catalytic mechanism of Fe-N-C is reasonably proposed. In detail, the electrochemical formation of Fe-N-C catalytic sites facilitates the release of fluorine in ester-based electrolyte, inducing Na+-conducting-enhanced solid-electrolyte interphase. Furthermore, it also effectively induces the dissociation energy of the P-P bond and promotes the reaction kinetics of P anode. As a result, the unconventional P/Fe-N-C anode demonstrates outstanding rate-capability (267 mAh g−1 at 100 A g−1) and cycling stability (72%, 10 000 cycles). Notably, the assembled pouch cell achieves high-energy density of 220 Wh kg−1. 相似文献
With the rapid development of mobile technology and smart devices, crowdsensing has shown its large potential to collect massive data. Considering the limitation of calculation power, edge computing is introduced to release unnecessary data transmission. In edge-computing-enabled crowdsensing, massive data is required to be preliminary processed by edge computing devices (ECDs). Compared with the traditional central platform, these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically. ECDs involved in one task are required to cooperate to process the task data. The privacy of participants is important in crowdsensing, so blockchain is used due to its decentralization and tamper-resistance. In crowdsensing tasks, it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced. As mentioned before, ECDs can’t process task data comprehensively and they are required to cooperate quality assessment. Therefore, a blockchain-based framework for data quality in edge-computing-enabled crowdsensing (BFEC) is proposed in this paper. DPoR (Delegated Proof of Reputation), which is proposed in our previous work, is improved to be suitable in BFEC. Iteratively, the final result is calculated without revealing the privacy of participants. Experiments on the open datasets Adult, Blog, and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks. 相似文献
Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network (CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion ( MMFF) is proposed. Specifically, first residual network ( Resnet )-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network (FPN), finally squeeze-and-excitation fusion ( SEF) module and self-attention network ( SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods. 相似文献
The main task of local rough set model is to avoid the interference of complicated calculation and invalid information in the formation of approximation space. In this paper, we first present a local rough set model based on dominance relation to make the local rough set theory applicable to the ordered information system, then two kinds of local multigranulation rough set models in the ordered information system are constructed by extending the single granulation environment to a multigranulation case. Moreover, the updating processes of dynamic objects based on global (classical) and local multigranulation rough sets in the ordered information system are analyzed and compared carefully. It is addressed about how the rough approximation spaces of global multigranulation rough set and local multigranulation rough set change when the object set increase or decrease in an ordered information system. The relevant algorithms for updating approximations with dynamic objects on global and local multigranulation rough sets are provided in ordered information systems. To illustrate the superiority and the effectiveness of the proposed dynamic updating approaches in the ordered information system, experimental evaluation is performed using six datasets coming from the University of California-Irvine repository.