Versatile Hypoxic Extracellular Vesicles Laden in an Injectable and Bioactive Hydrogel for Accelerated Bone Regeneration

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.     

Electrocatalysis of Fe-N-C Bonds Driving Reliable Interphase and Fast Kinetics for Phosphorus Anode in Sodium-Ion Batteries

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.     

A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

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.     

基于模型识别相关性的光伏发电并网联络线纵联保护

针对集中式光伏并网系统联络线的保护原理存在适应性问题,提出了一种基于模型识别相关性判别的并网联络线纵联保护方法。在分析并网联络线故障特性的基础上,利用差动电压导数与差动电流的相关系数,对并网联络线区内、外故障模型进行区分：当三相线路的相关系数都接近1时,判定为光伏发电并网联络线的区外故障,而当相关系数小于保护整定值时,判定为区内故障;当单相（或多相）的相关系数小于整定值时,则判定为单相（或多相）故障。建立了光伏发电并网系统模型,仿真结果表明,提出的保护方法不受控制暂态响应影响,具有较强的抗过渡电阻能力。     

Face anti-spoofing based on multi-modal and multi-scale features fusion

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.     

Updating approximations with dynamic objects based on local multigranulation rough sets in ordered information systems

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.

     

叶酸协同EDC改性高脱乙酰度壳聚糖微球的制备及工艺探究

使用高脱乙酰度壳聚糖制备了靶向药物载体-叶酸改性壳聚糖微球。首先通过碱法制备了高脱乙酰度的壳聚糖,采用酸碱滴定法和傅里叶变换红外光谱(FTIR)对其结构进行了表征,结果表明,经脱乙酰化处理后的壳聚糖脱乙酰度高达93.8%。然后以高脱乙酰度壳聚糖制备了叶酸改性壳聚糖,创新性地发现用1∶1的二甲基亚砜和水的混合溶剂可以得到壳聚糖和叶酸反应的均相体系。通过不同的改性方案,得到了不同改性程度的壳聚糖,改性程度分别达到了2.60%、5.10%、8.75%和9.49%。最后用三聚磷酸钠交联制备了叶酸改性壳聚糖微球,并用激光粒度仪系统地分析了三聚磷酸钠和改性壳聚糖的用量比例及浓度对微球的粒径、Zeta电位的影响。研究发现,随着三聚磷酸钠与改性壳聚糖的比值增大,微球的粒径和Zeta电位都增大,当比值增加到一定的程度,微球的粒径会快速增加。对三聚磷酸钠和改性壳聚糖加入浓度的研究,发现浓度的增大将导致粒径的增大和Zeta电位的降低。     

基于熔喷过程的等规聚丙烯中拟六方与α混合晶的构成及其稳定性

采用微型熔喷实验机制备得到等规立构聚丙烯熔喷非织造布,采用广角X射线衍射技术研究了熔喷过程中非织造布生产条件对所形成的非织造布中拟六方和α混合晶晶相结构的影响,采用等温热老化方法研究了混合晶的热力学稳定性及拟六方晶向α晶转变的热力学规律。结果表明,受热风牵伸和室温冷却的共同作用,等规立构聚丙烯熔喷非织造布在成型过程中,易形成含α晶型与拟六方晶型的混合晶。混合晶中α晶与拟六方晶型的比例随牵伸风力的增大和熔体冷却成型时温差的减小而增加。温度高于70℃的热老化可促使混合晶中拟六方晶型向α晶型转变。混合晶晶粒尺寸随α型晶体比例增加而增大。     

UHMWPE/SiO2渔用纳米复合单丝的力学性能与动态力学行为研究

采用熔融纺丝法制备超高分子量聚乙烯(UHMWPE)/二氧化硅(SiO_2)渔用纳米复合单丝,并研究了牵伸倍数和纳米粒子含量对UHMWPE单丝结构、热性能、力学性能及动态力学行为的影响。结果表明,纳米SiO_2在UHMWPE单丝基体中的分散为纳米级分散,且分布均匀。当纳米SiO_2含量增加时,熔点和结晶度基本不变,而UHMWPE/SiO_2纳米复合单丝的取向度增大,断裂强度和结节强度增大。动态力学分析表明,当纳米SiO_2含量增加时,纳米粒子与聚乙烯链段相互作用力增强,晶区附近受限的非晶区链段增多,α转变峰逐渐增强增宽。通过纳米改性技术,能够显著改变聚乙烯单丝的粘弹性能,并使聚乙烯单丝的力学性能提高,为实现渔用聚乙烯纤维材料的改性与高性能化提供了理论和技术支撑。