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Docetaxel-loaded liposomes: preparation, pH sensitivity,Pharmacokinetics, and tissue distribution

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2012年第12期13卷 981-989页

作者： Hong ZHANG Rui-ying LI Xia LU Zhen-zhen MOU gui-mei lin School of Pharmaceutical Science Shandong UniversityJinan 250012China

Docetaxel (DTX), as a member of taxoid family, has been widely used in the treatment of cancers. The present study prepared pH-sensitive DTX-loaded liposomes (DTX-Lips) by thin-film dispersion method and various physico-chemical and morphological properties were examined. The pH sensitivity of in vitro DTX release and the in vivo pharmacokinetics and tissue distribution using Kunming mice were also investigated. The mean particle size and zetapotential of DTX liposomes were (277±2) nm and (-32.60±0.26) mV, respectively. Additionally, in vitro drug release study showed that the cumulative release rate was 1.3 times more at pH 5.0 than at pH 7.4, suggesting a pH-dependent releaseability of DTX-Lips. Pharmacokinetic and pharmaceutical studies in comparison with Duopafei showed that the half-timeperiod (t1/2) and area under the curve (AUC) of DTX-Lips in mouse plasma were 1.8 times longer and 2.6 times higher,respectively, and that DTX-Lips selectively accumulated in macrophage-rich organs such as liver and spleen. The seresults together suggest that the DTX-Lips could be a promising formulation for the clinical administration of DTX.

关键词： Docetaxel-loaded liposomes pH sensitivity Pharmacokinetics Tissue distribution

Microcalorimetry studies of the antimicrobial actions of Aconitum alkaloids

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2015年第8期16卷 690-695页

作者： Yan-bin SHI Lian LIU Wei SHAO Ting WEI gui-mei lin School of Mechanical and Automotive Engineering Qilu University of Technology Beijing Health Vocational College School of Pharmaceutical Science Shandong University

The metabolic activity of organisms can be measured by recording the heat output using microcalorimetry. In this paper, the total alkaloids in the traditional Chinese medicine Radix Aconiti Lateralis were extracted and applied to Eschenchia coil and Staphylococcus aureus. The effect of alkaloids on bacteda growth was studied by microcalorimetry. The power-time curves were plotted with a thermal activity monitor （TAM） air isothermal microcalorimeter and pa- rameters such as growth rate constant （p）, peak-time （Trn）, inhibitory ratio （I）, and enhancement ratio （E） were cal- culated. The relationships between the concentration of Aconitum alkaloids and p of E. coil or S. aureus were discussed. The results showed that Aconitum alkaloids had little effect on E. coil and had a potentially inhibitory effect on the growth of S. aureus.

关键词： Aconitum alkaloids Escherichia coil Staphylococcus aureus Microcalorimetry

Offload Strategy for Edge Computing in Satellite Networks Based on Software Defined Network

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Computers, Materials & Continua 2025年第1期82卷 863-879页

作者： Zhiguo Liu Yuqing gui lin Wang Yingru Jiang Communication and Network Laboratory Dalian UniversityDalian116622China College of Environment and Chemical Engineering Dalian UniversityDalian116622China

Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable *** sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user *** this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their ***,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN ***,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of *** results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.

关键词： Satellite network edge computing task scheduling computing offloading

Observation of acoustic hybrid topological phases induced by the p-d orbital interactions

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 132-139页

作者： Xiao Xiang Feng Gao Yu-gui Peng Peng Wu Zong-lin Li Xue-Feng Zhu School of Physics and Innovation Institute Huazhong University of Science and Technology

In spinless systems,growing attention has recently been attracted to synthetic gauge fields,which redefine the fundamental crystal symmetries by utilizing the projective algebraic ***,synthetic gauge fields have been commonly investigated in single orbital systems,and explorations on hybrid orbitals remain elusive in ***,we report the experimental observation of hybrid topological phases induced by hybrid synthetic gauge flux,which is formed by the interaction between p and d orbitals embedded in acoustic *** breaking the translation symmetries of Lxand/or Ly,we unambiguously demonstrate the first-order M?bius edge states and higher-order corner *** work establishes a platform for seeking exotic topological phases induced by hybrid-orbitals interactions and initializing the framework of hybrid-orbitalsrelated topological *** applications can be anticipated in scenarios such as high-quality sensing and robust wave trapping due to the stepwise energy distribution of the hybrid topological phase.

关键词： topological insulator synthetic gauge field projective symmetry corner states

Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

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Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong mei State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical Engineering and Automation Luoyang Institute of Science and Technology School of Mechanical Engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Contrastive Learning

Clinical observation of acupuncture plus repetitive transcranial magnetic stimulation in the treatment of post-stroke insomnia

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Journal of Acupuncture and Tuina Science 2020年第2期18卷 122-128页

作者： Zhang Wei Ma Kun-qin Xiao Hong-bo Li Pei-fang gui mei-lin Lu Yin-feng Chen Rui-quan Zhu Zong-jun Wu Jiang-yun Anhui University of Chinese Medicine Hefei 230031China The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230031China The Second Affiliated Hospital of Anhui University of Chinese Medicine Hefei 230061China 不详

Objective:To evaluate the clinical efficacy of the Governor Vessel-unblocking and mind-regulating acupuncture method plus repetitive transcranial magnetic stimulation(rTMS)in the treatment of post-stroke ***:A total of 72 patients with post-stroke insomnia were randomly divided into 2 groups,with 36 cases in each *** control group received rTMS treatment with a frequency of 1 Hz and a motion threshold value of 90%.The observation group received acupuncture with Governor Vessel-unblocking and mind-regulating method based on the rTMS treatment of the control *** points were Baihui(GV 20),Shenting(GV 24),Yintang(GV 29),Fengfu(GV 16),Sishencong(EX-HN I),Shenmen(HT I),Sanyinjiao(SP 6);Shenmai(BL 62)and Zhaohai(Kl 6).The treatment was performed once a day for 5 d a week followed by 2 d of rest for 4 *** improvements of Pittsburgh sleep quality index(PSQI),self-rating anxiety scale(SAS)and self-rating depression scale(SDS)scores were observed after treatment,and the clinical efficacy was compared between the two ***:After 4 weeks of treatment,the PSQI,SAS,and SDS scores of the two groups were all reduced,and the intra-group differences were statistically significant(all Plinical efficacy(P<0.05).Conclusion:The therapeutic effect of the Governor Vessel-unblocking and mind-regulating acupuncture method plus rTMS in treating post-stroke insomnia is better than rTMS alone,and it can better improve the anxiety and depression of patients.

关键词： Acupuncture Therapy Governor Vessel Insomnia Depression Anxiety Stroke Complications Transcranial Magnetic Stimulation

一例钠牛磺胆酸共转运多肽缺陷病患儿临床和遗传学分析

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中国当代儿科杂志 2018年第4期20卷 279-284页

作者：李华邱建武林桂枝邓梅林伟霞程映宋元宗暨南大学附属第一医院儿科广东广州510630

钠牛磺胆酸共转运多肽(NTCP)缺陷病是SLC10A1基因变异引起的遗传性胆汁酸代谢病。本研究报道一例NTCP缺陷病患儿的临床及遗传学特征。患儿,男,因皮肤巩膜黄染3月余就诊。查体皮肤及巩膜中度黄染,肝脏肿大;转氨酶、总胆汁酸及胆红素升高... 详细信息

钠牛磺胆酸共转运多肽(NTCP)缺陷病是SLC10A1基因变异引起的遗传性胆汁酸代谢病。本研究报道一例NTCP缺陷病患儿的临床及遗传学特征。患儿,男,因皮肤巩膜黄染3月余就诊。查体皮肤及巩膜中度黄染,肝脏肿大;转氨酶、总胆汁酸及胆红素升高,以结合胆红素升高为主,伴25-OH-Vit D水平降低;肝组织病理示肝细胞气球样变,部分肝细胞内淤胆,肝小叶结构破坏,汇管区炎性细胞浸润。予醋酸泼尼松抗炎,以及护肝和补充脂溶性维生素等治疗,转氨酶和胆红素等逐渐恢复正常,但胆汁酸水平仍高。17.2月龄时行SLC10A1基因分析,发现患儿为c.800C>T(***267Phe)突变的纯合子,其父母均为该变异携带者,确诊NTCP缺陷病。现已随访至34.3月龄,患儿体格及神经行为发育正常,黄疸消退,肝脏大小、质地和肝功能均恢复正常,但胆汁酸仍高。

关键词： NTCP缺陷病 SLC10A1基因高胆汁酸血症胆汁淤积婴儿

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《庄子》精神生态隐喻的英译叙事重构

东北师大学报（哲学社会科学版） 2025年第1期 134-142页

作者：周桂君林琳东北师范大学外国语学院吉林长春130024 沈阳化工大学外国语学院辽宁沈阳110142

隐喻言说是《庄子》话语特色之一,其中不乏体现主体与其生存环境同构互动的精神生态隐喻。本文对此种隐喻进行界定并提出识别程序,选取《庄子》精神生态隐喻英译实例,探究何种叙事重构方法能将《庄子》原文有关精神生态的隐喻叙事内容... 详细信息

隐喻言说是《庄子》话语特色之一,其中不乏体现主体与其生存环境同构互动的精神生态隐喻。本文对此种隐喻进行界定并提出识别程序,选取《庄子》精神生态隐喻英译实例,探究何种叙事重构方法能将《庄子》原文有关精神生态的隐喻叙事内容更好地被译入语读者群接受并认可,进而将原文的精神生态内涵在异域文化中得以传递。《庄子》精神生态隐喻主要有喻体源于自然意象和非自然意象等两类。前者的英译可遵照选择性采用的诗化叙事重构方法,后者的英译宜采取人物事件再定位的语境重构策略。聚焦《庄子》中精神生态隐喻的译介叙事重构研究,可对以生态思想外译为导向的《庄子》英译实践与研究提供参考。

关键词：精神生态隐喻隐喻识别叙事重构选择性采用人物事件再定位

Channel pruning on frequency response

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Science China(Information Sciences) 2025年第1期68卷 159-170页

作者： Hang lin Yifan PENG lin BIE Chenggang YAN Xibin ZHAO Yue GAO School of Software Tsinghua University School of Automation Hangzhou Dianzi University

Network pruning has a significant role in reducing network parameters and accelerating the inference time of the network. Some existing methods prune the network based on the frequency of the data, and finally obtain a sub-network with high accuracy. However, according to our experimental analysis, different frequencies of information in the data contribute differently to the accuracy of the model, and using this information directly for pruning without making a selection will lead to incorrect results. We believe that pruning should retain the convolutional kernels in the network that process important information, while those kernels that process unimportant information should be removed. In this paper, we first investigate the meaning of each frequency band information in the spectrum and their contribution to the prediction accuracy of the network,and according to these results, we propose a new pruning method based on frequency response(PFR). Our PFR finds and removes the convolutional kernels in the network that specialize in processing unimportant information, resulting in a compact neural network model. PFR obtains significant experimental results on different datasets, for example, a 56.0% raduction of float points operations(FLOPs) on Res Net-50 and only 0.37% of Top-1 accuracy degradation on the Image Net dataset.

关键词： deep learning model compression filter pruning channel pruning frequency response

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