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EcoHealth - Interspecies transmission may play a key role in the evolution and ecology of influenza A viruses. The importance of marine mammals as hosts or carriers of potential zoonotic pathogens... 相似文献
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Hamid Reza Naghibi Beidokhti Reza Ghaffarzadegan Sasan Mirzakhanlouei Leila Ghazizadeh Farid Abedin Dorkoosh 《AAPS PharmSciTech》2017,18(1):115-129
The objective of this study was to investigate the combined influence of independent variables in the preparation of folic acid-chitosan-methotrexate nanoparticles (FA-Chi-MTX NPs). These NPs were designed and prepared for targeted drug delivery in tumor. The NPs of each batch were prepared by coaxial electrospray atomization method and evaluated for particle size (PS) and particle size distribution (PSD). The independent variables were selected to be concentration of FA-chitosan, ratio of shell solution flow rate to core solution flow rate, and applied voltage. The process design of experiments (DOE) was obtained with three factors in three levels by Design expert software. Box-Behnken design was used to select 15 batches of experiments randomly. The chemical structure of FA-chitosan was examined by FTIR. The NPs of each batch were collected separately, and morphologies of NPs were investigated by field emission scanning electron microscope (FE-SEM). The captured pictures of all batches were analyzed by ImageJ software. Mean PS and PSD were calculated for each batch. Polynomial equation was produced for each response. The FE-SEM results showed the mean diameter of the core-shell NPs was around 304 nm, and nearly 30% of the produced NPs are in the desirable range. Optimum formulations were selected. The validation of DOE optimization results showed errors around 2.5 and 2.3% for PS and PSD, respectively. Moreover, the feasibility of using prepared NPs to target tumor extracellular pH was shown, as drug release was greater in the pH of endosome (acidic medium). Finally, our results proved that FA-Chi-MTX NPs were active against the human epithelial cervical cancer (HeLa) cells. 相似文献
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Reza Mohammadi Bahram Abadi Amir Masoud Rahmani Sasan Hossein Alizadeh 《Cluster computing》2018,21(3):1711-1733
By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process. 相似文献
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Naoya Takayama Alex Murison Shin-ichiro Takayanagi Christopher Arlidge Stanley Zhou Laura Garcia-Prat Michelle Chan-Seng-Yue Sasan Zandi Olga I. Gan Héléna Boutzen Kerstin B. Kaufmann Aaron Trotman-Grant Erwin Schoof Ken Kron Noelia Díaz John J.Y. Lee Tiago Medina Daniel D. De Carvalho Mathieu Lupien 《Cell Stem Cell》2021,28(3):488-501.e10