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Macrophage therapy for liver fibrosis is on the cusp of meaningful clinical utility. Due to the heterogeneities of macrophages, it is urgent to develop safer macrophages with a more stable and defined phenotype for the treatment of liver fibrosis. Herein, a new macrophage‐based immunotherapy using macrophages stably expressing a pivotal cytokine from Toxoplasma gondii, a parasite that infects ≈ 2 billion people is developed. It is found that Toxoplasma gondii macrophage migration inhibitory factor‐transgenic macrophage (Mφtgmif) shows stable fibrinolysis and strong chemotactic capacity. Mφtgmif effectively ameliorates liver fibrosis and deactivates aHSCs by recruiting Ly6Chi macrophages via paracrine CCL2 and polarizing them into the restorative Ly6Clo macrophage through the secretion of CX3CL1. Remarkably, Mφtgmif exhibits even higher chemotactic potential, lower grade of inflammation, and better therapeutic effects than LPS/IFN‐γ‐treated macrophages, making macrophage‐based immune therapy more efficient and safer. Mechanistically, TgMIF promotes CCL2 expression by activating the ERK/HMGB1/NF‐κB pathway, and this event is associated with recruiting endogenous macrophages into the fibrosis liver. The findings do not merely identify viable immunotherapy for liver fibrosis but also suggest a therapeutic strategy based on the evolutionarily designed immunomodulator to treat human diseases by modifying the immune microenvironment. [ABSTRACT FROM AUTHOR]

In this study, we aim to examine how socialization practices predict newcomers' career adaptability during their organizational transitions. Drawing on career construction theory and conservation of resources theory, we argue that newcomers' job embeddedness, as predicted by their perceived organizational socialization tactics, positively predicts their career adaptability during career transitions. We investigate the role of past transition experiences (i.e., career variety) in moderating the relationship between job embeddedness and career adaptability. Data were collected at three time points from 492 newcomers in an information technology company in China. The newcomers' perceived organizational socialization tactics (i.e., training, future prospects and coworker support) positively predicted their job embeddedness, which was positively associated with their career adaptability. Additionally, career variety weakened the positive effect of job embeddedness on career adaptability. Furthermore, career variety moderated the indirect effects of future prospects and coworker support on career adaptability via job embeddedness, but not that of training. We conclude the article with discussions of our theoretical and practical contributions. [ABSTRACT FROM AUTHOR]

Summary: Cloud computing has gained more and more attention from industrial and academic circle since it offers pay‐as‐you‐go model, and business applications based on the cloud are also increasing. These applications meet the requirement of users while at the same time triggering the problem of high energy consumption in data centers. To deal with the problem, we propose a new algorithm named EEOM (Energy Efficiency Optimization of VM Migrations). Under considering CPU and memory factors, the key three steps for EEOM algorithm, including trigger time, VM selection, and host location, are optimized. EEOM algorithm takes use of the virtualization technology and migrates some VMs on the lightly loaded host and heavily loaded host to other hosts. The idle hosts are switched to low‐power mode or shut down so as to save energy consumption. The experimental results show that, as compared with Double Threshold (DT) algorithm, the EEOM algorithm saves 7% energy consumption and reduces 13% SLA violations. [ABSTRACT FROM AUTHOR]

Summary: With the increasing scale of tasks in cloud computing, the problem of high energy consumption becomes increasingly serious. To deal with the problem, we propose a cloud computing energy consumption model, which takes into account the execution and transmission cost of the processor. Then, based on this model, we put forward a task scheduling optimization algorithm named modified particle swarm optimization (M‐PSO) to handle the local optimum and slow convergence problem. Different from the PSO, M‐PSO can dynamically adjust the inertia weight coefficient to improve the speed of convergence according to the number of iterations. Finally, the performance of the proposed algorithm is evaluated through the CloudSim toolkit, and the experimental results show that the M‐PSO can efficiently reduce total cost compared with other algorithms. [ABSTRACT FROM AUTHOR]