Macrophages are the principal immune cells of the epididymis and testis, but their origins, heterogeneity, development, and maintenance are not well understood. Here, we describe distinct populations of epididymal and testicular macrophages that display an organ-specific cellular identity. Combining in vivo fate-mapping, chimeric and parabiotic mouse models with in-depth cellular analyses, we found that CD64hiMHCIIlo and CD64loMHCIIhi macrophage populations of epididymis and testis arise sequentially from yolk sac erythro-myeloid progenitors, embryonic hematopoiesis, and nascent neonatal monocytes. While monocytes were the major developmental source of both epididymal and testicular macrophages, both populations self-maintain in the steady-state independent of bone marrow hematopoietic precursors. However, after radiation-induced macrophage ablation or during infection, bone marrow-derived circulating monocytes are recruited to the epididymis and testis, giving rise to inflammatory macrophages that promote tissue damage. These results define the layered ontogeny, maintenance and inflammatory response of macrophage populations in the male reproductive organs.

No abstract

Task scheduling in cloud computing can directly affect the resource usage and operational cost of a system. To improve the efficiency of task executions in a cloud, various metaheuristic algorithms, as well as their variations, have been proposed to optimize the scheduling. In this work, for the first time, we apply the latest metaheuristics WOA (the whale optimization algorithm) for cloud task scheduling with a multiobjective optimization model, aiming at improving the performance of a cloud system with given computing resources. On that basis, we propose an advanced approach called IWC (Improved WOA for Cloud task scheduling) to further improve the optimal solution search capability of the WOA-based method. We present the detailed implementation of IWC and our simulation-based experiments show that the proposed IWC has better convergence speed and accuracy in searching for the optimal task scheduling plans, compared to the current metaheuristic algorithms. Moreover, it can also achieve better performance on system resource utilization, in the presence of both small and large-scale tasks. Index Terms-Cloud computing; task scheduling; whale optimization algorithm; metaheuristics; multi-objective optimization I. INTRODUCTION W Ith the ubiquitous growth of Internet access and big data, cloud computing becomes more and more popular in today's business world [1]. Compared to other distributed computing techniques (e.g., cluster and grid computing), cloud computing has provided an elastic and scalable way on delivering services to consumers. Namely, consumers do not need to possess the underlying technology and they can make use of computing resources and platforms in a pay-per-use fashion [2], [3]. The basic mechanism of cloud computing is to dispatch computing tasks to a resource pooling constituting of a

No abstract

Many chronic liver diseases will advance to hepatic fibrosis and, if without timely intervention, liver cirrhosis or even hepatocellular carcinoma. Anti-inflammation could be a standard therapeutic strategy for hepatic fibrosis treatment, but a “smart” strategy of hepatic fibrosis-targeted, either self-assembly or slow release of an anti-inflammation drug (e.g., dexamethasone, Dex), has not been reported. Herein, we rationally designed a hydrogelator precursor Nap-Phe-Phe-Lys­(Dex)-Tyr­(H2PO3)-OH (1-Dex-P) and proposed a tandem enzymatic strategy of self-assembly and slow release of Dex, with which the precursor exhibited much stronger antihepatic fibrosis effect than Dex both in vitro and in vivo. Enzymatic and cell experiments validated that 1-Dex-P was first dephosphorylated by alkaline phosphatase to yield Nap-Phe-Phe-Lys­(Dex)-Tyr-OH (1-Dex), which self-assembled into nanofiber 1-Dex. The nanofiber was then hydrolyzed by esterase to transform into nanofiber 1, accompanied by slow release of Dex. We anticipate that our “smart” tandem enzymatic strategy could be widely employed to design more sophisticated drug delivery systems to achieve enhanced therapeutic efficacy than free drugs in the future.