Opposite Regulation of Chondrogenesis and Angiogenesis in Cartilage Repair ECM Materials under Hypoxia

Although cartilage tissue engineering has been developed for decades, it is still unclear whether angio- genesis was the accompaniment of chondrogenesis in cartilage regeneration. This study aimed to explore the process of anti-angiogenesis during cartilage regenerative progress in cartilage repair extracellular matrix （ECM） materials under Hypoxia. C3H10T1/2 cell line, seeded as pellet or in ECM materials, was added with chondrogenic medium or DMEM medium for 21 days under hypoxia or normoxia environment. Genes and miRNAs related with chondrogenesis and angiogenesis were detected by RT-qPCR technique on Days 7, 14, and 21. Dual-luciferase report system was used to explore the regulating roles of miRNAs on angiogenesis. Results showed that the chondrogenic medium promotes chondrogenesis both in pellet and ECM materials culture. HIF1α was up-regulated under hypoxia compared with normoxia （P 〈 0.05）. Meanwhile, hypoxia enhanced chondrogenesis, miR-140-Sp exhibited higher expression while miR-146b exhibited lower expression. The chondrogenic phenotype was more stabilized in the ECM materials in chondrogenic medium than DMEM medium, with lower VEGFα expression even under hypoxia. Dual-luciferase report assays demonstrated that miR-140-5p directly targets VEGFct by binding its 3＇- UTR. Taken together, chondrogenic cytokines, ECM materials and hypoxia synergistically promoted chondrogenesis and inhibited angiogenesis, miR-140-5p olaved an imnortant role in this process.

Computer comparisons in the presence of performance variation

Performance variability,stemming from nondeterministic hardware and software behaviors or deterministic behaviors such as measurement bias,is a well-known phenomenon of computer systems which increases the difficulty of comparing computer performance metrics and is slated to become even more of a concern as interest in Big Data analytic increases.Conventional methods use various measures(such as geometric mean)to quantify the performance of different benchmarks to compare computers without considering this variability which may lead to wrong conclusions.In this paper,we propose three resampling methods for performance evaluation and comparison:a randomization test for a general performance comparison between two computers,bootstrapping confidence estimation,and an empirical distribution and five-number-summary for performance evaluation.The results show that for both PARSEC and highvariance BigDataBench benchmarks 1)the randomization test substantially improves our chance to identify the difference between performance comparisons when the difference is not large;2)bootstrapping confidence estimation provides an accurate confidence interval for the performance comparison measure(e.g.,ratio of geometric means);and 3)when the difference is very small,a single test is often not enough to reveal the nature of the computer performance due to the variability of computer systems.We further propose using empirical distribution to evaluate computer performance and a five-number-summary to summarize computer performance.We use published SPEC 2006 results to investigate the sources of performance variation by predicting performance and relative variation for 8,236 machines.We achieve a correlation of predicted performances of 0.992 and a correlation of predicted and measured relative variation of 0.5.Finally,we propose the utilization of a novel biplotting technique to visualize the effectiveness of benchmarks and cluster machines by behavior.We illustrate the results and conclusion through detailed Monte Carlo simulation studies and real examples.