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It has been reported that genomic DNA methylation decreases gradually during cell culture and an organism's aging. However, less is known about the methylation changes of age-related specific genes in aging. p21(Waf1/Cip1) and p16(INK4a) are cyclin-dependent kinase (Cdk) inhibitors that are critical for the replicative senescence of normal cells. In this study, we show that p21(Waf1/Cip1) and p16(INK4a) have different methylation patterns during the aging process of normal human 2BS and WI-38 fibroblasts. p21(Waf1/Cip1) promoter is gradually methylated up into middle-aged fibroblasts but not with senescent fibroblasts, whereas p16(INK4a) is always unmethylated in the aging process. Correspondently, the protein levels of DNA methyltransferase 1 (DNMT1) and DNMT3a increase from young to middle-aged fibroblasts but decrease in the senescent fibroblasts, while DNMT3b decreases stably from young to senescent fibroblasts. p21(Waf1/Cip1) promoter methylation directly represses its expression and blocks the radiation-induced DNA damage-signaling pathway by p53 in middle-aged fibroblasts. More importantly, demethylation by 5-aza-CdR or DNMT1 RNA interference (RNAi) resulted in an increased p21(Waf1/Cip1) level and premature senescence of middle-aged fibroblasts demonstrated by cell growth arrest and high beta-Galactosidase expression. Our results suggest that p21(Waf1/Cip1) but not p16(INK4a) is involved in the DNA methylation mediated aging process. p21(Waf1/Cip1) promoter methylation may be a critical biological barrier to postpone the aging process.

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The aim of this study was to investigate the molecular mechanism underlying the immunomodulatory effect of the purified Artemisia sphaerocephala Krasch seed polysaccharide (ASKP-1) in RAW264.7 macrophages. Chemical characteristic analysis revealed that ASKP-1 consisted of 14.1% mannose, 56.9% glucose and 19.6% galactose with the average molecular weight of 9.08 × 10 Da and the mixed glycan backbone structure containing 1→4)-Glcp (39.8%), 1→6)-Galp (18.8%), 1→3,6)-Manp (19.6%), 1→)-Glcp (10.8%), 2→6)-Manp (4.0%) and 2→3,5)-Araf (7.0%). In vitro studies showed that ASKP-1 markedly induced the release of cytotoxic molecules (NO and ROS) and secretion of the cytokines (TNF-α, INF-β, and IL-6) and significantly enhanced the phagocytosis of RAW264.7 macrophages. Furthermore, TLR4 was found to be a recognized target of ASKP-1 and its related mitogen-activated protein (MAPK) and phosphoinositide 3-kinase (PI3K)/Akt, including phosphorylated ERK, JNK, p38 and Akt, were rapidly activated by ASKP-1 in RAW264.7 macrophages. Moreover, ASKP-1 was found to cause the nuclear translocation of the nuclear factor NF-κB subunit p65 and the degradation of IκB-α in RAW264.7 macrophages. All these findings suggest that MAPK, PI3K/Akt and NF-κB pathways are involved in ASKP-1-induced macrophage activation, and ASKP-1 is a potential immunomodulating function food.

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Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering. We propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples. Our approach considers an existing Monte Carlo rendering algorithm as a black box. During a scene‐dependent training phase, we learn to generate samples with a desired density in the primary sample space of the renderer using maximum likelihood estimation. We leverage a recent neural network architecture that was designed to represent real‐valued non‐volume preserving (“Real NVP”) transformations in high dimensional spaces. We use Real NVP to non‐linearly warp primary sample space and obtain desired densities. In addition, Real NVP efficiently computes the determinant of the Jacobian of the warp, which is required to implement the change of integration variables implied by the warp. A main advantage of our approach is that it is agnostic of underlying light transport effects, and can be combined with an existing rendering technique by treating it as a black box. We show that our approach leads to effective variance reduction in several practical scenarios.

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