Benzo[a]pyrene diol epoxide suppresses retinoic acid receptor-β2 expression by recruiting DNA (cytosine-5-)-methyltransferase 3A

Tobacco smoke is an important risk factor for various human cancers, including esophageal cancer. How benzo [a]pyrene diol epoxide (BPDE), a carcinogen present in tobacco smoke as well as in environmental pollution, induces esophageal carcinogenesis has yet to be defined. In this study, we investigated the molecular mechanism responsible for BPDE-suppressed expression of retinoic acid receptor-beta2 (RAR-β2) in esophageal cancer cells. We treated esophageal cancer cells with BPDE before performing methylation-specific polymerase chain reaction (MSP) to find that BPDE induced methylation of the RAR-β2 gene promoter. We then performed chromatin immunoprecipitation (ChIP) assays to find that BPDE recruited genes of the methylation machinery into the RAR-β2 gene promoter. We found that BPDE recruited DNA (cytosine-5-)-methyltransferase 3 alpha (DNMT3A), but not beta (DNMT3B), in a time-dependent manner to methylate the RAR-β2 gene promoter, which we confirmed by reverse transcription-polymerase chain reaction (RT-PCR) analysis of the reduced RAR-β2 expression in these BPDE-treated esophageal cancer cell lines. However, BPDE did not significantly change DNMT3A expression, but it slightly reduced DNMT3B expression. DNA methylase inhibitor 5-aza-2'-deoxycytidine (5-Aza) and DNMT3A small hairpin RNA (shRNA) vector antagonized the effects of BPDE on RAR-β2 expressions. Transient transfection of the DNMT3A shRNA vector also antagonized BPDE's effects on expression of RAR-β2, c-Jun, phosphorylated extracellular signal-regulated protein kinases 1/2 (ERK1/2), and cyclooxygenase-2 (COX-2), suggesting a possible therapeutic effect. The results of this study form the link between the esophageal cancer risk factor BPDE and the reduced RAR-β2 expression

Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

Factorization Machines (FMs) are a supervised learning approach that enhances the linear regression model by incorporating the second-order feature interactions. Despite effectiveness, FM can be hindered by its modelling of all feature interactions with the same weight, as not all feature interactions are equally useful and predictive. For example, the interactions with useless features may even introduce noises and adversely degrade the performance. In this work, we improve FM by discriminating the importance of different feature interactions. We propose a novel model named Attentional Factorization Machine (AFM), which learns the importance of each feature interaction from data via a neural attention network. Extensive experiments on two real-world datasets demonstrate the effectiveness of AFM. Empirically, it is shown on regression task AFM betters FM with a $8.6\%$ relative improvement, and consistently outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross with a much simpler structure and fewer model parameters. Our implementation of AFM is publicly available at: https://github.com/hexiangnan/attentional_factorization_machineComment: 7 pages, 5 figure

Inflammatory responses to Hydroxyapatite implants in middle ear in rats

AbstractObjectiveTo study local inflammatory response after implantation of hydroxyapatite synthetic ossicular prosthesis.MethodsHydroxyapatite granules were implanted in the bulla in 32 rats. Sham surgical procedures were performed in 10 rats as the control. Animals were sacrificed at 1 to 300 days after surgery. Bulla sections, stained with HE and Mallory’s azan, were examined for numbers and percentages of various inflammatory cell types.ResultsSlightly more inflammatory reaction was seen in animals with the implant than in the controls, mostly during the early stage following the implantation procedure. Few inflammatory cells were observed at later times. There were satisfactory fibrosis in both implanted and control ears.ConclusionThe results indicate that hydroxyapatite synthetic prosthesis is a biocompatible implantation material in the middle ear. Nonetheless, the presence of inflammatory reaction immediately following implantation implies that control of infection is important in the early times after the implantation procedure

Fault diagnosis and fault-tolerant control for system with fast time-varying delay

This paper proposes a fault diagnosis and fault-tolerant control method for a system with a fast time-varying delay and time-varying parameters. A fault observer is designed to estimate faults, and an improved fast adaptive fault estimation (FAFE) algorithm is developed to reduce the relevant constraints in the general form of this algorithm. With newly introduced relaxation matrices, this study estimates faults in a system exhibiting a fast time-varying delay. Based on the estimated faults, an output feedback controller is designed to accommodate the faults. The fault-tolerant control is realized using the introduced relaxation matrices. An algorithm is derived to solve for the observer and controller. Finally, the theory and method are validated using a real example of a helicopter system

Follicular thyroid carcinoma but not adenoma recruits tumor-associated macrophages by releasing CCL15

Immunostain of CD68 in entire tissue of FTC/FA samples. (A) An example of immunohistochemistry analysis of CD68 in whole tissue samples of FTC (left panel) and FA (right panel). Stars indicated blank areas were taked out for tissue microarrays constructing. CD68+ cells in ten 200 μm *300 μm areas (green-red marked) of every sample were counted. Bar = 1 mm. (B) Enlarged picture of one count area (one green-red area in A). Arrows indicate the CD68+ macrophages in FTC (left panel) or FA (right panel). Bar = 20 μm. Figure S2. Densities of CD206+ cells in FTC are significantly higher than those in FA. Immunohistochemistry analysis of CD206 in 55 cases of tissue samples from FTC and FA patients. Arrows indicate the CD206+ macrophages. Bar = 20 μm. (PDF 509 kb