Advanced glycation end product (AGE) accumulation on Bruch's membrane: links to age-related RPE dysfunction.

PURPOSE: Advanced glycation end products (AGEs) accumulate during aging and have been observed in postmortem eyes within the retinal pigment epithelium (RPE), Bruch's membrane, and subcellular deposits (drusen). AGEs have been associated with age-related dysfunction of the RPE-in particular with development and progression to age-related macular degeneration (AMD). In the present study the impact of AGEs at the RPE-Bruch's membrane interface was evaluated, to establish how these modifications may contribute to age-related disease. METHODS: AGEs on Bruch's membrane were evaluated using immunohistochemistry. A clinically relevant in vitro model of substrate AGE accumulation was established to mimic Bruch's membrane ageing. Responses of ARPE-19 growing on AGE-modified basement membrane (AGE-BM) for 1 month were investigated by using a microarray approach and validated by quantitative (q)RT-PCR. In addition to identified AGE-related mRNA alterations, lysosomal enzyme activity and lipofuscin accumulation were also studied in ARPE-19 grown on AGE-BM. RESULTS: Autofluorescent and glycolaldehyde-derived AGEs were observed in clinical specimens on Bruch's membrane and choroidal extracellular matrix. In vitro analysis identified a range of dysregulated mRNAs in ARPE-19 exposed to AGE-BM. Altered ARPE-19 degradative enzyme mRNA expression was observed on exposure to AGE-BM. AGE-BM caused a significant reduction in cathepsin-D activity in ARPE-19 (P

Prediction of Fresh and Hardened Properties of Self-Consolidating Concrete Using Neurofuzzy Approach

Self-consolidating concrete (SCC) developed in Japan in the late 80s has enabled the construction industry to reduce demand on the resources, improve the work conditions and also reduce the impact on the environment by elimination of the need for compaction. This investigation aimed at exploring the potential use of the neurofuzzy (NF) approach to model the fresh and hardened properties of SCC containing pulverised fuel ash (PFA) as based on experimental data investigated in this paper. Twenty six mixes were made with water-to-binder ratio ranging from 0.38 to 0.72, cement content ranging from 183 to 317 kg/m3 , dosage of PFA ranging from 29 to 261 kg/m3 , and percentage of superplasticizer, by mass of powder, ranging from 0 to 1%. Nine properties of SCC mixes modeled by NF were the slump flow, JRing combined to the Orimet, JRing combined to cone, V-funnel, L-box blocking ratio, segregation ratio, and the compressive strength at 7, 28, and 90 days. These properties characterized the filling ability, the passing ability, the segregation resistance of fresh SCC, and the compressive strength. NF model is constructed by training and testing data using the experimental results obtained in this study. The results of NF models were compared with experimental results and were found to be quite accurate. The proposed NF models offers useful modeling approach of the fresh and hardened properties of SCC containing PFA.

Genetic programming based formulation for fresh and hardened properties of self-compacting concrete containing pulverised fuel ash

Self-compacting concrete (SCC) flows into place and around obstructions under its own weight to fill the formwork completely and self-compact without any segregation and blocking. Elimination of the need for compaction leads to better quality concrete and substantial improvement of working conditions. This investigation aimed to show possible applicability of genetic programming (GP) to model and formulate the fresh and hardened properties of self-compacting concrete (SCC) containing pulverised fuel ash (PFA) based on experimental data. Twenty-six mixes were made with 0.38 to 0.72 water-to-binder ratio (W/B), 183–317 kg/m³ of cement content, 29–261 kg/m³ of PFA, and 0 to 1% of superplasticizer, by mass of powder. Parameters of SCC mixes modelled by genetic programming were the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength at 7, 28 and 90 days. GP is constructed of training and testing data using the experimental results obtained in this study. The results of genetic programming models are compared with experimental results and are found to be quite accurate. GP has showed a strong potential as a feasible tool for modelling the fresh properties and the compressive strength of SCC containing PFA and produced analytical prediction of these properties as a function as the mix ingredients. Results showed that the GP model thus developed is not only capable of accurately predicting the slump flow, JRing combined to the Orimet, JRing combined to cone, and the compressive strength used in the training process, but it can also effectively predict the above properties for new mixes designed within the practical range with the variation of mix ingredients.