Methodology for designing accelerated aging tests for predicting life of photovoltaic arrays

A methodology for designing aging tests in which life prediction was paramount was developed. The methodology builds upon experience with regard to aging behavior in those material classes which are expected to be utilized as encapsulant elements, viz., glasses and polymers, and upon experience with the design of aging tests. The experiences were reviewed, and results are discussed in detail

A histological and micro-CT investigation in to the effect of NGF and EGF on the periodontal, alveolar bone, root and pulpal healing of replanted molars in a rat model - a pilot study

Background: This study aims to investigate, utilising micro-computed tomography (micro-CT) and histology, whether the topical application of nerve growth factor (NGF) and/or epidermal growth factor (EGF) can enhance periodontal, alveolar bone, root and pulpal tissue regeneration while minimising the risk of pulpal necrosis, root resorption and ankylosis of replanted molars in a rat model. Methods: Twelve four-week-old male Sprague-Dawley rats were divided into four groups: sham, collagen, EGF and NGF. The maxillary right first molar was elevated and replanted with or without a collagen membrane impregnated with either the growth factors EGF or NGF, or a saline solution. Four weeks after replantation, the animals were sacrificed and the posterior maxilla was assessed using histological and micro-CT analysis. The maxillary left first molar served as the control for the corresponding right first molar. Results: Micro-CT analysis revealed a tendency for all replanted molars to have reduced root length, root volume, alveolar bone height and inter-radicular alveolar bone volume. It appears that the use of the collagen membrane had a negative effect while no positive effect was noted with the incorporation of EGF or NGF. Histologically, the incorporation of the collagen membrane was found to negatively affect pulpal, root, periodontal and alveolar bone healing with pulpal inflammation and hard tissue formation, extensive root resorption and alveolar bone fragmentation. The incorporation of EGF and NGF did not improve root, periodontal or alveolar bone healing. However, EGF was found to improve pulp vascularisation while NGF improved pulpal architecture and cell organisation, although not to the level of the control group.Conclusions: Results indicate a possible benefit on pulpal vascularisation and pulpal cell organisation following the incorporation of EGF and NGF, respectively, into the alveolar socket of replanted molars in the rat model. No potential benefit of EGF and NGF was detected in periodontal or root healing, while the use of a collagen membrane carrier was found to have a negative effect on the healing response

Evolving neural network optimization of cholesteryl ester separation by reversed-phase HPLC

Cholesteryl esters have antimicrobial activity and likely contribute to the innate immunity system. Improved separation techniques are needed to characterize these compounds. In this study, optimization of the reversed-phase high-performance liquid chromatography separation of six analyte standards (four cholesteryl esters plus cholesterol and tri-palmitin) was accomplished by modeling with an artificial neural network–genetic algorithm (ANN-GA) approach. A fractional factorial design was employed to examine the significance of four experimental factors: organic component in the mobile phase (ethanol and methanol), column temperature, and flow rate. Three separation parameters were then merged into geometric means using Derringer’s desirability function and used as input sources for model training and testing. The use of genetic operators proved valuable for the determination of an effective neural network structure. Implementation of the optimized method resulted in complete separation of all six analytes, including the resolution of two previously co-eluting peaks. Model validation was performed with experimental responses in good agreement with model-predicted responses. Improved separation was also realized in a complex biological fluid, human milk. Thus, the first known use of ANN-GA modeling for improving the chromatographic separation of cholesteryl esters in biological fluids is presented and will likely prove valuable for future investigators involved in studying complex biological samples

Pitfalls of predicting complex traits from SNPs

The success of genome-wide association studies (GWASs) has led to increasing interest in making predictions of complex trait phenotypes, including disease, from genotype data. Rigorous assessment of the value of predictors is crucial before implementation. Here we discuss some of the limitations and pitfalls of prediction analysis and show how naive implementations can lead to severe bias and misinterpretation of results