Pelophylaxins: novel antimicrobial peptide homologs from the skin secretion of the Fukien gold-striped pond frog, Pelophylax plancyi fukienensis: identification by “shotgun” cDNA cloning and sequence analysis.

Amphibian skin secretions are rich in antimicrobial peptides that act as important components of an innate immune system. Here, we describe a novel “shotgun” skin peptide precursor cloning technique that facilitates rapid access to these genetically encoded molecules and effects their subsequent identification and structural characterization from the secretory peptidome. Adopting this approach on a skin secretion-derived library from a hitherto unstudied Chinese species of frog, we identified a family of novel antimicrobial peptide homologs, named pelophylaxins, that belong to previously identified families (ranatuerins, brevinins and temporins) found predominantly in the skin secretions from frogs of the genus Rana. These data further substantiate the scientifically robust nature of applying parallel transcriptome and peptidome analyses on frog defensive skin secretions that can be obtained in a non-invasive, non-destructive manner. In addition, the present data illustrate that rapid structural characterization of frog skin secretion peptides can be achieved from an unstudied species without prior knowledge of primary structures of endogenous peptides.

A geotechnical description of fresh cement grout - filtration and consolidation behaviour

The removal of water from three Portland cement grouts by pressure filtration is examined, and the consolidation behaviour of the filtered material clarified. The filtration takes place by the laying down of a very stiff filter cake through the removal of excess water. The behaviour due to further loading resembles that of a re-constituted silt. For stress levels above the filtration pressure the calculated permeability values are similar to those from the filtration phase only if the data sampling rate was sufficiently rapid to discriminate the first portion of the observed primary consolidation curve. The change in void ratio for incremental loading is roughly linear with the change in the logarithm of the vertical effective stress. The characterisation of fresh cement paste using standard soil mechanics models is both appropriate and useful, at least during the first few hours after mixing.

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.