Comparison of LIMPET contra-rotating wells turbine with theoretical and model test predictions

The performance of the contra-rotating Wells turbine installed in the LIMPET wave power station is compared to the predicted performance from theoretical analysis and model tests. A reasonable agreement was found between the predicted and measured turbine damping characteristic, however the turbine efficiency was found to be poorly predicted. It is postulated that this is due to the unsteady nature and mal-distribution of flow through the LIMPET turbine, which were not considered in the predictions. It is suggested that the reduced performance of the contra-rotating Wells turbine makes biplane or monoplane Wells turbines with guide vanes better solutions for OWC's.

Elafin prevents lipopolysaccharide-induced AP-1 and NF-kB activation via an effect on the ubiquitin-proteasome pathway

In this paper we follow on from our research into SLPI by assessing the immunomodulatory activity of elafin - an antiprotease related to SLPI and also present on the respiratory tract. We demonstrate for the first time that exogenously applied elafin inhibits lipopolysaccharide-induced activation of the NF-kappaB and AP-1 pathways in monocytes. I designed this project and supervised Marcus Butler during his MD thesis.

Development of a rapid screening test for veterinary sedatives and the beta-blocker carazolol in porcine kidney by ELISA

Sedatives and tranquillisers are frequently used to reduce stress during the transportation of food producing animals. The most widely used classes of sedatives include the butyrophenone azaperone, the phenothiazines acepromazine, propionylpromazine, chlorpromazine and the beta-blocker, carazolol. For regulatory control purposes, tolerances for azaperone and carazolol have been set by the European Union as 100 and 25 mug kg(-1), respectively. Furthermore, the use of the phenothiazines is prohibited and therefore has a zero tolerance. A method for the detection of residues of five tranquillisers and one beta-blocker using a single ELISA plate has been developed. Kidney samples (2.5 g) were extracted with dichloromethane and applied to a competitive enzyme immunoassay using three polyclonal antibodies raised in rabbits against azaperol, propionylpromazine and carazolol conjugates. In sample matrix, the azaperol antibody cross-reacted 28.0% with azaperone and the propionylpromazine antibody cross-reacted 24.9% with acepromazine and 11.7% with chlorpromazine. In the ELISA, the detection capabilities of the six sedatives, azaperol, azaperone, carazolol, acepromazine, chlorpromazine, and propionylpromazine are 5, 15, 5, 5, 20 and 5 mug kg(-1), respectively. The proposed method is a sensitive and rapid multi-residue technique that offers a cost effective alternative to current published procedures, without any concession on the ability to detect sedative misuse.

Ubiquitin-dependent proteolysis of trihydrophobin 1 (TH1) by the human papilloma virus E6-associated protein (E6-AP).

Human Papilloma virus E6-associated protein (E6-AP), which is known as an E3 ubiquitin ligase, mediates ubiquitination and subsequent degradation of a series of cellular proteins. In this paper, we identify here trihydrophobin 1 (TH1), an integral subunit of the human negative transcription elongation factor (NELF) complex, as a novel E6-AP interaction protein and a target of E6-AP-mediated degradation. Overexpression of E6-AP results in degradation of TH1 in a dose-dependent manner, whereas knock-down of endogenous E6-AP elevates the TH1 protein level. TH1 protein turnover is substantially faster, compared to controls, in cells that overexpressed E6-AP. Wild-type E6-AP promotes the ubiquitination of TH1, while a catalytically inactive point mutant of E6-AP abolishes its ubiquitination. Furthermore, in vitro ubiquitination assay also demonstrates that TH1 can be ubiquitinated by E6-AP. The degradation is blocked by treatment with proteasome inhibitor MG132. Herein, we provide strong evidence that TH1 is a specific substrate that is targeted for degradation through E6-AP-catalyzed polyubiquitination.

Gene selection by cooperative competition clustering

Clustering analysis of data from DNA microarray hybridization studies is an essential task for identifying biologically relevant groups of genes. Attribute cluster algorithm (ACA) has provided an attractive way to group and select meaningful genes. However, ACA needs much prior knowledge about the genes to set the number of clusters. In practical applications, if the number of clusters is misspecified, the performance of the ACA will deteriorate rapidly. In fact, it is a very demanding to do that because of our little knowledge. We propose the Cooperative Competition Cluster Algorithm (CCCA) in this paper. In the algorithm, we assume that both cooperation and competition exist simultaneously between clusters in the process of clustering. By using this principle of Cooperative Competition, the number of clusters can be found in the process of clustering. Experimental results on a synthetic and gene expression data are demonstrated. The results show that CCCA can choose the number of clusters automatically and get excellent performance with respect to other competing methods.

Robust fuzzy Gustafson-Kessel clustering for nonlinear system identification

This paper deals with Takagi-Sugeno (TS) fuzzy model identification of nonlinear systems using fuzzy clustering. In particular, an extended fuzzy Gustafson-Kessel (EGK) clustering algorithm, using robust competitive agglomeration (RCA), is developed for automatically constructing a TS fuzzy model from system input-output data. The EGK algorithm can automatically determine the 'optimal' number of clusters from the training data set. It is shown that the EGK approach is relatively insensitive to initialization and is less susceptible to local minima, a benefit derived from its agglomerate property. This issue is often overlooked in the current literature on nonlinear identification using conventional fuzzy clustering. Furthermore, the robust statistical concepts underlying the EGK algorithm help to alleviate the difficulty of cluster identification in the construction of a TS fuzzy model from noisy training data. A new hybrid identification strategy is then formulated, which combines the EGK algorithm with a locally weighted, least-squares method for the estimation of local sub-model parameters. The efficacy of this new approach is demonstrated through function approximation examples and also by application to the identification of an automatic voltage regulation (AVR) loop for a simulated 3 kVA laboratory micro-machine system.