2-COMPONENT EPOXY NETWORK-LICLO4 POLYMER ELECTROLYTE

An epoxy network-LiClO4 electrolyte system was prepared from diglycidyl ether of polyethylene glycol and triglycidyl ether of glycerol, cured in the presence of LiClO4 only. Various techniques were used to characterize the chemical structure of the precursors and the correlation between the viscoelasticity and conductivity of the cured films was examined.

Investigation of EscA as a chaperone for the Edwardsiella tarda type III secretion system putative translocon component EseC

Edwardsiella tarda is an important Gram-negative enteric pathogen affecting both animals and humans. It possesses a type III secretion system (T3SS) essential for pathogenesis. EseB, EseC and EseD have been shown to form a translocon complex after secretion, while EscC functions as a T3SS chaperone for EseB and EseD. In this paper we identify EscA, a protein required for accumulation and proper secretion of another translocon component, EseC. The escA gene is located upstream of eseC and the EscA protein has the characteristics of T3SS chaperones. Cell fractionation experiments indicated that EscA is located in the cytoplasm and on the cytoplasmic membrane. Mutation with in-frame deletion of escA greatly decreased the secretion of EseC, while complementation of escA restored the wild-type secretion phenotype. The stabilization and accumulation of EseC in the cytoplasm were also affected in the absence of EscA. Mutation of escA did not affect the transcription of eseC but reduced the accumulation level of EseC as measured by using an EseC-LacZ fusion protein in Ed. tarda. Co-purification and co-immunoprecipitation studies demonstrated a specific interaction between EscA and EseC. Further analysis showed that residues 31-137 of EseC are required for EseC-EscA interaction, Mutation of EseC residues 31-137 reduced the secretion and accumulation of EseC in Ed. tarda. Finally, infection experiments showed that mutations of EscA and residues 31-137 of EseC increased the LD50 by approximately 10-fold in blue gourami fish. These results indicated that EscA functions as a specific chaperone for EseC and contributes to the virulence of Ed. tarda.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Comparison of nonlinear and linear PCA on surface wind, surface height, and SST in the South China Sea

We compared nonlinear principal component analysis (NLPCA) with linear principal component analysis (LPCA) with the data of sea surface wind anomalies (SWA), surface height anomalies (SSHA), and sea surface temperature anomalies (SSTA), taken in the South China Sea (SCS) between 1993 and 2003. The SCS monthly data for SWA, SSHA and SSTA (i.e., the anomalies with climatological seasonal cycle removed) were pre-filtered by LPCA, with only three leading modes retained. The first three modes of SWA, SSHA, and SSTA of LPCA explained 86%, 71%, and 94% of the total variance in the original data, respectively. Thus, the three associated time coefficient functions (TCFs) were used as the input data for NLPCA network. The NLPCA was made based on feed-forward neural network models. Compared with classical linear PCA, the first NLPCA mode could explain more variance than linear PCA for the above data. The nonlinearity of SWA and SSHA were stronger in most areas of the SCS. The first mode of the NLPCA on the SWA and SSHA accounted for 67.26% of the variance versus 54.7%, and 60.24% versus 50.43%, respectively for the first LPCA mode. Conversely, the nonlinear SSTA, localized in the northern SCS and southern continental shelf region, resulted in little improvement in the explanation of the variance for the first NLPCA.

Yellow perch strain evaluation I: Genetic variance of six broodstock populations

As a prelude to strain selection for domestication and future marker assisted selection, genetic variation revealed by microsatellite DNA was evaluated in yellow perch, Perca flavescens, from four wild North American populations collected in 2003-2004 (Maine, New York, North Carolina, and Pennsylvania,), and two captive populations (Michigan and Ohio). For the loci examined, levels of heterozygosity ranged from H-e=0.04 to 0.88, genetic differentiation was highly significant among all population pairs, and effective migration ranged from low (N(e)m=0.3) to high (N(e)m=4.5). Deviation from Hardy-Weinberg equilibrium was regularly observed indicating significant departures from random mating. Instantaneous measures of inbreeding within these populations ranged from near zero to moderate (median F=0.16) and overall inbreeding levels averaged F-IS=0.18. Estimates of genetic diversity, Phi(ST), and genetic distance were highest between Michigan and all other broodstock groups and lowest between New York and Ohio. Genetic differentiation among groups did not correlate with geographic distance. Overall, the patterns of variation exhibited by the captive (Michigan and Ohio) populations were similar to patterns exhibited by the other wild populations, indicating that spawning and management practices to date have not significantly reduced levels of genetic variation. (C) 2007 Elsevier B.V. All rights reserved.

Growing season ecosystem respirations and associated component fluxes in two alpine meadows on the Tibetan Plateau

From 30 June to 24 September in 2003 ecosystem respiration (Re) in two alpine meadows on the Tibetan Plateau were measured using static chamber- and gas chromatography- (GC) based techniques. Simultaneously, plant removal treatments were set to partition Re into plant autotrophic respiration (Ra) and microbial heterotrophic respiration (Rh). Results indicated that Re had clear diurnal and seasonal variation patterns in both of the meadows. The seasonal variability of Re at both meadow sites was caused mainly by changes in Ra, rather than Rh. Moreover, at the Kobresia humilis meadow site (K_site), Ra and Rh accounted for 54% and 46% of Re, respectively. While at the Potentilla fruticosa scrub meadow (P_site), the counterparts accounted for 61% and 39%, respectively. T test showed that there was significant difference in Re rates between the two meadows (t = 2.387, P = 0.022). However, no significant difference was found in Rh rates, whereas a significant difference was observed in Ra rates between the two meadows. Thus, the difference in Re rate between the two meadows was mainly attributed to plant autotrophic respirations. During the growing season, the two meadows showed relatively low Q(10) values, suggesting that Re, especially Rh was not sensitive to temperature variation in the growing season. Additionally, Re and Rh at the K_site, as well as Rh at the P_site was negatively correlated with soil moisture, indicating that soil moisture would also play an important role in respirations.