Contribution of the UV component of natural sunlight to photoinhibition of photosynthesis in six species of subtidal brown and red seaweeds.

Field-collected specimens of three species of Laminaria and three species of subtidal red algae (Delesseria sanguinea, Plocamium cartilagineum and Phyllophora pseudoceranoides) were exposed to natural summer sunlight on Helgoland (southern North Sea) for up to 4 h at 15 °C. Dark-adapted variable fluorescence (Fv : Fm) was measured immediately after these treatments, and following 6, 24 and 48 h of recovery in moderate irradiances of white light. The response of plants to the full spectrum of natural sunlight was compared with that to PAR alone, UV-A + visible, UV-A + UV-B, or UV-A alone. The Fv : Fm values of all species were reduced to minimal values after 4 h in all of these treatments, but those of the more resistant species (Laminaria spp. and P. pseudoceranoides) were higher after shorter exposures to UV radiation alone than to PAR with or without UV. The recovery of Fv : Fm in all species was also more rapid in the two treatments that contained UV radiation alone than in those that included PAR. These results suggest that it is the high irradiances of PAR in natural sunlight which are responsible for the photoinhibition of photosynthesis of subtidal seaweeds and that the current ambient irradiances of UV radiation (either UV-B or UV-A) in northern temperate latitudes would not contribute significantly to this photoinhibition.

Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems

Coloured effluents from textile industries are a problem in many rivers and waterways. Prediction of adsorption capacities of dyes by adsorbents is important in design considerations. The sorption of three basic dyes, namely Basic Blue 3, Basic Yellow 21 and Basic Red 22, onto peat is reported. Equilibrium sorption isotherms have been measured for the three single component systems. Equilibrium was achieved after twenty-one days. The experimental isotherm data were analysed using Langmuir, Freundlich, Redlich-Peterson, Temkin and Toth isotherm equations. A detailed error analysis has been undertaken to investigate the effect of using different error criteria for the determination of the single component isotherm parameters and hence obtain the best isotherm and isotherm parameters which describe the adsorption process. The linear transform model provided the highest R2 regression coefficient with the Redlich-Peterson model. The Redlich-Peterson model also yielded the best fit to experimental data for all three dyes using the non-linear error functions. An extended Langmuir model has been used to predict the isotherm data for the binary systems using the single component data. The correlation between theoretical and experimental data had only limited success due to competitive and interactive effects between the dyes and the dye-surface interactions.

Improved Principal Component Monitoring of Large-Scale Processes

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Introduction of a Nonlinearity Measure for Principal Component Models

This is the first paper that introduces a nonlinearity test for principal component models. The methodology involves the division of the data space into disjunct regions that are analysed using principal component analysis using the cross-validation principle. Several toy examples have been successfully analysed and the nonlinearity test has subsequently been applied to data from an internal combustion engine.

Kv1.5 is a major component underlying the A-type potassium current in retinal arteriolar smooth muscle.

Little is known about the molecular characteristics of the voltage-activated K(+) (K(v)) channels that underlie the A-type K(+) current in vascular smooth muscle cells of the systemic circulation. We investigated the molecular identity of the A-type K(+) current in retinal arteriolar myocytes using patch-clamp techniques, RT-PCR, immunohistochemistry, and neutralizing antibody studies. The A-type K(+) current was resistant to the actions of specific inhibitors for K(v)3 and K(v)4 channels but was blocked by the K(v)1 antagonist correolide. No effects were observed with pharmacological agents against K(v)1.1/2/3/6 and 7 channels, but the current was partially blocked by riluzole, a K(v)1.4 and K(v)1.5 inhibitor. The current was not altered by the removal of extracellular K(+) but was abolished by flecainide, indicative of K(v)1.5 rather than K(v)1.4 channels. Transcripts encoding K(v)1.5 and not K(v)1.4 were identified in freshly isolated retinal arterioles. Immunofluorescence labeling confirmed a lack of K(v)1.4 expression and revealed K(v)1.5 to be localized to the plasma membrane of the arteriolar smooth muscle cells. Anti-K(v)1.5 antibody applied intracellularly inhibited the A-type K(+) current, whereas anti-K(v)1.4 antibody had no effect. Co-expression of K(v)1.5 with K(v)beta1 or K(v)beta3 accessory subunits is known to transform K(v)1.5 currents from delayed rectifers into A-type currents. K(v)beta1 mRNA expression was detected in retinal arterioles, but K(v)beta3 was not observed. K(v)beta1 immunofluorescence was detected on the plasma membrane of retinal arteriolar myocytes. The findings of this study suggest that K(v)1.5, most likely co-assembled with K(v)beta1 subunits, comprises a major component underlying the A-type K(+) current in retinal arteriolar smooth muscle cells

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.