Influence of particle characteristics and organic matter content on the bioavailability and bioaccumulation of pyrene by clams

Hydrophobic chemicals are known to associate with sediment particles including those from both suspended particulate matter and bottom deposits. The complex and variable composition of natural particles makes it very difficult therefore, to predict the bioavailability of sediment-bound contaminants. To overcome these problems we have previously devised a test system using artificial particles, with or without humic acids, for use as an experimental model of natural sediments. In the present work we have applied this experimental technique to investigate the bioavailability and bioaccumulation of pyrene by the freshwater fingernail clam Sphaerium corneum. The uptake and accumulation of pyrene in clams exposed to the chemical in the presence of a sample of natural sediment was also investigated. According to the results obtained, particle surface properties and organic matter content are the key factors for assessing the bioavailability and bioaccumulation of pyrene by clams. (C) 2002 Elsevier Science Ltd. All rights reserved.

Analysis of ochres from Clearwell Caves: the role of particle size in determining colour

Three ochre samples (A (orange-red in colour), B (red) and C (purple)) from Clearwell Caves, (Gloucestershire, UK) have been examined using an integrated analytical methodology based on the techniques of IR and diffuse reflectance UV-visible-NIR spectroscopy, X-ray diffraction, elemental analysis by ICP-AES and particle size analysis. It is shown that the chromophore in each case is haematite. The differences in colour may be accounted for by (i) different mineralogical and chemical composition in the case of the orange ochre, where hi,,her levels of dolomite and copper are seen and (ii) an unusual particle size distribution in the case of the purple ochre. When the purple ochre was ground to give the same particle size distribution as the red ochre then the colours of the two samples became indistinguishable. An analysis has now been completed of a range of ochre samples with colours from yellow to purple from the important site of Clearwell Caves. (C) 2004 Elsevier B.V. All rights reserved.

Silica-encapsulated nanomagnetic particle as a new recoverable biocatalyst carrier

Enzymes are versatile biocatalysts with major advantages of ultrahigh reaction selectivity and specificity under mild conditions, which currently find increasing applications. However, their applications are often hampered by difficulties in recovery and recycling. As a result, we carried out detailed investigations on the synthesis and characterization of silica-encapsulated iron oxide magnetic nanoparticles of controlled dimension as an enzyme carrier. It is shown that the relatively smaller sized silica-coated magnetic nanoparticle prepared by the microemlusion technique can a carry bulky enzyme, beta-lactamase, via chemical linkages on the silica overlayer without severely blocking the enzymatic active center ( which is commonly encountered in conventional solid supports). An activity study by Michalis-Menten kinetics reflects that this new type of immobilization allows enzyme isolation with accessibility as good as free enzyme. The recovery and reusability of the nanoparticle-supported enzyme upon application of magnetic separation are also demonstrated.

Ultra-thin porous silica coated silver-platinum alloy nano-particle as a new catalyst precursor

Templated sol-gel encapsulation of surfactant-stabilised micelles containing metal precursor(s) with ultra-thin porous silica coating allows solvent extraction of organic based stabiliser from the composites in colloidal state hence a new method of preparing supported alloy catalysts using the inorganic silica-stabilised nano-sized, homogenously mixed, silver - platinum (Ag-Pt) colloidal particles is reported.

Chylomicron particle size and number, factor VII activation and dietary monounsaturated fatty acids

This study evaluated the effects of substituting dietary saturated fatty acids (SFAs) with monounsaturated fatty acids (MUFAs) on postprandial chylomicron (triacylglycerol (TAG), apolipoprotein B-48 (apo B-48) and retinyl ester (RE)), chylomicron particle size and factor VII (FVII) response when subjects were given a standard meal. In a controlled sequential design, 51 healthy young subjects followed an SFA-rich diet (Reference diet) for 8 weeks after which half of the subjects followed a moderate MUFA diet (n = 25) and half followed a high MUFA diet (n = 26) for 16 weeks. Fasting lipoprotein and lipid measurements were evaluated at baseline and at 8-week intervals during the Reference and MUFA diets. In 25 of the subjects (n = 12 moderate MUFA, n = 13 high MUFA), postprandial responses to a standard test meal containing RE and 13 C-tripalmitin were investigated at the end of the Reference and the MUFA diet periods. Although there were no differences in the postprandial lipid markers (TAG, RE, C-13-TAG) on the two diets, the postprandial apo B-48 response (incremental area under the curve (IAUC) was reduced by 21% on the moderate MUFA diet (NS) and by 54% on the high MUFA diet (P < 0.01). The postprandial peak concentrations of apo B-48 were reduced by 33% on the moderate MUFA diet (P < 0.01) and 48% on the high MUFA diet (P < 0.001). Fasting values for factor VII activity (FVIIc), activated factor VII (FVIIa) or factor VII antigen (FVIIag) did not differ significantly when subjects were transferred from Reference to MUFA diets. However, the postprandial increases in coagulation FVII activity (FVIIc) were 18% lower and of activated FVII (FVIIa) were 17% lower on the moderate MUFA diet (NS). Postprandial increases in FVIIc and FVIIa were 50% (P < 0.05) and 29% (P < 0.07) lower on the high MUFA diet and the area under the postprandial FVIIc response curve (AUC) was also lower on the high MUFA diet (P < 0.05). Significantly higher ratios of RE:apo B-48 (P < 0.001) and 13 C-palmitic acid:apo B-48 (P < 0.01) during both MUFA diets suggest that the CMs formed carry larger amounts of dietary lipids per particle, reflecting an adaptation to form larger lipid droplets in the enterocyte when increased amounts of dietary MUFAs are fed. Smaller numbers of larger chylomicrons may explain attenuated activation of factor VII during the postprandial state when the background diet is rich in MUFA. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.

Saturated fat-induced changes in S-f 60-400 particle composition reduces uptake of LDL by HepG2 cells

The ability of human postprandial triacylglycerol-rich lipoproteins (TRLs), isolated after meals enriched in saturated fatty acids (SFAs), n-6 PUFAs, and MUFAs, to inhibit the uptake of I-125-labeled LDL by the LDL receptor was investigated in HepG2 cells. Addition of TRLs resulted in a dose-dependent inhibition of heparin-releasable binding, cell-associated radioactivity, and degradation products of I-125-labeled LDL (P < 0.001). SFA-rich Svedberg flotation rate (S-f) 60-400 resulted in significantly greater inhibition of cell-associated radioactivity than PUFA-rich particles (P = 0.016) and total uptake of I-125-labeled LDL compared with PUFA- and MUFA-rich particles (P = 0.02). Normalization of the apolipoprotein (apo)E but not apoC-III content of the TRLs removed the effect of meal fatty acid composition, and addition of an anti-apoE antibody reversed the inhibitory effect of TRLs on the total uptake of I-125-labeled LDL. Real time RT-PCR showed that the SFA-rich Sf 60-400 increased the expression of genes involved in hepatic lipid synthesis (P < 0.05) and decreased the expression of the LDL receptor-related protein 1 compared with MUFAs (P = 0.008). In conclusion, these findings suggest an alternative or additional mechanism whereby acute fat ingestion can influence LDL clearance via competitive apoE-dependent effects of TRL on the LDL receptor.-Jackson, K. G., V. Maitin, D. S. Leake, P. Yaqoob, and C. M. Williams. Saturated fat-induced changes in Sf 60 400 particle composition reduces uptake of LDL by HepG2 cells.

Towards the automatic reconstruction of dendritic trees using particle filters

The 3D reconstruction of a Golgi-stained dendritic tree from a serial stack of images captured with a transmitted light bright-field microscope is investigated. Modifications to the bootstrap filter are discussed such that the tree structure may be estimated recursively as a series of connected segments. The tracking performance of the bootstrap particle filter is compared against Differential Evolution, an evolutionary global optimisation method, both in terms of robustness and accuracy. It is found that the particle filtering approach is significantly more robust and accurate for the data considered.

A tunable radial basis function model for nonlinear system identification using particle swarm optimisation

A tunable radial basis function (RBF) network model is proposed for nonlinear system identification using particle swarm optimisation (PSO). At each stage of orthogonal forward regression (OFR) model construction, PSO optimises one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is computationally more efficient.

Nonlinear system identification using particle swarm optimisation tuned radial basis function models

A novel particle swarm optimisation (PSO) tuned radial basis function (RBF) network model is proposed for identification of non-linear systems. At each stage of orthogonal forward regression (OFR) model construction process, PSO is adopted to tune one RBF unit's centre vector and diagonal covariance matrix by minimising the leave-one-out (LOO) mean square error (MSE). This PSO aided OFR automatically determines how many tunable RBF nodes are sufficient for modelling. Compared with the-state-of-the-art local regularisation assisted orthogonal least squares algorithm based on the LOO MSE criterion for constructing fixed-node RBF network models, the PSO tuned RBF model construction produces more parsimonious RBF models with better generalisation performance and is often more efficient in model construction. The effectiveness of the proposed PSO aided OFR algorithm for constructing tunable node RBF models is demonstrated using three real data sets.

Mixed initial-boundary value problem in particle modeling of microelectronic devices

The Boltzmann equation in presence of boundary and initial conditions, which describes the general case of carrier transport in microelectronic devices is analysed in terms of Monte Carlo theory. The classical Ensemble Monte Carlo algorithm which has been devised by merely phenomenological considerations of the initial and boundary carrier contributions is now derived in a formal way. The approach allows to suggest a set of event-biasing algorithms for statistical enhancement as an alternative of the population control technique, which is virtually the only algorithm currently used in particle simulators. The scheme of the self-consistent coupling of Boltzmann and Poisson equation is considered for the case of weighted particles. It is shown that particles survive the successive iteration steps.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Assessing the impact of nano- and micro-scale zerovalent iron particles on soil microbial activities: Particle reactivity interferes with assay conditions and interpretation of genuine microbial effects

The effects of nano-scale and micro-scale zerovalent iron (nZVI and mZVI) particles on general (dehydrogenase and hydrolase) and specific (ammonia oxidation potential, AOP) activities mediated by the microbial community in an uncontaminated soil were examined. nZVI (diameter 12.5 nm; 10 mg gÿ1 soil)apparently inhibited AOP and nZVI and mZVI apparently stimulated dehydrogenase activity but had minimal influence on hydrolase activity. Sterile experiments revealed that the apparent inhibition of AOP could not be interpreted as such due to the confounding action of the particles, whereas, the nZVIenhanced dehydrogenase activity could represent the genuine response of a stimulated microbial population or an artifact of ZVI reactivity. Overall, there was no evidence for negative effects of nZVI or mZVI on the processes studied. When examining the impact of redox active particles such as ZVI on microbial oxidation–reduction reactions, potential confounding effects of the test particles on assay conditions should be considered.

Prediction of Parkinson’s Disease tremor onset using a radial basis function neural network based on particle swarm optimization

Deep Brain Stimulation (DBS) has been successfully used throughout the world for the treatment of Parkinson's disease symptoms. To control abnormal spontaneous electrical activity in target brain areas DBS utilizes a continuous stimulation signal. This continuous power draw means that its implanted battery power source needs to be replaced every 18–24 months. To prolong the life span of the battery, a technique to accurately recognize and predict the onset of the Parkinson's disease tremors in human subjects and thus implement an on-demand stimulator is discussed here. The approach is to use a radial basis function neural network (RBFNN) based on particle swarm optimization (PSO) and principal component analysis (PCA) with Local Field Potential (LFP) data recorded via the stimulation electrodes to predict activity related to tremor onset. To test this approach, LFPs from the subthalamic nucleus (STN) obtained through deep brain electrodes implanted in a Parkinson patient are used to train the network. To validate the network's performance, electromyographic (EMG) signals from the patient's forearm are recorded in parallel with the LFPs to accurately determine occurrences of tremor, and these are compared to the performance of the network. It has been found that detection accuracies of up to 89% are possible. Performance comparisons have also been made between a conventional RBFNN and an RBFNN based on PSO which show a marginal decrease in performance but with notable reduction in computational overhead.