Recovery of astaxanthin using colloidal gas aphrons (CGA): a mechanistic study

The aim of this study is to investigate the mechanism responsible for the recovery of astaxanthin using Colloidal Gas Aphrons (CGA), which are surfactant stabilised microbubbles. The latter were produced using different surfactant solutions (Cetyl Trimethyl Ammonium Bromide (CTAB)-cationic, Sodium Dodecyl Sulfate (SDS)-anionic, TWEEN 60-non-ionic and mixtures of TWEEN 60-SPAN 80- non-ionic with varying hydrophobicity) at stirring speed 8000 rpm and stirring time 5 min. Experiments were carried out at varying pH and volumetric ratios of astaxanthin to CGA, and with two different astaxanthin standard suspensions: (i) astaxanthin dispersed in aqueous solutions and (ii) astaxanthin dispersed in ethanolic/aqueous solutions with different compositions of ethanol (20/80 (v/v) and 40/60 (v/v)). When astaxanthin is dispersed in aqueous solutions the separation seems to occur mainly by electrostatic interactions. Therefore the recoveries are higher in the case of the cationic surfactant when astaxanthin particles are strongly negatively charged, as shown by the zeta potential measurements. When ethanol is present, highest recoveries are achieved with CGA produced from the non-ionic surfactant, which indicates that, under these conditions, separation is driven mainly by hydrophobic interactions. In experiments with ethanolic/aqueous suspensions, when the hydrophobicity of the surfactant was increased by increasing volumes of SPAN 80, the CGA produced were less stable; thus higher recoveries of astaxanthin under conditions that favour hydrophobic interactions were not observed. (C) 2008 Elsevier B.V All rights reserved.

The use of colloidal gas aphrons as novel downstream processing for the recovery of astaxanthin from cells of Phaffia rhodozyma

BACKGROUND: There is an increasing interest in obtaining natural products with bioactive properties, using fermentation technology. However, the downstream processing consisting of multiple steps can be complicated, leading to increase in the final cost of the product. Therefore there is a need for integrated, cost-effective and scalable separation processes. RESULTS: The present study investigates the use of colloidal gas aphrons (CGA), which are surfactant-stabilized microbubbles, as a novel method for downstream processing. More particularly, their application for the recovery of astaxanthin from the cells of Phaffia rhodozyma is explored. Research carried out with standard solutions of astaxanthin and CGA generated from the cationic surfactant hexadecyl. trimethyl ammonium bromide (CTAB) showed that up to 90% recovery can be achieved under optimum conditions, i.e., pH 11 with NaOH 0.2 mol L-1. In the case of the cells' suspension from the fermentation broth, three different approaches were investigated: (a) the conventional integrated approach where CGA were applied directly; (b) CGA were applied to the clarified suspension of cells; and finally (c) the in situ approach, where CGA are generated within the clarified suspension of cells. Interestingly, in the case of the whole suspension (approach a) highest recoveries (78%) were achieved under the same conditions found to be optimal for the standard solutions. In addition, up to 97% recovery of total carotenoids could be achieved from the clarified suspension after pretreatment with NaOH. This pretreatment led to maximum cell disruption as well as optimum conditioning for subsequent CGA separation. CONCLUSIONS: These results demonstrate the potential of CGA for the recovery of bioactive components from complex feedstock. (c) 2008 Society of Chemical Industry.

Recovery of norbixin from a raw extraction solution of annatto pigments using colloidal gas aphrons (CGAs)

Annatto dyes are widely used in food and are finding increasing interest also for their application in the pharmaceutical and cosmetics industry. Bixin is the main pigment extracted from annatto seeds and accounts for 80% of the carotenoids in the outer coat of the seeds; norbixin being the water-soluble form of the bixin. Typically annatto dyes are extracted from the seeds by mechanical means or solutions of alkali, edible oil or organic solvents, or a combination of the two depending on the desired final product. In this work CGAs are investigated as an alternative separation method for the recovery of norbixin from a raw extraction solution of annatto pigments in KOH. A volume of CGAs generated from a cationic surfactant (CTAB) solution is mixed with a volume of annatto solution and when the mixture is allowed to settle it separates into the top aphron phase and the bottom liquid phase. Potassium norbixinate presented in the annatto solution will interact with the surfactant in the aphron phase, which results in the effective separation of norbixin. Recovery= 94% was achieved at a CTAB to norbixin molar ratio of 3.3. In addition a mechanism of separation is proposed here based on the separation results with the cationic surfactant and an anionic surfactant (bis-2-ethyl hexyl sulfosuccinate, AOT) and measurements of surfactant to norbixin ratio in the aphron phase; electrostatic interactions between the surfactant and norbixin molecules result in the fort-nation of a coloured complex and effective separation of norbixin. (c) 2005 Elsevier B.V. All rights reserved.

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.

Integrating features for man-made target tracking from FLIR image sequence using particle filter

A new man-made target tracking algorithm integrating features from (Forward Looking InfraRed) image sequence is presented based on particle filter. Firstly, a multiscale fractal feature is used to enhance targets in FLIR images. Secondly, the gray space feature is defined by Bhattacharyya distance between intensity histograms of the reference target and a sample target from MFF (Multi-scale Fractal Feature) image. Thirdly, the motion feature is obtained by differencing between two MFF images. Fourthly, a fusion coefficient can be automatically obtained by online feature selection method for features integrating based on fuzzy logic. Finally, a particle filtering framework is developed to fulfill the target tracking. Experimental results have shown that the proposed algorithm can accurately track weak or small man-made target in FLIR images with complicated background. The algorithm is effective, robust and satisfied to real time tracking.

Transport in the low-latitude tropopause zone diagnosed using particle trajectories

Recent literature has described a “transition zone” between the average top of deep convection in the Tropics and the stratosphere. Here transport across this zone is investigated using an offline trajectory model. Particles were advected by the resolved winds from the European Centre for Medium-Range Weather Forecasts reanalyses. For each boreal winter clusters of particles were released in the upper troposphere over the four main regions of tropical deep convection (Indonesia, central Pacific, South America, and Africa). Most particles remain in the troposphere, descending on average for every cluster. The horizontal components of 5-day trajectories are strongly influenced by the El Niño–Southern Oscillation (ENSO), but the Lagrangian average descent does not have a clear ENSO signature. Tropopause crossing locations are first identified by recording events when trajectories from the same release regions cross the World Meteorological Organization lapse rate tropopause. Most crossing events occur 5–15 days after release, and 30-day trajectories are sufficiently long to estimate crossing number densities. In a further two experiments slight excursions across the lapse rate tropopause are differentiated from the drift deeper into the stratosphere by defining the “tropopause zone” as a layer bounded by the average potential temperature of the lapse rate tropopause and the profile temperature minimum. Transport upward across this zone is studied using forward trajectories released from the lower bound and back trajectories arriving at the upper bound. Histograms of particle potential temperature (θ) show marked differences between the transition zone, where there is a slow spread in θ values about a peak that shifts slowly upward, and the troposphere below 350 K. There forward trajectories experience slow radiative cooling interspersed with bursts of convective heating resulting in a well-mixed distribution. In contrast θ histograms for back trajectories arriving in the stratosphere have two distinct peaks just above 300 and 350 K, indicating the sharp change from rapid convective heating in the well-mixed troposphere to slow ascent in the transition zone. Although trajectories slowly cross the tropopause zone throughout the Tropics, all three experiments show that most trajectories reaching the stratosphere from the lower troposphere within 30 days do so over the west Pacific warm pool. This preferred location moves about 30°–50° farther east in an El Niño year (1982/83) and about 30° farther west in a La Niña year (1988/89). These results could have important implications for upper-troposphere–lower-stratosphere pollution and chemistry studies.

A cluster computing implementation of a Monte Carlo simulation of a particle growth mechanism

Clusters of computers can be used together to provide a powerful computing resource. Large Monte Carlo simulations, such as those used to model particle growth, are computationally intensive and take considerable time to execute on conventional workstations. By spreading the work of the simulation across a cluster of computers, the elapsed execution time can be greatly reduced. Thus a user has apparently the performance of a supercomputer by using the spare cycles on other workstations.