Higher harmonic large-amplitude fourier transformed alternating current voltammetry : analytical attributes derived from studies of the oxidation of ferrocenemethanol and uric acid at a glassy carbon electrode

An analytical evaluation of the higher ac harmonic components derived from large amplitude Fourier transformed voltammetry is provided for the reversible oxidation of ferrocenemethanol (FcMeOH) and oxidation of uric acid by an EEC mechanism in a pH 7.4 phosphate buffer at a glassy carbon (GC) electrode. The small background current in the analytically optimal fifth harmonic is predominantly attributed to faradaic current associated with the presence of electroactive functional groups on the GC electrode surface, rather than to capacitive current which dominates the background in the dc, and the initial three ac harmonics. The detection limits for the dc and the first to fifth harmonic ac components are 1.9, 5.89, 2.1, 2.5, 0.8, and 0.5 µM for FcMeOH, respectively, using a sine wave modulation of 100 mV at 21.46 Hz and a dc sweep rate of 111.76 mV s−1. Analytical performance then progressively deteriorates in the sixth and higher harmonics. For the determination of uric acid, the capacitive background current was enhanced and the reproducibility lowered by the presence of surface active uric acid, but the rapid overall 2e− rather than 1e– electron transfer process gives rise to a significantly enhanced fifth harmonic faradaic current which enabled a detection limit of 0.3 µM to be achieved which is similar to that reported using chemically modified electrodes. Resolution of overlapping voltammetric signals for a mixture of uric acid and dopamine is also achieved using higher fourth or fifth harmonic components, under very low background current conditions. The use of higher fourth and fifth harmonics exhibiting highly favorable faradaic to background (noise) current ratios should therefore be considered in analytical applications under circumstances where the electron transfer rate is fast.

Method of making metal oxides

A method for producing particles having at least regions of at least one metal oxide having nano-sized grains comprises providing particles of material having an initial, non-equiaxed particle shape, making a mixture of the particles of material and one or more precursors of the metal oxide, and treating the mixture such that the one or more precursors of the metal oxide react with the particles of material to thereby form at least regions of metal oxide on or within the particles, wherein atoms from the particles of material form part of a matrix of the at least one metal oxide and the at least one metal oxide has nano-sized grains and wherein at least some of the regions of metal oxide on or within the particles have a non-equiaxed grain shape.

Production of metal oxide particles with nano-sized grains

A method for producing metal oxide particles having nano-sized grains is disclosed. A solution of metal cations is mixed with surfactant under conditions such that surfactant micelles are formed. This mixture is then heated to form the metal oxide particles; this heating step removing the surfactant, forming the metal oxide and creating the pore structure of the particles. The pore structures are disordered. This method is particularly advantageous for production of complex (multi-component) metal oxides in which the different atomic species are homogeneously dispersed.

Design, implementation and multisite evaluation of a system suitability protocol for the quantitative assessment of instrument performance in liquid chromatography-multiple reaction monitoring-MS (LC-MRM-MS)

Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation <0.15, peak width coefficient of variation <0.15, standard deviation of RT <0.15 min (9 s), and the RT drift <0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Role of electromechanical wave propagation in power systems

In a large interconnected power system, disturbances initiated by a fault or other events cause acceleration in the generator rotors with respect to their synchronous reference frame. This acceleration of rotors can be described by two different dynamic phenomena, as shown in existing literature. One of the phenomena is simultaneous acceleration and the other is electromechanical wave propagation, which is characterized by travelling waves in terms of a wave equation. This paper demonstrates that depending on the structure of the system, the exhibited dynamic response will be dominated by one phenomenon or the other or a mixture of both. Two system structures of choice are examined, with each structure exemplifying each phenomenon present to different degrees in their dynamic responses. Prediction of dominance of either dynamic phenomenon in a particular system can be determined by taking into account the relative sizes of the values of its reduced admittance matrix.

Modelling survival data to account for model uncertainty: a single model or model averaging?

This study considered the problem of predicting survival, based on three alternative models: a single Weibull, a mixture of Weibulls and a cure model. Instead of the common procedure of choosing a single “best” model, where “best” is defined in terms of goodness of fit to the data, a Bayesian model averaging (BMA) approach was adopted to account for model uncertainty. This was illustrated using a case study in which the aim was the description of lymphoma cancer survival with covariates given by phenotypes and gene expression. The results of this study indicate that if the sample size is sufficiently large, one of the three models emerge as having highest probability given the data, as indicated by the goodness of fit measure; the Bayesian information criterion (BIC). However, when the sample size was reduced, no single model was revealed as “best”, suggesting that a BMA approach would be appropriate. Although a BMA approach can compromise on goodness of fit to the data (when compared to the true model), it can provide robust predictions and facilitate more detailed investigation of the relationships between gene expression and patient survival. Keywords: Bayesian modelling; Bayesian model averaging; Cure model; Markov Chain Monte Carlo; Mixture model; Survival analysis; Weibull distribution

A recommendation approach dealing with multiple market segments

A new community and communication type of social networks - online dating - are gaining momentum. With many people joining in the dating network, users become overwhelmed by choices for an ideal partner. A solution to this problem is providing users with partners recommendation based on their interests and activities. Traditional recommendation methods ignore the users’ needs and provide recommendations equally to all users. In this paper, we propose a recommendation approach that employs different recommendation strategies to different groups of members. A segmentation method using the Gaussian Mixture Model (GMM) is proposed to customize users’ needs. Then a targeted recommendation strategy is applied to each identified segment. Empirical results show that the proposed approach outperforms several existing recommendation methods.

Learning information literacy

Introduction This paper reports on university students' experiences of learning information literacy. Method Phenomenography was selected as the research approach as it describes the experience from the perspective of the study participants, which in this case is a mixture of undergraduate and postgraduate students studying education at an Australian university. Semi-structured, one-on-one interviews were conducted with fifteen students. Analysis The interview transcripts were iteratively reviewed for similarities and differences in students' experiences of learning information literacy. Categories were constructed from an analysis of the distinct features of the experiences that students reported. The categories were grouped into a hierarchical structure that represents students' increasingly sophisticated experiences of learning information literacy. Results The study reveals that students experience learning information literacy in six ways: learning to find information; learning a process to use information; learning to use information to create a product; learning to use information to build a personal knowledge base; learning to use information to advance disciplinary knowledge; and learning to use information to grow as a person and to contribute to others. Conclusions Understanding the complexity of the concept of information literacy, and the collective and diverse range of ways students experience learning information literacy, enables academics and librarians to draw on the range of experiences reported by students to design academic curricula and information literacy education that targets more powerful ways of learning to find and use information.

Structural characterization of glycerophospholipids by combinations of ozone- and collision-induced dissociation mass spectrometry: The next step towards "top-down" lipidomics

The complete structural elucidation of complex lipids, including glycerophospholipids, using only mass spectrometry represents a major challenge to contemporary analytical technologies. Here, we demonstrate that product ions arising from the collision-induced dissociation (CID) of the [M + Na] + adduct ions of phospholipids can be isolated and subjected to subsequent gas-phase ozonolysis-known as ozone-induced dissociation (OzID)-in a linear ion-trap mass spectrometer. The resulting CID/OzID experiment yields abundant product ions that are characteristic of the acyl substitution on the glycerol backbone (i.e., sn-position). This approach is shown to differentiate sn-positional isomers, such as the regioisomeric phosphatidylcholine pair of PC 16:0/18:1 and PC 18:1/16:0. Importantly, CID/OzID provides a sensitive diagnostic for the existence of an isomeric mixture in a given sample. This is of very high value for the analysis of tissue extracts since CID/OzID analyses can reveal changes in the relative abundance of isomeric constituents even within different tissues from the same animal. Finally, we demonstrate the ability to assign carbon-carbon double bond positions to individual acyl chains at specific backbone positions by adding subsequent CID and/or OzID steps to the workflow and that this can be achieved in a single step using a hybrid triple quadrupole-linear ion trap mass spectrometer. This unique approach represents the most complete and specific structural analysis of lipids by mass spectrometry demonstrated to date and is a significant step towards comprehensive top-down lipidomics. This journal is © The Royal Society of Chemistry 2014. Grant Number ARC/DP0986628, ARC/FT110100249, ARC/LP110200648

Bisresorcinols and arbutin derivatives from Grevillea banksii R. Br

This work is part of a series of chemical investigations of the genus Grevillea. Two new arbutin derivatives, seven new bisresorcinols, including a mixture of two isomers, three known flavonol glycosides, and four known resorcinols, including a mixture of two homologous compounds, were isolated from the ethyl acetate extract of the leaves and methanol extract of the stems of Grevillea banksii. The new compounds were identified, on the basis of spectroscopic data, as 6'-O-(3-(2(hydroxymethyl)acryloyloxy)-2-methylpropanoyl)arbutin (1), 6'-O-(2-methylacryloyl)arbutin (2), 5,5'-(4(Z)-dodecen-1,12diyl)bisresorcinol (6), 2'-methyl-5,5'-(4(Z)-tetradecen-1,14-diyl)bisresorcinol (8), 2,2'-di(4-hydroxyprenyl)-5,5'-(6(Z)-tetradecen-1,14-diyl)bisresorcinol (9), 2-(4-acetoxyprenyl)-2'-(4-hydroxyprenyl) 5,5'-(6(Z)-tetradecen-1,14-diyl)bisresorcinol (10), 2-(4-acetoxyprenyl)-2'-(4-hydroxyprenyl)5,5'-(8(Z)-tetradecen-l,14-diyl)bisresorcinol (11), 5,5'-(10(Z)-tetradecen-1-on-diyl)bisresorcinol (12) and 5,5'-(4(Z)-tetradecen-1-on-diyl)bisresorcinol (13).

Approximate Bayesian computation using auxiliary model based estimates

We present a novel approach for developing summary statistics for use in approximate Bayesian computation (ABC) algorithms using indirect infer- ence. We embed this approach within a sequential Monte Carlo algorithm that is completely adaptive. This methodological development was motivated by an application involving data on macroparasite population evolution modelled with a trivariate Markov process. The main objective of the analysis is to compare inferences on the Markov process when considering two di®erent indirect mod- els. The two indirect models are based on a Beta-Binomial model and a three component mixture of Binomials, with the former providing a better ¯t to the observed data.

Lattice mixing and vanishing trapdoors : a framework for fully secure short signatures and more

We propose a framework for adaptive security from hard random lattices in the standard model. Our approach borrows from the recent Agrawal-Boneh-Boyen families of lattices, which can admit reliable and punctured trapdoors, respectively used in reality and in simulation. We extend this idea to make the simulation trapdoors cancel not for a specific forgery but on a non-negligible subset of the possible challenges. Conceptually, we build a compactly representable, large family of input-dependent “mixture” lattices, set up with trapdoors that “vanish” for a secret subset which we hope the forger will target. Technically, we tweak the lattice structure to achieve “naturally nice” distributions for arbitrary choices of subset size. The framework is very general. Here we obtain fully secure signatures, and also IBE, that are compact, simple, and elegant.

Antibacterial anthranilic acid derivatives from Geijera parviflora

Five anthranilic acid derivatives, a mixture I of three new compounds 11′-hexadecenoylanthranilic acid (1), 9′-hexadecenoylanthranilic acid (2), and 7′-hexadecenoylanthranilic acid (3), as well as a new compound 9,12,15-octadecatrienoylanthranilic acid (4) together with a new natural product, hexadecanoylanthranilic acid (5), were isolated from Geijera parviflora Lindl. (Rutaceae). Their structures were elucidated by extensive spectroscopic measurements, and the positions of the double bonds in compounds 1-3 of the mixture I were determined by tandem mass spectrometry employing ozone-induced dissociation. The mixture I and compound 5 showed good antibacterial activity against several Gram-positive strains. © 2013 Elsevier B.V.

Bauxite residue neutralisation precipitate stability in acidic environments

This investigation used a combination of techniques, such as X-ray diffraction, inductively coupled plasma optical emission spectroscopy and infrared spectroscopy, to determine the dissolution mechanisms of the Bayer precipitate and the associated rate of dissolution in acetic, citric and oxalic acid environments. The Bayer precipitate is a mixture of hydrotalcite, calcium carbonate and sodium chloride that forms during the seawater neutralisation of Bayer liquors (waste residue of the alumina industry). The dissolution rate of a Bayer precipitate is found to be dependent on (1) the strength of the organic acid and (2) the number of donating H+ ions. The dissolution mechanism for a Bayer precipitate consists of several steps involving: (1) the dissolution of CaCO3, (2) formation of whewellite (calcium oxalate) when oxalic acid is used and (3) multiple dissolution steps for hydrotalcite that are highly dependent on the pH of solution. The decomposition of the Al–OH hydrotalcite layers resulted in the immediate formation of Al(OH)3, which is stable until the pH decreases below 5.5. This investigation has found that the Bayer precipitate is stable across a wide pH range in the presence of common organic acids found in the rhizosphere, and that initial decomposition steps are likely to be beneficial in supporting plant growth through the release of nutrients such as Ca2þ and Mg2þ.