A pseudo-marginal sequential Monte Carlo algorithm for random effects models in Bayesian sequential design

A computationally efficient sequential Monte Carlo algorithm is proposed for the sequential design of experiments for the collection of block data described by mixed effects models. The difficulty in applying a sequential Monte Carlo algorithm in such settings is the need to evaluate the observed data likelihood, which is typically intractable for all but linear Gaussian models. To overcome this difficulty, we propose to unbiasedly estimate the likelihood, and perform inference and make decisions based on an exact-approximate algorithm. Two estimates are proposed: using Quasi Monte Carlo methods and using the Laplace approximation with importance sampling. Both of these approaches can be computationally expensive, so we propose exploiting parallel computational architectures to ensure designs can be derived in a timely manner. We also extend our approach to allow for model uncertainty. This research is motivated by important pharmacological studies related to the treatment of critically ill patients.

Understanding public response to a congestion charge : a random-effects ordered logit approach

Public acceptance is consistently listed as having an enormous impact on the implementation and success of a congestion charge scheme. This paper investigates public acceptance of such a scheme in Australia. Surveys were conducted in Brisbane and Melbourne, the two fastest growing Australian cities. Using an ordered logit modeling approach, the survey data including stated preferences were analyzed to pinpoint the important factors influencing people’s attitudes to a congestion charge and, in turn, to their transport mode choices. To accommodate the nature of, and to account for the resulting heterogeneity of the panel data, random effects were considered in the models. As expected, this study found that the amount of the congestion charge and the financial benefits of implementing it have a significant influence on respondents’ support for the charge and on the likelihood of their taking a bus to city areas. However, respondents’ current primary transport mode for travelling to the city areas has a more pronounced impact. Meanwhile, respondents’ perceptions of the congestion charge’s role in protecting the environment by reducing vehicle emissions, and of the extent to which the charge would mean that they travelled less frequently to the city for shopping or entertainment, also have a significant impact on their level of support for its implementation. We also found and explained notable differences across two cities. Finally, findings from this study have been fully discussed in relation to the literature.

Surface plasmon-enhanced zeolite catalysis under light irradiation and its correlation with molecular polarity of reactants

Enhanced catalytic performance of zeoltes via the plasmonic effect of gold nanoparticles has been discovered to be closely correlated with the molecular polarity of reactants. The intensified polarised electrostatic field of Na+ in NaY plays a critical role in stretching the C=O bond of aldehydes to improve the reaction rate.

Potential-dependent surface-enhanced resonance raman spectroscopy at nanostructured TiO2 : A case study on cytochrome b5

Abstract: Nanostructured titanium dioxide (TiO2) electrodes, prepared by anodization of titanium, are employed to probe the electron-transfer process of cytochrome b5 (cyt b5) by surface-enhanced resonance Raman (SERR) spectroscopy. Concomitant with the increased nanoscopic surface roughness of TiO2, achieved by raising the anodization voltage from 10 to 20 V, the enhancement factor increases from 2.4 to 8.6, which is rationalized by calculations of the electric field enhancement. Cyt b 5 is immobilized on TiO2 under preservation of its native structure but it displays a non-ideal redox behavior due to the limited conductivity of the electrode material. The electron-transfer efficiency which depends on the crystalline phase of TiO2 has to be improved by appropriate doping for applications in bioelectrochemistry. Nanostructured TiO2 electrodes are employed to probe the electron-transfer process of cytochrome b5 by surface-enhanced resonance Raman spectroscopy. Concomitant with the increased nanoscopic surface roughness of TiO2, the enhancement factor increases, which can be attributed to the electric field enhancement. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Complementary surface-enhanced resonance raman spectroscopic biodetection of mixed protein solutions by chitosan- and silica-coated plasmon-tuned silver nanoparticles

Silver nanoparticles with identical plasmonic properties but different surface functionalities are synthesized and tested as chemically selective surface-enhanced resonance Raman (SERR) amplifiers in a two-component protein solution. The surface plasmon resonances of the particles are tuned to 413 nm to match the molecular resonance of protein heme cofactors. Biocompatible functionalization of the nanoparticles with a thin film of chitosan yields selective SERR enhancement of the anionic protein cytochrome b5, whereas functionalization with SiO2 amplifies only the spectra of the cationic protein cytochrome c. As a result, subsequent addition of the two differently functionalized particles yields complementary information on the same mixed protein sample solution. Finally, the applicability of chitosan-coated Ag nanoparticles for protein separation was tested by in situ resonance Raman spectroscopy.

Tailored silica coated Ag nanoparticles for non-invasive surface enhanced Raman spectroscopy of biomolecular targets

Silica coated Ag nanoparticles with defined surface plasmon resonances are used to selectively detect and analyze protein cofactors in solution and on interfaces via surface enhanced resonance Raman spectroscopy. The silica coating has a surprisingly small effect on optical amplification but minimizes unwanted interactions between the protein and the nanoparticle.

Adsorption thermodynamics and kinetics study for the self-assembly of 1,8,15,22-tetraaminophthalocyanatocobalt(II) on glassy carbon surface

Spontaneous adsorption of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) on glassy carbon (GC) electrode leads to the formation of a stable self-assembled monolayer (SAM). Since the SAM of 4α-CoIITAPc is redox active, its adsorption on GC electrode was followed by cyclic voltammetry. SAM of 4α-CoIITAPc on GC electrode shows two pairs of well-defined redox peaks corresponding to CoIII/CoII and CoIIIPc−1/CoIIIPc−2. The surface coverage (Γ) value, calculated by integrating the charge under CoII oxidation, was used to study the adsorption thermodynamics and kinetics of 4α-CoIITAPc on GC surface. Cyclic voltammetric studies show that the adsorption of 4α-CoIITAPc on GC electrode has reached the saturation coverage (Γs) within 3 h. The Γs value for the SAM of 4α-CoIITAPc on GC electrode was found to be 2.37 × 10−10 mol cm−2. Gibbs free energy (ΔGads) and adsorption rate constant (kad) for the adsorption of 4α-CoIITAPc on GC surface were found to be −16.76 kJ mol−1 and 7.1 M−1 s−1, respectively. The possible mechanism for the self-assembly of 4α-CoIITAPc on GC surface is through the addition of nucleophilic amines to the olefinic bond on the GC surface in addition to a meager contribution from π stacking. The contribution of π stacking was confirmed from the adsorption of unsubstituted phthalocyanatocobalt(II) (CoPc) on GC electrode. Raman spectra for the SAM of 4α-CoIITAPc on carbon surface shows strong stretching and breathing bands of Pc macrocycle, pyrrole ring and isoindole ring. Raman and CV studies suggest that 4α-CoIITAPc is adopting nearly a flat orientation or little bit tilted orientation.

Electrochemical and spectral studies of self-assembled monolayer of 1,8,15,22-tetraaminophthalocyanatocobalt(II) on indium tin oxide surface

Self-assembled monolayer (SAM) of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) was prepared on indium tin oxide (ITO) electrode by spontaneous adsorption from dimethylformamide (DMF) solution containing 4α-CoIITAPc. The SAM of 4α-CoIITAPc formed on ITO electrode was characterized by cyclic voltammetry, Raman and UV–visible spectroscopic techniques. The cyclic voltammogram (CV) of 4α-CoIITAPc SAM shows two pairs of well-defined redox peaks corresponding to CoIII/CoII and CoIIIPc−1/CoIIIPc−2. The surface coverage (Γ) was calculated by integrating the charge under the anodic wave corresponding to CoII oxidation and it was found to be 2.25 × 10−10 mol cm−2. Raman spectrum obtained for the SAM of 4α-CoIITAPc on ITO surface shows strong stretching and breathing bands of Pc macrocycle, pyrrole ring and isoindole ring. Further, the –NH2 bending mode of vibration was absent for the SAM of 4α-CoIITAPc on ITO surface which indirectly confirmed that all the amino groups of 4α-CoIITAPc are involved in bonding with ITO surface. UV–visible spectrum for the SAM of 4α-CoIITAPc on ITO surface shows an intense B-band, Q-band and n–π∗ transition with slight broadening when compared to that of 4α-CoIITAPc in DMF.

Towards improved precision in the quantification of surface-enhanced Raman scattering (SERS) enhancement factors: A renewed approach

This paper demonstrates a renewed procedure for the quantification of surface-enhanced Raman scattering (SERS) enhancement factors with improved precision. The principle of this method relies on deducting the resonance Raman scattering (RRS) contribution from surface-enhanced resonance Raman scattering (SERRS) to end up with the surface enhancement (SERS) effect alone. We employed 1,8,15,22-tetraaminophthalocyanato-cobalt(II) (4α-CoIITAPc), a resonance Raman- and electrochemically redox-active chromophore, as a probe molecule for RRS and SERRS experiments. The number of 4α-CoIITAPc molecules contributing to RRS and SERRS phenomena on plasmon inactive glassy carbon (GC) and plasmon active GC/Au surfaces, respectively, has been precisely estimated by cyclic voltammetry experiments. Furthermore, the SERS substrate enhancement factor (SSEF) quantified by our approach is compared with the traditionally employed methods. We also demonstrate that the present approach of SSEF quantification can be applied for any kind of different SERS substrates by choosing an appropriate laser line and probe molecule.

Detection of DNA mutations using novel SERS (Surface-Enhanced Raman Spectroscopy) diagnostic platform

This article describes the detection of DNA mutations using novel Au-Ag coated GaN substrate as SERS (surface-enhanced Raman spectroscopy) diagnostic platform. Oligonucleotide sequences corresponding to the BCR-ABL (breakpoint cluster region-Abelson) gene responsible for development of chronic myelogenous leukemia were used as a model system to demonstrate the discrimination between the wild type and Met244Val mutations. The thiolated ssDNA (single-strand DNA) was immobilized on the SERS-active surface and then hybridized to a labeled target sequence from solution. An intense SERS signal of the reporter molecule MGITC was detected from the complementary target due to formation of double helix. The SERS signal was either not observed, or decreased dramatically for a negative control sample consisting of labeled DNA that was not complementary to the DNA probe. The results indicate that our SERS substrate offers an opportunity for the development of novel diagnostic assays.

Factors influencing robustness and effectiveness of conditional random fields in active learning frameworks

Active learning approaches reduce the annotation cost required by traditional supervised approaches to reach the same effectiveness by actively selecting informative instances during the learning phase. However, effectiveness and robustness of the learnt models are influenced by a number of factors. In this paper we investigate the factors that affect the effectiveness, more specifically in terms of stability and robustness, of active learning models built using conditional random fields (CRFs) for information extraction applications. Stability, defined as a small variation of performance when small variation of the training data or a small variation of the parameters occur, is a major issue for machine learning models, but even more so in the active learning framework which aims to minimise the amount of training data required. The factors we investigate are a) the choice of incremental vs. standard active learning, b) the feature set used as a representation of the text (i.e., morphological features, syntactic features, or semantic features) and c) Gaussian prior variance as one of the important CRFs parameters. Our empirical findings show that incremental learning and the Gaussian prior variance lead to more stable and robust models across iterations. Our study also demonstrates that orthographical, morphological and contextual features as a group of basic features play an important role in learning effective models across all iterations.

Phenotyping cotton ovule fibre initiation with spatial statistics

Yield in cultivated cotton (Gossypium spp.) is affected by the number and distribution of fibres initiated on the seed surface but, apart from simple statistical summaries, little has been done to assess this phenotype quantitatively. Here we use two types of spatial statistics to describe and quantify differences in patterning of cotton ovule fibre initials (FI). The following five different species of Gossypium were analysed: G. hirsutum L., G. barbadense L., G. arboreum, G. raimondii Ulbrich. and G. trilobum (DC.) Skovsted. Scanning electron micrographs of FIs were taken on the day of anthesis. Cell centres for fibre and epidermal cells were digitised and analysed by spatial statistics methods appropriate for marked point processes and tessellations. Results were consistent with previously published reports of fibre number and spacing. However, it was shown that the spatial distributions of FIs in all of species examined exhibit regularity, and are not completely random as previously implied. The regular arrangement indicates FIs do not appear independently of each other and we surmise there may be some form of mutual inhibition specifying fibre-initial development. It is concluded that genetic control of FIs differs from that of stomata, another well studied plant idioblast. Since spatial statistics show clear species differences in the distribution of FIs within this genus, they provide a useful method for phenotyping cotton. © CSIRO 2007.

Using extended random set to find specific patterns

With the overwhelming increase in the amount of data on the web and data bases, many text mining techniques have been proposed for mining useful patterns in text documents. Extracting closed sequential patterns using the Pattern Taxonomy Model (PTM) is one of the pruning methods to remove noisy, inconsistent, and redundant patterns. However, PTM model treats each extracted pattern as whole without considering included terms, which could affect the quality of extracted patterns. This paper propose an innovative and effective method that extends the random set to accurately weigh patterns based on their distribution in the documents and their terms distribution in patterns. Then, the proposed approach will find the specific closed sequential patterns (SCSP) based on the new calculated weight. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms other state-of-the-art methods in different popular measures.