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.

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.

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.

Common values in assessing health outcomes from disease and injury: Disability weights measurement study for the Global Burden of Disease Study 2010

BACKGROUND Measurement of the global burden of disease with disability-adjusted life-years (DALYs) requires disability weights that quantify health losses for all non-fatal consequences of disease and injury. There has been extensive debate about a range of conceptual and methodological issues concerning the definition and measurement of these weights. Our primary objective was a comprehensive re-estimation of disability weights for the Global Burden of Disease Study 2010 through a large-scale empirical investigation in which judgments about health losses associated with many causes of disease and injury were elicited from the general public in diverse communities through a new, standardised approach. METHODS We surveyed respondents in two ways: household surveys of adults aged 18 years or older (face-to-face interviews in Bangladesh, Indonesia, Peru, and Tanzania; telephone interviews in the USA) between Oct 28, 2009, and June 23, 2010; and an open-access web-based survey between July 26, 2010, and May 16, 2011. The surveys used paired comparison questions, in which respondents considered two hypothetical individuals with different, randomly selected health states and indicated which person they regarded as healthier. The web survey added questions about population health equivalence, which compared the overall health benefits of different life-saving or disease-prevention programmes. We analysed paired comparison responses with probit regression analysis on all 220 unique states in the study. We used results from the population health equivalence responses to anchor the results from the paired comparisons on the disability weight scale from 0 (implying no loss of health) to 1 (implying a health loss equivalent to death). Additionally, we compared new disability weights with those used in WHO's most recent update of the Global Burden of Disease Study for 2004. FINDINGS 13,902 individuals participated in household surveys and 16,328 in the web survey. Analysis of paired comparison responses indicated a high degree of consistency across surveys: correlations between individual survey results and results from analysis of the pooled dataset were 0·9 or higher in all surveys except in Bangladesh (r=0·75). Most of the 220 disability weights were located on the mild end of the severity scale, with 58 (26%) having weights below 0·05. Five (11%) states had weights below 0·01, such as mild anaemia, mild hearing or vision loss, and secondary infertility. The health states with the highest disability weights were acute schizophrenia (0·76) and severe multiple sclerosis (0·71). We identified a broad pattern of agreement between the old and new weights (r=0·70), particularly in the moderate-to-severe range. However, in the mild range below 0·2, many states had significantly lower weights in our study than previously. INTERPRETATION This study represents the most extensive empirical effort as yet to measure disability weights. By contrast with the popular hypothesis that disability assessments vary widely across samples with different cultural environments, we have reported strong evidence of highly consistent results.

Wide-band spectral power fluctuations characterize the response of simulated cortical networks to increasing stimulus intensity

The hippocampus is an anatomically distinct region of the medial temporal lobe that plays a critical role in the formation of declarative memories. Here we show that a computer simulation of simple compartmental cells organized with basic hippocampal connectivity is capable of producing stimulus intensity sensitive wide-band fluctuations of spectral power similar to that seen in real EEG. While previous computational models have been designed to assess the viability of the putative mechanisms of memory storage and retrieval, they have generally been too abstract to allow comparison with empirical data. Furthermore, while the anatomical connectivity and organization of the hippocampus is well defined, many questions regarding the mechanisms that mediate large-scale synaptic integration remain unanswered. For this reason we focus less on the specifics of changing synaptic weights and more on the population dynamics. Spectral power in four distinct frequency bands were derived from simulated field potentials of the computational model and found to depend on the intensity of a random input. The majority of power occurred in the lowest frequency band (3-6 Hz) and was greatest to the lowest intensity stimulus condition (1% maximal stimulus). In contrast, higher frequency bands ranging from 7-45 Hz show an increase in power directly related with an increase in stimulus intensity. This trend continues up to a stimulus level of 15% to 20% of the maximal input, above which power falls dramatically. These results suggest that the relative power of intrinsic network oscillations are dependent upon the level of activation and that above threshold levels all frequencies are damped, perhaps due to over activation of inhibitory interneurons.

Catching drink drivers in Queensland through Random Breath Testing : what does the last decade of data tell us

Background Random Breath Testing (RBT) has proven to be a cornerstone of enforcement attempts to deter (as well as apprehend) motorists from drink driving in Queensland (Australia) for decades. However, scant published research has examined the relationship between the frequency of implementing RBT activities and subsequent drink driving apprehension rates across time. Aim This study aimed to examine the prevalence of apprehending drink drivers in Queensland over a 12 year period. It was hypothesised that an increase in breath testing rates would result in a corresponding decrease in the frequency of drink driving apprehension rates over time, which would reflect general deterrent effects. Method The Queensland Police Service provided RBT data that was analysed. Results Between the 1st of January 2000 and 31st of December 2011, 35,082,386 random breath tests (both mobile and stationary) were conducted in Queensland, resulting in 248,173 individuals being apprehended for drink driving offences. A total of 342,801 offences were recorded during this period, representing an intercept rate of .96. Of these offences, 276,711 (80.72%) were recorded against males and 66,024 (19.28%) offences committed by females. The most common drink driving offence was between 0.05 and 0.08 BAC limit. The largest proportion of offences was detected on the weekends, with Saturdays (27.60%) proving to be the most common drink driving night followed by Sundays (21.41%). The prevalence of drink driving detection rates rose steadily across time, peaking in 2008 and 2009, before slightly declining. This decline was observed across all Queensland regions and any increase in annual figures was due to new offence types being developed. Discussion This paper will further outline the major findings of the study in regards to tailoring RBT operations to increase detection rates as well as improve the general deterrent effect of the initiative.

Aspect-based opinion mining from product reviews using conditional random fields

Product reviews are the foremost source of information for customers and manufacturers to help them make appropriate purchasing and production decisions. Natural language data is typically very sparse; the most common words are those that do not carry a lot of semantic content, and occurrences of any particular content-bearing word are rare, while co-occurrences of these words are rarer. Mining product aspects, along with corresponding opinions, is essential for Aspect-Based Opinion Mining (ABOM) as a result of the e-commerce revolution. Therefore, the need for automatic mining of reviews has reached a peak. In this work, we deal with ABOM as sequence labelling problem and propose a supervised extraction method to identify product aspects and corresponding opinions. We use Conditional Random Fields (CRFs) to solve the extraction problem and propose a feature function to enhance accuracy. The proposed method is evaluated using two different datasets. We also evaluate the effectiveness of feature function and the optimisation through multiple experiments.

Monoclonal antibody screening of a phage-displayed random peptide library reveals mimotopes of chemokine receptor CCR5: Implications for the tertiary structure of the receptor and for an n-terminal binding site for HIV-1 gp120

The chemokine receptor CCR5 contains seven transmembrane-spanning domains. It binds chemokines and acts as co-receptor for macrophage (m)-tropic (or R5) strains of HIV-1. Monoclonal antibodies (mAb) to CCR5, 3A9 and 5C7, were used for biopanning a nonapeptide cysteine (C)-constrained phage-displayed random peptide library to ascertain contact residues and define tertiary structures of possible epitopes on CCR5. Reactivity of antibodies with phagotopes was established by enzyme-linked immunosorbent assay (ELISA). mAb 3A9 identified a phagotope C-HASIYDFGS-C (3A9/1), and 5C7 most frequently identified C-PHWLRDLRV-C (5C7/1). Corresponding peptides were synthesized. Phagotopes and synthetic peptides reacted in ELISA with corresponding antibodies and synthetic peptides inhibited antibody binding to the phagotopes. Reactivity by immunofluorescence of 3A9 with CCR5 was strongly inhibited by the corresponding peptide. Both mAb 3A9 and 5C7 reacted similarly with phagotopes and the corresponding peptide selected by the alternative mAb. The sequences of peptide inserts of phagotopes could be aligned as mimotopes of the sequence of CCR5. For phage 3A9/1, the motif SIYD aligned to residues at the N terminus and FG to residues on the first extracellular loop; for 5C7/1, residues at the N terminus, first extracellular loop, and possibly the third extracellular loop could be aligned and so would contribute to the mimotope. The synthetic peptides corresponding to the isolated phagotopes showed a CD4-dependent reactivity with gp120 of a primary, m-tropic HIV-1 isolate. Thus reactivity of antibodies raised to CCR5 against phage-displayed peptides defined mimotopes that reflect binding sites for these antibodies and reveal a part of the gp120 binding sites on CCR5.

Characterization of epitopes for virus-neutralizing monoclonal antibodies to Ross River virus E2 using phage-displayed random peptide libraries

Ross River virus (RRV) is the predominant cause of epidemic polyarthritis in Australia, yet the antigenic determinants are not well defined. We aimed to characterize epitope(s) on RRV-E2 for a panel of monoclonal antibodies (MAbs) that recognize overlapping conformational epitopes on the E2 envelope protein of RRV and that neutralize virus infection of cells in vitro. Phage-displayed random peptide libraries were probed with the MAbs T1E7, NB3C4, and T10C9 using solution-phase and solid-phase biopanning methods. The peptides VSIFPPA and KTAISPT were selected 15 and 6 times, respectively, by all three of the MAbs using solution-phase biopanning. The peptide LRLPPAP was selected 8 times by NB3C4 using solid-phase biopanning; this peptide shares a trio of amino acids with the peptide VSIFPPA. Phage that expressed the peptides VSIFPPA and LRLPPAP were reactive with T1E7 and/or NB3C4, and phage that expressed the peptides VSIFPPA, LRLPPAP, and KTAISPT partially inhibited the reactivity of T1E7 with RRV. The selected peptides resemble regions of RRV-E2 adjacent to sites mutated in neutralization escape variants of RRV derived by culture in the presence of these MAbs (E2 210-219 and 238-245) and an additional region of E2 172-182. Together these sites represent a conformational epitope of E2 that is informative of cellular contact sites on RRV.

Multiple alignment and sorting of peptides derived from phage-displayed random peptide libraries with polyclonal sera allows discrimination of relevant phagotopes

Biopanning of phage-displayed random peptide libraries is a powerful technique for identifying peptides that mimic epitopes (mimotopes) for monoclonal antibodies (mAbs). However, peptides derived using polyclonal antisera may represent epitopes for a diverse range of antibodies. Hence following screening of phage libraries with polyclonal antisera, including autoimmune disease sera, a procedure is required to distinguish relevant from irrelevant phagotopes. We therefore applied the multiple sequence alignment algorithm PILEUP together with a matrix for scoring amino acid substitutions based on physicochemical properties to generate guide trees depicting relatedness of selected peptides. A random heptapeptide library was biopanned nine times using no selecting antibodies, immunoglobulin G (IgG) from sera of subjects with autoimmune diseases (primary biliary cirrhosis (PBC) and type 1 diabetes) and three murine ascites fluids that contained mAbs to overlapping epitope(s) on the Ross River Virus envelope protein 2. Peptides randomly sampled from the library were distributed throughout the guide tree of the total set of peptides whilst many of the peptides derived in the absence of selecting antibody aligned to a single cluster. Moreover peptides selected by different sources of IgG aligned to separate clusters, each with a different amino acid motif. These alignments were validated by testing all of the 53 phagotopes derived using IgG from PBC sera for reactivity by capture ELISA with antibodies affinity purified on the E2 subunit of the pyruvate dehydrogenase complex (PDC-E2), the major autoantigen in PBC: only those phagotopes that aligned to PBC-associated clusters were reactive. Hence the multiple sequence alignment procedure discriminates relevant from irrelevant phagotopes and thus a major difficulty with biopanning phage-displayed random peptide libraries with polyclonal antibodies is surmounted.

Phagotopes derived by antibody screening of phage-displayed random peptide libraries vary in immunoreactivity: Studies using an exemplary monoclonal antibody, CII-C1, to type II collagen

Antibody screening of phage-displayed random peptide libraries to identify mimotopes of conformational epitopes is promising. However, because interpretations can be difficult, an exemplary system has been used in the present study to investigate whether variation in the peptide sequences of selected phagotopes corresponded with variation in immunoreactivity. The phagotopes, derived using a well-characterized monoclonal antibody, CII-C1, to a known conformational epitope on type II collagen, C1, were tested by direct and inhibition ELISA for reactivity with CII-C1. A multiple sequence alignment algorithm, PILEUP, was used to sort the peptides expressed by the phagotopes into clusters. A model was prepared of the C1 epitope on type II collagen. The 12 selected phagotopes reacted with CII-C1 by both direct ELISA (titres from < 100-11 200) and inhibition ELISA (20-100% inhibition); the reactivity varied according to the peptide sequence and assay format. The differences in reactivity between the phagotopes were mostly in accord with the alignment, by PILEUP, of the peptide sequences. The finding that the phagotopes functionally mimicked the C1 epitope on collagen was validated in that amino acids RRL at the amino terminal of many of the peptides were topographically demonstrable on the model of the C1 epitope. Notably, one phagotope that expressed the widely divergent peptide C-IAPKRHNSA-C also mimicked the C1 epitope, as judged by reactivity in each of the assays used: these included cross-inhibition of CII-C1 reactivity with each of the other phagotopes and inhibition by a synthetic peptide corresponding to that expressed by the most frequently selected phagotope, RRLPFGSQM. Thus, it has been demonstrated that multiple phage-displayed peptides can mimic the same epitope and that observed immunoreactivity of selected phagotopes with the selecting mAb can depend on the primary sequence of the expressed peptide and also on the assay format used.

Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study

Background Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival. Methods Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20–84 years diagnosed during 1997–2007 from Queensland, Australia. Results Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients. Conclusions With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings