Computational learning of the conditional phase-type (C-Ph) distribution: Learning C-Ph distributions

This paper presents a new algorithm for learning the structure of a special type of Bayesian network. The conditional phase-type (C-Ph) distribution is a Bayesian network that models the probabilistic causal relationships between a skewed continuous variable, modelled by the Coxian phase-type distribution, a special type of Markov model, and a set of interacting discrete variables. The algorithm takes a dataset as input and produces the structure, parameters and graphical representations of the fit of the C-Ph distribution as output.The algorithm, which uses a greedy-search technique and has been implemented in MATLAB, is evaluated using a simulated data set consisting of 20,000 cases. The results show that the original C-Ph distribution is recaptured and the fit of the network to the data is discussed.

Discrete Conditional Phase-type model (DC_Ph) for patient waiting time with a logistic regression component to predict patient admission to hospital

Discrete Conditional Phase-type (DC-Ph) models are a family of models which represent skewed survival data conditioned on specific inter-related discrete variables. The survival data is modeled using a Coxian phase-type distribution which is associated with the inter-related variables using a range of possible data mining approaches such as Bayesian networks (BNs), the Naïve Bayes Classification method and classification regression trees. This paper utilizes the Discrete Conditional Phase-type model (DC-Ph) to explore the modeling of patient waiting times in an Accident and Emergency Department of a UK hospital. The resulting DC-Ph model takes on the form of the Coxian phase-type distribution conditioned on the outcome of a logistic regression model.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.

Utility in Willingness to Pay Space: A Tool to Address Confounding Random Scale Effects in Destination Choice to the Alps

We compare two approaches for estimating the distribution of consumers' willingness to pay (WTP) in discrete choice models. The usual procedure is to estimate the distribution of the utility coefficients and then derive the distribution of WTP, which is the ratio of coefficients. The alternative is to estimate the distribution of WTP directly. We apply both approaches to data on site choice in the Alps. We find that the alternative approach fits the data better, reduces the incidence of exceedingly large estimated WTP values, and provides the analyst with greater control in specifying and testing the distribution of WTP. © 2008 Agricultural and Applied Economics Association.

Computational techniques for a simple theory of conditional preferences

A simple logic of conditional preferences is defined, with a language that allows the compact representation of certain kinds of conditional preference statements, a semantics and a proof theory. CP-nets and TCP-nets can be mapped into this logic, and the semantics and proof theory generalise those of CP-nets and TCP-nets. The system can also express preferences of a lexicographic kind. The paper derives various sufficient conditions for a set of conditional preferences to be consistent, along with algorithmic techniques for checking such conditions and hence confirming consistency. These techniques can also be used for totally ordering outcomes in a way that is consistent with the set of preferences, and they are further developed to give an approach to the problem of constrained optimisation for conditional preferences.

The Utility of Fractional Exhaled Nitric Oxide Suppression in the Identification of Nonadherence in Difficult Asthma

Rationale: Nonadherence to inhaled corticosteroid therapy (ICS) is a major contributor to poor control in difficult asthma, yet it is challenging to ascertain. Objectives: Identify a test for nonadherence using fractional exhaled nitric oxide (FENO) suppression after directly observed inhaled corticosteroid (DOICS) treatment. Methods: Difficult asthma patients with an elevated FENO (>45 ppb) were recruited as adherent (ICS prescription filling >80%) or nonadherent (filling <50%). They received 7 days of DOICS (budesonide 1,600 µg) and a test for nonadherence based on changes in FENO was developed. Using this test, clinic patients were prospectively classified as adherent or nonadherent and this was then validated against prescription filling records, prednisolone assay, and concordance interview. Measurements and Main Results: After 7 days of DOICS nonadherent (n = 9) compared with adherent subjects (n = 13) had a greater reduction in FENO to 47 ± 21% versus 79 ± 26% of baseline measurement (P = 0.003), which was also evident after 5 days (P = 0.02) and a FENO test for nonadherence (area under the curve = 0.86; 95% confidence interval, 0.68-1.00) was defined. Prospective validation in 40 subjects found the test identified 13 as nonadherent; eight confirmed nonadherence during interview (three of whom had excellent prescription filling but did not take medication), five denied nonadherence, two had poor inhaler technique (unintentional nonadherence), and one also denied nonadherence to prednisolone despite nonadherent blood level. Twenty-seven participants were adherent on testing, which was confirmed in 21. Five admitted poor ICS adherence but of these, four were adherent with oral steroids and one with omalizumab. Conclusions: FENO suppression after DOICS provides an objective test to distinguish adherent from nonadherent patients with difficult asthma. Clinical trial registered with www.clinicaltrials.gov (NCT 01219036). Copyright © 2012 by the American Thoracic Society.

Production and evaluation of the utility of novel phage display-derived peptide ligands to Salmonella spp. for magnetic separation

Aims: The objectives of this study were to produce Salmonella-specific peptide ligands by phage display biopanning and evaluate their use for magnetic separation (MS).
Methods and Results: Four phage display biopanning rounds were performed and the peptides expressed by the two most Salmonella-specific (on the basis of phage binding ELISA results) phage clones, MSal020401 and MSal020417, were chemically synthesized and coupled to MyOne™ tosylactivated Dynabeads®. Peptide capture capability for whole Salmonella cells from non-enriched broth cultures was quantified by MS + plate counts and MS + Greenlight™ detection, and compared to capture capability of anti-Salmonella (antibody-coated) Dynabeads®. MS + Greenlight™ gave a more comprehensive picture of capture capability than MS + plate counts and showed that Peptide MSal020417-coated beads exhibited at least similar, if not better, capture capability to anti-Salmonella Dynabeads® (mean capture values of 36.0 ± 18.2 % and 31.2 ± 20.1 %, respectively, over Salmonella spp. concentration range 3 x 101 - 3 x 106 cfu ml-1) with minimal cross-reactivity (= 1.9 %) to three other foodborne bacteria.
Conclusions: One of the phage display-derived peptide ligands was demonstrated by MS + Greenlight™ to be a viable antibody-alternative for MS of Salmonella spp.
Significance and Impact of Study: This study demonstrates an antibody-free approach to Salmonella detection and opens substantial possibilities for more rapid tests for this bacterium.

Dual-colour HER2/chromosome 17 chromogenic in situ hybridisation assay enables accurate assessment of HER2 genomic status in gastric cancer and has potential utility in HER2 testing of biopsy samples

Determination of HER2 protein expression by immunohistochemistry (IHC) and genomic status by fluorescent in situ hybridisation (FISH) are important in identifying a subset of high HER2-expressing gastric cancers that might respond to trastuzumab. Although FISH is considered the standard for determination of HER2 genomic status, brightfield ISH is being increasingly recognised as a viable alternative. Also, the impact of HER2 protein expression/genomic heterogeneity on the accuracy of HER2 testing has not been well studied in the context of gastric biopsy samples.