Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/

A roadmap of strain in doped anatase TiO2

Anatase titanium oxide is important for its high chemical stability and photocatalytic properties, however, the latter are plagued by its large band gap that limits its activity to only a small percentage of the solar spectrum. In that respect, straining the material can reduce its band gap increasing the photocatalytic activity of titanium oxide. We apply density functional theory with the introduction of the Hubbard + U model, to investigate the impact of stress on the electronic structure of anatase in conjunction with defect engineering by intrinsic defects (oxygen/titanium vacancies and interstitials), metallic dopants (iron, chromium) and non-metallic dopants (carbon, nitrogen). Here we show that both biaxial and uniaxial strain can reduce the band gap of undoped anatase with the use of biaxial strain being marginally more beneficial reducing the band gap up to 2.96 eV at a tensile stress of 8 GPa. Biaxial tensile stress in parallel with doping results in reduction of the band gap but also in the introduction of states deep inside the band gap mainly for interstitially doped anatase. Dopants in substitutional positions show reduced deep level traps. Chromium-doped anatase at a tensile stress of 8 GPa shows the most significant reduction of the band gap as the band gap reaches 2.4 eV

Telehealth, sustainable economic development, and social welfare

Country experiences in Australia, New Zealand, Norway, Taiwan, and UK have been in favor of telehealth services since the early 1990s. Though a few studies do discuss evidence of the efficacy and cost-effectiveness of telehealth programs, the literature might limit to financial evaluation. This research investigates the welfare implications of conventional in-person and telecommunications health care as improving health levels or preventing health from deterioration for efficient resource allocation by incorporating government intervention for equal accessibility of health care in the economic progress perspective. Analytical findings indicate that the inverse U shape relationship between telehealth expenditure share and social welfare status exists as the nonlinear nexus between telehealth expenditure share and economic growth presents. The health dividend in terms of an enhanced economic growth rate can be achieved only when the initial share of telehealth expenditure is smaller than the growth-maximizing share. For economic sustainable development, telehealth initiatives strengthen rather than compete with conventional in-person health care. Research results guide the countries, which have or will have telehealth systems, for effectively allocating medical resources to stimulate economic growth and improve the population's well-being

The severity of pandemic H1N1 influenza in the United States, from April to July 2009: A Bayesian analysis

Background: Accurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources. Methods and Findings: We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data - medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York - were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%-0.096%), sCIR of 0.239% (0.134%-0.458%), and sCHR of 1.44% (0.83%-2.64%). Using self-reported ILI, we obtained estimates approximately 7-96lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5-17 y. sCHR appears to be lowest in persons aged 5-17; our data were too sparse to allow us to determine the group in which it was the highest. Conclusions: These estimates suggest that an autumn-winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0-4 and adults 18-64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.published_or_final_versio