University of Warwick

About: University of Warwick is a education organization based out in Coventry, Warwickshire, United Kingdom. It is known for research contribution in the topics: Population & White dwarf. The organization has 26212 authors who have published 77127 publications receiving 2666552 citations. The organization is also known as: Warwick University & The University of Warwick.

Aggregate data meta-analysis with time-to-event outcomes

Abstract: In a meta-analysis of randomized controlled trials with time-to-event outcomes, an aggregate data approach may be required for some or all included studies Variation in the reporting of survival analyses in journals suggests that no single method for extracting the log(hazard ratio) estimate will suffice Methods are described which improve upon a previously proposed method for estimating the log(HR) from survival curves These methods extend to life-tables In the situation where the treatment effect varies over time and the trials in the meta-analysis have different lengths of follow-up, heterogeneity may be evident In order to assess whether the hazard ratio changes with time, several tests are proposed and compared A cohort study comparing life expectancy of males and females with cerebral palsy and a systematic review of five trials comparing two anti-epileptic drugs, carbamazepine and sodium valproate, are used for illustration

Development of a Multilocus Sequence Typing Scheme for the Opportunistic Pathogen Pseudomonas aeruginosa

Abstract: A multilocus sequence typing (MLST) scheme has been developed for Pseudomonas aeruginosa which provides molecular typing data that are highly discriminatory and electronically portable between laboratories. MLST data confirm the data from previous studies that suggest that P. aeruginosa is best described as nonclonal but as having an epidemic population. The index of association was 0.17, indicating a freely recombining population; however, there was evidence of clusters of closely related strains or clonal complexes among the members of this population. It is apparent that the sequence types (STs) from single isolates, representing each of the present epidemic clones in the United Kingdom from Liverpool, Manchester, and the West Midlands, are not closely related to each other. This suggests distinct evolutionary origins for each of these epidemic clones in the United Kingdom. Furthermore, these clones are distinct from European clone C. Comparison of the results of MLST with those of toxA typing and serotyping revealed that strains with identical STs may possess different toxA types and diverse serotypes. Given that recombination is important in the population of P. aeruginosa, the lack of a linkage between toxA type and serotype is not surprising and reveals the strength of the MLST approach for obtaining a better understanding of the epidemiology of P. aeruginosa.

Lost, but Found with Nile Red: A Novel Method for Detecting and Quantifying Small Microplastics (1 mm to 20 μm) in Environmental Samples

Abstract: Marine plastic debris is a global environmental problem. Surveys have shown that <5 mm plastic particles, known as microplastics, are significantly more abundant in surface seawater and on shorelines than larger plastic particles are. Nevertheless, quantification of microplastics in the environment is hampered by a lack of adequate high-throughput methods for distinguishing and quantifying smaller size fractions (<1 mm), and this has probably resulted in an underestimation of actual microplastic concentrations. Here we present a protocol that allows high-throughput detection and automated quantification of small microplastic particles (20-1000 μm) using the dye Nile red, fluorescence microscopy, and image analysis software. This protocol has proven to be highly effective in the quantification of small polyethylene, polypropylene, polystyrene, and nylon-6 particles, which frequently occur in the water column. Our preliminary results from sea surface tows show a power-law increase in small microplastics (i.e., <1 mm) with a decreasing particle size. Hence, our data help to resolve speculation about the "apparent" loss of this fraction from surface waters. We consider that this method presents a step change in the ability to detect small microplastics by substituting the subjectivity of human visual sorting with a sensitive and semiautomated procedure.

Enhanced stability and efficiency in hole-transport-layer-free CsSnI3 perovskite photovoltaics

Abstract: Photovoltaics based on tin halide perovskites have not yet benefitted from the same intensive research effort that has propelled lead perovskite photovoltaics to >20% power conversion efficiency, due to the susceptibility of tin perovskites to oxidation, the low energy of defect formation and the difficultly in forming pin-hole free films. Here we report CsSnI3 perovskite photovoltaic devices without a hole-selective interfacial layer that exhibit a stability 10 times greater than devices with the same architecture using methylammonium lead iodide perovskite, and the highest efficiency to date for a CsSnI3 photovoltaic: 3.56%. The latter results in large part from a high device fill-factor, achieved using a strategy that removes the need for an electron blocking layer or an additional processing step to minimise the pinhole density in the perovskite film, based on co-depositing the perovskite precursors with SnCl2. These two findings raise the prospect that this class of lead-free perovskite photovoltaic may yet prove viable for applications.

A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

Abstract: Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.