206 research outputs found

    Autonomous analysis to identify bijels from two-dimensional images

    Get PDF
    Bicontinuous interfacially jammed emulsion gels (bijels) are novel composite materials that can be challenging to manufacture. As a step towards automating production, we have developed a machine learning tool to classify fabrication attempts. We use training and testing data in the form of confocal images from both successful and unsuccessful attempts at bijel fabrication. We then apply machine learning techniques to this data in order to classify whether an image is a bijel or a non-bijel. Our principal approach is to process the images to find their autocorrelation function and structure factor, and from these functions we identify variables that can be used for training a supervised machine learning model to identify a bijel image. We are able to categorise images with reasonable accuracies of 85.4% and 87.5% for two different approaches. We find that using both the liquid and particle channels helps to achieve optimal performance and that successful classification relies on the bijel samples sharing a characteristic length scale. Our second approach is to classify the shapes of the liquid domains directly; the shape descriptors are then used to classify fabrication attempts via a decision tree. We have used an adaptive design approach to find an image pre-processing step that yields the optimal classification results. Again, we find that the characteristic length scale of the images is crucial in performing the classification

    Redefining Leadership in the 21st Century: the view from cybernetics

    Get PDF
    A white paper developed by the ANU School of Cybernetics powered by The Menzies Foundation

    Evidence that faecal carriage of resistant Escherichia coli by 16-week-old dogs in the United Kingdom is associated with raw feeding

    Get PDF
    We report a survey (August 2017 to March 2018) and risk factor analysis of faecal carriage of antibacterial-resistant (ABR) Escherichia coli in 223 16-week-old dogs in the United Kingdom. Raw feeding was associated with the presence of fluoroquinolone-resistant (FQ-R) E. coli and those resistant to tetracycline, amoxicillin, and streptomycin, but not to cefalexin. Whole genome sequencing of 36 FQ-R E. coli isolates showed a wide range of sequence types (STs), with almost exclusively mutational FQ-R dominated by ST744 and ST162. Comparisons between E. coli isolates from puppies known to be located within a 50 × 50 km region with those isolated from human urinary tract infections (isolated in parallel in the same region) identified an ST744 FQ-R lineage that was carried by one puppy and caused one urinary tract infection. Accordingly, we conclude that raw feeding is associated with carriage of ABR E. coli in dogs even at 16 weeks of age and that bacteria carried by puppies are shared with humans. We therefore suggest that those who feed their dogs raw meat seriously consider the potential ABR-transmission threat their pet may become as a result and deploy appropriate hygiene practices in mitigation

    Examining the reproducibility of meta-analyses in psychology:A preliminary report

    Get PDF
    Meta-analyses are an important tool to evaluate the literature. It is essential that meta-analyses can easily be reproduced to allow researchers to evaluate the impact of subjective choices on meta-analytic effect sizes, but also to update meta-analyses as new data comes in, or as novel statistical techniques (for example to correct for publication bias) are developed. Research in medicine has revealed meta-analyses often cannot be reproduced. In this project, we examined the reproducibility of meta-analyses in psychology by reproducing twenty published meta-analyses. Reproducing published meta-analyses was surprisingly difficult. 96% of meta-analyses published in 2013-2014 did not adhere to reporting guidelines. A third of these meta-analyses did not contain a table specifying all individual effect sizes. Five of the 20 randomly selected meta-analyses we attempted to reproduce could not be reproduced at all due to lack of access to raw data, no details about the effect sizes extracted from each study, or a lack of information about how effect sizes were coded. In the remaining meta-analyses, differences between the reported and reproduced effect size or sample size were common. We discuss a range of possible improvements, such as more clearly indicating which data were used to calculate an effect size, specifying all individual effect sizes, adding detailed information about equations that are used, and how multiple effect size estimates from the same study are combined, but also sharing raw data retrieved from original authors, or unpublished research reports. This project clearly illustrates there is a lot of room for improvement when it comes to the transparency and reproducibility of published meta-analyses

    Pneumococcal Serotypes Colonise the Nasopharynx in Children at Different Densities.

    Get PDF
    Prevalence of pneumococcal serotypes in carriage and disease has been described but absolute serotype colonisation densities have not been reported. 515 paediatric nasal swab DNA extracts were subjected to lytA qPCR and molecular serotyping by microarray. Absolute serotype densities were derived from total pneumococcal density (qPCR cycle threshold and standard curve) and relative abundance (microarray) and varied widely. Compared to all serotype densities observed, the strongest evidence of differences was seen for serotypes 21 and 35B (higher) and 3, 38 and non-typeables (lower) (p<0.05) with a similar hierarchy when only a single serotype carriage was assessed. There was no evidence of any overall density differences between children with single or multiple serotypes detected but serotypes with mid-range densities were more prevalent. The hierarchy of distinct pneumococcal serotype carriage densities described here for the first time, may help explain the dynamics of transmission between children

    Colorectal and other cancer risks for carriers and noncarriers from families with a DNA mismatch repair gene mutation: A Prospective Cohort Study

    Get PDF
    To determine whether cancer risks for carriers and noncarriers from families with a mismatch repair (MMR) gene mutation are increased above the risks of the general population. We prospectively followed a cohort of 446 unaffected carriers of an MMR gene mutation (MLH1, n = 161; MSH2, n = 222; MSH6, n = 47; and PMS2, n = 16) and 1,029 their unaffected relatives who did not carry a mutation every 5 years at recruitment centers of the Colon Cancer Family Registry. For comparison of cancer risk with the general population, we estimated country-, age-, and sex-specific standardized incidence ratios (SIRs) of cancer for carriers and noncarriers. Over a median follow-up of 5 years, mutation carriers had an increased risk of colorectal cancer (CRC; SIR, 20.48; 95% CI, 11.71 to 33.27; P < .001), endometrial cancer (SIR, 30.62; 95% CI, 11.24 to 66.64; P < .001), ovarian cancer (SIR, 18.81; 95% CI, 3.88 to 54.95; P < .001), renal cancer (SIR, 11.22; 95% CI, 2.31 to 32.79; P < .001), pancreatic cancer (SIR, 10.68; 95% CI, 2.68 to 47.70; P = .001), gastric cancer (SIR, 9.78; 95% CI, 1.18 to 35.30; P = .009), urinary bladder cancer (SIR, 9.51; 95% CI, 1.15 to 34.37; P = .009), and female breast cancer (SIR, 3.95; 95% CI, 1.59 to 8.13; P = .001). We found no evidence of their noncarrier relatives having an increased risk of any cancer, including CRC (SIR, 1.02; 95% CI, 0.33 to 2.39; P = .97). We confirmed that carriers of an MMR gene mutation were at increased risk of a wide variety of cancers, including some cancers not previously recognized as being a result of MMR mutations, and found no evidence of an increased risk of cancer for their noncarrier relatives

    Atomic Resonance and Scattering

    Get PDF
    Contains reports on eight research projects.National Science Foundation (Grant PHY79-09743)National Bureau of Standards (Grant NB-8-NAHA-3017)Joint Services Electronics Program (Contract DAAG29-80-C-0104)National Science Foundation (Grant PHY82-10486)U.S. Navy - Office of Naval Research (Contract N00014-79-C-0183)National Science Foundation (Grant CHE79-02967-A04)U.S. Air Force - Office of Scientific Research (Contract AFOSR-81-0067)Joint Services Electronics Program (Contract DAAG29-83-K-0003
    corecore