Early Australian Optical and Radio Observations of Centaurus A

The discoveries of the radio source Centaurus A and its optical counterpart NGC 5128 were important landmarks in the history of Australian astronomy. NGC 5128 was first observed in August 1826 by James Dunlop during a survey of southern objects at the Parramatta Observatory, west of the settlement at Sydney Cove. The observatory had been founded a few years earlier by Thomas Brisbane, the new governor of the British colony of New South Wales. Just over 120 years later, John Bolton, Gordon Stanley and Bruce Slee discovered the radio source Centaurus A at the Dover Heights field station in Sydney, operated by CSIRO's Radiophysics Laboratory (the forerunner of the Australia Telescope National Facility). This paper will describe this early historical work and summarise further studies of Centaurus A by other Radiophysics groups up to 1960.Comment: 45 pages, 43 figure

Pancreatic cancer and predictors of survival: comparing the CA 19-9/bilirubin ratio with the McGill Brisbane Symptom Score

AbstractIntroductionFew tools predict survival from pancreatic cancer (PAC). The McGill Brisbane Symptom Score (MBSS) based on symptoms at presentation (weight loss, pain, jaundice and smoking) was recently validated. The present study compares the ability of four strategies to predict 9-month survival: MBSS, carbohydrate antigen 19-9 (CA 19-9) alone, CA19-9-to-bilirubin ratio and a combination of MBSS and the CA19-9-to-bilirubin ratio.MethodologyA retrospective review of 133 patients diagnosed with PAC between 2005 and 2011 was performed. Survival was determined from the Quebec civil registry. Blood CA 19-9 and bilirubin values were collected (n = 52) at the time of diagnosis. Receiver-operating characteristic (ROC) curves were used to determine a cutoff for optimal test characteristics of CA 19-9 and CA19-9-to-total bilirubin ratio in predicting survival at 9 months. Predictive characteristics were then calculated for the four strategies.ResultsOf the four strategies, the one with the greatest negative predictive value was the MBSS: negative predictive value (NPV) was 90.2% (76.9–97.3%) and the positive likelihood ratio (LR) was the greatest. The ability of CA 19-9 levels alone, at baseline, to predict survival was low. For the CA19-9-to-bilirubin ratio, the test characteristics improved but remained non-significant. The best performing strategy according to likelihood ratios was the combined MBSS and CA19-9 to the bilirubin ratio.ConclusionCA19-9 levels and the CA19-9-to-bilirubin ratio are poor predictors of survival for PAC, whereas the MBSS is a far better predictor, confirming its clinical value. By adding the CA19-9-to-bilirubin ratio to the MBSS the predictive characteristics improved

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Mixtures of phenol and phthalate urinary biomarkers in relation to the ovarian reserve among women attending a fertility clinic

International audienc

Mixtures of urinary concentrations of phenols and phthalate biomarkers in relation to the ovarian reserve among women attending a fertility clinic

International audienceAlthough prior studies have found associations of the ovarian reserve with urinary concentrations of some individual phenols and phthalate metabolites, little is known about the potential associations of these chemicals as a mixture with the ovarian reserve. We investigated whether mixtures of four urinary phenols (bisphenol A, butylparaben, methylparaben, propylparaben) and eight metabolites of five phthalate diesters including di(2-ethylhexyl) phthalate were associated with markers of the ovarian reserve among 271 women attending a fertility center who enrolled in the Environment and Reproductive Health study (2004-2017). The analysis was restricted to one outcome per study participant using the earliest outcome after the last exposure assessment. Ovarian reserve markers included lower antral follicle count (AFC) defined as AFC < 7, circulating serum levels of day 3 follicle stimulating hormone (FSH) assessed by immunoassays, and diminished ovarian reserve (DOR) defined as either AFC < 7, FSH > 10 UI/L or primary infertility diagnosis of DOR. We applied Bayesian Kernel Machine Regression (BKMR) and quantile g-computation to estimate the joint associations and assess the interactions between chemical exposure biomarkers on the markers of the ovarian reserve while adjusting for confounders. Among all 271 women, 738 urine samples were collected. In quantile g-computation models, a quartile increase in the exposure biomarkers mixture was not significantly associated with lower AFC (OR = 1.10, 95 % CI = 0.52, 2.30), day 3 FSH levels (Beta = 0.30, 95 % CI = -0.32, 0.93) or DOR (OR = 1.02, 95 % CI = 0.52, 2.05). Similarly, BKMR did not show any evidence of associations between the mixture and any of the studied outcomes, or interactions between chemicals. Despite the lack of associations, these results need to be explored among women in other study cohorts

Mixtures of phenol and phthalate exposure biomarkers in relation to semen parameters among men from a fertility clinic

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