An XMM-Newton view of M101 - III. Diffuse X-ray emission

We present a study of the X-ray properties of the nearby face-on Scd spiral galaxy M101 based on recent XMM–Newton observations. In this third and final paper in the present series, we focus on the spatial and spectral properties of the residual emission, after excluding bright X-ray sources with LX > 1037 erg s−1. Within a central region of radius 10 arcmin (21 kpc), the X-ray emission broadly traces the pattern of the spiral arms, establishing a strong link with recent star formation, but it also exhibits a radial scalelength of ≈2.6 arcmin (5.4 kpc) consistent with optical data. We estimate the soft X-ray luminosity within the central 5 arcmin (10.5 kpc) region to be LX ≈ 2.1 × 1039 erg s−1 (0.5–2 keV), the bulk of which appears to originate as diffuse emission. We find a two-temperature thermal model best fits the spectral data with derived temperatures of keV which are very typical of the diffuse components seen in other normal and starburst galaxies. More detailed investigation of the X-ray morphology reveals a strong correlation with images recorded in the far-ultraviolet through to V band, with the best match being with the U band. We interpret these results in terms of a clumpy thin-disc component which traces the spiral arms of M101 plus an extended lower halo component with large filling factor

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults

This is the final version. Available from the publisher via the DOI in this record.The data that support the findings of this study are available from University of Exeter Medical School/Oxford University but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of University of Exeter Medical School/Oxford University. R code is made available in supplementary file (see Additional file 2).Background: There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models. Methods: We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18–50 years) with clinically diagnosed diabetes recruited from primary and secondary care (n = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset (n = 504, 21% with type 1 diabetes). Results: Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities. Conclusion: Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.National Institute for Health Research (NIHR

3D modelling of angiogenesis and vascular tumour growth

This paper was presented at the 3rd Micro and Nano Flows Conference (MNF2011), which was held at the Makedonia Palace Hotel, Thessaloniki in Greece. The conference was organised by Brunel University and supported by the Italian Union of Thermofluiddynamics, Aristotle University of Thessaloniki, University of Thessaly, IPEM, the Process Intensification Network, the Institution of Mechanical Engineers, the Heat Transfer Society, HEXAG - the Heat Exchange Action Group, and the Energy Institute

Dust formation in the outflows of catastrophically evaporating planets

Ultra-short period planets offer a window into the poorly understood interior composition of exoplanets through material evaporated from their rocky interiors. Among these objects are a class of disintegrating planets, observed when their dusty tails transit in front of their host stars. These dusty tails are thought to originate from dust condensation in thermally-driven winds emanating from the sublimating surfaces of these planets. Existing models of these winds have been unable to explain their highly variable nature and have not explicitly modelled how dust forms in the wind. Here we present new radiation-hydrodynamic simulations of the winds from these planets, including a minimal model for the formation and destruction of dust, assuming that nucleation can readily take place. We find that dust forms readily in the winds, a consequence of large dust grains obtaining lower temperatures than the planet’s surface. As hypothesised previously, we find that the coupling of the planet’s surface temperature to the outflow properties via the dust’s opacity can drive time-variable flows when dust condensation is sufficiently fast. In agreement with previous work, our models suggest that these dusty tails are a signature of catastrophically evaporating planets that are close to the end of their lives. Finally, we discuss the implications of our results for the dust’s composition. More detailed hydrodynamic models that self-consistently compute the nucleation and composition of the dust and gas are warranted in order to use these models to study the planet’s interior composition

Investigations on the Peach 4 Debrite, a Late Pleistocene Mass Movement on the Northwest British Continental Margin

The Peach 4 debrite is the most recent in a series of large scale Pleistocene MTDs within the Barra fan on the northwest British continental margin. Geophysical data indicate that Peach 4 was formed through a combination of blocky and muddy debris flows and affects an area of ~ 700 km2. BGS core sample 56 -10 36, located directly over the Peach 4 debrite, provides a minimum age of 14.68 ka cal BP for the last major failure. An upwards fining turbidite sequence in BGS core sample 56 -10 239 is associ-ated with increased As and S concentrations, indicators of diagenetic pyrite which forms under anoxic conditions. It is proposed that As and S concentrations may pro-vide a method of distinguishing between contourite and turbidite sedimentation, though further research is required

Effect of Silicon Content on Carbide Precipitation and Low-Temperature Toughness of Pressure Vessel Steels

Cr – Mn – Mo – Ni pressure vessel steels containing 0.54 and 1.55% Si are studied. Metallographic and fractographic analyses of the steels after tempering at 650 and 700°C are performed. The impact toughness at – 30°C and the hardness of the steels are determined. The mass fraction of the carbide phase in the steels is computed with the help of the J-MatPro 4.0 software

Regular breakfast consumption and type 2 diabetes risk markers in 9- to 10-year-old children in the child heart and health study in England (CHASE): a cross-sectional analysis.

BACKGROUND: Regular breakfast consumption may protect against type 2 diabetes risk in adults but little is known about its influence on type 2 diabetes risk markers in children. We investigated the associations between breakfast consumption (frequency and content) and risk markers for type 2 diabetes (particularly insulin resistance and glycaemia) and cardiovascular disease in children. METHODS AND FINDINGS: We conducted a cross-sectional study of 4,116 UK primary school children aged 9-10 years. Participants provided information on breakfast frequency, had measurements of body composition, and gave fasting blood samples for measurements of blood lipids, insulin, glucose, and glycated haemoglobin (HbA1c). A subgroup of 2,004 children also completed a 24-hour dietary recall. Among 4,116 children studied, 3,056 (74%) ate breakfast daily, 450 (11%) most days, 372 (9%) some days, and 238 (6%) not usually. Graded associations between breakfast frequency and risk markers were observed; children who reported not usually having breakfast had higher fasting insulin (percent difference 26.4%, 95% CI 16.6%-37.0%), insulin resistance (percent difference 26.7%, 95% CI 17.0%-37.2%), HbA1c (percent difference 1.2%, 95% CI 0.4%-2.0%), glucose (percent difference 1.0%, 95% CI 0.0%-2.0%), and urate (percent difference 6%, 95% CI 3%-10%) than those who reported having breakfast daily; these differences were little affected by adjustment for adiposity, socioeconomic status, and physical activity levels. When the higher levels of triglyceride, systolic blood pressure, and C-reactive protein for those who usually did not eat breakfast relative to those who ate breakfast daily were adjusted for adiposity, the differences were no longer significant. Children eating a high fibre cereal breakfast had lower insulin resistance than those eating other breakfast types (p for heterogeneity <0.01). Differences in nutrient intakes between breakfast frequency groups did not account for the differences in type 2 diabetes markers. CONCLUSIONS: Children who ate breakfast daily, particularly a high fibre cereal breakfast, had a more favourable type 2 diabetes risk profile. Trials are needed to quantify the protective effect of breakfast on emerging type 2 diabetes risk. Please see later in the article for the Editors' Summary