A new super-soft X-ray source in the Small Magellanic Cloud: Discovery of the first Be/white dwarf system in the SMC?

The Small Magellanic Cloud (SMC) hosts a large number of Be/X-ray binaries, however no Be/white dwarf system is known so far, although population synthesis calculations predict that they might be more frequent than Be/neutron star systems. XMMUJ010147.5-715550 was found as a new faint super-soft X-ray source (SSS) with a likely Be star optical counterpart. We investigate the nature of this system and search for further high-absorbed candidates in the SMC. We analysed the XMM-Newton X-ray spectrum and light curve, optical photometry, and the I-band OGLE III light curve. The X-ray spectrum is well represented by black-body and white dwarf atmosphere models with highly model-dependent temperature between 20 and 100 eV. The likely optical counterpart AzV 281 showed low near infrared emission during X-ray activity, followed by a brightening in the I-band afterwards. We find further candidates for high-absorbed SSSs with a blue star as counterpart. We discuss XMMUJ010147.5-715550 as the first candidate for a Be/white dwarf binary system in the SMC.Comment: 6 pages, 4 figures, accepted by A&

Early education pilot for two year old children : evaluation

This report provides the findings of the evaluation of the early education pilot for disadvantaged two year old children (the pilot). This study aimed to assess the impact of the pilot by looking at: how well the pilot was targeted, parents’ experiences of taking up a pilot place, the quality of the pilot settings, the impact on the children’s behaviour, and parents’ views and experiences of using a pilot place. The pilot provided free early years education to over 13,500 disadvantaged two year olds between 2006 and 2008. The main purpose of the pilot was to improve children’s social and cognitive outcomes, e.g. their social confidence and independence, and their verbal skills and reasoning ability. Additional aims were to have a positive impact on children’s parents and wider family e.g. on the relationship between parents and their children, or on parent’s emotional wellbeing. The funding offered these children 7.5 or in a small number of local authorities 12.5 hours of early years education per week for 38 weeks of the year. The pilot places were available in a variety of early years settings e.g. nurseries, play groups and with childminders, but all were required to operate the Birth to Three Matters curriculum.© National Centre for Social Research 2009. The full text of this report is not available in ORA. You may be able to access the report at https://www.gov.uk/government/publications/early-education-pilot-for-2-year-old-children-evaluation (URL checked 26 March 2014) or via the publication website link above

Translationally-invariant coupled-cluster method for finite systems

The translational invariant formulation of the coupled-cluster method is presented here at the complete SUB(2) level for a system of nucleons treated as bosons. The correlation amplitudes are solution of a non-linear coupled system of equations. These equations have been solved for light and medium systems, considering the central but still semi-realistic nucleon-nucleon S3 interaction.Comment: 16 pages, 2 Postscript figures, to be published in Nucl. Phys.

NKX2-2 (NK2 homeobox 2)

Review on NKX2-2 (NK2 homeobox 2), with data on DNA, on the protein encoded, and where the gene is implicated

Tools for computing the AGN feedback: radio-loudness distribution and the kinetic luminosity function

We studied the Active Galactic Nuclei (AGN) radio emission from a compilation of hard X-ray selected samples, all observed in the 1.4 GHz band. A total of more than 1600 AGN with 2-10 keV de-absorbed luminosities higher than 10^42 erg/s were used. For a sub-sample of about 50 z\lsim 0.1 AGN it was possible to reach a ~80% fraction of radio detections and therefore, for the first time, it was possible to almost completely measure the probability distribution function of the ratio between the radio and the X-ray luminosity Rx=log[L(1.4)/Lx]. The probability distribution function of Rx was functionally fitted as dependent on the X-ray luminosity and redshift, P(Rx|Lx,z). It roughly spans over 6 decades (-7<Rx<-1), and does not show any sign of bi-modality. It resulted that the probability of finding large values of the Rx ratio increases with decreasing X-ray luminosities and (possibly) with increasing redshift. No statistical significant difference was found between the radio properties of the X-ray absorbed and unabsorbed AGN. The measure of the probability distribution function of Rx allowed us to compute the kinetic luminosity function and the kinetic energy density which, at variance with what assumed in many galaxy evolution models, is observed to decrease of about a factor of five at redshift below 0.5. About half of the kinetic energy density results to be produced by the more radio quiet (Rx<-4) AGN. In agreement with previous estimates, the AGN efficiency in converting the accreted mass energy into kinetic power is, on average, ~5x10-3.Comment: 13 pages, ApJsty; ApJ in pres

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

Kinetic control over CdS nanocrystal nucleation using a library of thiocarbonates, thiocarbamates, and thioureas

We report a family of substituted thiocarbonates, thiocarbamates, and thioureas and their reaction with cadmium oleate at 180-240 degrees C to form zincblende CdS nanocrystals (d = 2.25.9 nm). To monitor the kinetics of CdS formation with UV-vis spectroscopy, the size dependence of the extinction coefficient for lambda(max)(1S(e)-1S(1/2h)) is determined. The precursor conversion reactivity spans 5 orders of magnitude depending on the precursor structure (2 degrees-thioureas > 3 degrees-thioureas >= 2 degrees-thiocarbamates > 2 degrees-thiocarbonates > 4 degrees-thioureas >= 3 degrees-thiocarbamates). The concentration of nanocrystals formed during nucleation increases when more reactive precursors are used, allowing the final size to be controlled by the precursor structure. H-1 NMR spectroscopy is used to monitor the reaction of di-p-tolyl thiocarbonate and cadmium oleate where di-p-tolyl carbonate and oleic anhydride coproducts can be identified. These coproducts further decompose into p-tolyl oleate and p-cresol. The spectral features of CdS nanocrystals produced from thiocarbonates are exceptionally narrow (95-161 meV fwhm) as compared to those made from thioureas (137-174 meV fwhm) under otherwise identical conditions, indicating that particular precursors nucleate narrower size distributions than others

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