In this paper, we report the evidence for a planetary mass body orbiting the close binary star KIC 5095269. This detection arose from a search for eclipse timing variations amongst the more than 2000 eclipsing binaries observed by Kepler. Light curve and periodic eclipse time variations have been analysed using systemic and a custom Binary Eclipse Timings code based on the Transit Analysis Package which indicates a 7.70 ± 0.08M_Jup object orbiting every 237.7 ± 0.1 d around a 1.2 M_⊙ primary and a 0.51 M_⊙ secondary in an 18.6 d orbit. A dynamical integration over 10⁷ yr suggests a stable orbital configuration. Radial velocity observations are recommended to confirm the properties of the binary star components and the planetary mass of the companion.

The Jovian Trojans are two swarms of objects located around the L4 and L5 Lagrange points. The population is thought to have been captured by Jupiter during the Solar system’s youth. Within the swarms, six collisional families have been identified in previous work, with four in the L4 swarm, and two in the L5. Our aim is to investigate the stability of the two Trojan swarms, with a particular focus on these collisional families. We find that the members of Trojan swarms escape the population at a linear rate, with the primordial L4 (23.35 per cent escape) and L5 (24.89 per cent escape) population sizes likely 1.31 and 1.35 times larger than today. Given that the escape rates were approximately equal between the two Trojan swarms, our results do not explain the observed asymmetry between the two groups, suggesting that the numerical differences are primordial in nature, supporting previous studies. Upon leaving the Trojan population, the escaped objects move on to orbits that resemble those of the Centaur and short-period comet populations. Within the Trojan collisional families, the 1996 RJ and 2001 UV209 families are found to be dynamically stable over the lifetime of the Solar system, whilst the Hektor, Arkesilos and Ennomos families exhibit various degrees of instability. The larger Eurybates family shows 18.81 per cent of simulated members escaping the Trojan population. Unlike the L4 swarm, the escape rate from the Eurybates family is found to increase as a function of time, allowing an age estimation of approximately 1.045 ± 0.364 × 109 yr.

Traumatic brain injury (TBI) is a much researched topic in medical health, which requires additional studies to understand various effects of demographic and geographic factors that can assist in developing the most effective treatments. Thousands of people of different ages are suffering from lifelong disabilities, either mild or severe, from TBI and the number is increasing. This study aims to increase our understanding of the effect of sex and age by applying five different statistical methods to evaluate the effect of these covariates on two independent TBI data sets representing patients from different geographical cohorts. A primary data was collected from Bangladesh and it was compared with CRASH (Corticosteroid Randomisation after Significant Head Injury) data, representing various countries around the world. The outcome variable for TBI considered in this paper is Glasgow Outcome Scale, which is a four point scale. It was converted to a binary outcome scale for fitting of Fisher’s exact test, a test of proportions and a binary linear model. For analyzing ordinal outcomes, the proportional odds model and the sliding dichotomy model were fitted. As the sample size of the Bangladeshi data set was small, parametric bootstrapping was applied for the consistency of results. Females were the worse sufferers of TBI compared to men, according to CRASH data set. The old (aged above 58 years) followed by adults (age 25 to 58) were the most vulnerable victims. Interaction effects concluded that old women tended to endure the worst outcomes of TBI. This conclusion came from the CRASH data set representing the world in general, whereas such effects were not present in the Bangladesh data set. Additional application of parametric bootstrapping for the smaller Bangladesh data set did not result into any significant outcome. The effect of gender and age could be stronger in some countries than others which is driving the significance in CRASH and was not found in Bangladesh. It reflects the necessity of incorporating geographic patterns as well as demographic features of patients while developing treatments and designing clinical trials.

We present a spectropolarimetric snapshot survey of solar-type planet-hosting stars. In addition to 14 planet-hosting stars observed as part of the BCool magnetic snapshot survey, we obtained magnetic observations of a further 19 planet-hosting solar-type stars in order to see if the presence of close-in planets had an effect on the measured surface magnetic field (|B_ℓ|). Our results indicate that the magnetic activity of this sample is congruent with that of the overall BCool sample. The effects of the planetary systems on the magnetic activity of the parent star, if any, are too subtle to detect compared to the intrinsic dispersion and correlations with rotation, age and stellar activity proxies in our sample. Four of the 19 newly observed stars, two of which are subgiants, have unambiguously detected magnetic fields and are future targets for Zeeman–Doppler mapping.

The use of spatially referenced data in agricultural systems modelling has grown in recent decades, however, the use of spatial modelling techniques in agricultural science is limited. In this paper, we test an effective and efficient technique for spatially modelling and analysing agricultural data using Bayesian hierarchical spatial models (BHSM). These models utilise analytical approximations and numerical integration called Integrated Nested Laplace Approximations (INLA). We critically analyse and compare the performance of the INLA and INLA-SPDE (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation) approaches against the more commonly used generalised linear model (glm), by modelling binary geostatistical species presence/absence data for several agro-ecologically significant Australian grassland species. The INLA-SPDE approach showed excellent predictive performance (ROCAUC 0.9271–0.9623) for all species. Further, the glm approach not accounting for spatial autocorrelation had inconsistent parameter estimates (switching between significantly positive and negative) when the dataset was subsetted and modelled at different scales. In contrast, the INLA-SPDE approach which accounted for spatial autocorrelation had stable parameter estimates. Using approaches which explicitly account for spatial autocorrelation, such as INLA-SPDE, improves model predictive performance and may provide a significant advantage for researchers by reducing the potential for Type I or false-positive errors in inferences about the significance of predictors.