Improved representation of female scientists in the media can show future generations of women that they belong

The attrition of women from STEM careers has been attributed to many factors, such as work/life balance, biased hiring committees, and prejudiced editorial boards. But might it also be that women still do not see themselves as "real" scientists, or lack female role models? Miranda Hart reports on research examining women's visibility in two high-profile scientific publications. Not only were men far more likely to be the corresponding author on scientific papers and more likely to appear as a featured scientist, there were also far fewer photos of women, even among advertisements and stock photos. Science journals continue to portray the image of a scientist as almost exclusively male, which undoubtedly trickles down to young girls, who are struggling to see where they fit in

A robust Bayesian analysis of the impact of policy decisions on crop rotations.

We analyse the impact of a policy decision on crop rotations, using the imprecise land use model that was developed by the authors in earlier work. A specific challenge in crop rotation models is that farmer’s crop choices are driven by both policy changes and external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where crop transition probabilities are multinomial logistic functions of such external factors. We use a robust Bayesian approach to estimate the parameters of our model, and validate it by comparing the model response with a non-parametric estimate, as well as by cross validation. Finally, we use the resulting predictions to solve a hypothetical yet realistic policy problem

Community assembly and stability in the root microbiota during early plant development

Little is known about how community composition in the plant microbiome is affected by events in the life of a plant. For example, when the plant is exposed to soil, microbial communities may be an important factor in root community assembly. We conducted two experiments asking whether the composition of the root microbiota in mature plants could be determined by either the timing of root exposure to microbial communities or priority effects by early colonizing microbes. Timing of microbial exposure was manipulated through an inoculation experiment, where plants of different ages were exposed to a common soil inoculum. Priority effects were manipulated by challenging roots with established microbiota with an exogenous microbial community. Results show that even plants with existing microbial root communities were able to acquire new microbial associates, but that timing of soil exposure affected root microbiota composition for both bacterial and fungal communities in mature plants. Plants already colonized were only receptive to colonizers at 1 week post-germination. Our study shows that the timing of soil exposure in the early life stages of a plant is important for the development of the root microbiota in mature plants

Further theoretical and practical insight to the do-validated bandwidth selector

Recent contributions to kernel smoothing show that the performance of cross-validated bandwidth selectors improves significantly from indirectness and that the recent do-validated method seems to provide the most practical alternative among these methods. In this paper we show step by step how classical cross-validation improves in theory, as well as in practice, from indirectness and that do-validated estimators improve in theory, but not in practice, from further indirectness. This paper therefore provides a strong support for the practical and theoretical properties of do-validated bandwidth selection. Do-validation is currently being introduced to survival analysis in a number of contexts and this paper provides evidence that this might be the immediate step forward

In-Sample Forecasting with Local Linear Survival Densities

In this paper, in-sample forecasting is defined as forecasting a structured density to sets where it is unobserved. The structured density consists of one-dimensional in-sample components that identify the density on such sets. We focus on the multiplicative density structure, which has recently been seen as the underlying structure of non-life insurance forecasts. In non-life insurance the in-sample area is defined as one triangle and the forecasting area as the triangle that 20 added to the first triangle produces a square. Recent approaches estimate two one-dimensional components by projecting an unstructured two-dimensional density estimator onto the space of multiplicatively separable functions. We show that time-reversal reduces the problem to two one-dimensional problems, where the one-dimensional data are left-truncated and a one-dimensional survival density estimator is needed. This paper then uses the local linear density smoother with 25 weighted cross-validated and do-validated bandwidth selectors. Full asymptotic theory is provided, with and without time reversal. Finite sample studies and an application to non-life insurance are included

Smoothing survival densities in practice

Many nonparametric smoothing procedures consider independent identically distributed stochastic variables. There are also many important nonparametric smoothing applications where the data is more complicated. Survival data or filtered data, defined as following Aalen’s multiplicative hazard model and aggregated versions of this model, are considered. Aalen’s model based on counting process theory allows multiple left truncations and multiple right censoring to be present in the data. This type of filtering is omnipresent in biostatistical and demographical applications, where people can join a study, leave the study and perhaps join the study again. The estimation methodology is based on a recent class of local linear density estimators. A new stable bandwidth-selector is developed for these estimators. A data application to aggregated national mortality data is provided, where immigrations to and from the country correspond to respectively left truncation and right censoring. The aggregated mortality data study illustrates that the new practical density estimators provide an important extra element in the visual toolbox for understanding survival data

Study protocol for two pilot randomised controlled trials aimed at increasing physical activity using electrically assisted bicycles to enhance prostate or breast cancer survival

BACKGROUND: In 2020, 1.4 and 2.3 million new cases of prostate cancer and breast cancer respectively were diagnosed globally. In the UK, prostate cancer is the most common male cancer, while breast cancer is the most common female cancer. Engaging in physical activity (PA) is a key component of treatment. However, rates of PA are low in these clinical populations. This paper describes the protocol of CRANK-P and CRANK-B, two pilot randomised controlled trials, involving an e-cycling intervention aimed at increasing PA in individuals with prostate cancer or breast cancer respectively.METHODS: These two trials are single-centre, stratified, parallel-group, two-arm randomised waitlist-controlled pilot trials in which forty individuals with prostate cancer (CRANK-P) and forty individuals with breast cancer (CRANK-B) will be randomly assigned, in a 1:1 allocation ratio, to an e-cycling intervention or waitlist control. The intervention consists of e-bike training with a certified cycle instructor, followed by the provision of an e-bike for 12 weeks. Following the intervention period, participants in the e-bike condition will be directed to community-based initiatives through which they can access an e-bike. Data will be collected at baseline (T0), immediately post intervention (T1) and at 3-month follow-up (T2). In addition, in the intervention group, data will be collected during the intervention and follow-up periods. Quantitative and qualitative methods will be used. The primary objectives are to determine effective recruitment strategies, establish recruitment and consent rates, adherence and retention in the study, and determine the feasibility and acceptability of the study procedures and intervention. The potential impact of the intervention on clinical, physiological and behavioural outcomes will be assessed to examine intervention promise. Data analyses will be descriptive.DISCUSSION: The findings from these trials will provide information on trial feasibility and highlight the potential of e-cycling as a strategy to positively impact the health and behaviour of individuals with prostate cancer and breast cancer. If appropriate, this information can be used to design and deliver a fully powered definitive trial.TRIAL REGISTRATION: CRANK-B: [ISRCTN39112034]. CRANK-P [ISRCTN42852156]. Registered [08/04/2022] https://www.isrctn.com .</p

A robust Bayesian analysis of the impact of policy decisions on crop rotations

We analyse the impact of a policy decision on crop rotations, using the imprecise land use model that was developed by the authors in earlier work. A specific challenge in crop rotation models is that farmer’s crop choices are driven by both policy changes and external non-stationary factors, such as rainfall, temperature and agricultural input and output prices. Such dynamics can be modelled by a non-stationary stochastic process, where crop transition probabilities are multinomial logistic functions of such external factors. We use a robust Bayesian approach to estimate the parameters of our model, and validate it by comparing the model response with a non-parametric estimate, as well as by cross validation. Finally, we use the resulting predictions to solve a hypothetical yet realistic policy problem