On local structures of cubicity 2 graphs

A 2-stab unit interval graph (2SUIG) is an axes-parallel unit square intersection graph where the unit squares intersect either of the two fixed lines parallel to the $X$-axis, distance $1 + \epsilon$ ($0 < \epsilon < 1$) apart. This family of graphs allow us to study local structures of unit square intersection graphs, that is, graphs with cubicity 2. The complexity of determining whether a tree has cubicity 2 is unknown while the graph recognition problem for unit square intersection graph is known to be NP-hard. We present a polynomial time algorithm for recognizing trees that admit a 2SUIG representation

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Seasonal variation of food security among the Batwa of Kanungu, Uganda

Climate change is projected to increase the burden of food insecurity (FI) globally, particularly among populations that depend on subsistence agriculture. The impacts of climate change will have disproportionate effects on populations with higher existing vulnerability. Indigenous people consistently experience higher levels of FI than their non-Indigenous counterparts and are more likely to be dependent upon land-based resources. The present study aimed to understand the sensitivity of the food system of an Indigenous African population, the Batwa of Kanungu District, Uganda, to seasonal variation. A concurrent, mixed methods (quantitative and qualitative) design was used. Six cross-sectional retrospective surveys, conducted between January 2013 and April 2014, provided quantitative data to examine the seasonal variation of self-reported household FI. This was complemented by qualitative data from focus group discussions and semi-structured interviews collected between June and August 2014. Ten rural Indigenous communities in Kanungu District, Uganda. FI data were collected from 130 Indigenous Batwa Pygmy households. Qualitative methods involved Batwa community members, local key informants, health workers and governmental representatives. The dry season was associated with increased FI among the Batwa in the quantitative surveys and in the qualitative interviews. During the dry season, the majority of Batwa households reported greater difficulty in acquiring sufficient quantities and quality of food. However, the qualitative data indicated that the effect of seasonal variation on FI was modified by employment, wealth and community location. These findings highlight the role social factors play in mediating seasonal impacts on FI and support calls to treat climate associations with health outcomes as non-stationary and mediated by social sensitivity

Scenario-led modelling of broadleaf forest expansion in Wales

Context Significant changes in the composition and extent of the UK forest cover are likely to take place in the coming decades. Current policy targets an increase in forest area, for example the Welsh Government aims for forest expansion by 2030, and a purposeful shift from non-native conifers to broadleaved tree species, as identified by the UK Forestry Standard Guidelines on Biodiversity. Objectives Using the example of Wales, we aim to generate evidence-based projection of impact of contrasting policy scenarios on the state of forests in the near future, with the view of stimulating debate and aiding decisions concerning plausible outcomes of different policies. Methods We quantified changes in different land use and land cover (LULC) classes in Wales between 2007 and 2015 and used a Multi-layer perceptron-Markov chain ensemble modelling approach to project the state of Welsh forests in 2030 under the current and an alternate policy scenario. Results The current level of expansion and restoration of broadleaf forest in Wales is sufficient to deliver on existing policy goals. We also show effects of a more ambitious afforestation policy on the Welsh landscape. In a key finding, the highest intensity of broadleaf expansion is likely to shift from south-eastern to a more central areas of Wales. Conclusion The study identifies the key predictors of LULC change in Wales. High resolution future land cover simulation maps using these predictors offers an evidence-based tool for forest managers and government officials to test effects of existing and alternative policy scenarios

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Mobile phone text message intervention to reduce binge drinking among young adults: Study protocol for a randomized controlled trial

Background: Heavy episodic (binge) drinking is common among young adults and can lead to injury and illness. Young adults who seek care in the Emergency Department (ED) may be disproportionately affected with binge drinking behavior, therefore provide an opportunity to reduce future risk through screening, brief intervention and referral to treatment (SBIRT). Mobile phone text messaging (SMS) is a common form of communication among young adults and has been shown to be effective at providing behavioral support to young adult drinkers after ED discharge. Efficacy of SMS programs to reduce binge drinking remains unknown.Methods/Design: We will conduct a three parallel arm, randomized trial. A convenience sample of adults aged 18 to 25 years attending three EDs in Pittsburgh, PA and willing to participate in the study will be screened for hazardous alcohol consumption. Participants identified as hazardous drinkers will then be allocated to either 12 weeks of weekly SMS drinking assessments with feedback (SA+F), SMS drinking assessments without feedback (SA), or a control group. Randomization will be via an independent and remote computerized randomization and will be stratified by study site. The SA+F group will be asked to provide pre-weekend drinking intention as well as post-weekend consumption via SMS and will receive feedback messages focused on health consequences of alcohol consumption, personalized normative feedback, protective drinking strategies and goal setting. Follow-up data on alcohol use and injury related to alcohol will be collected through a password-protected website three, six and nine months later. The primary outcome for the study is binge drinking days (≥4 drinks for women; ≥5 drinks for men) during the previous month, and the main secondary outcome is the proportion of participants who report any injury related to alcohol in the prior three months.Discussion: This study will test the hypothesis that a mobile phone text-messaging program will result in immediate and durable reductions in binge drinking among at-risk young adults. By testing an intervention group to an assessment-only and control group, we will be able to separate the effect of assessment reactivity. By collecting pre-weekend drinking intentions and post-weekend consumption data in the SA+F group, we will be able to better understand mechanism of change.Trial registration: Clinicaltrials.gov NCT01688245. © 2013 Suffoletto et al.; licensee BioMed Central Ltd

S-COL: A Copernican turn for the development of flexibly reusable collaboration scripts

Collaboration scripts are usually implemented as parts of a particular collaborative-learning platform. Therefore, scripts of demonstrated effectiveness are hardly used with learning platforms at other sites, and replication studies are rare. The approach of a platform-independent description language for scripts that allows for easy implementation of the same script on different platforms has not succeeded yet in making the transfer of scripts feasible. We present an alternative solution that treats the problem as a special case of providing support on top of diverse Web pages: In this case, the challenge is to trigger support based on the recognition of a Web page as belonging to a specific type of functionally equivalent pages such as the search query form or the results page of a search engine. The solution suggested has been implemented by means of a tool called S-COL (Scripting for Collaborative Online Learning) and allows for the sustainable development of scripts and scaffolds that can be used with a broad variety of content and platforms. The tool’s functions are described. In order to demonstrate the feasibility and ease of script reuse with S-COL, we describe the flexible re-implementation of a collaboration script for argumentation in S-COL and its adaptation to different learning platforms. To demonstrate that a collaboration script implemented in S-COL can actually foster learning, an empirical study about the effects of a specific script for collaborative online search on learning activities is presented. The further potentials and the limitations of the S-COL approach are discussed