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

Insulin resistance in type 1 diabetes: what is ‘double diabetes’ and what are the risks?

In this review, we explore the concept of ‘double diabetes’, a combination of type 1 diabetes with features of insulin resistance and type 2 diabetes. After considering whether double diabetes is a useful concept, we discuss potential mechanisms of increased insulin resistance in type 1 diabetes before examining the extent to which double diabetes might increase the risk of cardiovascular disease (CVD). We then go on to consider the proposal that weight gain from intensive insulin regimens may be associated with increased CV risk factors in some patients with type 1 diabetes, and explore the complex relationships between weight gain, insulin resistance, glycaemic control and CV outcome. Important comparisons and contrasts between type 1 diabetes and type 2 diabetes are highlighted in terms of hepatic fat, fat partitioning and lipid profile, and how these may differ between type 1 diabetic patients with and without double diabetes. In so doing, we hope this work will stimulate much-needed research in this area and an improvement in clinical practice

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management

Applying refinement to the use of mice and rats in rheumatoid arthritis research

Rheumatoid arthritis (RA) is a painful, chronic disorder and there is currently an unmet need for effective therapies that will benefit a wide range of patients. The research and development process for therapies and treatments currently involves in vivo studies, which have the potential to cause discomfort, pain or distress. This Working Group report focuses on identifying causes of suffering within commonly used mouse and rat ‘models’ of RA, describing practical refinements to help reduce suffering and improve welfare without compromising the scientific objectives. The report also discusses other, relevant topics including identifying and minimising sources of variation within in vivo RA studies, the potential to provide pain relief including analgesia, welfare assessment, humane endpoints, reporting standards and the potential to replace animals in RA research

Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis : A Tutorial

Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot be ascertained from the data at hand. Guidelines recommend conducting sensitivity analyses to assess the robustness of conclusions to plausible MNAR assumptions, but this is rarely done in practice, possibly because of a lack of practical guidance. This tutorial aims to address this by presenting an accessible framework and practical guidance for conducting sensitivity analysis for MNAR data in trial-based CEA. We review some of the methods for conducting sensitivity analysis, but focus on one particularly accessible approach, where the data are multiply-imputed and then modified to reflect plausible MNAR scenarios. We illustrate the implementation of this approach on a weight-loss trial, providing the software code. We then explore further issues around its use in practice

SPRING: an RCT study of probiotics in the prevention of gestational diabetes mellitus in overweight and obese women

Background: Obesity is increasing in the child-bearing population as are the rates of gestational diabetes. Gestational diabetes is associated with higher rates of Cesarean Section for the mother and increased risks of macrosomia, higher body fat mass, respiratory distress and hypoglycemia for the infant. Prevention of gestational diabetes through life style intervention has proven to be difficult. A Finnish study showed that ingestion of specific probiotics altered the composition of the gut microbiome and thereby metabolism from early gestation and decreased rates of gestational diabetes in normal weight women. In SPRING (the Study of Probiotics IN the prevention of Gestational diabetes), the effectiveness of probiotics ingestion for the prevention of gestational diabetes will be assessed in overweight and obese women

Evaluation of a minimally invasive glucose biosensor for continuous tissue monitoring

We describe here a minimally invasive glucose biosensor based on a microneedle array electrode fabricated from an epoxy-based negative photoresist (SU8 50) and designed for continuous measurement in the dermal compartment with minimal pain. These minimally invasive, continuous monitoring sensor devices (MICoMS) were produced by casting the structures in SU8 50, crosslinking and then metallising them with platinum or silver to obtain the working and reference electrodes, respectively. The metallised microneedle array electrodes were subsequently functionalised by entrapping glucose oxidase in electropolymerised polyphenol (PP) film. Sensor performance in vitro showed that glucose concentrations down to 0.5 mM could be measured with a response times (T90) of 15 s. The effect of sterilisation by Co60 irradiation was evaluated. In preparation for further clinical studies, these sensors were tested in vivo in a healthy volunteer for a period of 3–6 h. The sensor currents were compared against point measurements obtained with a commercial capillary blood glucometer. The epoxy MICoMS devices showed currents values that could be correlated with these

SETD2 loss-of-function promotes renal cancer branched evolution through replication stress and impaired DNA repair

The research leading to these results is supported by Cancer Research UK (XYG, RAB, EG, PM, PE, SG, C Santos, AJR, NM, PAB, AS and C Swanton), Breast Cancer Research Foundation (C Swanton and NK), Medical Research Council (ID: G0902275 to MG and C Santos; ID: G0701935/2 to AJR and C Swanton), the Danish Cancer Society (AMM, J Bartkova and J Bartek), the Lundbeck Foundation (R93-A8990 to J Bartek), the Ministry of the interior of the Czech Republic (grant VG20102014001 to MM and J Bartek), the National Program of Sustainability (grant LO1304 to MM and J Bartek), the Danish Council for Independent Research (grant DFF-1331-00262 to J Bartek), NIHR RMH/ICR Biomedical Research Centre for Cancer (JL), the EC Framework 7 (PREDICT 259303 to XYG, EG, PM, MG, TJ and C Swanton; DDResponse 259892 to J Bartek and J Bartkova and RESPONSIFY ID:259303 to C Swanton), UCL Overseas Research Scholarship (SG). C Swanton is also supported by the European Research Council, Rosetrees Trust and The Prostate Cancer Foundation. This research is supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance