New frontiers in translational research: Touchscreens,open science,and the mouse translational research accelerator platform

Many neurodegenerative and neuropsychiatric diseases and other brain disorders are accompanied by impairments in high-level cognitive functions including memory, attention, motivation, and decision-making. Despite several decades of extensive research, neuroscience is little closer to discovering new treatments. Key impediments include the absence of validated and robust cognitive assessment tools for facilitating translation from animal models to humans. In this review, we describe a state-of-the-art platform poised to overcome these impediments and improve the success of translational research, the Mouse Translational Research Accelerator Platform (MouseTRAP), which is centered on the touchscreen cognitive testing system for rodents. It integrates touchscreen-based tests of high-level cognitive assessment with state-of-the art neurotechnology to record and manipulate molecular and circuit level activity in vivo in animal models during human-relevant cognitive performance. The platform also is integrated with two Open Science platforms designed to facilitate knowledge and data-sharing practices within the rodent touchscreen community, touchscreencognition.org and mousebytes.ca. Touchscreencognition.org includes the Wall, showcasing touchscreen news and publications, the Forum, for community discussion, and Training, which includes courses, videos, SOPs, and symposia. To get started, interested researchers simply create user accounts. We describe the origins of the touchscreen testing system, the novel lines of research it has facilitated, and its increasingly widespread use in translational research, which is attributable in part to knowledge-sharing efforts over the past decade. We then identify the unique features of MouseTRAP that stand to potentially revolutionize translational research, and describe new initiatives to partner with similar platforms such as McGill's M3 platform (m3platform.org).     

New evidence on the impact of the Great Recession on health-compromising behaviours

Using data from the English Longitudinal Study of Ageing over the period 2004–2017, this paper explores the effects of the Great Recession and its aftermath upon health-compromising behaviours in adults aged 50 and over. We introduce new techniques into this area of research, namely dynamic random-effects logit estimators which control for initial conditions and correlated individual effects. We observe a lack of crisis effect upon the probabilities of smoking and being physically inactive, as well as of transitioning in and out of these behaviours. In line with other recent literature, this suggests that the relationship between economic recessions and smoking and physical inactivity may have broken down. Alternatively, the over 50s may have been protected from the crisis and subsequent austerity measures. Nonetheless, both the crisis and post-crisis period were associated with a lower probability of drinking frequently.     

C-reactive protein-to-albumin ratio is an independent poor prognostic factor in newly diagnosed chronic lymphocytic leukaemia: A clinical analysis of 322 cases

Chronic lymphocytic leukaemia is one of the most common types of adult leukaemia. Cancer-related systemic inflammation response has been characterized to correlate with therapeutic outcome in patients with cancer. The C-reactive protein-to-albumin (CRP/ALB) ratio (CAR), which is an inflammatory marker, has been reported as a novel prognostic factor in several cancers. The aim of our study was to evaluate the prognostic value of the CAR in patients with chronic lymphocytic leukaemia (CLL). We retrospectively reviewed the clinical characteristics of 322 newly diagnosed CLL patients, investigated the correlations among pretreatment CAR, treatment-free survival (TFS) and overall survival (OS), assessed the prognostic effect of the CAR to compare with other inflammation-related prognostic index by the area under the curve (AUC), and combined CAR and CLL-international prognostic index (CLL-IPI) together to improve the current prognostic system. The results showed that CAR was an independent prognostic factor for OS. Furthermore, the predictive and discriminatory capacity of CLL-IPI together with CAR level was superior to that of CLL-IPI alone for OS. In conclusion, serum CRP and ALB levels are both simple and easily accessible parameters, whose ratio CAR may be good candidates for predicting prognosis in the future clinical practice of CLL.     

Mapping scrub vegetation cover from photogrammetric point clouds: A useful tool in reserve management

Scrub vegetation is a valuable habitat and resource for wildlife, but if unmanaged can encroach and dominate adjacent habitats, reducing biodiversity value. A primary task in the management of terrestrial nature reserves in the UK is monitoring and controlling scrub. The methods used to monitor and assess scrub cover are often basic, relying on qualitative assessment. Inaccurate assessments may fail to inform appropriate management of the habitats and lead to loss or degradation of important ecological features. Scrub can be monitored using UAV or satellite‐derived imagery, but it can be difficult to distinguish between other vegetation types without using high‐cost hyperspectral sensors. An alternative method using high‐resolution surface models from photogrammetric point clouds enables the isolation of vegetation types based on height. Scrub can be isolated from woodland, hedgerows, and tall ground vegetation. In this study, we calculate scrub cover using a photogrammetric point cloud modeling approach using UAVs.We illustrate the method with two case studies from the UK. The scrub cover at Daneway Banks, a calcareous grassland site in Gloucestershire, was calculated at 21.8% of the site. The scrub cover at Flat Holm Island, a maritime grassland in the Severn Estuary, was calculated at 7%. This approach enabled the scrub layer to be readily measured and if required, modeled to provide a visual guide of what a projected management objective would look like. This approach provides a new tool in reserve management, enabling habitat management strategies to be informed, and progress toward objectives monitored.     

Nonparametric scanning tests of homogeneity for hierarchical models with continuous covariates

In many applications of hierarchical models, there is often interest in evaluating the inherent heterogeneity in view of observed data. When the underlying hypothesis involves parameters resting on the boundary of their support space such as variances and mixture proportions, it is a usual practice to entertain testing procedures that rely on common heterogeneity assumptions. Such procedures, albeit omnibus for general alternatives, may entail a substantial loss of power for specific alternatives such as heterogeneity varying with covariates. We introduce a novel and flexible approach that uses covariate information to improve the power to detect heterogeneity, without imposing unnecessary restrictions. With continuous covariates, the approach does not impose a regression model relating heterogeneity parameters to covariates or rely on arbitrary discretizations. Instead, a scanning approach requiring continuous dichotomizations of the covariates is proposed. Empirical processes resulting from these dichotomizations are then used to construct the test statistics, with limiting null distributions shown to be functionals of tight random processes. We illustrate our proposals and results on a popular class of two-component mixture models, followed by simulation studies and applications to two real datasets in cancer and caries research.     

Proof-of-Concept Study of Using Supervised Machine Learning Algorithms to Predict Self-Care and Glycemic Control in Type 1 Diabetes Patients on Insulin Pump Therapy

ObjectiveUsing supervised machine learning algorithms (SMLAs), we built models to predict the probability of type 1 diabetes mellitus patients on insulin pump therapy for meeting insulin pump self-management behavioral (IPSMB) criteria and achieving good glycemic response within 6 months.MethodsThis was a single-center retrospective chart review of 100 adult type 1 diabetes mellitus patients on insulin pump therapy (≥6 months). Three SMLAs were deployed: multivariable logistic regression (LR), random forest (RF), and K-nearest neighbor (k-NN); validated using repeated three-fold cross-validation. Performance metrics included area under the curve-Receiver of characteristics for discrimination and Brier scores for calibration.ResultsVariables predictive of adherence with IPSMB criteria were baseline hemoglobin A1c, continuous glucose monitoring, and sex. The models had comparable discriminatory power (LR = 0.74; RF = 0.74; k-NN = 0.72), with the RF model showing better calibration (Brier = 0.151). Predictors of the good glycemic response included baseline hemoglobin A1c, entering carbohydrates, and following the recommended bolus dose, with models comparable in discriminatory power (LR = 0.81, RF = 0.80, k-NN = 0.78) but the RF model being better calibrated (Brier = 0.099).ConclusionThese proof-of-concept analyses demonstrate the feasibility of using SMLAs to develop clinically relevant predictive models of adherence with IPSMB criteria and glycemic control within 6 months. Subject to further study, nonlinear prediction models may perform better.