Adaptation and Evaluation of the Neighborhood Environment Walkability Scale in India (NEWS-India)

Physical inactivity is the fourth leading risk factor for global mortality, with most of these deaths occurring in low and middle-income countries (LMICs) like India. Research from developed countries has consistently demonstrated associations between built environment features and physical activity levels of populations. The development of culturally sensitive and reliable measures of the built environment is a necessary first step for accurate analysis of environmental correlates of physical activity in LMICs. This study systematically adapted the Neighborhood Environment Walkability Scale (NEWS) for India and evaluated aspects of test-retest reliability of the adapted version among Indian adults. Cultural adaptation of the NEWS was conducted by Indian and international experts. Semi-structured interviews were conducted with local residents and key informants in the city of Chennai, India. At baseline, participants (N = 370; female = 47.2%) from Chennai completed the adapted NEWS-India surveys on perceived residential density, land use mix-diversity, land use mix-access, street connectivity, infrastructure and safety for walking and cycling, aesthetics, traffic safety, and safety from crime. NEWS-India was administered for a second time to consenting participants (N = 62; female = 53.2%) with a gap of 2–3 weeks between successive administrations. Qualitative findings demonstrated that built environment barriers and constraints to active commuting and physical activity behaviors intersected with social ecological systems. The adapted NEWS subscales had moderate to high test-retest reliability (ICC range 0.48–0.99). The NEWS-India demonstrated acceptable measurement properties among Indian adults and may be a useful tool for evaluation of built environment attributes in India. Further adaptation and evaluation in rural and suburban settings in India is essential to create a version that could be used throughout India.

Local Computation in Credal Networks

The goal of this contribution is to discuss local computation in credal networks — graphical models that can represent imprecise and indeterminate probability values. We analyze the inference problem in credal networks, discuss how inference algorithms can benefit from local computation, and suggest that local computation can be particularly important in approximate inference algorithms.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating ‘big data’ across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.