Modelling the cost-effectiveness of adopting risk stratified approaches to extended screening intervals in the national diabetic retinopathy screening programme in Scotland

Funding sources: The study was funded by a research grant from the Chief Scientist’s Office of the Scottish Government Health and Social Care Directorates (CZH/4/971). The funder played no role in study design, data collection, data analysis, manuscript preparation and/or publication decisions. The views expressed herein are those of the authors and do not necessarily reflect those of the funder.

The role of time and risk preferences in adherence to physician advice on health behaviour change

Acknowledgements The Interdisciplinary Chronic Disease Collaboration (ICDC) is funded through the Alberta Heritage Foundation for Medical Research (AHFMR) Inter-disciplinary Team Grants Program. AHFMR is now Alberta Innovates – Health Solutions (AI-HS). The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The Chief Scientist Office of the Scottish Government Health and Social Care Directorates funds HERU. The views expressed in this paper are those of the authors only and not those of the funding bodies.

Targeting androgen receptor activation function-1 with EPI to overcome resistance mechanisms in castration-resistant prostate cancer

Financial support: This research was supported by grants to MDS from the NCI (2R01CA105304), the Canadian Institutes of Health Research (MOP79308) and the US Army Medical Research and Materiel Command Prostate Cancer Research Program (E81XWH-11-1-0551). Research by IJM’s group was supported by the Chief Scientist’s Office of the Scottish Government (ETM-258 and -382). We are grateful to Country Meadows Senior Men’s Golf Charity Classic for financial support of this research.

The evolutionary relationships and age of Homo naledi : an assessment using dated Bayesian phylogenetic methods

Acknowledgements We wish to express our gratitude to the National Geographic Society and the National Research Foundation of South Africa for funding the discovery, recovery, and analysis of the H. naledi material. The study reported here was also made possible by grants from the Social Sciences and Humanities Research Council of Canada, the Canada Foundation for Innovation, the British Columbia Knowledge Development Fund, the Canada Research Chairs Program, Simon Fraser University, the DST/NRF Centre of Excellence in Palaeosciences (COE-Pal), as well as by a Discovery Grant from the Natural Sciences and Engineering Research Council of Canada, a Young Scientist Development Grant from the Paleontological Scientific Trust (PAST), a Baldwin Fellowship from the L.S.B. Leakey Foundation, and a Seed Grant and a Cornerstone Faculty Fellowship from the Texas A&M University College of Liberal Arts. We would like to thank the South African Heritage Resource Agency for the permits necessary to work on the Rising Star site; the Jacobs family for granting access; Wilma Lawrence, Bonita De Klerk, Merrill Van der Walt, and Justin Mukanku for their assistance during all phases of the project; Lucas Delezene for valuable discussion on the dental characters of H. naledi. We would also like to thank Peter Schmid for the preparation of the Dinaledi fossil material; Yoel Rak for explaining in detail some of the characters used in previous studies; William Kimbel for drawing our attention to the possibility that there might be a problem with Dembo et al.’s (2015) codes for the two characters related to the articular eminence; Will Stein for helpful discussion about the Bayesian analyses; Mike Lee for his comments on this manuscript; John Hawks for his support in organizing the Rising Star workshop; and the associate editor and three anonymous reviewers for their valuable comments. We are grateful to S. Potze and the Ditsong Museum, B. Billings and the School of Anatomical Sciences at the University of the Witwatersrand, and B. Zipfel and the Evolutionary Studies Institute at the University of the Witwatersrand for providing access to the specimens in their care; the University of the Witwatersrand, the Evolutionary Studies Institute, and the South African National Centre of Excellence in PalaeoSciences for hosting a number of the authors while studying the material; and the Western Canada Research Grid for providing access to the high-performance computing facilities for the Bayesian analyses. Last but definitely not least, we thank the head of the Rising Star project, Lee Berger, for his leadership and support, and for encouraging us to pursue the study reported here.

A primary care web-based intervention modelling experiment replicated behaviour changes seen in earlier paper-based experiment

Acknowledgements and funding We would like to thank the GPs who took part in this study. We would also like to thank Marie Pitkethly and Gail Morrison for their help and support in recruiting GPs to the study. WIME was funded by the Chief Scientist Office, grant number CZH/4/610. The Health Services Research Unit, University of Aberdeen, is core funded by the Chief Scientist Office of the Scottish Government Health Directorates.

Predictors of functional dependency in Parkinson’s disease

Financial disclosures/conflicts of interest: Dr Macleod was funded by a Clinical Academic Fellowship from the Chief Scientist Office of the Scottish Government and received grant funding from Parkinson’s UK, the Wellcome Trust, University of Aberdeen, and NHS Grampian endowments relating to this research. Dr Counsell received grant funding from Parkinson’s UK, National Institute for Health Research, the Scottish Chief Scientist Office, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, NHS Grampian endowments and SPRING relating to this research. We declare we have no conflicts of interest. Financial support: This study was funded by Parkinson’s UK, the Scottish Chief Scientist Office, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING.  

A theory-informed approach to developing visually mediated interventions to change behaviour using an asthma and physical activity intervention exemplar

Funding for this study was received from the Chief Scientist Office for Scotland. We would like to thank Asthma UK and Asthma UK Scotland for facilitating the advertisement of the study pilot and consultative user group. Thanks to Dr Mark Grindle for his helpful discussions concerning narrative. Thanks also to Mr Mark Haldane who designed the characters, backgrounds, and user interface used within the 3D computer animation. Particular thanks to the participants of the consultative user group for their enthusiasm, comments, and suggestions at all stages of the intervention design.

Data feedback and behavioural change intervention to improve primary care prescribing safety (EFIPPS) : multicentre, three arm, cluster-randomised controlled trial

Funding and trial registration: Scottish Government Chief Scientist Office grant CZH/3/17. ClinicalTrials.gov registration NCT01602705.

Predictors of functional dependency in Parkinson’s disease

Financial disclosures/conflicts of interest: Dr Macleod was funded by a Clinical Academic Fellowship from the Chief Scientist Office of the Scottish Government and received grant funding from Parkinson’s UK, the Wellcome Trust, University of Aberdeen, and NHS Grampian endowments relating to this research. Dr Counsell received grant funding from Parkinson’s UK, National Institute for Health Research, the Scottish Chief Scientist Office, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, NHS Grampian endowments and SPRING relating to this research. We declare we have no conflicts of interest. Financial support: This study was funded by Parkinson’s UK, the Scottish Chief Scientist Office, NHS Grampian endowments, the BMA Doris Hillier award, RS Macdonald Trust, the BUPA Foundation, and SPRING.  

Patterns of weight change after the diagnosis of type 2 diabetes in Scotland and their relationship with glycaemic control, mortality and cardiovascular outcomes: a retrospective cohort study

Acknowledgements The authors would like to thank the Scottish Diabetes Research Network Epidemiology Group for granting permission to use this database. They also thank the data management team in the University of Aberdeen who were the initial conduit for access to these data and also provided validation to the various data cleaning criteria applied. Jeremy J Walker, University of Edinburgh, was invaluable for the original funding application and initial exploration of data. HSRU is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates. Funding Chief Scientist Office (CSO) reference number: CZG/2/571.

Vulnerable children, stigmatised smokers : The social construction of target audiences in media debates on policies regulating smoking in vehicles

Acknowledgements S.H., S.S. and S.D. developed the study concept and gained funding for the work. S.H. developed the study design. J.B. and H.W. drafted the manuscript. J.B. and H.W. developed the coding frame and coded the articles. S.H., S.S. and S.D. critically revised the manuscript. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was funded by Cancer Research UK (C47682/A16930) and the Scottish School of Public Health Research. Sheila Duffy is Chief Executive of ASH Scotland. Heide Weishaar and Shona Hilton are funded by the UK Medical Research Council as part of the Informing Healthly Public Policy programme (MC_UU12017-15) at the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow. The authors declare no additional conflicting interest.

Long term effects of gestational hypertension and pre-eclampsia on kidney function : Record linkage study

This research was funded by the Chief Scientists Office, Scotland (CZH/4/659).

An Audit and Feedback Intervention for Reducing Antibiotic Prescribing in General Dental Practice : The RAPiD Cluster Randomised Controlled Trial

Funding: This study was conducted as part of the TRiaDS programme of implementation research which is funded by NHS Education for Scotland (NES). The Health Services Research Unit which is funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorates supported the study. The funder had no influence over the design, conduct, analysis and write up of the study.

Design and protocol for the Focusing on Clozapine Unresponsive Symptoms (FOCUS) trial : a randomised controlled trial

Acknowledgements Thank you to all the participants who agreed to take part in the trial. This study was supported NHS Research Scotland (NRS), through Chief Scientist Office (CSO) and the Scottish Mental Health Research Network, and the Clinical Research Network-Mental Health. We are grateful to the Psychosis Research Unit (PRU) Service User Reference Group (SURG) for their consultation regarding the design of the study and contribution to the developments of study related materials. We are grateful to our Independent Trial Steering Committee and Independent Data Monitoring Committee for provided oversight of the trial. Funding This project was funded by the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme (project number10/101/02) and will be published in full in Health Technology Assessment. Visit the HTA programme website for further project information. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the HTA programme, NIHR, NHS or the Department of Health.

Studying Recommender Systems to Enhance Distributed Computing Schedulers

Distributed Computing frameworks belong to a class of programming models that allow developers to

launch workloads on large clusters of machines. Due to the dramatic increase in the volume of

data gathered by ubiquitous computing devices, data analytic workloads have become a common

case among distributed computing applications, making Data Science an entire field of

Computer Science. We argue that Data Scientist's concern lays in three main components: a dataset,

a sequence of operations they wish to apply on this dataset, and some constraint they may have

related to their work (performances, QoS, budget, etc). However, it is actually extremely

difficult, without domain expertise, to perform data science. One need to select the right amount

and type of resources, pick up a framework, and configure it. Also, users are often running their

application in shared environments, ruled by schedulers expecting them to specify precisely their resource

needs. Inherent to the distributed and concurrent nature of the cited frameworks, monitoring and

profiling are hard, high dimensional problems that block users from making the right

configuration choices and determining the right amount of resources they need. Paradoxically, the

system is gathering a large amount of monitoring data at runtime, which remains unused.

In the ideal abstraction we envision for data scientists, the system is adaptive, able to exploit

monitoring data to learn about workloads, and process user requests into a tailored execution

context. In this work, we study different techniques that have been used to make steps toward

such system awareness, and explore a new way to do so by implementing machine learning

techniques to recommend a specific subset of system configurations for Apache Spark applications.

Furthermore, we present an in depth study of Apache Spark executors configuration, which highlight

the complexity in choosing the best one for a given workload.