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Chronic low back disorders are the leading cause of direct and indirect healthcare burden globally. Exercise training improves pain intensity, mental health and physical function. However, the optimal prescription variables are unknown. We aim to compare the efficacy of various exercise dosages for chronic low back disorders to identify the optimal prescription variables. Six databases (Medline, SPORTDiscus, CINAHL, PsycINFO, EMBASE and CENTRAL), trial registries (ClinicalTrials.gov and WHO International Clinical Trials Registry Platform) and reference lists of prior systematic reviews will be searched, and we will conduct forward and backward citation tracking. We will include peer-reviewed randomised controlled trials (individual, cluster or cross-over trials) published in English or German language comparing exercise training to other exercise training or non-exercise training interventions (conservative, non-surgical, non-pharmacological, non-invasive treatments, placebo, sham, usual/standard care, no-treatment control, waitlist control) in adults with chronic low back disorders. Outcomes will include pain intensity, disability, mental health, adverse events, adherence rate, dropout rate and work capacity. Version 2 of the Cochrane risk-of-bias tool will be employed. The dose will be categorised as cumulative dose (total and weekly minutes of exercise training) and individual dose prescription variables (intervention duration, session duration, frequency and intensity). Dose-response model-based network meta-analysis will be used to assess the comparative efficacy of different exercise doses to determine a dose-response relationship. The certainty of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation. Information about optimal exercise training dosage will help in enhancing treatment outcomes., Competing Interests: None declared., (Copyright © Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Motivation: Photoactivatable-Ribonucleoside-Enhanced Crosslinking and Immunoprecipitation (PAR-CLIP) is a biochemical method for detecting interaction sites of proteins with mRNA. This method introduces T-to-C substitutions at sequenced cDNA that help to detect binding sites on mRNA. However, T-to-C substitutions can also occur due to other reasons such as mismatches or SNPs. Only few statistical procedures exist for detecting binding sites in PAR-CLIP data. Most of these methods do not account for other types of substitutions than those induced by PAR-CLIP, and therefore, also report positions with high T-to-C substitution rates, e.g. SNPs, as binding sites. Moreover, none of these procedures allow to include additional information, e.g. the type of mRNA region, relevant for the biology of microRNA-binding sites., Results: We have developed BayMAP, a procedure based on a fully Bayesian hierarchical model that takes other sources of substitutions into account. Furthermore, this model enables the incorporation of additional information into the analysis of PAR-CLIP data. This incorporation does not only permit a better detection of binding sites, but also a better understanding of the data and the biology of binding sites. In applications to simulated PAR-CLIP data, BayMAP distinguishes binding sites from noise better than existing methods. Additionally, it yields good estimates of the influence of the additional information. We here demonstrate BayMAP's usability for real datasets even when noisy data is present., Availability and Implementation: BayMAP is freely available as an R package at http://stat.math.uni-duesseldorf.de/baymap., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)