Compositional data analysis of Holocene sediments from the West Bengal Sundarbans, India: Geochemical proxies for grain-size variability in a delta environment

This paper is part of a special issue of Applied Geochemistry focusing on reliable applications of compositional multivariate statistical methods. This study outlines the application of compositional data analysis (CoDa) to calibration of geochemical data and multivariate statistical modelling of geochemistry and grain-size data from a set of Holocene sedimentary cores from the Ganges-Brahmaputra (G-B) delta. Over the last two decades, understanding near-continuous records of sedimentary sequences has required the use of core-scanning X-ray fluorescence (XRF) spectrometry, for both terrestrial and marine sedimentary sequences. Initial XRF data are generally unusable in ‘raw-format’, requiring data processing in order to remove instrument bias, as well as informed sequence interpretation. The applicability of these conventional calibration equations to core-scanning XRF data are further limited by the constraints posed by unknown measurement geometry and specimen homogeneity, as well as matrix effects. Log-ratio based calibration schemes have been developed and applied to clastic sedimentary sequences focusing mainly on energy dispersive-XRF (ED-XRF) core-scanning. This study has applied high resolution core-scanning XRF to Holocene sedimentary sequences from the tidal-dominated Indian Sundarbans, (Ganges-Brahmaputra delta plain). The Log-Ratio Calibration Equation (LRCE) was applied to a sub-set of core-scan and conventional ED-XRF data to quantify elemental composition. This provides a robust calibration scheme using reduced major axis regression of log-ratio transformed geochemical data. Through partial least squares (PLS) modelling of geochemical and grain-size data, it is possible to derive robust proxy information for the Sundarbans depositional environment. The application of these techniques to Holocene sedimentary data offers an improved methodological framework for unravelling Holocene sedimentation patterns.

Prior knowledge transfer across transcriptional data sets and technologies using compositional statistics yields new mislabelled ovarian cell line

Here, we describe gene expression compositional assignment (GECA), a powerful, yet simple method based on compositional statistics that can validate the transfer of prior knowledge, such as gene lists, into independent data sets, platforms and technologies. Transcriptional profiling has been used to derive gene lists that stratify patients into prognostic molecular subgroups and assess biomarker performance in the pre-clinical setting. Archived public data sets are an invaluable resource for subsequent in silico validation, though their use can lead to data integration issues. We show that GECA can be used without the need for normalising expression levels between data sets and can outperform rank-based correlation methods. To validate GECA, we demonstrate its success in the cross-platform transfer of gene lists in different domains including: bladder cancer staging, tumour site of origin and mislabelled cell lines. We also show its effectiveness in transferring an epithelial ovarian cancer prognostic gene signature across technologies, from a microarray to a next-generation sequencing setting. In a final case study, we predict the tumour site of origin and histopathology of epithelial ovarian cancer cell lines. In particular, we identify and validate the commonly-used cell line OVCAR-5 as non-ovarian, being gastrointestinal in origin. GECA is available as an open-source R package.

SYSTEMATIC REVIEW AND META-ANALYSIS: EFFECTS OF WALKING EXERCISE IN CHRONIC MUSCULOSKELETAL PAIN

SYSTEMATIC REVIEW AND META-ANALYSIS: EFFECTS OF WALKING EXERCISE IN CHRONIC MUSCULOSKELETAL PAIN O'Connor S.R.1, Tully M.A.2, Ryan B.3, Baxter D.G.3, Bradley J.M.1, McDonough S.M.11University of Ulster, Health & Rehabilitation Sciences Research Institute, Newtownabbey, United Kingdom, 2Queen's University, UKCRC Centre of Excellence for Public Health (NI), Belfast, United Kingdom, 3University of Otago, Centre for Physiotherapy Research, Dunedin, New ZealandPurpose: To examine the effects of walking exercise on pain and self-reported function in adults with chronic musculoskeletal pain.Relevance: Chronic musculoskeletal pain is a major cause of morbidity, exerting a substantial influence on long-term health status and overall quality of life. Current treatment recommendations advocate various aerobic exercise interventions for such conditions. Walking may represent an ideal form of exercise due to its relatively low impact. However, there is currently limited evidence for its effectiveness.Participants: Not applicable.Methods: A comprehensive search strategy was undertaken by two independent reviewers according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and the recommendations of the Cochrane Musculoskeletal Review Group. Six electronic databases (Medline, CINAHL, PsychINFO, PEDro, Sport DISCUS and the Cochrane Central Register of Controlled Trials) were searched for relevant papers published up to January 2010 using MeSH terms. All randomised or non-randomised studies published in full were considered for inclusion. Studies were required to include adults aged 18 years or over with a diagnosis of chronic low back pain, osteoarthritis or fibromyalgia. Studies were excluded if they involved peri-operative or post-operative interventions or did not include a comparative, non exercise or non-walking exercise control group. The U.S. Preventative Services Task Force system was used to assess methodological quality. Data for pain and self-reported function were extracted and converted to a score out of 100.Analysis: Data were pooled and analyzed using RevMan (v.5.0.24). Statistical heterogeneity was assessed using the X2 and I2 test statistics. A random effects model was used to calculate the mean differences and 95% CIs. Data were analyzed by length of final follow-up which was categorized as short (≤8 weeks post randomisation), mid (2-12 months) or long-term (>12 months).Results: A total of 4324 articles were identified and twenty studies (1852 participants) meeting the inclusion criteria were included in the review. Overall, studies were judged to be of at least fair methodological quality. The most common sources of likely bias were identified as lack of concealed allocation and failure to adequately address incomplete data. Data from 12 studies were suitable for meta-analysis. Walking led to reductions in pain at short (<8 weeks post randomisation) (-8.44 [-14.54, -2.33]) and mid-term (>8 weeks - 12 month) follow-up (-9.28 [-16.34, -2.22]). No effect was observed for long-term (>12 month) data (-2.49 [-7.62, 2.65]). For function, between group differences were observed for short (-11.57 [-16.06, -7.08]) and mid-term data (-13.26 [-16.91, -9.62]). A smaller effect was also observed at long-term follow-up (-5.60 [-7.70, -3.50]).Conclusions: Walking interventions were associated with statistically significant improvements in pain and function at short and mid-term follow-up. Long-term data were limited but indicated that these effects do not appear to be maintained beyond twelve months.Implications: Walking may be an effective form of exercise for individuals with chronic musculoskeletal pain. However, further research is required which examines longer term follow-up and dose-response issues in this population.Key-words: 1. Walking exercise 2. Musculoskeletal pain 3. Systematic reviewFunding acknowledgements: Department of Employment and Learning, Northern Ireland.Ethics approval: Not applicable.