Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines : the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)

Galileo Galilei’s quote “measure what is measurable, and make measurable what is not so” has particular relevance to health behaviours, such as physical activity (PA), sitting and sleep, whose measurement during free living is notoriously difficult. To date, much of what we know about how these behaviours affect our health is based on self-report by questionnaires which have limited validity, are prone to bias, and inquire about selective aspects of these behaviours. Although self-reported evidence has made great contributions to shaping public health and exercise medicine policy and guidelines until now1, the ongoing advancements of accelerometry-based measurement and evidence synthesis methods are set to change the landscape. The aim of this editorial is to outline new directions in PA and sleep related epidemiology that open new horizons for guideline development and improvement; and to describe a new research collaboration platform: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)

Recent Change—North Sea

This chapter discusses past and ongoing change in the following physical variables within the North Sea: temperature, salinity and stratification; currents and circulation; mean sea level; and extreme sea levels. Also considered are carbon dioxide; pH and nutrients; oxygen; suspended particulate matter and turbidity; coastal erosion, sedimentation and morphology; and sea ice. The distinctive character of the Wadden Sea is addressed, with a particular focus on nutrients and sediments. This chapter covers the past 200 years and focuses on the historical development of evidence (measurements, process understanding and models), the form, duration and accuracy of the evidence available, and what the evidence shows in terms of the state and trends in the respective variables. Much work has focused on detecting long-term change in the North Sea region, either from measurements or with models. Attempts to attribute such changes to, for example, anthropogenic forcing are still missing for the North Sea. Studies are urgently needed to assess consistency between observed changes and current expectations, in order to increase the level of confidence in projections of expected future conditions

Selenium, Selenoenzymes, Oxidative Stress and Risk of Neoplastic Progression from Barrett's Esophagus: Results from Biomarkers and Genetic Variants

Clinical trials have suggested a protective effect of selenium supplementation on the risk of esophageal cancer, which may be mediated through the antioxidant activity of selenoenzymes. We investigated whether serum selenium concentrations, selenoenzyme activity, oxidative stress and genetic variation in selenoenzymes were associated with the risk of neoplastic progression to esophageal adenocarcinoma (EA) and two intermediate endpoints, aneuploidy and tetraploidy. In this prospective cohort study, during an average follow-up of 7.3 years, 47 EA cases, 41 aneuploidy cases and 51 tetraploidy cases accrued among 361 participants from the Seattle Barrett's Esophagus Research Study who were free of EA at the time of blood draw and had at least one follow-up visit. Development to EA was assessed histologically and aneuploidy and tetraploidy by DNA content flow cytometry. Serum selenium concentrations were measured using atomic absorption spectrometry, activity of glutathione peroxidase (GPX) 1 and GPX3 by substrate-specific coupled test procedures, selenoprotein P (SEPP1) concentrations and protein carbonyl content by ELISA method and malondialdehyde concentrations by HPLC. Genetic variants in GPX1-4 and SEPP1 were genotyped. Serum selenium was not associated with the risk of neoplastic progression to EA, aneuploidy or tetraploidy (P for trend = 0.25 to 0.85). SEPP1 concentrations were positively associated with the risk of EA [hazard ratio (HR) = 3.95, 95% confidence intervals (CI) = 1.42–10.97 comparing the third tertile with the first] and with aneuploidy (HR = 6.53, 95% CI = 1.31–32.58), but not selenoenzyme activity or oxidative stress markers. No genetic variants, overall, were associated with the risk of neoplastic progression to EA (global p = 0.12–0.69). Our results do not support a protective effect of selenium on risk of neoplastic progression to EA. Our study is the first to report positive associations of plasma SEPP1 concentrations with the risk of EA and aneuploidy, which warrants further investigation

Whole Exome Sequencing of HIV-1 long-term non-progressors identifies rare variants in genes encoding innate immune sensors and signaling molecules

Abstract Common CCR5-∆32 and HLA alleles only explain a minority of the HIV long-term non-progressor (LTNP) and elite controller (EC) phenotypes. To identify rare genetic variants contributing to the slow disease progression phenotypes, we performed whole exome sequencing (WES) on seven LTNPs and four ECs. HLA and CCR5 allele status, total HIV DNA reservoir size, as well as variant-related functional differences between the ECs, LTNPs, and eleven age- and gender-matched HIV-infected non-controllers on antiretroviral therapy (NCARTs) were investigated. Several rare variants were identified in genes involved in innate immune sensing, CD4-dependent infectivity, HIV trafficking, and HIV transcription mainly within the LTNP group. ECs and LTNPs had a significantly lower HIV reservoir compared to NCARTs. Furthermore, three LTNPs with variants affecting HIV nuclear import showed integrated HIV DNA levels below detection limit after in vitro infection. HIV slow progressors with variants in the TLR and NOD2 pathways showed reduced pro-inflammatory responses compared to matched controls. Low-range plasma levels of fibronectin was observed in a LTNP harboring two FN1 variants. Taken together, this study identified rare variants in LTNPs as well as in one EC, which may contribute to understanding of HIV pathogenesis and these slow progressor phenotypes, especially in individuals without protecting CCR5-∆32 and HLA alleles

Land- and water-based exercise intervention in women with fibromyalgia: the al-andalus physical activity randomised controlled trial

Background The al-Andalus physical activity intervention study is a randomised control trial to investigate the effectiveness of a land- and water-based exercise intervention for reducing the overall impact of fibromyalgia (primary outcome), and for improving tenderness and pain-related measures, body composition, functional capacity, physical activity and sedentary behaviour, fatigue, sleep quality, health-related quality of life, and cognitive function (secondary outcomes) in women with fibromyalgia. Methods/Design One hundred eighty women with fibromyalgia (age range: 35-65 years) will be recruited from local associations of fibromyalgia patients in Andalucía (Southern Spain). Patients will be randomly assigned to a usual care (control) group (n = 60), a water-based exercise intervention group (n = 60) or a land-based exercise intervention group (n = 60). Participants in the usual care group will receive general physical activity guidelines and participants allocated in the intervention groups will attend three non-consecutive training sessions (60 min each) per week during 24 weeks. Both exercise interventions will consist of aerobic, muscular strength and flexibility exercises. We will also study the effect of a detraining period (i.e., 12 weeks with no exercise intervention) on the studied variables. Discussion Our study attempts to reduce the impact of fibromyalgia and improve patients' health status by implementing two types of exercise interventions. Results from this study will help to assess the efficacy of exercise interventions for the treatment of fibromyalgia. If the interventions would be effective, this study will provide low-cost and feasible alternatives for health professionals in the management of fibromyalgia. Results from the al-Andalus physical activity intervention will help to better understand the potential of regular physical activity for improving the well-being of women with fibromyalgia.This study was supported by the Consejeria de Turismo, Comercio y Deporte (CTCD-201000019242-TRA), the Spanish Ministry of Science and Innovation (I + D + I DEP2010-15639, grants: BES-2009-013442, BES-2011-047133, RYC-2010-05957, RYC-2011-09011), the Swedish Heart-Lung Foundation (20090635), the Spanish Ministry of Education (AP-2009-3173), Granada Research of Excelence Initiative on Biohealth (GREIB), Campus BioTic, University of Granada, Spain and European University of Madrid. Escuela de Estudios Universitarios Real Madrid. 2010/04RM

Emerging collaborative research platforms for the next generation of physical activity, sleep and exercise medicine guidelines: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS)

Galileo Galilei’s quote ‘measure what is measurable, and make measurable what is not so’ has particular relevance to health behaviours, such as physical activity (PA), sitting and sleep, whose measurement during free living is notoriously difficult. To date, much of what we know about how these behaviours affect our health is based on self-report by questionnaires which have limited validity, are prone to bias and inquire about selective aspects of these behaviours. Although self-reported evidence has made great contributions to shaping public health and exercise medicine policy and guidelines until now,1 the ongoing advancements of accelerometry-based measurement and evidence synthesis methods are set to change the landscape. The aim of this editorial is to outline new directions in PA and sleep-related epidemiology that open new horizons for guideline development and improvement; and to describe a new research collaboration platform: the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS

GeneRIF indexing: sentence selection based on machine learning

BACKGROUND: A Gene Reference Into Function (GeneRIF) describes novel functionality of genes. GeneRIFs are available from the National Center for Biotechnology Information (NCBI) Gene database. GeneRIF indexing is performed manually, and the intention of our work is to provide methods to support creating the GeneRIF entries. The creation of GeneRIF entries involves the identification of the genes mentioned in MEDLINE®; citations and the sentences describing a novel function. RESULTS: We have compared several learning algorithms and several features extracted or derived from MEDLINE sentences to determine if a sentence should be selected for GeneRIF indexing. Features are derived from the sentences or using mechanisms to augment the information provided by them: assigning a discourse label using a previously trained model, for example. We show that machine learning approaches with specific feature combinations achieve results close to one of the annotators. We have evaluated different feature sets and learning algorithms. In particular, Naïve Bayes achieves better performance with a selection of features similar to one used in related work, which considers the location of the sentence, the discourse of the sentence and the functional terminology in it. CONCLUSIONS: The current performance is at a level similar to human annotation and it shows that machine learning can be used to automate the task of sentence selection for GeneRIF annotation. The current experiments are limited to the human species. We would like to see how the methodology can be extended to other species, specifically the normalization of gene mentions in other species

MeSH indexing based on automatically generated summaries

BACKGROUND: MEDLINE citations are manually indexed at the U.S. National Library of Medicine (NLM) using as reference the Medical Subject Headings (MeSH) controlled vocabulary. For this task, the human indexers read the full text of the article. Due to the growth of MEDLINE, the NLM Indexing Initiative explores indexing methodologies that can support the task of the indexers. Medical Text Indexer (MTI) is a tool developed by the NLM Indexing Initiative to provide MeSH indexing recommendations to indexers. Currently, the input to MTI is MEDLINE citations, title and abstract only. Previous work has shown that using full text as input to MTI increases recall, but decreases precision sharply. We propose using summaries generated automatically from the full text for the input to MTI to use in the task of suggesting MeSH headings to indexers. Summaries distill the most salient information from the full text, which might increase the coverage of automatic indexing approaches based on MEDLINE. We hypothesize that if the results were good enough, manual indexers could possibly use automatic summaries instead of the full texts, along with the recommendations of MTI, to speed up the process while maintaining high quality of indexing results. RESULTS: We have generated summaries of different lengths using two different summarizers, and evaluated the MTI indexing on the summaries using different algorithms: MTI, individual MTI components, and machine learning. The results are compared to those of full text articles and MEDLINE citations. Our results show that automatically generated summaries achieve similar recall but higher precision compared to full text articles. Compared to MEDLINE citations, summaries achieve higher recall but lower precision. CONCLUSIONS: Our results show that automatic summaries produce better indexing than full text articles. Summaries produce similar recall to full text but much better precision, which seems to indicate that automatic summaries can efficiently capture the most important contents within the original articles. The combination of MEDLINE citations and automatically generated summaries could improve the recommendations suggested by MTI. On the other hand, indexing performance might be dependent on the MeSH heading being indexed. Summarization techniques could thus be considered as a feature selection algorithm that might have to be tuned individually for each MeSH heading