Coffee consumption and prostate cancer risk: further evidence for inverse relationship

<p>Abstract</p> <p>Background</p> <p>Higher consumption of coffee intake has recently been linked with reduced risk of aggressive prostate cancer (PC) incidence, although meta-analysis of other studies that examine the association between coffee consumption and overall PC risk remains inconclusive. Only one recent study investigated the association between coffee intake and grade-specific incidence of PC, further evidence is required to understand the aetiology of aggressive PCs. Therefore, we conducted a prospective study to examine the relationship between coffee intake and overall as well as grade-specific PC risk.</p> <p>Methods</p> <p>We conducted a prospective cohort study of 6017 men who were enrolled in the Collaborative cohort study in the UK between 1970 and 1973 and followed up to 31st December 2007. Cox Proportional Hazards Models were used to evaluate the association between coffee consumption and overall, as well as Gleason grade-specific, PC incidence.</p> <p>Results</p> <p>Higher coffee consumption was inversely associated with risk of high grade but not with overall risk of PC. Men consuming 3 or more cups of coffee per day experienced 55% lower risk of high Gleason grade disease compared with non-coffee drinkers in analysis adjusted for age and social class (HR 0.45, 95% CI 0.23-0.90, p value for trend 0.01). This association changed a little after additional adjustment for Body Mass Index, smoking, cholesterol level, systolic blood pressure, tea intake and alcohol consumption.</p> <p>Conclusion</p> <p>Coffee consumption reduces the risk of aggressive PC but not the overall risk.</p

Land of Addicts? An Empirical Investigation of Habit-Based Asset Pricing Models

A popular explanation of aggregate stock market behavior suggests that assets are priced as if there were a representative investor whose utility is a power function of the difference between aggregate consumption and a “habit” level, where the habit is some function of lagged and (possibly) contemporaneous consumption. But theory does not provide precise guidelines about the parametric functional relationship between the habit and aggregate consumption. This makes for- mal estimation and testing challenging; at the same time, it raises an empirical question about the functional form of the habit that best explains asset pricing data. This paper studies the ability of a general class of habit-based asset pricing models to match the conditional moment restrictions implied by asset pricing theory. Our approach is to treat the functional form of the habit as unknown, and to estimate it along with the rest of the model’s finite dimensional parameters. This semiparametric approach allows us to empirically evaluate a number of interesting hypotheses about the specification of habit-based asset pricing models. Using stationary quarterly data on consumption growth, assets returns and instruments, our empirical results indicate that the estimated habit function is nonlinear, the habit formation is internal, and the estimated time-preference parameter and the power utility parameter are sensible. In addition, our estimated habit function generates a positive stochastic discount factor (SDF) proxy and performs well in explaining cross-sectional stock return data. We find that an internal habit SDF proxy can explain a cross-section of size and book-market sorted portfolio equity returns better than (i) the Fama and French (1993) three-factor model, (ii) the Lettau and Ludvigson (2001b) scaled consumption CAPM model, (iii) an external habit SDF proxy, (iv) the classic CAPM, and (v) the classic consumption CAPM

Inspiratory muscle warm-up does not improve cycling time-trial performance

Purpose: This study examined the effects of an active cycling warm-up, with and without the addition of an inspiratory muscle warm-up (IMW), on 10-km cycling time-trial performance

Simpson's Paradox, Lord's Paradox, and Suppression Effects are the same phenomenon – the reversal paradox

This article discusses three statistical paradoxes that pervade epidemiological research: Simpson's paradox, Lord's paradox, and suppression. These paradoxes have important implications for the interpretation of evidence from observational studies. This article uses hypothetical scenarios to illustrate how the three paradoxes are different manifestations of one phenomenon – the reversal paradox – depending on whether the outcome and explanatory variables are categorical, continuous or a combination of both; this renders the issues and remedies for any one to be similar for all three. Although the three statistical paradoxes occur in different types of variables, they share the same characteristic: the association between two variables can be reversed, diminished, or enhanced when another variable is statistically controlled for. Understanding the concepts and theory behind these paradoxes provides insights into some controversial or contradictory research findings. These paradoxes show that prior knowledge and underlying causal theory play an important role in the statistical modelling of epidemiological data, where incorrect use of statistical models might produce consistent, replicable, yet erroneous results

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Height and timing of growth spurt during puberty in young people living with vertically acquired HIV in Europe and Thailand.

OBJECTIVE: The aim of this study was to describe growth during puberty in young people with vertically acquired HIV. DESIGN: Pooled data from 12 paediatric HIV cohorts in Europe and Thailand. METHODS: One thousand and ninety-four children initiating a nonnucleoside reverse transcriptase inhibitor or boosted protease inhibitor based regimen aged 1-10 years were included. Super Imposition by Translation And Rotation (SITAR) models described growth from age 8 years using three parameters (average height, timing and shape of the growth spurt), dependent on age and height-for-age z-score (HAZ) (WHO references) at antiretroviral therapy (ART) initiation. Multivariate regression explored characteristics associated with these three parameters. RESULTS: At ART initiation, median age and HAZ was 6.4 [interquartile range (IQR): 2.8, 9.0] years and -1.2 (IQR: -2.3 to -0.2), respectively. Median follow-up was 9.1 (IQR: 6.9, 11.4) years. In girls, older age and lower HAZ at ART initiation were independently associated with a growth spurt which occurred 0.41 (95% confidence interval 0.20-0.62) years later in children starting ART age 6 to 10 years compared with 1 to 2 years and 1.50 (1.21-1.78) years later in those starting with HAZ less than -3 compared with HAZ at least -1. Later growth spurts in girls resulted in continued height growth into later adolescence. In boys starting ART with HAZ less than -1, growth spurts were later in children starting ART in the oldest age group, but for HAZ at least -1, there was no association with age. Girls and boys who initiated ART with HAZ at least -1 maintained a similar height to the WHO reference mean. CONCLUSION: Stunting at ART initiation was associated with later growth spurts in girls. Children with HAZ at least -1 at ART initiation grew in height at the level expected in HIV negative children of a comparable age

Antimicrobial consumption and resistance in adult hospital inpatients in 53 countries:results of an internet-based global point prevalence survey

Summary: Background: The Global Point Prevalence Survey (Global-PPS) established an international network of hospitals to measure antimicrobial prescribing and resistance worldwide. We aimed to assess antimicrobial prescribing and resistance in hospital inpatients. Methods: We used a standardised surveillance method to collect detailed data about antimicrobial prescribing and resistance from hospitals worldwide, which were grouped by UN region. The internet-based survey included all inpatients (adults, children, and neonates) receiving an antimicrobial who were on the ward at 0800 h on one specific day between January and September, 2015. Hospitals were classified as primary, secondary, tertiary (including infectious diseases hospitals), and paediatric hospitals. Five main ward types were defined: medical wards, surgical wards, intensive-care units, haematology oncology wards, and medical transplantation (bone marrow or solid transplants) wards. Data recorded included patient characteristics, antimicrobials received, diagnosis, therapeutic indication according to predefined lists, and markers of prescribing quality (eg, whether a stop or review date were recorded, and whether local prescribing guidelines existed and were adhered to). We report findings for adult inpatients. Findings: The Global-PPS for 2015 included adult data from 303 hospitals in 53 countries, including eight lower-middle-income and 17 upper-middle-income countries. 86 776 inpatients were admitted to 3315 adult wards, of whom 29 891 (34·4%) received at least one antimicrobial. 41 213 antimicrobial prescriptions were issued, of which 36 792 (89·3%) were antibacterial agents for systemic use. The top three antibiotics prescribed worldwide were penicillins with β-lactamase inhibitors, third-generation cephalosporins, and fluoroquinolones. Carbapenems were most frequently prescribed in Latin America and west and central Asia. Of patients who received at least one antimicrobial, 5926 (19·8%) received a targeted antibacterial treatment for systemic use, and 1769 (5·9%) received a treatment targeting at least one multidrug-resistant organism. The frequency of health-care-associated infections was highest in Latin America (1518 [11·9%]) and east and south Asia (5363 [10·1%]). Overall, the reason for treatment was recorded in 31 694 (76·9%) of antimicrobial prescriptions, and a stop or review date in 15 778 (38·3%). Local antibiotic guidelines were missing for 7050 (19·2%) of the 36 792 antibiotic prescriptions, and guideline compliance was 77·4%. Interpretation: The Global-PPS showed that worldwide surveillance can be accomplished with voluntary participation. It provided quantifiable measures to assess and compare the quantity and quality of antibiotic prescribing and resistance in hospital patients worldwide. These data will help to improve the quality of antibiotic prescribing through education and practice changes, particularly in low-income and middle-income countries that have no tools to monitor antibiotic prescribing in hospitals. Funding: bioMérieux

Identifying risk factors for exposure to culturable allergenic moulds in energy efficient homes by using highly specific monoclonal antibodies.

PublishedThis is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The aim of this study was to determine the accuracy of monoclonal antibodies (mAbs) in identifying culturable allergenic fungi present in visible mould growth in energy efficient homes, and to identify risk factors for exposure to these known allergenic fungi. Swabs were taken from fungal contaminated surfaces and culturable yeasts and moulds isolated by using mycological culture. Soluble antigens from cultures were tested by ELISA using mAbs specific to the culturable allergenic fungi Aspergillus and Penicillium spp., Ulocladium, Alternaria, and Epicoccum spp., Cladosporium spp., Fusarium spp., and Trichoderma spp. Diagnostic accuracies of the ELISA tests were determined by sequencing of the internally transcribed spacer 1 (ITS1)-5.8S-ITS2-encoding regions of recovered fungi following ELISA. There was 100% concordance between the two methods, with ELISAs providing genus-level identity and ITS sequencing providing species-level identities (210 out of 210 tested). Species of Aspergillus/Penicillium, Cladosporium, Ulocladium/Alternaria/Epicoccum, Fusarium and Trichoderma were detected in 82% of the samples. The presence of condensation was associated with an increased risk of surfaces being contaminated by Aspergillus/Penicillium spp. and Cladosporium spp., whereas moisture within the building fabric (water ingress/rising damp) was only associated with increased risk of Aspergillus/Penicillium spp. Property type and energy efficiency levels were found to moderate the risk of indoor surfaces becoming contaminated with Aspergillus/Penicillium and Cladosporium which in turn was modified by the presence of condensation, water ingress and rising damp, consistent with previous literature.Richard Sharpe's PhD scholarship was funded by the European Social Fund Convergence Program for Cornwall and the Isles of Scilly, and was undertaken in collaboration with Coastline Housing. The European Centre for Environment and Human Health (part of the University of Exeter Medical School) is part financed by the European Regional Development Fund Program 2007–2013 and European Social Fund Convergence Program for Cornwall and the Isles of Scill

Increasing citizen scientist accuracy with artificial intelligence on UK camera‐trap data

As camera traps have become more widely used, extracting information from images at the pace they are acquired has become challenging, resulting in backlogs that delay the communication of results and the use of data for conservation and management. To ameliorate this, artificial intelligence (AI), crowdsourcing to citizen scientists and combined approaches have surfaced as solutions. Using data from the UK mammal monitoring initiative MammalWeb, we assess the accuracies of classifications from registered citizen scientists, anonymous participants and a convolutional neural network (CNN). The engagement of anonymous volunteers was facilitated by the strategic placement of MammalWeb interfaces in a natural history museum with high footfall related to the ‘Dippy on Tour’ exhibition. The accuracy of anonymous volunteer classifications gathered through public interfaces has not been reported previously, and here we consider this form of citizen science in the context of alternative forms of data acquisition. While AI models have performed well at species identification in bespoke settings, here we report model performance on a dataset for which the model in question was not explicitly trained. We also consider combining AI output with that of human volunteers to demonstrate combined workflows that produce high accuracy predictions. We find the consensus of registered users has greater overall accuracy (97%) than the consensus from anonymous contributors (71%); AI accuracy lies in between (78%). A combined approach between registered citizen scientists and AI output provides an overall accuracy of 96%. Further, when the contributions of anonymous citizen scientists are concordant with AI output, 98% accuracy can be achieved. The generality of this last finding merits further investigation, given the potential to gather classifications much more rapidly if public displays are placed in areas of high footfall. We suggest that combined approaches to image classification are optimal when the minimisation of classification errors is desired