Sports activity and the use of cigarettes and snus among young males in Finland in 1999-2010

<p>Abstract</p> <p>Background</p> <p>Studies of the relationship between sports activity and smoking among adolescents and young adults report contradictory results. We examined the association between sports activity (intensity and type of sport) and the current use of snus (Swedish snuff), cigarette smoking, and the combined use of cigarettes and snus (dual use) among young males in Finland.</p> <p>Methods</p> <p>Data were collected from 16,746 male conscripts who completed a survey during the first days of their conscription during the years 1999-2010 (median age 19 years, response rate 95%). Main outcome measures were self-reported daily/occasional use of snus, cigarette smoking, and dual use. The association between sports activity, type of sport, and several sociodemographic background variables was assessed using logistic regression analysis.</p> <p>Results</p> <p>Over the study period (1999-2010), the prevalence of cigarette smoking decreased from 42% to 34%, while snus use increased from 5% to 12%, and dual use increased from 7% to 13% (<it>p </it>< 0.001). Compared with no physical activity, regular competitive sports activity (defined as high-intensity sports activity) was positively associated with use of snus (odds ratio [OR] 10.2; 95% confidence interval [CI]: 7.8-13.5) and negatively with cigarette smoking (OR 0.2; 95% CI: 0.1-0.3). When stratified by type of sport in multivariate models, ice hockey was most strongly associated with snus use (OR 1.6; 95% CI: 1.4-1.9) and dual use (OR 2.0; 95% CI 1.8-2.3) compared with those not playing ice-hockey, followed by other team sports for snus use (OR 1.5; 95% CI: 1.3-1.8) and dual use (OR 1.8; 95% CI: 1.6-2.0) compared with those not participating in other team-sports.</p> <p>Conclusions</p> <p>Our results show a clear association between snus use and intensity and type of training. Team sports were associated with increased use of snus and dual use compared with no participation in team sports. These findings should be acknowledged when planning and implementing preventive strategies.</p

Cross-sectional area of the paraspinal muscles and its association with muscle strength among fighter pilots : A 5-year follow-up

Background: A small cross sectional area (CSA) of the paraspinal muscles may be related to low back pain among military aviators but previous studies have mainly concentrated on spinal disc degeneration. Therefore, the primary aim of the study was to investigate the changes in muscle CSA and composition of the psoas and paraspinal muscles during a 5-year follow up among Finnish Air Force (FINAF) fighter pilots. Methods: Study population consisted of 26 volunteered FINAF male fighter pilots (age: 20.6 (±0.6) at the baseline). The magnetic resonance imaging (MRI) examinations were collected at baseline and after 5 years of follow-up. CSA and composition of the paraspinal and psoas muscles were obtained at the levels of 3-4 and 4-5 lumbar spine. Maximal isometric strength tests were only performed on one occasion at baseline. Results: The follow-up comparisons indicated that the mean CSA of the paraspinal muscles increased (p <0.01) by 8% at L3-4 level and 7% at L4-5 level during the 5-year period. There was no change in muscle composition during the follow-up period. The paraspinal and psoas muscles' CSA was positively related to overall maximal isometric strength at the baseline. However, there was no association between LBP and muscle composition or CSA. Conclusions: The paraspinal muscles' CSA increased among FINAF fighter pilots during the first 5 years of service. This might be explained by physically demanding work and regular physical activity. However, no associations between muscle composition or CSA and low back pain (LBP) experienced were observed after the five-year follow-up. © 2019 The Author(s).Peer reviewe

Own or shared silage feeding place for dairy cows?

We studied behaviour, silage eating and milk production of cows when every animal had an own silage feeding place and when the feeding place was shared with two other cows

Genomic prediction for Nordic Red Cattle using one-step and selection index blending

v2012o

The effects of dietary resin acid inclusion on productive, physiological and rumen microbiome responses of dairy cows during early lactation

Dairy cows have intense fluctuations in digestive, metabolic and hormonal systems around calving which predispose them to various disorders and health problems. The aim of the current experiment was to investigate feed and nutrient intake, rumen fermentation, rumen bacterial communities, milk production, milk fatty acid composition and plasma biomarker profiles of dairy cows to assess the modulation of these functions by in-feed resin acid inclusion. Thirty-six Nordic Red cows were used in a continuous feeding trial starting 3 weeks prepartum and lasting for 10 weeks into the lactation. The cows were fed grass silage ad libitum and the dietary treatments were 1) control with basal concentrate (CON), 2) CON supplemented with tall oil fatty acids (TOFA; 90 % fatty acids and 9% resin acids) at 7.0 g/cow/day and 3) CON supplemented with resin acid concentrate (RAC; 37.5% resin acids) at 1.7 g/cow/day. The mixture of resin acids in TOFA and RAC, consisting mostly of abietic and dehydroabietic acids, originated from coniferous tree species Pinus sylvestris L. and Picea abies L. Feed intake and milk production were measured throughout the experimental period. Milk and blood samples were collected at weeks 2, 3, 6 and 10, and rumen fluid was sampled at weeks 2 and 10 of lactation to analyse rumen fermentation and rumen bacterial communities. The dynamics in feed intake and milk production with progressing lactation showed typical curvilinear trends (P for timePeer reviewe

Principal component and factor analytic models in international sire evaluation

<p>Abstract</p> <p>Background</p> <p>Interbull is a non-profit organization that provides internationally comparable breeding values for globalized dairy cattle breeding programmes. Due to different trait definitions and models for genetic evaluation between countries, each biological trait is treated as a different trait in each of the participating countries. This yields a genetic covariance matrix of dimension equal to the number of countries which typically involves high genetic correlations between countries. This gives rise to several problems such as over-parameterized models and increased sampling variances, if genetic (co)variance matrices are considered to be unstructured.</p> <p>Methods</p> <p>Principal component (PC) and factor analytic (FA) models allow highly parsimonious representations of the (co)variance matrix compared to the standard multi-trait model and have, therefore, attracted considerable interest for their potential to ease the burden of the estimation process for multiple-trait across country evaluation (MACE). This study evaluated the utility of PC and FA models to estimate variance components and to predict breeding values for MACE for protein yield. This was tested using a dataset comprising Holstein bull evaluations obtained in 2007 from 25 countries.</p> <p>Results</p> <p>In total, 19 principal components or nine factors were needed to explain the genetic variation in the test dataset. Estimates of the genetic parameters under the optimal fit were almost identical for the two approaches. Furthermore, the results were in a good agreement with those obtained from the full rank model and with those provided by Interbull. The estimation time was shortest for models fitting the optimal number of parameters and prolonged when under- or over-parameterized models were applied. Correlations between estimated breeding values (EBV) from the PC19 and PC25 were unity. With few exceptions, correlations between EBV obtained using FA and PC approaches under the optimal fit were ≥ 0.99. For both approaches, EBV correlations decreased when the optimal model and models fitting too few parameters were compared.</p> <p>Conclusions</p> <p>Genetic parameters from the PC and FA approaches were very similar when the optimal number of principal components or factors was fitted. Over-fitting increased estimation time and standard errors of the estimates but did not affect the estimates of genetic correlations or the predictions of breeding values, whereas fitting too few parameters affected bull rankings in different countries.</p

Simulation Study on Heterogeneous Variance Adjustment for Observations with Different Measurement Error Variance

Abstract The Nordic Holstein yield evaluation model describes all available milk, protein and fat test-day yields from Denmark, Finland and Sweden. In its current form all variance components are estimated from observations recorded under conventional milking systems. Also the model for heterogeneity of variance correction is developed for the same observations. As automated milking systems are becoming more popular the current evaluation model needs to be enhanced to account for the different measurement error variances of observations from automated milking systems. In this simulation study different models and different approaches to account for heterogeneous variance when observations have different measurement error variances were investigated. Based on the results we propose to upgrade the currently applied models and to calibrate the heterogeneous variance adjustment method to yield same genetic variance for both milking systems

Principal component approach in variance component estimation for international sire evaluation

<p>Abstract</p> <p>Background</p> <p>The dairy cattle breeding industry is a highly globalized business, which needs internationally comparable and reliable breeding values of sires. The international Bull Evaluation Service, Interbull, was established in 1983 to respond to this need. Currently, Interbull performs multiple-trait across country evaluations (MACE) for several traits and breeds in dairy cattle and provides international breeding values to its member countries. Estimating parameters for MACE is challenging since the structure of datasets and conventional use of multiple-trait models easily result in over-parameterized genetic covariance matrices. The number of parameters to be estimated can be reduced by taking into account only the leading principal components of the traits considered. For MACE, this is readily implemented in a random regression model.</p> <p>Methods</p> <p>This article compares two principal component approaches to estimate variance components for MACE using real datasets. The methods tested were a REML approach that directly estimates the genetic principal components (direct PC) and the so-called bottom-up REML approach (bottom-up PC), in which traits are sequentially added to the analysis and the statistically significant genetic principal components are retained. Furthermore, this article evaluates the utility of the bottom-up PC approach to determine the appropriate rank of the (co)variance matrix.</p> <p>Results</p> <p>Our study demonstrates the usefulness of both approaches and shows that they can be applied to large multi-country models considering all concerned countries simultaneously. These strategies can thus replace the current practice of estimating the covariance components required through a series of analyses involving selected subsets of traits. Our results support the importance of using the appropriate rank in the genetic (co)variance matrix. Using too low a rank resulted in biased parameter estimates, whereas too high a rank did not result in bias, but increased standard errors of the estimates and notably the computing time.</p> <p>Conclusions</p> <p>In terms of estimation's accuracy, both principal component approaches performed equally well and permitted the use of more parsimonious models through random regression MACE. The advantage of the bottom-up PC approach is that it does not need any previous knowledge on the rank. However, with a predetermined rank, the direct PC approach needs less computing time than the bottom-up PC.</p