Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a ...successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle.
Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls.
For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy.All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time.
The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended.
Several countries have come close to eliminating leprosy, but leprosy cases continue to be detected at low levels. Due to the long, highly variable delay from infection to detection, the relationship ...between observed cases and transmission is uncertain. The World Health Organization's new technical guidance provides a path for countries to reach elimination. We use a simple probabilistic model to simulate the stochastic dynamics of detected cases as transmission declines, and evaluate progress through the new public health milestones. In simulations where transmission is halted, 5 years of zero incidence in autochthonous children, combined with 3 years of zero incidence in all ages is a flawed indicator that transmission has halted (54% correctly classified). A further 10 years of only occasional sporadic cases is associated with a high probability of having interrupted transmission (99%). If, however, transmission continues at extremely low levels, it is possible that cases could be misidentified as historic cases from the tail of the incubation period distribution, although misleadingly achieving all three milestones is unlikely (less than 1% probability across a 15-year period of ongoing low-level transmission). These results demonstrate the feasibility and challenges of a phased progression of milestones towards interruption of transmission, allowing assessment of programme status. This article is part of the theme issue 'Challenges and opportunities in the fight against neglected tropical diseases: a decade from the London Declaration on NTDs'.
Seeing one's practice as a high antibiotic prescriber compared to general practices with similar patient populations can be one of the best motivators for change. Current comparisons are based on ...age-sex weighting of the practice population for expected prescribing rates (STAR-PU). Here, we investigate whether there is a need to additionally account for further potentially legitimate medical reasons for higher antibiotic prescribing.
Publicly available data from 7376 general practices in England between April 2014 and March 2015 were used. We built two different negative binomial regression models to compare observed versus expected antibiotic dispensing levels per practice: one including comorbidities as covariates and another with the addition of smoking prevalence and deprivation. We compared the ranking of practices in terms of items prescribed per STAR-PU according to i) conventional STAR-PU methodology, ii) observed vs expected prescribing levels using the comorbidity model, and iii) observed vs expected prescribing levels using the full model.
The median number of antibiotic items prescribed per practice per STAR-PU was 1.09 (25th-75th percentile, 0.92-1.25). 1133 practices (76.8% of 1476) were consistently identified as being in the top 20% of high antibiotic prescribers. However, some practices that would be classified as high prescribers using the current STAR-PU methodology would not be classified as high prescribers if comorbidity was accounted for (n = 269, 18.2%) and if additionally smoking prevalence and deprivation were accounted for (n = 312, 21.1%).
Current age-sex weighted comparisons of antibiotic prescribing rates in England are fair for many, but not all practices. This new metric that accounts for legitimate medical reasons for higher antibiotic prescribing may have more credibility among general practitioners and, thus, more likely to be acted upon.
Findings of this study indicate that the antibiotic prescribing metric by which practices are measured (and need to implement interventions determined) may be inadequate, and therefore raises the question of how they should be measured. Substantial variation between practices remains after accounting for comorbidities, deprivation and smoking. There is a need for a better understanding of why such variation remains and, more importantly, what can be done to reduce it. While antibiotics are more frequently indicated in patients with comorbidities, it is unclear to what extent antibiotic prescribing can be lowered among that patient population and how this could be achieved.
Records from 9429 pigs raised in 353 grower groups in a commercial Australian piggery were analysed to determine whether grower-group characteristics affected daily gain and backfat of individual ...pigs. Individual and group effects as well as their interactions were tested for significance (P < 0.05) in a mixed model, with sire fitted as a random effect. Group characteristics affected average daily gain (ADG) more than backfat (BF). The proportion of males in a group influenced both traits significantly, as did the average number of full siblings. Groups with 10-30% of the opposite sex had the highest BF and a 21-30 g/day reduction in ADG compared with the highest-performing groups with less than 10% of males. Each additional full sibling per group increased ADG by 5.5 ± 1.60 g/day and BF by 0.12 ± 0.05 mm. Additionally, ADG increased by 9.8 ± 2.64 g/day per second of group mean flight time and by 4.5 g/day per 10% increase in the proportion of Duroc pigs per group. Group size affected ADG (linear and quadratic) and BF (linear); however, the effect on ADG was considerably larger during the warmer grower season. In commercial piggeries, it may be possible to optimise individual daily gain through the manipulation of grower-group characteristics. Advantages for growth rate arose from including a portion of a calmer line of pigs within groups, optimising the stocking density in warmer months and maximising the proportion of quieter, less fearful pigs in grower groups.
Quantitative analysis and mathematical models are useful tools in informing strategies to control or eliminate disease. Currently, there is an urgent need to develop these tools to inform policy to ...achieve the 2020 goals for neglected tropical diseases (NTDs). In this paper we give an overview of a collection of novel model-based analyses which aim to address key questions on the dynamics of transmission and control of nine NTDs: Chagas disease, visceral leishmaniasis, human African trypanosomiasis, leprosy, soil-transmitted helminths, schistosomiasis, lymphatic filariasis, onchocerciasis and trachoma. Several common themes resonate throughout these analyses, including: the importance of epidemiological setting on the success of interventions; targeting groups who are at highest risk of infection or re-infection; and reaching populations who are not accessing interventions and may act as a reservoir for infection,. The results also highlight the challenge of maintaining elimination 'as a public health problem' when true elimination is not reached. The models elucidate the factors that may be contributing most to persistence of disease and discuss the requirements for eventually achieving true elimination, if that is possible. Overall this collection presents new analyses to inform current control initiatives. These papers form a base from which further development of the models and more rigorous validation against a variety of datasets can help to give more detailed advice. At the moment, the models' predictions are being considered as the world prepares for a final push towards control or elimination of neglected tropical diseases by 2020.
Bacterioplankton community composition was compared across 10 lakes and 14 streams within the catchment of Toolik Lake, a tundra lake in Arctic Alaska, during seven surveys conducted over three years ...using denaturing gradient gel electrophoresis (DGGE) of PCR-amplified rDNA. Bacterioplankton communities in streams draining tundra were very different than those in streams draining lakes. Communities in streams draining lakes were similar to communities in lakes. In a connected series of lakes and streams, the stream communities changed with distance from the upstream lake and with changes in water chemistry, suggesting inoculation and dilution with bacteria from soil waters or hyporheic zones. In the same system, lakes shared similar bacterioplankton communities (78% similar) that shifted gradually down the catchment. In contrast, unconnected lakes contained somewhat different communities (67% similar). We found evidence that dispersal influences bacterioplankton communities via advection and dilution (mass effects) in streams, and via inoculation and subsequent growth in lakes. The spatial pattern of bacterioplankton community composition was strongly influenced by interactions among soil water, stream, and lake environments. Our results reveal large differences in lake-specific and stream-specific bacterial community composition over restricted spatial scales (<10 km) and suggest that geographic distance and connectivity influence the distribution of bacterioplankton communities across a landscape.
Abstract
Objectives
To investigate whether baseline social participation modifies the effect of a long-term structured physical activity (PA) program on major mobility disability (MMD).
Methods
1,635 ...sedentary adults (70–89 years) with physical limitations were randomized to either a structured PA or health education (HE) intervention. Social participation was defined categorically at baseline. High social participation was defined as attending organized group functions at least once per week and visiting with noncohabitating friends and family ≥7 hr per week. Anything less was considered limited social participation. Participants performed a standardized walking test at baseline and every 6 months for up to 42 months. MMD was defined as the loss in the ability to walk 400 m.
Results
There was a significant intervention by social participation interaction (p = .003). Among individuals with high levels of social participation, those randomized to PA had significantly lower incidence of MMD (hazard ratio HR, 0.43 95% confidence interval (CI), 0.27–0.68; p < .01) than those randomized to HE. Individuals with limited social participation showed no mobility benefit of the PA intervention when compared with their HE counterparts (HR, 0.92 95% CI, 0.77–1.11; p = .40).
Discussion
Our findings suggest that baseline social participation is an important factor for the success of a PA intervention aimed at delaying mobility disability.
Proteome expression profiles can be used as biomarkers to predict traits with low heritability and large environmental influences. Porcine meat quality typically is such a trait. Proteome profiles of ...150 meat samples were determined and associated with measured technical meat quality traits. Biomarkers tests were developed and their predictive potential determined. Differentially expressed proteins related to variation in drip loss and ultimate pH were determined and the underlying biological mechanism evaluated with bioinformatics. The biological mechanism also explains the connections between these two meat quality traits.
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