Posterior Means Research Articles

Theoretical aspects of the development of partially accelerated life testing using Bayesian estimation

In this article likelihood and Bayesian estimates of partially accelerated step-stress life testing model are compared using Type-II censored data from Gompertz distribution. Maximum likelihood estimates and the posterior means under the squared error loss function using Lindley’s approximation technique are obtained. Moreover, Markov chain Monte Carlo (MCMC) point estimates and the highest posterior density credible intervals of the model parameters based on Gibbs sampling procedure are also obtained. Monte Carlo simulation studies are performed under different sizes of samples and different parameter values for comparison purpose.

Reduced Form Information Design: Persuading a Privately Informed Receiver

We study information design problems where the designer’s payoff is a step function of the posterior mean of the state induced by her signals. Settings where the designer’s payoff depends on the receiver’s actions, the receiver’s payoff is affine in the state, and the receiver’s actions belong to a finite set are special cases. To maximize her payoff, the designer needs to induce certain atomic distributions over posterior means. We show that the relevant set of posterior means can be characterized in terms of a collection of convex constraints. Leveraging this characterization, we provide a reduced form approach to information design. In this approach, the designer first solves a convex optimization problem, where she optimizes over the aforementioned set and then she constructs an information structure that is consistent with the optimal solution. The approach is versatile and tractable. We apply it to characterize the optimal information structures when the receiver is privately informed and establish the optimality of information structures based on a laminar partition of the set of states.

PSVIII-29 Analysis of genotype by environment interaction across time for Nile tilapia reared in different production systems

Abstract Nile tilapia is the second most important freshwater fish for human consumption worldwide and it is produced under many different conditions. Even though genotype by environment interaction (GEI) can arise due to many factors, most studies with tilapia have focused on water quality on pond and cage systems. Therefore, the objective of the current work was to evaluate GEI on body weight (BW) of Nile tilapia raised in biofloc technology (BFT), Cage, and recirculation aquaculture systems (RAS) from 100 to 350 days of age. A Bayesian multi-trait random regression model (MTRRM) with age as random regression covariable was employed to evaluate the temporal trend of GEI. Estimated genetic correlations between BFT and RAS were above 0.7 for almost any combination of ages evaluated (Figure 1). However, genetic correlations between Cage at ages below 150 days with either BFT or RAS at any other age were positive but below 0.5. The posterior means of heritability for BW were found to reach peaks of 0.40, 0.38 and 0.29 around 230 days for RAS, BFT, and Cage respectively (Figure 2). Conversely, the ratio between common family environment and phenotypic variances presented an overall decreasing trend with minimums around 0.22, 0.15 and 0.10 at 230 days for Cage, RAS, and BFT, respectively. The MTRRM has shown to be a powerful statistical tool to assess changes across time for genetic parameters of interest. In conclusion, GEI was found to be dynamic across time, with stronger effects between Cage and both BFT and RAS if selection is promoted prior to 150 days. However, GEI is negligible for most of the situations considered if selection is performed after 200 days of age in either BTF or RAS, with the possibility of achieving better indirect selection results if selection is performed around 230 days.

Modeling and Simulation of Pretomanid Pharmacodynamics in Pulmonary Tuberculosis Patients.

Pretomanid (PA-824) is a nitroimidazole in clinical testing for the treatment of tuberculosis. A population pharmacodynamic model for pretomanid was developed using a Bayesian analysis of efficacy data from two early bactericidal activity (EBA) studies, PA-824-CL-007 and PA-824-CL-010, conducted in Cape Town, South Africa. The two studies included 122 adult male and female participants with newly diagnosed pulmonary tuberculosis who received once daily oral pretomanid doses of either 50, 100, 150, 200, 600, 1,000, or 1,200 mg for 14 days. The structural model described capacity-limited growth and saturable drug-induced bacterial killing with separate rate equations for sputum solid culture colony forming unit (CFU) counts and liquid culture time to positivity (TTP) that were linked through a time constant. The posterior population geometric means and interindividual variability percent coefficients of variation were, respectively; 0.152±0.013 log10 CFU/mL sputum/day and 54%±6% for the maximum kill rate constant, 20.4±1.0 h and 20.8%±0.1% for the time constant of proportionality between the CFU and TTP rate equations, and 770±140 ng/mL and 48%±17% for the pretomanid half-maximum effect plasma concentration. Model simulations showed once daily pretomanid at 100 mg, 200 mg, and 300 mg, attained 58%, 73%, and 80%, respectively, of an expected maximum 14-day EBA of 0.136 log10CFU/mL sputum/day. These results establish a pretomanid exposure-efficacy relationship with dual outcomes for CFU counts and TTP, and with potential applications to dose optimization of pretomanid-containing regimens.

Application of a Modified g -Parameter Prior in Bayesian Model Averaging to Water Pollution in Ibadan

A special technique that measures the uncertainties embedded in model selection processes is Bayesian Model Averaging (BMA) which depends on the appropriate choices of model and parameter priors. Inspite the importance of the parameter priors' specification in BMA, the existing parameter priors give exitremely low Posterior Model Probability (PMP). Therefore, this paper elicits modified g-parameter priors to improve the performance of the PMP and predictive ability of the model with an application to the Water Pollution of Asejire in Ibadan. The modified g-parameter priors g j = , established the consistency conditions and asymptotic properties using the models in the literature. The results show that the PMP with the best prior (g j = ) had the least standard deviations (0.0411 at n=100,000 and 0:000 at n=1000) for models 1 & 2 respectively; and had the highest posterior means (0.9577 at n=100,000 and 1.000 at n=1000) for models 1 & 2 respectively. The point and overall predictive performances for the best prior were 2.357 at n=50 and 2.335 at n=100,000 when compared with the BMA Log Predictive Score threshold of 2.335. Applying this best g-parameter prior in modeling the Asejire river, it indicates that the dissolved solids (mg/l) and total solids (mg/l) are the most important pollutants in the river model with their PIP of 6.14% and 6.1% respectively. Keywords : Posterior Inclusion Probability (PIP), Log-Predictive Score, Model Uncertainty, Dissolved Solids DOI : 10.7176/JEES/9-11-06 Publication date: November 30 th 2019

Option pricing with bivariate risk-neutral density via copula and heteroscedastic model: A Bayesian approach

Multivariate options are adequate tools for multi-asset risk management. The pricing models derived from the pioneer Black and Scholes method under the multivariate case consider that the asset-object prices follow a Brownian geometric motion. However, the construction of such methods imposes some unrealistic constraints on the process of fair option calculation, such as constant volatility over the maturity time and linear correlation between the assets. Therefore, this paper aims to price and analyze the fair price behavior of the call-on-max (bivariate) option considering marginal heteroscedastic models with dependence structure modeled via copulas. Concerning inference, we adopt a Bayesian perspective and computationally intensive methods based on Monte Carlo simulations via Markov Chain (MCMC). A simulation study examines the bias, and the root mean squared errors of the posterior means for the parameters. Real stocks prices of Brazilian banks illustrate the approach. For the proposed method is verified the effects of strike and dependence structure on the fair price of the option. The results show that the prices obtained by our heteroscedastic model approach and copulas differ substantially from the prices obtained by the model derived from Black and Scholes. Empirical results are presented to argue the advantages of our strategy.

Bayesian spatial analysis of cholangiocarcinoma in Northeast Thailand

Cholangiocarcinoma (CCA) is a malignant neoplasm of the biliary tract. Thailand reports the highest incidence of CCA in the world. The aim of this study was to map the distribution of CCA and identify spatial disease clusters in Northeast Thailand. Individual-level data of patients with histopathologically confirmed CCA, aggregated at the sub-district level, were obtained from the Cholangiocarcinoma Screening and Care Program (CASCAP) between February 2013 and December 2017. For analysis a multivariate Zero-inflated, Poisson (ZIP) regression model was developed. This model incorporated a conditional autoregressive (CAR) prior structure, with posterior parameters estimated using Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling. Covariates included in the models were age, sex, normalized vegetation index (NDVI), and distance to water body. There was a total of 1,299 cases out of 358,981 participants. CCA incidence increased 2.94 fold (95% credible interval [CrI] 2.62–3.31) in patients >60 years as compared to ≤60 years. Males were 2.53 fold (95% CrI: 2.24–2.85) more likely to have CCA when compared to females. CCA decreased with a 1 unit increase of NDVI (Relative Risk =0.06; 95% CrI: 0.01–0.63). When posterior means were mapped spatial clustering was evident after accounting for the model covariates. Age, sex and environmental variables were associated with an increase in the incidence of CCA. When these covariates were included in models the maps of the posterior means of the spatially structured random effects demonstrated evidence of spatial clustering.

The Reliability of the Posterior Probability of Skill Attainment in Diagnostic Classification Models

One common score reported from diagnostic classification assessments is the vector of posterior means of the skill mastery indicators. As with any assessment, it is important to derive and report estimates of the reliability of the reported scores. After reviewing a reliability measure suggested by Templin and Bradshaw, this article suggests three new measures of reliability of the posterior means of skill mastery indicators and methods for estimating the measures when the number of items on the assessment and the number of skills being assessed render exact calculation computationally burdensome. The utility of the new measures is demonstrated using simulated and real data examples. Two of the suggested measures are recommended for future use.

66 A genome-wide association study for gestation length in swine

Abstract The objective of the current study was to conduct a genome-wide association on gestation length (GL) in different parities in swine. Sows (n = 831) belonging to the University of Nebraska – Lincoln resource population (Landrace X Nebraska Index Line) were utilized. GL was defined as the number of days between the final insemination and farrowing. Four traits, GL at parity 1, 2, 3 and 4 (GL1, GL2, GL3 and GL4, respectively) were investigated. Animals which were induced 24h prior to a farrowing event were removed from the analysis. Sows were genotyped with the Illumina SNP60 BeadArray. A Bayes C model with π=0.995 was implemented with fixed effects of contemporary group, development pen, diet, linear and quadratic terms for age at puberty (GL1; P < 0.01), and linear and quadratic terms for farrowing age (GL2; P < 0.01). Results are posterior means of 55,000 samples.Single marker association analysis (SMA) was performed in R utilizing a linear model on SNP from 1-Mb windows (n = 10) which explained the largest proportion of genetic variation in GL1. Top 10 (0.5% of all windows) 1-Mb windows accounted for a limited proportion of genetic variance, 7.75, 4.66, 3.45 and 2.05% in GL1, GL2, GL3 and GL4, respectively. Posterior mean heritability estimates (posterior SD) for GL1, GL2, GL3 and GL4 were 0.33 (0.06), 0.34 (0.07), 0.32 (0.08) and 0.20 (0.08), respectively. The top SNP (ASGA0017859, SSC4, 7.8 Mb) located in one of the two top common genomic regions associated with GL1, GL2 and GL3 displayed a difference of 1.1d in GL1 between alternate homozygotes (P < 0.01). The top SNP from nine of the ten regions were significant (P < 0.05) in the SMA. Two of these regions were in common with GL2 and GL3 where SNP with potential functional effects were found in ZFAT, MAML2 and CCDC82. Results suggest GL is a largely polygenic trait.

Planned staging for posterior surgical correction of multi-planar spinal deformities, does it differ?

Planned staging for posterior surgical correction of multi-planar spinal deformities, does it differ?

Phase Retrieval From Quantized Measurements via Approximate Message Passing

In this letter, the problem of sparse signal reconstruction from quantized noisy magnitudes y = Q (|z + w| + n) is studied, where z = Ax, Q(·) denotes a quantizer, and x is the sparse signal. According to expectation propagation, the abovementioned problem can be solved by exchanging extrinsic information between the standard linear model (SLM) module and the minimum mean square error (MMSE) module. For the MMSE module, exploiting the fact that the likelihood is only a function of the absolute value of z, the magnitude and phase information of z are decoupled, and only the posterior means and variances of the magnitude of z are calculated. While for the SLM module, the approximate message passing (AMP) is used. To obtain the closed-form expression during the iteration, a novel approximation is adopted. We refer to the resulting algorithm as generalized AMP (Gr-AMP) based quantized phase retrieval algorithm. Finally, several numerical simulations are conducted to demonstrate the performances of the proposed approach.

Cauchy and other shrinkage priors for logistic regression in the presence of separation

AbstractIn recent years, the choice of prior distributions for Bayesian logistic regression has received considerable interest. It is widely acknowledged that noninformative, improper priors have to be used with caution because posterior propriety may not always hold. As an alternative, heavy‐tailed priors such as Cauchy prior distributions have been proposed by Gelman et al. (2008). The motivation for using Cauchy prior distributions is that they are proper, and thus, unlike noninformative priors, they are guaranteed to yield proper posterior distributions. The heavy tails of the Cauchy distribution allow the posterior distribution to adapt to the data. Thus Gelman et al. (2008) suggested the use of these prior distributions as a default weakly informative choice, in the absence of prior knowledge about the regression coefficients. While these prior distributions are guaranteed to have proper posterior distributions, Ghosh, Li, and Mitra (2018), showed that the posterior means may not exist, or can be unreasonably large, when they exist, for datasets with separation. In this paper, we provide a short review of the concept of separation, and we discuss how common prior distributions like the Cauchy and normal prior perform in data with separation. Theoretical and empirical results suggest that lighter tailed prior distributions such as normal prior distributions can be a good default choice when there is separation.This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Models

Modeling the growth curve in Kordi sheep: The comparison of non-linear models and estimation of genetic parameters for the growth curve traits

In the present study, 43,441 body weight-age records (21,361 records on male lambs and 22,060 records on female lambs) on 6899 lambs descended from 235 sires and 2842 dams that collected from 1991 to 2015 in Kordi sheep breeding station, located in Shirvan city, North Khorasan province, North-eastern area of Iran, were used. Five non-linear models including Brody, Negative exponential, von Bertalanffy, Logistic and Gompertz were fitted on body weight-age data and compared applying Akaike's information criterion (AIC), mean square error (MSE) and adjusted coefficient of determination (R2adg) for determining the most appropriate model describing the growth curve for all, male and female Kordi lambs. The Brody model showed the lowest AIC and MSE and the highest R2adg among the tested models for all, male and female Kordi lambs. Therefore, Brody model was selected as the best one for describing growth curve in Kordi sheep. The estimated values for parameters of growth curve under Brody model including parameters A (estimated mature weight), B (an integration constant related to initial animal weight) and K (maturation rate) were 43.372, 0.905 and 0.007, respectively. Parameters of A, B and K were estimated as 48.630, 0.911 and 0.006, respectively for male lambs and 38.444, 0.898 and 0.008 for female lambs, respectively. A nultivariate animal model was considered for genetic analysis of the growth curve parameters via Gibbs sampling under a Bayesian approach with 100,000 iterations, considering the first 20,000 samples as the burn-in period and a thinning interval of 10 samples. Posterior means for heritability estimates of the studied growth curve parameters of Kordi sheep were 0.08, 0.14 and 0.04 for parameters A, B and K, respectively; showed low level of genetic variation for the parameters of growth curve in Kordi sheep. Posterior means for the estimates of genetic correlations among the studied growth curve traits were 0.48 (A-B), -0.22 (A-K) and -0.28 (B-K). The Brody model can be considered for predicting live weight of Kordi lambs at later ages from early body weight data and can help planning farm management practices such as feeding and drug administration (since these practices are commonly focused on live body weight) and also decision making regarding the selecting of highly productive animals by monitoring their growth curve.

A Bayesian risk analysis for Trisomy 21 in isolated choroid plexus cyst: combining a prenatal database with a meta-analysis

Objective The purpose of this study was to quantify the possible additional risk of a fetus with an isolated choroid plexus cyst (ICPC) for Trisomy 21 by combining a large controlled cohort study with data from existent studies. Methods We searched our prenatal database between 2000 and 2014 for all singleton pregnancies between 18 + 0 and 26 + 6 gestational weeks with either an isolated choroid plexus cyst (study group) or no abnormality found in the detailed ultrasound scan (control group). We assessed all prenatal karyotyping results if invasive testing was performed and attempted to collect the postnatal outcome reports of all patients. The prevalence of Down syndrome was calculated. By using previous studies that met our inclusion criteria, a meta-analysis following the Bayesian Independent Model was created. From this meta-analysis, we computed the posterior predictive distribution of the probability (Trisomy 21 | ICPC) = P1 including posterior means, standard deviations, quantiles (2.5, 50, and 97.5%). By calculating the posterior of the difference (Δ) between the probability (Trisomy 21 | ICPC) and the probability (Trisomy 21 | Normal Ultrasound) = P2, we investigated the additional risk of an ICPC (ΔB = P1–P2). Results Overall, we detected 1220 fetuses with an isolated plexus cyst at 19–27 weeks of gestational age (GA). In our study group, the prevalence of Trisomy 21 was 2/1220 (0.16, 95% CI: 0.1–0.6%). The median of the pooled probability of Trisomy 21 given isolated PC across the studies included in the meta-analysis was 0.2% (CI: 0.1–0.4%). In the given periods (GA and time), 66,606 (74.8%) out of 89,056 investigated fetuses met the inclusion criteria and had a normal ultrasound result without any abnormality. The Δ between our study group and the control group was 0.08% (CIΔA: 0–0.5%). Including the meta-analysis, the median of the posterior distribution of Δ between P1 and P2 was 0.08% (CIΔB: 0–0.4%) (ΔB = P1–P2). Conclusion The posterior distribution of Δ between P1 and P2 including the meta-analysis corresponds to showing no difference between the cases and controls (95% CIΔB: 0–0.4%). The additional risk of a fetus with an ICPC for Trisomy 21 is 97.5% likely to be lower than 0.4% (about 1/250). However, in our collective, the positive predictive value of ICPC for Down syndrome was 0.16% (about 1/625). In prenatal counseling, the additional risk should be added to the individual risk (based on maternal age, earlier screening test results, and sonographic markers) and the diagnostic options including fetal DNA and diagnostic procedures should be discussed according to the posterior individual risk.

Evaluation of non-linear models for genetic parameters estimation of growth curve traits in Kermani sheep.

In the present study, 10,116 body weight-age records were measured on 2537 Kermani lambs. The records were collected from Kermani Sheep Breeding Station, located in Shahrbabak city, Kerman Province, south-eastern part of Iran, between 1993 and 2013 and used for evaluation of non-linear models describing growth curve from birth to yearling age and estimation of genetic parameters for growth curve traits in Kermani sheep. Six non-linear models including Brody, negative exponential, von Bertalanffy, Richards, Verhulst, and Gompertz were compared applying Akaike's information criterion (AIC), root mean square error (RMSE) and Durbin-Watson statistic (DW) for determining the most appropriate model describing the growth curve in Kermani sheep. The von Bertalanffy model showed the lowest AIC and RMSE among the tested models. Furthermore, positive autocorrelations were found between residuals under the all tested model with the lowest value under the von Bertalanffy model. Therefore, von Bertalanffy model was selected as the best one for describing growth curve in Kermani sheep. A multivariate animal model was used for genetic analysis of the growth curve traits including parameters A (estimated mature weight), B (an integration constant related to initial animal weight), K (maturation rate), inflection age (IA), and inflection weight (IW) under a Bayesian approach. Posterior means for heritability estimates of A, B, K, IA, and IW were significant values of 0.10, 0.03, 0.04, 0.15, and 0.10, respectively. The parameter A had significant and positive genetic and phenotypic correlations with parameters B, IA, and IW. The posterior means for genetic and phenotypic correlations between parameters A and K were negative estimates of - 0.32 and - 0.22, respectively, implied that the lambs with slower maturation rate had higher mature weight. Positive and medium estimates were obtained for posterior means of phenotypic (0.31) and genetic (0.35) correlations between B and K. The posterior means for phenotypic and genetic correlations of B with IA and IW were not statistically significant. High and positive estimates were obtained for posterior means of genetic (0.6) and phenotypic (0.84) correlations between IA and IW. Generally, von Bertalanffy model showed high level of adequacy for describing the growth curve in Kermani sheep. Low additive genetic variations were found for all the studied growth curve traits. Therefore, the traits highly influenced by environmental which necessitate improving environmental influencing factors on the studied traits for achieving desired shape of growth curve and developing an efficient breeding strategy in Kermani sheep.

Genetic and phenotypic aspects of early reproductive performance in Raeini Cashmere goats.

This study used pedigree information and data collected from 1979 to 2012 at the Raeini Cashmere goat breeding station, located in Baft City in Kerman Province in southeastern Iran. Genetic and phenotypic parameters for early reproductive traits of breeding does, including total numbers of kids born at first kidding (LSB1), total numbers of kids weaned at first kidding (LSW1), total birth weight of all kids born at first kidding (LWB1), total weaning weight of all kids weaned at first kidding (LWW1), and age at first kidding (AFK), were estimated using a Bayesian approach via Gibbs sampling. Posterior means for heritability estimates of LSB1, LSW1, LWB1, LWW1, and AFK were statistically significant, with values of 0.12, 0.23, 0.17, 0.15, and 0.46, respectively. Low-to-moderate additive genetic variation was present for the studied reproductive traits. Estimated genetic correlations among LSB1, LSW1, LWB1, and LWW1 were statistically significant and ranged from 0.12 between LWB1 and LWW1 to 0.72 between LSB1 and LSW1. Corresponding phenotypic correlation estimates were also statistically significant and ranged from 0.04 between LWB1 and LWW1 to 0.55 between LSB1 and LSW1. Posterior means of genetic and phenotypic correlations between AFK and other studied traits were statistically significant only for LSB1 and LWB1. For LSB1, LSW1, LWB1, and LWW1, we conclude that genetic and phenotypic improvement in any of these traits in Raeini Cashmere does would favorably influence all of the other traits. However, does that first kidded at younger ages have smaller litters at birth and lower litter birth weights at their first parity.

Robust Bayesian analysis of a multivariate dynamic model

Recently, a Bayesian approach has been widely used to infer parameters of complex models from finite sample data in business, finance, social and physical sciences. Bayesian applications in finance literature are growing due to their exposure to prior beliefs that macroeconomic variables influence the financial behavior of customers, investors and firm’s performance. This paper employs a robust Bayesian analysis model to capture a complex multi-dimensional process and estimate the required parameters of interest. It considers a class of prior distributions for the parameters that is a mixture of natural conjugate base priors and a contamination class of prior distributions. The conditional type-II maximum likelihood posterior densities and posterior means for the coefficients vector and autoregressive parameter are derived for the coefficients vector and autoregressive parameter of interest. The Markov Chain Monte Carlo (MCMC) method has been used for obtaining the marginal posterior densities and Bayes estimators for various combinations of the parameters. The usefulness of the model is demonstrated on Indian financial firms’ balance sheets data between 1992 to 2015 to observe the sensitivity of the proposed model. The result shows most of the corporate performance variables are significantly affecting the firm’s profitability with the exception of interest coverage ratio.

Genetic and genomic analyses of latent variables related to the milk fatty acid profile, milk composition, and udder health in dairy cattle

The aim of this study was to perform genetic, genome-wide association (GWAS), and gene-set enrichment analyses with latent variables related to milk fatty acid profile (i.e., fatty acids factor scores; FAF), milk composition, and udder health in a cohort of 1,158 Italian Brown Swiss cows. The phenotypes under study were 12 FAF previously identified through factor analysis and classified as follows: de novo FA (F1), branched-chain FA-milk yield (F2), biohydrogenation (F3), long-chain fatty acids (F4), desaturation (F5), short-chain fatty acids (F6), milk protein and fat contents (F7), odd fatty acids (F8), conjugated linoleic acids (F9), linoleic acid (F10), udder health (F11) and vaccelenic acid (F12). (Co)variance components were estimated for factor scores using a Bayesian linear animal model via Gibbs sampling. The animals were genotyped with the Illumina BovineSNP50 BeadChip v.2 (Illumina Inc., San Diego, CA). A single marker regression model was fitted for GWAS analysis. The gene-set enrichment analysis was run on the GWAS results using the Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathway databases to identify the ontologies and pathways associated with the FAF. Marginal posterior means of the heritabilities of the aforementioned FAF ranged from 0.048 for F12 to 0.310 for F5. Factors F1 and F6 had the highest number of relevant genetic correlations with the other traits. The genomic analysis detected a total of 39 significant SNP located on 17 Bos taurus autosomes. All latent variables produced signals except for F2 and F10. The traits with the highest number of significant associations were F11 (17) and F12 (7). Gene-set enrichment analyses identified significant pathways (false discovery rate 5%) for F3 and F7. In particular, systemic lupus erythematosus was enriched for F3, whereas the MAPK (mitogen-activated protein kinase) signaling pathway was overrepresented for F7. The results support the existence of important and exploitable genetic and genomic variation in these latent explanatory phenotypes. Information acquired might be exploited in selection programs and when designing further studies on the role of the putative candidate genes identified in the regulation of milk composition and udder health.

Inbreeding depression load for litter size in Entrepelado and Retinto Iberian pig varieties1.

Individual-specific hidden inbreeding depression load (IDL) can be accounted for in livestock populations by appropriate best linear unbiased prediction approaches. This genetic effect has a recessive pattern and reveals when inherited in terms of identity-by-descent. Nevertheless, IDL inherits as a pure additive genetic background and can be selected using standard breeding values. The main target of this research was to evaluate IDL for litter size in 2 Iberian pig varieties (Entrepelado and Retinto) from a commercial breeding-stock. Analyses were performed on the total number of piglets born (both alive and dead) and used data from 3,200 (8.02 ± 0.04 piglets/litter) Entrepelado and 4,744 Retinto litters (8.40 ± 0.03 piglets/litter). Almost 50% of Entrepelado sows were inbred (1.7% to 25.0%), whereas this percentage reduced to 37.4% in the Retinto variety (0.2% to 25.0%). The analytical model was solved by Bayesian inference and accounted for 2 systematic effects (sow age and breed/variety of the artificial insemination boar), 2 permanent environmental effects (herd-year-season and sow), and 2 genetic effects (IDL and infinitesimal additive). In terms of posterior means (PM), additive genetic and IDL variances were similar in the Entrepelado variety (PM, 0.68 vs. 0.76 piglets2, respectively) and their 95% credibility intervals (95CI) overlapped, although without including zero (0.38 to 0.94 vs. 0.15 to 1.31 piglets2, respectively). The same pattern revealed in the Retinto variety, with IDL variance (PM, 0.41 piglets2; 95CI, 0.07 to 0.88 piglets2) slightly larger than the additive genetic variance (PM, 0.37 piglets2; 95CI, 0.16 to 0.59 piglets2). The relevance of IDL was also checked by a Bayes factor and the deviance information criterion, the model including this effect being clearly favored in both cases. Although the analysis assumed null genetic covariance between IDL and infinitesimal additive effects, a moderate negative correlation (-0.31) was suggested when plotting the PM of breeding values in the Entrepelado variety; a negative genetic trend for IDL was also revealed in this Iberian pig variety (-0.25 piglets for 100% inbred offspring of individuals born in 2014), whereas no trend was detected in Retinto breeding-stock. Those were the first estimates of IDL in a commercial livestock population, they giving evidence of a relevant genetic background with potential consequences on the reproductive performance of Iberian sows.

Healthcare-associated bloodstream infection trends under a provincial surveillance program.

BACTOT, Quebec's healthcare-associated bloodstream infection (HABSI) surveillance program has been operating since 2007. In this study, we evaluated the changes in HABSI rates across 10 years of BACTOT surveillance under a Bayesian framework. A retrospective, cohort study of eligible hospitals having participated in BACTOT for at least 3 years, regardless of their entry date. Multilevel Poisson regressions were fitted independently for cases of HABSI, catheter-associated bloodstream infections (CA-BSIs), non-catheter-associated primary BSIs (NCA-BSIs), and BSIs secondary to urinary tract infections (BSI-UTIs) as the outcome and log of patient days as the offset. The log of the mean Poisson rate was decomposed as the sum of a surveillance year effect, period effect, and hospital effect. The main estimate of interest was the cohort-level rate in years 2-10 of surveillance relative to year 1. Overall, 17,479 cases and 33,029,870 patient days were recorded for the cohort of 77 hospitals. The pooled 10-year HABSI rate was 5.20 per 10,000 patient days (95% CI, 5.12-5.28). For HABSI, CA-BSI, and BSI-UTI, there was no difference between the estimated posterior rates of years 2-10 compared to year 1. The posterior means of the NCA-BSI rate ratios increased from the seventh year until the tenth year, when the rate was 29% (95% confidence interval, 1%-89%) higher than the first year rate. HABSI rates and those of the most frequent subtypes remained stable over the surveillance period. To achieve reductions in incidence, we recommend that more effort be expended in active interventions against HABSI alongside surveillance.