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The sale of Holbein’s ‘Portrait of Christina, Duchess of Milan’ to Henry Clay Frick in 1909 – a painting that had been on long-term loan to the National Gallery in London from the Duke of Norfolk – and the painting’s eleventh-hour rescue, following a campaign by the National Art Collections Fund, became a national cause célèbre. Coming at a time of heightened sensitivities about the loss of Britain’s heritage and her declining importance as a world superpower, the threatened export of the painting provoked a huge outcry in the press, in which Frick, the Duke of Norfolk and the art dealers Colnaghi were cast in the role of villains in an Edwardian melodrama. However, new evidence presented in this article shows that the truth was considerably more complicated and prompts an examination of Edwardian debates about the protection of the national heritage.

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 d of age). Rectal swabs were taken from each animal at 158 ± 4 d of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including 4 kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), 2 dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and 2 ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.Gut microbiota has received significant research attention in farm animals because of its close relationship with host performance. We chose eight approaches to create a square covariance matrix that characterizes the relationship among animals based on their gut microbiota composition. Then, we fitted this information with linear models to evaluate the proportion of phenotypic variance explained by gut microbiota composition and predict host growth and body composition traits in three pig breeds. We found that different matrices had varying performance in predicting host phenotypes, but the results highly depended on the trait and breed considered in the prediction. Our findings highlight possible alternative approaches to incorporate gut microbiome data in regression models and emphasize the value of gut microbiome data in better understanding complex traits in pigs with diverse genetic backgrounds.

TPS433 Background: As cure rates for patients with advanced TGCT improve, there is increasing focus on quality of survival. Increasingly, primary RPLND is being used as an alternative to chemotherapy (and radiotherapy) as primary treatment for selected individuals, due to the development of low morbidity surgical techniques (robot-assisted and extraperitoneal approaches), and recognition of acute and long-term toxicity of chemotherapy and radiotherapy. We hypothesise that primary RPLND compared to chemotherapy and radiotherapy will have less detrimental impact on HRQoL with comparable oncological outcomes. Methods: PRESTIGE is a prospective cohort study among adults with histologically confirmed advanced TGCT planned for primary RPLND, chemotherapy or radiotherapy. Eligible patients may have de novo advanced disease, or relapse within two- (NSGCT) or three-years (seminoma) following orchidectomy, without prior chemotherapy or radiotherapy (including adjuvant), and able to complete HRQoL questionnaires in English. See table for cohort-specific eligibility. Surgical approach (modality, template, adjuvant chemotherapy), chemotherapy regimen and radiotherapy dose will be administered at physician discretion and captured in Australia’s national multi-centre TGCT registry, iTestis, with clinical data in follow-up. Participants will complete HRQoL questionnaires focusing on physical, social, emotional, and functional domains commencing prior to treatment and continuing for 24 months. Questionnaires include EORTC QLQ-C30, EORTC QLQ-TC26, Brief Male Sexual Function Inventory and specific questions focusing on retrograde ejaculation and fertility. Patients undergoing RPLND will also have miR-371 evaluation pre-operatively and serially for 12 months after surgery. The primary outcome is change in EORTC QLQ-C30 global health status over 24 months for participants undergoing primary RPLND. Key secondary outcomes are changes in HRQoL, patterns of recurrence, surgical complications, retrograde ejaculation, and infertility. A sample size of 90 participants (30 for RPLND, 60 for chemotherapy) provides >80% power to detect a mean difference of 28% in global health status at 0.05 significance favouring RPLND. Up to 30 individuals receiving radiotherapy may also be enrolled. The first site was activated 4-Aug-22; 2 of 3 study sites are active and 5 out of a planned 120 (4%) participants have enrolled. [Table: see text]

TPS432 Background: Retrograde ejaculation is a known complication of RPLND occurring due to interruption of sympathetic nerve fibres intraoperatively. Whilst common immediately after surgery, the prevalence of persistent symptoms, and impact on health-related quality-of-life (HRQoL) in TC survivors is unknown. Whilst few effective treatments are available, alpha-sympathomimetics such as pseudoephedrine may form a viable treatment option due to effects on bladder neck constriction at sexual climax propagating antegrade ejaculation. Existing studies have shown pseudoephedrine may be helpful in individuals with retrograde ejaculation; however, these studies have only included a small number (n=5) of patients following RPLND. Methods: In a two-part, single-arm phase 2 clinical trial, participants with a history of TC receiving follow-up after RPLND at least >6 months prior will be invited to participate. In part A (ACTRN12622000537752), eligible participants complete questionnaires regarding survivorship issues including sexual function and fertility to explore the prevalence of retrograde ejaculation and HRQoL. Questionnaires include EORTC QLQ-C30, sexual function items from EORTC QLQ-TC26, Brief Male Sexual Function Inventory and tailored questions focusing on retrograde ejaculation. 15 out of a planned 50 participants (30%) have been enrolled and completed questionnaires. If retrograde ejaculation is reported, eligible participants are invited to enrol in Part B (ACTRN12622000542796), where if the participant has no contraindications to pseudoephedrine, nor are they receiving testosterone replacement and retrograde ejaculation is confirmed during screening, they receive a short course of pseudoephedrine hydrochloride. Pseudoephedrine will be given 60mg QID for one-day, followed by 60mg 4 hours and 1 hour prior to ejaculation to evaluate changes in ejaculation volume, sperm count within (any) antegrade ejaculate and within the post-ejaculatory urine. The primary endpoint is total sperm count in antegrade ejaculate of at least 39 million (5th centile) following treatment. We assumed that pseudoephedrine is ineffective if 80% power to detect 36% (9 out of 25) of participants achieving a normal sperm count against a reference proportion of 10% at the 0.05 significance level. 4 out of a planned 30 participants (16%) have been enrolled. All participants with retrograde ejaculation will be invited to an optional, semi-structured interview to further evaluate impact(s) of retrograde ejaculation on issues around sexual health, relationships, body image and masculinity. Clinical trial information: ACTRN12622000537752 , ACTRN12622000542796 .

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The Doherty Model is being used in Australia to justify partial reopening with 70% of adults vaccinated. However, we have identified six critical failures of the model: failure to model uncertainties; failure to use appropriate premises; failure to model subgroup vaccine takeup; failure to correctly model child transmission; failure to include relevant outcomes; and failure to consider longer time-frames. These failures result in missing over 200,000 cases of long covid in children, underestimating death counts by a factor of up to ten, underestimating the severity of the delta variant by a factor of two, and greatly underestimating the potential downside risk.

It is of interest to evaluate crossbred pigs for hot carcass weight (HCW) and birth weight (BW); however, obtaining a HCW record is dependent on livability (LIV) and retained tag (RT). The purpose of this study is to analyze how HCW evaluations are affected when herd removal and missing identification are included in the model and examine if accounting for the reasons for missing traits improves the accuracy of predicting breeding values. Pedigree information was available for 1,965,077 purebred and crossbred animals. Records for 503,716 commercial three-way crossbred terminal animals from 2014 to 2019 were provided by Smithfield Premium Genetics. Two pedigree-based models were compared; model 1 (M1) was a threshold-linear model with all four traits (BW, HCW, RT, and LIV), and model 2 (M2) was a linear model including only BW and HCW. The fixed effects used in the model were contemporary group, sex, age at harvest (for HCW only), and dam parity. The random effects included direct additive genetic and random litter effects. Accuracy, dispersion, bias, and Pearson correlations were estimated using the linear regression method. The heritabilities were 0.11, 0.07, 0.02, and 0.04 for BW, HCW, RT, and LIV, respectively, with standard errors less than 0.01. No difference was observed in heritabilities or accuracies for BW and HCW between M1 and M2. Accuracies were 0.33, 0.37, 0.19, and 0.23 for BW, HCW, RT, and LIV, respectively. The genetic correlation between BW and RT was 0.34 ± 0.03, and between BW and LIV was 0.56 ± 0.03. Similarly, the genetic correlation between HCW and RT was 0.26 ± 0.04, and between HCW and LIV was 0.09 ± 0.05, respectively. The positive and moderate genetic correlations between BW and other traits imply a heavier BW resulted in a higher probability of surviving to harvest. Genetic correlations between HCW and other traits were lower due to the large quantity of missing records. Despite the heritable and correlated aspects of RT and LIV, results imply no major differences between M1 and M2; hence, it is unnecessary to include these traits in classical models for BW and HCW.

Inbreeding depression is a growing concern in livestock because it can detrimentally affect animal fitness, health, and production levels. Genomic information can be used to more effectively capture variance in Mendelian sampling, thereby enabling more accurate estimation of inbreeding, but further progress is still required. The calculation of inbreeding for herd management purposes is largely still done using pedigree information only, although inbreeding coefficients calculated in this manner have been shown to be less accurate than genomic inbreeding measures. Continuous stretches of homozygous genotypes, so called runs of homozygosity, have been shown to provide a better estimate of autozygosity at the genomic level than conventional measures based on inbreeding coefficients calculated through conventional pedigree information or even genomic relationship matrices. For improved and targeted management of genomic inbreeding at the population level, the development of methods that incorporate genomic information in mate selection programs may provide a more precise tool for reducing the detrimental effects of inbreeding in dairy herds. Additionally, a better understanding of the genomic architecture of inbreeding and incorporating that knowledge into breeding programs could significantly refine current practices. Opportunities to maintain high levels of genetic progress in traits of interest while managing homozygosity and sustaining acceptable levels of heterozygosity in highly selected dairy populations exist and should be examined more closely for continued sustainability of both the dairy cattle population as well as the dairy industry. The inclusion of precise genomic measures of inbreeding, such as runs of homozygosity, inbreeding, and mating programs, may provide a path forward. In this symposium review article, we describe traditional measures of inbreeding and the recent developments made toward more precise measures of homozygosity using genomic information. The effects of homozygosity resulting from inbreeding on phenotypes, the identification and mapping of detrimental homozygosity haplotypes, management of inbreeding with genomic data, and areas in need of further research are discussed.

5027 Background: The Phase I PRINCE trial (NCT03658447) is evaluating the efficacy of 177Lu-PSMA-617 plus pembrolizumab for mCRPC. The utility of serial monitoring of CTCs and PET as biomarkers of prognosis and clinical benefit of 177Lu-PSMA-617-based therapy remains unknown. Methods: 36 of 37 pts with high PSMA expression on PSMA PET underwent serial CTC collections in conjunction with PSMA PET at baseline, every 12 weeks for 48 weeks and every 24 weeks thereafter. CK+, CD45- CTCs were enumerated from 3ml of blood and stained for PSMA (Epic Sciences platform). Associations between PSMA+ CTC counts, PET molecular tumor volume (MTV), total lesional activity (TLA; MTVxSUVmean) were assessed by Spearman correlation. Cox models assessed the association of CTC and PSMA PET parameters with radiographic progression-free survival (rPFS) and PSA PFS. A subset of pre-treatment CTCs underwent single cell low-pass whole genome sequencing to characterize copy number aberrations. Results: 32/36 pt (89%) had detectable CTCs (median 7, range 0-514) with 23 (64%) being PSMA+ (median 1, range 0-224) at baseline. At w12, 23/33 (70%) had CTCs detected with 10 (30%) being PSMA+. Baseline PSMA+ CTC count and MTV were moderately correlated (rs= 0.57, p < 0.001). Of 22 evaluable pts with baseline PSMA+ CTC, 18 (82%) showed decrease by w12 with clearance in 13. This paralleled reductions in MTV (-18% med relative change, IQR: -57 to -1) and TLA (-48% med relative change, IQR: -77 to -28). Total CTC and PSMA+ CTC counts at baseline, and PET parameters were not associated with PSA PFS or rPFS. Clearance of PSMA+ CTC by w12 (13/22 pts) was associated with improved rPFS (med NR vs 3.0 mos, HR 0.23, 95%CI 0.07-0.74, p = 0.007) and PSA PFS (med 11.2 vs 3.5 mos, HR 0.28, 95%CI 0.11-0.73, p = 0.006). Persisting PSMA-neg CTCs at w12 trended to worse rPFS (med 4.1 vs 12.3 mos, p = 0.11) and PSA PFS (med 5.1 vs 12.3 mos, p = 0.07). Of pts not progressing by w12, decrease in PSA (HR 0.83 per 10% decrease, 95%CI 0.74-0.93, p < 0.001), MTV (HR 0.85 per 10% decrease, 95%CI 0.75-0.96, p = 0.008), MTV > 30% decrease (HR 0.30, 95% CI 0.08-1.08, p = 0.05) and TLA (HR 0.88 per 10% decrease, 95%CI 0.78-1.00, p = 0.04) associated with improved rPFS beyond w12. Pre-treatment CTCs (18 pts) exhibited genomic heterogeneity and frequent loss of PTEN, TP53 and RB1. Pts with compound TP53 and RB1 loss at baseline nonetheless had PSMA+ CTCs in high proportion (med 91.3% of total CTCs). Conclusions: PSMA PET-positive mCRPC is associated with high rates of PSMA+ CTCs which decline with 177Lu-PSMA-617 plus pembrolizumab in parallel with PSMA MTV/TLA. Despite imaging suitability for therapy, CTCs had heterogenous PSMA expression and genomic alterations associated with aggressive disease. Early changes in PSMA+ CTCs and MTV/TLA were associated with outcomes and may aid in determining clinical activity of LuPSMA-based therapy.

The present study aimed to explore gut microbiota’s overall influence on feeding behavior and identify specific microbes associated with the traits in three commercial swine breeds at different growth stages. Feeding behavior measures were obtained from 651 pigs of three breeds (Duroc, Landrace, and Large White) from 67 to 175 days of age. Seven feeding behavior traits cover the information of feed intake, feeder occupation time, feeding rate, and the number of visits to the feeder. Rectal swabs were collected from each pig at 73 ± 3, 123 ± 4, and 158 ± 4 days of age. DNA was extracted and subjected to 16S rRNA gene sequencing. Sequences were analyzed using the QIIME2. The amplicon sequence variant table was constructed using the DADA2. Mixed linear models were used to perform statistical analyses. Differences in feeding behavior traits among breeds and across time periods were found. The proportion of phenotypic variances explained by the microbiome ranged from 0.10 to 0.26, 0.14 to 0.25, and 0.12 to 0.28 in Duroc pigs, from 0.09 to 0.20, 0.10 to 0.23, and 0.12 to 0.23 in Landrace pigs, from 0.10 to 0.17, 0.11 to 0.19, and 0.10 to 0.31 in Large White pigs at three time points, respectively. A total of 21, 10, and 35 amplicon sequence variants were found to be significantly (q-value < 0.05) associated with feeding behavior traits for Duroc, Landrace, and Large White across three time points. This study demonstrated the importance of the gut microbiome composition in influencing the host feeding behavior and identified multiple microbes associated with the feeding behavior of pigs from either Duroc, Landrace, or Large White breeds at three growth stages. Our study provides insight into the interaction between gut microbiota and feeding behavior and highlights the breed effects in swine microbial studies.

A timely and accurate estimation of body weight in finishing pigs is critical in determining profits by allowing pork producers to make informed marketing decisions on group-housed pigs while reducing labor and feed costs. This study investigated the usefulness of feeding behavior data in predicting the body weight of pigs at the finishing stage. We obtained data on 655 pigs of three breeds (Duroc, Landrace, and Large White) from 75 to 166 days of age. Feeding behavior, feed intake, and body weight information were recorded when a pig visited the Feed Intake Recording Equipment in each pen. Data collected from 75 to 158 days of age were split into six slices of 14 days each and used to calibrate predictive models. LASSO regression and two machine learning algorithms (Random Forest and Long Short-term Memory network) were selected to forecast the body weight of pigs aged from 159 to 166 days using four scenarios: individual-informed predictive scenario, individual- and group-informed predictive scenario, breed-specific individual- and group-informed predictive scenario, and group-informed predictive scenario. We developed four models for each scenario: Model_Age included only age, Model_FB included only feeding behavior variables, Model_Age_FB and Model_Age_FB_FI added feeding behavior and feed intake measures on the basis of Model_Age as predictors. Pearson’s correlation, root mean squared error, and binary diagnostic tests were used to assess predictive performance. The greatest correlation was 0.87, and the highest accuracy was 0.89 for the individual-informed prediction, while they were 0.84 and 0.85 for the individual- and group-informed predictions, respectively. The least root mean squared error of both scenarios was about 10 kg. The best prediction performed by Model_FB had a correlation of 0.83, an accuracy of 0.74, and a root mean squared error of 14.3 kg in the individual-informed prediction. The effect of the addition of feeding behavior and feed intake data varied across algorithms and scenarios from a small to moderate improvement in predictive performance. We also found differences in predictive performance associated with the time slices or pigs used in the training set, the algorithm employed, and the breed group considered. Overall, this study’s findings connect the dynamics of feeding behavior to body growth and provide a promising picture of the involvement of feeding behavior data in predicting the body weight of group-housed pigs.

The science around the use of masks by the public to impede COVID-19 transmission is advancing rapidly. In this narrative review, we develop an analytical framework to examine mask usage, synthesizing the relevant literature to inform multiple areas: population impact, transmission characteristics, source control, wearer protection, sociological considerations, and implementation considerations. A primary route of transmission of COVID-19 is via respiratory particles, and it is known to be transmissible from presymptomatic, paucisymptomatic, and asymptomatic individuals. Reducing disease spread requires two things: limiting contacts of infected individuals via physical distancing and other measures and reducing the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces transmissibility per contact by reducing transmission of infected respiratory particles in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. Given the current shortages of medical masks, we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory particles become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask wearing by infectious people (“source control”) with benefits at the population level, rather than only mask wearing by susceptible people, such as health care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Made available in DSpace on 2022-04-28T19:40:47Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-12-01 National Institute of Food and Agriculture Background: There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait. Results: The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs. Conclusions: We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs. Department of Animal Sciences Purdue University Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province Sichuan Agricultural University Centre for Genetic Improvement of Livestock Department of Animal Biosciences University of Guelph Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP) Smithfield Premium Genetics Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP) National Institute of Food and Agriculture: 2020-67015-31575

Genomic predictivity is expected to decay over time as predictions are evaluated to more distant generations. More data increases predictive ability; however, data from distant ancestors may not add a significant amount of value compared to the data from closely related individuals. The objective of this study was to evaluate the decay in genomic predictivity over time and to compare the magnitude of decay when including ancestral data versus data from 2 and 3 most recent generations for body weight at off-test (BW). The data set included 211,812 phenotypic records. The pedigree included 406,983 animals from 2001 to 2020, of which 55,118 were genotyped. A single-trait model was used with all ancestral data and sliding subsets of two- and three-generation intervals. Single-step GBLUP was used to calculate GEBVs. Predictive abilities were calculated by the correlation between GEBVs and adjusted phenotypes. Validation populations consisted of single generations succeeding the training population and continued for all generations available. Predictive ability was slightly higher, with all ancestral data in the training population compared to three- and two-generation intervals. The decay of predictivity was similar when comparing the three training population subsets. The average predictivity for the validation population immediately following the training population was 0.40 for 2016, 0.39 for 2017, 0.35 for 2018, and 0.29 for 2019. Predictive ability reached a maximum in the year 2017 (0.45) for the ancestral training population, 2017 (0.43) for the 3-year training population, and 2016 (0.38) for the 2-year training population. The average decay of predictive ability from the first year after the training population to the second year was -0.08. Realized predictivity is affected by selection pressure. The drop in predictive ability suggests declining heritability. With more data and with consistent selection pressure, predictive abilities should increase.

Genomic information has a limited dimensionality (number of independent chromosome segments [Me]) related to the effective population size. Under the additive model, the persistence of genomic accuracies over generations should be high when the nongenomic information (pedigree and phenotypes) is equivalent to Me animals with high accuracy. The objective of this study was to evaluate the decay in accuracy over time and to compare the magnitude of decay with varying quantities of data and with traits of low and moderate heritability. The dataset included 161,897 phenotypic records for a growth trait (GT) and 27,669 phenotypic records for a fitness trait (FT) related to prolificacy in a population with dimensionality around 5,000. The pedigree included 404,979 animals from 2008 to 2020, of which 55,118 were genotyped. Two single-trait models were used with all ancestral data and sliding subsets of 3-, 2-, and 1-generation intervals. Single-step genomic best linear unbiased prediction (ssGBLUP) was used to compute genomic estimated breeding values (GEBV). Estimated accuracies were calculated by the linear regression (LR) method. The validation population consisted of single generations succeeding the training population and continued forward for all generations available. The average accuracy for the first generation after training with all ancestral data was 0.69 and 0.46 for GT and FT, respectively. The average decay in accuracy from the first generation after training to generation 9 was −0.13 and −0.19 for GT and FT, respectively. The persistence of accuracy improves with more data. Old data have a limited impact on the predictions for young animals for a trait with a large amount of information but a bigger impact for a trait with less information.

Les confins, les cœurs Certes, Strasbourg (ou Strasburg) et Riga etaient aux confins d’empires autour de 1900, que ce soit celui de Guillaume II ou celui des Romanov. Et certes, ces deux villes etaient le lieu d’un melange cosmopolite de populations, parmi lesquelles on comptait celles dont l’identite ethnique locale, alsacienne ou lettone, differait de celles du centre des empires qui les dominaient, c’est-a-dire allemande ou russe. Que ces populations locales aient pu developper des langage...

The science around the use of masks by the general public to impede COVID-19 transmission is advancing rapidly. Policymakers need guidance on how masks should be used by the general population to combat the COVID-19 pandemic. In this narrative review, we develop an analytical framework to examine mask usage, considering and synthesizing the relevant literature to inform multiple areas: population impact; transmission characteristics; source control; PPE; sociological considerations; and implementation considerations. A primary route of transmission of COVID-19 is via respiratory droplets, and is known to be transmissible from presymptomatic and asymptomatic individuals. Reducing disease spread requires two things: first, limit contacts of infected individuals via physical distancing and other measures, and second, reduce the transmission probability per contact. The preponderance of evidence indicates that mask wearing reduces the transmissibility per contact by reducing transmission of infected droplets in both laboratory and clinical contexts. Public mask wearing is most effective at reducing spread of the virus when compliance is high. The decreased transmissibility could substantially reduce the death toll and economic impact while the cost of the intervention is low. Given the current shortages of medical masks we recommend the adoption of public cloth mask wearing, as an effective form of source control, in conjunction with existing hygiene, distancing, and contact tracing strategies. Because many respiratory droplets become smaller due to evaporation, we recommend increasing focus on a previously overlooked aspect of mask usage: mask-wearing by infectious people ("source control") with benefits at the population-level, rather than mask-wearing by susceptible people, such as health-care workers, with focus on individual outcomes. We recommend that public officials and governments strongly encourage the use of widespread face masks in public, including the use of appropriate regulation.

Background Feed efficiency is a crucial parameter in swine production, given both its economic and environmental impact. The gut microbiota plays an essential role in nutrient digestibility and is, therefore, likely to affect feed efficiency. This study aimed to characterize feed efficiency, fatness traits, and gut microbiome composition in three major breeds of domesticated swine and investigate a possible link between feed efficiency and gut microbiota composition. Results Average daily feed intake (ADFI), average daily gain (ADG), feed conversion ratio (FCR), residual feed intake (RFI), backfat, loin depth, and intramuscular fat of 615 pigs belonging to the Duroc (DR), Landrace (LR), and Large White (LW) breeds were measured. Gut microbiota composition was characterized by 16S rRNA gene sequencing. Orthogonal contrasts between paternal line (DR) and maternal lines (LR+LW) and between the two maternal lines (LR versus LW) were performed. Average daily feed intake and ADG were statistically different with DR having lower ADFI and ADG compared to LR and LW. Landrace and LW had a similar ADG and RFI, with higher ADFI and FCR for LW. Alpha diversity was higher in the fecal microbial communities of LR pigs than in those of DR and LW pigs for all time points considered. Duroc communities had significantly higher proportional representation of the Catenibacterium and Clostridium genera compared to LR and LW, while LR pigs had significantly higher proportions of Bacteroides than LW for all time points considered. Amplicon sequence variants from multiple genera (including Anaerovibrio, Bacteroides, Blautia, Clostridium, Dorea, Eubacterium, Faecalibacterium, Lactobacillus, Oscillibacter, and Ruminococcus) were found to be significantly associated with feed efficiency, regardless of the time point considered. Conclusions In this study, we characterized differences in the composition of the fecal microbiota of three commercially relevant breeds of swine, both over time and between breeds. Correlations between different microbiome compositions and feed efficiency were established. This suggests that the microbial community may contribute to shaping host productive parameters. Moreover, our study provides important insights into how the intestinal microbial community might influence host energy harvesting capacity. A deeper understanding of this process may allow us to modulate the gut microbiome in order to raise more efficient animals. Video Abstract

Background Feed efficiency is a crucial parameter in swine production given both its economic and environmental impact. The gut microbiota plays an essential role in nutrient digestibility and is therefore likely to affect feed efficiency. This study aimed to characterize feed efficiency, production traits, and gut microbiome composition of three major breeds of domesticated swine, and to investigate a possible link between feed efficiency and gut microbiota composition. Results Average daily feed intake ( ADFI ), average daily gain ( ADG ), feed conversion ratio ( FCR ), residual feed intake ( RFI ), backfat, loin depth, and intramuscular fat of 615 pigs belonging to the Duroc ( DR ), Landrace ( LR ), and Large White ( LW ) breeds were measured. Gut microbiota composition was characterized by 16S rRNA gene sequencing. Orthogonal contrasts between paternal line (DR) and maternal lines (LR+LW) and between the two maternal lines (LR versus LW) were performed. Average daily feed intake and ADG were statistically different with DR having lower ADFI and ADG compared to LR and LW. Landrace and LW had a similar ADG and RFI, with higher ADFI and FCR for LW. Alpha diversity was higher in the fecal microbial communities of LR pigs than in those of DR and LW pigs for all time points considered. Duroc communities had significantly higher proportional representation of the Catenibacterium and Clostridium genera compared to LR and LW, while LR pigs had significantly higher proportions of Bacteroides than LW for all time points considered. We identified several amplicon sequencing variants members of certain genera including Anaerovibrio , Bacteroides , Blautia , Clostridium , Dorea , Eubacterium , Faecalibacterium , Lactobacillus, Oscillibacter , and Ruminococcus significantly associated with feed efficiency of pigs for all time points considered. Conclusions In this study we characterized differences in the composition of the fecal microbiota of three commercially relevant breeds of swine, both over time and between breeds. Correlations between different microbiome compositions and feed efficiency were established. This suggests that the microbial community may contribute in shaping host productive parameters. Moreover, our study provides important insights to understand how the intestinal microbial community might influence the host energy harvesting capacity. A deeper understanding of this process may allow us to modulate the gut microbiome for more efficient animals.

Across the majority livestock species, routinely collected genomic and pedigree information has been incorporated into evaluations using single-step methods. As a result, strategies that reduce genotyping costs without reducing the response to selection are important as they could have substantial economic impacts on breeding programs. Therefore, the objective of the current study was to investigate the impact of selectively genotyping selection candidates on the selection response using simulation. Populations were simulated to mimic the genome and population structure of a swine and cattle population undergoing selection on an index comprised of the estimated breeding values (EBV) for 2 genetically correlated quantitative traits. Ten generations were generated and genotyping began generation 7. Two phenotyping scenarios were simulated that assumed the first trait was recorded early in life on all individuals and the second trait was recorded on all versus a random subset of the individuals. The EBV were generated from a bivariate animal model. Multiple genotyping scenarios were generated that ranged from not genotyping any selection candidates, a proportion of the selection candidates based on either their index value or chosen at random, and genotyping all selection candidates. An interim index value was utilized to decide who to genotype for the selective genotype strategy. The interim value assumed only the first trait was observed and the only genotypic information available was on animals in previous generations. Within each genotyping scenario 25 replicates were generated. Within each genotyping scenario the mean response per generation and the degree to which EBV were inflated/deflated was calculated. Across both species and phenotyping strategies, the plateau of diminishing returns was observed when 60% of the selection candidates with the largest index values were genotyped. When randomly genotyping selection candidates, either 80 or 100% of the selection candidates needed to be genotyped for there not to be a reduction in the index response. Across both populations, no differences in the degree that EBV were inflated/deflated for either trait 1 or 2 were observed between nongenotyped and genotyped animals. The current study has shown that animals can be selectively genotyped in order to optimize the response to selection as a function of the cost to conduct a breeding program using single-step genomic best linear unbiased prediction.

Simulated and swine industry data sets were utilized to assess the impact of removing older data on the predictive ability of selection candidate estimated breeding values (EBV) when using single-step genomic best linear unbiased prediction (ssGBLUP). Simulated data included thirty replicates designed to mimic the structure of swine data sets. For the simulated data, varying amounts of data were truncated based on the number of ancestral generations back from the selection candidates. The swine data sets consisted of phenotypic and genotypic records for three traits across two breeds on animals born from 2003 to 2017. Phenotypes and genotypes were iteratively removed 1 year at a time based on the year an animal was born. For the swine data sets, correlations between corrected phenotypes (Cp) and EBV were used to evaluate the predictive ability on young animals born in 2016-2017. In the simulated data set, keeping data two generations back or greater resulted in no statistical difference (p-value > 0.05) in the reduction in the true breeding value at generation 15 compared to utilizing all available data. Across swine data sets, removing phenotypes from animals born prior to 2011 resulted in a negligible or a slight numerical increase in the correlation between Cp and EBV. Truncating data is a method to alleviate computational issues without negatively impacting the predictive ability of selection candidate EBV.

Vaginal and rectal prolapse incidence has been increasing in the U.S. commercial breeding sow population in recent years. This has resulted in increased sow breeding mortality rates in the U.S. due to both increased mortality and euthanasia. The objective of the present study was to estimate the potential role that genetics plays in the prolapse incidence of the U.S. commercial sow population. Data from 11,481 purebred Large White sows from two commercial farms were used in this study. Genetic parameters for prolapse incidence were estimated based on linear and threshold animal models utilizing AIREMLF90 and THRGIBBS1F90, respectively. Across both models, the fixed effects included contemporary group as year-month of removal and removal parity and a random effect was sow. A pedigree-based relationship matrix was included in the model. The highest prolapse incidence occurred in the 1st when compared to other parities. Heritability estimates for combined vaginal and rectal prolapse incidence were 0.03 ± 0.01 and 0.003 ± 0.003 for the linear and threshold models, respectively. The results from this study indicate that there is very little if any genetic impact on the prolapse incidence. Pork producers should focus on the environmental effects contributing to reducing prolapse incidence.

Traditionally, pedigree-based relationship coefficients have been used to manage the inbreeding and degree of inbreeding depression that exists within a population. The widespread incorporation of genomic information in dairy cattle genetic evaluations allows for the opportunity to develop and implement methods to manage populations at the genomic level. As a result, the realized proportion of the genome that 2 individuals share can be more accurately estimated instead of using pedigree information to estimate the expected proportion of shared alleles. Furthermore, genomic information allows genome-wide relationship or inbreeding estimates to be augmented to characterize relationships for specific regions of the genome. Region-specific stretches can be used to more effectively manage areas of low genetic diversity or areas that, when homozygous, result in reduced performance across economically important traits. The use of region-specific metrics should allow breeders to more precisely manage the trade-off between the genetic value of the progeny and undesirable side effects associated with inbreeding. Methods tailored toward more effectively identifying regions affected by inbreeding and their associated use to manage the genome at the herd level, however, still need to be developed. We have reviewed topics related to inbreeding, measures of relatedness, genetic diversity and methods to manage populations at the genomic level, and we discuss future challenges related to managing populations through implementing genomic methods at the herd and population levels.