Computational fluid dynamics modeling for HPV fermentation bioreactors

The fermentation processes for the manufacture of HPV (GARDASIL AND GARDASIL 9) are currently conducted at multiple manufacturing scales. The goal of the bioreactor modeling project for HPV is to generate data and models to support robust manufacturing and process understanding initiatives across the multiple scales. Additionally, this knowledge can be utilized to aid in future process transfers. The modeling work is performed utilizing computer models (Computational Fluid Dynamics via Fluent) and historical data to predict metabolic behavior based on bioreactor configurations and processing conditions

Animal board invited review: Practical applications of genomic information in livestock

Access to high-dimensional genomic information in many livestock species is accelerating. This has been greatly aided not only by continual reductions in genotyping costs but also an expansion in the services available that leverage genomic information to create a greater return-on-investment. Genomic information on individual animals has many uses including (1) parentage verification and discovery, (2) traceability, (3) karyotyping, (4) sex determination, (5) reporting and monitoring of mutations conferring major effects or congenital defects, (6) better estimating inbreeding of individuals and coancestry among individuals, (7) mating advice, (8) determining breed composition, (9) enabling precision management, and (10) genomic evaluations; genomic evaluations exploit genome-wide genotype information to improve the accuracy of predicting an animal’s (and by extension its progeny’s) genetic merit. Genomic data also provide a huge resource for research, albeit the outcome from this research, if successful, should eventually be realised through one of the ten applications already mentioned. The process for generating a genotype all the way from sample procurement to identifying erroneous genotypes is described, as are the steps that should be considered when developing a bespoke genotyping panel for practical application

Bull-half sib steer comparisons: phenotypic correlation and carcass prediction using ultrasound

Records from Angus bulls (n=257) and steers from Angus sires (n=212) over a four-year period were used in this analysis. The bulls and steers shared in common twenty Angus sires. All animals were serially scanned from weaning to slaughter for the following ultrasound traits: 12th-13th rib fat thickness (FTK), ribeye area (REA), rump fat thickness (RF), percentage intramuscular fat (PFAT), and weight at scanning (WT). Phenotypic correlation estimates between bull data adjusted to a year of age and steer data adjusted to average age at slaughter (390 days) were derived by the CORR procedure from SAS and were correlated by sire. Two sets of correlation estimates were derived, no age of dam adjustments in either sex and bull data adjusted using pooled estimates from the American Angus Association (AAA). The more notable estimates are as follows (AAA adjustments) .19, -.41, .19, .42, .40, -.43 for SREA (steer REA) and BREA (bull REA), SREA and BFTK, SFTK and BFTK, SPFAT and BFTK, SPFAT and BPFAT, SREA and BRF, respectively. Prediction models were derived using bull measures adjusted to a year of age and for age of dam (AAA) to explain steer marbling score (MS) and percent retail product (PRP). All explanatory and response variables were averaged by sire. The final prediction model for PRP explained 47.5% of the variation and included BFTK, BRF, BPFAT and the interaction between BFTK and BPFAT. The final prediction model for MS explained 34.2% of the variation and included BRF, BPFAT, the interaction between BRF and BFTK, and the interaction between BPFAT and BFTK

Computational fluid dynamics modeling for fermentation risk reduction during technology transfer and risk understanding

Computational Fluid Dynamics modeling and in-depth scaling calculations have been utilized in partnership to generate data to support equipment design and facility fit during commercialization of a fermentation and primary recovery process for a vaccine candidate across multiple technical transfers. This analysis utilizing representative computer models for tank configurations, supplemented with traditional computational scaling approaches (ungassed P/V, gassed P/V, kLa, etc.), ensures full knowledge of a tank’s mixing and oxygen transfer capabilities allowing process understanding and robust manufacturing across technology transfer to multiple sites. Implementation of this approach across process steps as well as manufacturing sites allows increased knowledge prior to use in a process and/or prior to construction of a new vessel, therefore contributing to successful process transfer with reduced risks upon scale-up/scale-down and new facility introductions

The impact of training strategies on the accuracy of genomic predictors in United States Red Angus cattle

Genomic selection (GS) has become an integral part of genetic evaluation methodology and has been applied to all major livestock species, including beef and dairy cattle, pigs, and chickens. Significant contributions in increased accuracy of selection decisions have been clearly illustrated in dairy cattle after practical application of GS. In the majority of U.S. beef cattle breeds, similar efforts have also been made to increase the accuracy of genetic merit estimates through the inclusion of genomic information into routine genetic evaluations using a variety of methods. However, prediction accuracies can vary relative to panel density, the number of folds used for folds cross-validation, and the choice of dependent variables (e.g., EBV, deregressed EBV, adjusted phenotypes). The aim of this study was to evaluate the accuracy of genomic predictors for Red Angus beef cattle with different strategies used in training and evaluation. The reference population consisted of 9,776 Red Angus animals whose genotypes were imputed to 2 medium-density panels consisting of over 50,000 (50K) and approximately 80,000 (80K) SNP. Using the imputed panels, we determined the influence of marker density, exclusion (deregressed EPD adjusting for parental information [DEPD-PA]) or inclusion (deregressed EPD without adjusting for parental information [DEPD]) of parental information in the deregressed EPD used as the dependent variable, and the number of clusters used to partition training animals (3, 5, or 10). A BayesC model with π set to 0.99 was used to predict molecular breeding values (MBV) for 13 traits for which EPD existed. The prediction accuracies were measured as genetic correlations between MBV and weighted deregressed EPD. The average accuracies across all traits were 0.540 and 0.552 when using the 50K and 80K SNP panels, respectively, and 0.538, 0.541, and 0.561 when using 3, 5, and 10 folds, respectively, for cross-validation. Using DEPD-PA as the response variable resulted in higher accuracies of MBV than those obtained by DEPD for growth and carcass traits. When DEPD were used as the response variable, accuracies were greater for threshold traits and those that are sex limited, likely due to the fact that these traits suffer from a lack of information content and excluding animals in training with only parental information substantially decreases the training population size. It is recommended that the contribution of parental average to deregressed EPD should be removed in the construction of genomic prediction equations. The difference in terms of prediction accuracies between the 2 SNP panels or the number of folds compared herein was negligible

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between estimated genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genomebased connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation r to a combination of computer simulation and real data (mice and cattle). We found that genomic information (G) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (A). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using G- rather than A-based relatedness, suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling G to values between 0 and 2, which is intrinsic to A; connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G; instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species

Do stronger measures of genomic connectedness enhance prediction accuracies across management units?

Genetic connectedness assesses the extent to which estimated breeding values can be fairly compared across management units. Ranking of individuals across units based on best linear unbiased prediction (BLUP) is reliable when there is a sufficient level of connectedness due to a better disentangling of genetic signal from noise. Connectedness arises from genetic relationships among individuals. Although a recent study showed that genomic relatedness strengthens the estimates of connectedness across management units compared with that of pedigree, the relationship between connectedness measures and prediction accuracies only has been explored to a limited extent. In this study, we examined whether increased measures of connectedness led to higher prediction accuracies evaluated by a cross-validation (CV) based on computer simulations. We applied prediction error variance of the difference, coefficient of determination (CD), and BLUP-type prediction models to data simulated under various scenarios. We found that a greater extent of connectedness enhanced accuracy of whole-genome prediction. The impact of genomics was more marked when large numbers of markers were used to infer connectedness and evaluate prediction accuracy. Connectedness across units increased with the proportion of connecting individuals and this increase was associated with improved accuracy of prediction. The use of genomic information resulted in increased estimates of connectedness and improved prediction accuracies compared with those of pedigree-based models when there were enough markers to capture variation due to QTL signals

Ultra-Fast List-Mode Reconstruction of Short PET Frames and Example Applications

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