bluuebunny/crossref_metadata_2025_split huggingface dataset:
126 Prevalence of Right Sided Heart Failure in Angus Influenced and Beef on Dairy Fattening Feedlot Cattle
Authors: Kukor Isabella , Mark Enns R , Holt Timothy N , Cleveland Matthew , Holland Ben P , Word Alyssa B , Ellis Guy , Theurer Miles , Speidel Scott E 
Published: November 2023

Abstract
Bovine Congestive Heart Failure (BCHF) exists in feedlot cattle at low to moderate elevations, but previous studies focused solely on beef cattle. The economic advantage of breeding beef sires to dairy dams results in an increase in the number of beef on dairy (DXB) crossed cattle in the feedlot system, though not much is known on the comparative prevalence of BCHF in DXB compared with beef cattle breeds. The inclusion of a new breeding program for dairy producers leads to another potential source of variability (i.e., dairy influenced genetics) for heart disease research in the feedlot sector. Heart failure in the feedlot is typically known as Feedlot Heart Disease (FHD) and has been reported to result in unfavorable carcass and growth performance damaging quality and efficiency throughout the lifetime of cattle. The unknown prevalence of heart failure in DXB cattle may be a problem for producers since little is known about which breeds are at greater risk. The severity of FHD for both beef and DXB cattle was determined by individually grading hearts at harvest using a 1 to 5 grading system corresponding to the stage of heart remodeling with a score of 1 (normal heart) and greater scores indicating increased remodeling. The objective of this study was to examine the prevalence of heart remodeling in both an Angus influenced group of beef cattle and a sample of DXB cattle. Heart scores were collected on an Angus influenced group raised in the panhandle of Texas (1,103m) and totaled 1,634 animals (Grade 1: 347; Grade 2: 848; Grade 3: 371; Grade 4: 68; Grade 5: 0). Heart scores were collected on a DXB group raised in western Kansas (846 m) and totaled 2,328 animals (Grade 1: 913; Grade 2: 1,162; Grade 3: 225; Grade 4: 27; Grade 5: 1). Proportions of moderate to severe heart remodeling were 0.27 and 0.11 for Angus influenced and DXB, respectively. In conclusion, preliminary research on heart remodeling in both samples indicates a greater proportion of cattle experiencing heart remodeling in beef breeds versus the DXB cattle, although further investigation is needed to make conclusions about the consistency of heart remodeling and breed effects.
137 Longitudinal Heritability of Ocular Microbiota in Preweaned Beef Cattle
Authors: Lakamp Drew , Neujahr Alison , Hille Matthew , Loy J Dustin , Fernando Samodha C , Spangler Matthew L 
Published: November 2023

Abstract
The bovine ocular microbiome is of interest because of its potential role in ocular disease, such as infectious bovine keratoconjunctivitis. The ocular bacterial community of 223 pre-weaned beef calves from a single cohort were sampled four times; day 0, day 21, day 41, and day 139 (mean calf ages 65, 86, 99, and 204 days, respectively). The bacterial community was phenotyped using the V4 region of the 16S rRNA gene and were grouped into amplicon sequence variants (ASV) which were taxonomically classified at the phylum and family level. Heritability was estimated for the log transformed relative abundance of each phylum, family, and ASV at each time point using ASReml v. 4.2. The model included calf age at time of sampling, calf sex, pinkeye vaccination treatment group, breed fractions, and retained heterosis as fixed effects. The random effect of animal had a (co)variance structure given by an H matrix comprised of 3,025 animals of which 1,207 were genotyped including all of the sampled animals. Heritability estimates ranged from 0.16 to 0.95 at the ASV level, 0.18 to 0.91 at the family level, and 0.18 to 0.81 at the phylum level. Standard errors were approximately 0.20 on average. The number of features (ASV, family, or phylum) with estimates greater than their standard error changed across time points (Table 1). Overall, time point 1 had the largest number of features with estimates greater than their standard error, followed by time point 4. Time point 3 had a greater number ASV with estimates greater than their standard error than time point 2; however, time point 2 had greater numbers of families and phyla with estimates greater than their standard errors. This trend likely stems from the fact that time points 1 and 4 had the greatest diversity of features while time point 2 had the least diversity. Average Chao1 index diversity estimates of ASV were 144.6, 28.1, 64.7, and 194.6, for time points 1 through 4, respectively. We hypothesize this is due to the perturbation of the ocular microbiome at sampling and the fact that overall diversity in the microbiome recovered by the final sampling, although, species composition differed compared with time point 1. While no ASV, family, or phylum had a heritability estimate greater than its standard error across all time points, certain features had heritabilities greater than their standard errors at multiple time points. The phylum Armatimonadota had heritability estimates greater than their standard error at time points 1, 2, and 4 (0.52, 0.18, and 0.37, respectively). Additionally, an ASV of the Streptococcus genus had estimates of 0.26, 0.45, and 0.26 at time points 1, 2, and 3, respectively. These estimates demonstrate that certain features of the ocular microbiome are influenced by host genetics.
144 Using Digital Twin Simulations to Optimize Genetic Selection in an Admixed Beef Herd
Authors: Engle Bailey N , Moser Gerhard , Villiers Kira , Suarez Mathias , Grey Eliza , Hayes Ben J , Kelly Matthew 
Published: November 2023

Abstract
Polled cattle, or those that are naturally hornless, are highly advantageous from both an economic and animal welfare perspective. While there are a number of different mutations that result in a polled phenotype, a duplication on bovine chromosome 1, referred to as the Celtic mutation, is the most common within beef cattle. Tropically adapted beef cattle typically have some degree of indicine influence, which are all naturally horned breeds. Polled genes must therefore be introgressed into tropically adapted cattle from naturally polled breeds, such as Angus, carrier of the Celtic mutation. However, Angus are not as well suited for hot, humid climates and introgression cannot happen at the expense of productivity or adaptation. Therefore, strategies must be developed to balance the introgression of poll alleles into a population with continued genetic improvement for economically important traits. This study utilized a privately owned, commercial population of admixed cattle to address this challenge. These cattle (n = ~3,000; single year cohort) were previously genotyped and SNP effects were estimated for heifer pregnancy as part of the internal genetic evaluation of the private enterprise. Management and selection of male and female replacements were modelled using actual production parameters in a digital twin framework. Four different breeding systems, multi-sire natural service, artificial insemination, and invitro fertilization collecting either five or 10 oocytes, were modelled and compared with account for differences in selection intensity. Selection index weighting of poll vs heifer pregnancy was modelled in eight different combinations. Genetic crosses were then simulated using the real genotypes in the genomicSimulation package in R for 5, 10, and 30 years of crosses to evaluate long and short-term consequences. We found that this approach of combining digital twin modelling with simulated crosses can be used to identify the selection strategy that should be applied to optimally balance selection for poll and economically important traits, such as fertility. This modelling approach provides a framework that may be customized to fit specific production parameters or genetic populations representing individual operations. More generally, this particular study used cattle and management practices representative of the northern Australian beef industry, and may be used as a reference for producers in that production system of strategies for increasing the rate of polled animals in their herds. Furthermore, this approach may be applied to the selection for or against other single gene traits, such as many genetic disorders. The USDA is an equal opportunity provider and employer.
143 Validation Strategies of Sensor Data in the Context of Heat Stress Detection
Authors: Lemal Pauline , Tran Marie-Nguyet , Schroyen Martine , Gengler Nicolas 
Published: November 2023

Abstract
Evaluating genetically heat stress (HS) of cattle is still a major challenge, also because of the need to have access to HS sensitive phenotypes relating their reaction to temperature and humidity as described by the temperature-humidity index (THI). A solution could be to add the information of data from sensors. However, activity (ACT) and rumination (RUM) sensors reflect no “production” [i.e., milk yields (MY), milk composition, but also intake or growth] related aspects which is suboptimal. Therefore, the objectives of this study were thus to assess the possible usefulness for adding sensors data for HS genetic evaluation based on a dairy situation. But the context described can be easily enlarged and include beef cattle and pigs. Strategy was illustrated using daily SenseHub collar records that were obtained from October 2019 to July 2022 in six dairy herds in Wallonia in Southern Belgium. A total of 453 Walloon Holstein cows were followed during this period for activity time. Meteorological data and a HS sensitive “production” trait (here MY) were also obtained from 2015 to 2022 for 1,740 cows from the same herds. In total 32,154 MY, 130,867 ACT and 130,848 RUM records were available. The thresholds at which the different traits start to be affected by HS were estimated at a THI of 63, 64 and 66, respectively, for MY, ACT and RUM. A three-trait random regression reaction norm model was fitted with these different thresholds. Estimated heritability values were 0.19 ± 0.03, 0.14 ± 0.06 and 0.21 ± 0.05 respectively for MY, ACT and RUM using AIREML. Ratios of variance associated to THI and constant genetic effect were 0.002, 0.02 and 0.04 indicating a stronger reaction by RUM but also ACT than MY to THI. However, it is uncertain if the reactions of these sensor traits are reflected in the production traits. Estimated genetic correlations of regressions on THI for ACT and RUM showed values of 0.51 ± 0.70 and 0.08 ± 0.54 with MY and 0.77 ± 0.69 between ACT and RUM. The first eigenvector of the correlation matrix explained 65% of the total variability and standardized coefficients of this eigenvector were 0.42, 0.69 and 0.59 for MY, ACT and RUM. A total of 50% of the variance associated with the regression on THI for MY could be explained by the regressions on THI for ACT and RUM. Adding the constant genetic effects for ACT and RUM only marginally increased to 51% the ability to explain the effect of THI on MY. Based on these results, reactions of different indicator traits, here sensor data, may reflect a different reaction than production and therefore economically important traits. Additional research, also beyond dairy cattle, should continue to address this topic that is very important for the validation of sensor data detecting HS.
141 Exploring Alternative Approaches for the National Argentine Angus Cattle Genomic Evaluation Program
Authors: Curutchet Agustin R , Fragomeni Breno O , Guitou Horacio , Monti Aldo 
Published: November 2023

Abstract
Beef cattle production is a relevant component of the Argentine economy. The Argentine Angus is the most common beef cattle breed, and this population has a large economic and cultural importance for the country. In 2019, a national genomic selection program was implemented in the Argentine Angus population. The objective of this study was to explore alternative approaches for the genomic selection program in Argentinean Angus so that it will better serve the stakeholders. For this study, a total of 9,944 phenotypes for weaning weight from Argentine Angus born in 2018 were used. All animals had pedigree information for at least 3 generations. Genomic information on 1,078 individuals for 150k SNPs was used. The genotyped individuals were connected to the population by pedigree. Weaning weight EPDs were calculated under an animal model considering contemporary group as fixed effects and additive direct and maternal genetic effects as random. Variance components were estimated using AIREMLF90, and EPDs were calculated using BLUPF90. The enhanced EPDs were the genomic estimated breeding values obtained by single-step GBLUP, by including the genomic information into the original model. Validation of the EPDs and enhanced EPDs were made by cross-validation, where a random sample of 70 genotyped individuals with more than 20 progeny was the validation population. The reference breeding values were the EPDs of the validation population with complete data. In a second analysis, the phenotype of the progeny of the validation population was removed. The correlation between the reduced and complete EPDs was the accuracy for this study, according to the LR method. The accuracy obtained by this method was overestimated due to potential relationships between animals in the training and validation population. However, the enhanced EPDs were 4% more accurate than the pedigree EPDs. The next steps of this research are exploring alternative approaches of the validation methodology, which will be accomplished by implementing a forward-in-time cross-validation. Such validation will be performed once more data is included in the model. Additionally, other models will be tested to identify the ideal combination of effects that will yield higher accuracies. Details regarding the construction of the genomic relationship matrix will be tested with the overarching goal of improving convergence without affecting accuracy. Moreover, a weighted single-step GBLUP approach will be tested to verify if prediction accuracy can improve even further. Finally, indirect prediction models will be tested so that the Argentine Angus Association can implement interim enhanced EPDs for young animals. In conclusion, including genomic data into this dataset yielded greater accuracy and such values are expected to improve even further after optimization.
151 Depth Video Data-Enabled Predictions of Longitudinal Dairy Cow Body Weight Using Thresholding and Mask R-Cnn Algorithms
Authors: Bi Ye , Campos Leticia , Wang Jin , Yu Haipeng , Hanigan Mark , Morota Gota 
Published: November 2023

Abstract
Cow body weight monitoring is critical in farm management because it is associated with the growth, nutritional status, and health of dairy cows. Regularly collecting body weight records often requires human labor or expensive equipment. Recently, a low-cost 3D depth sensor camera-enabled computer vision analysis has been proposed to address the body weight prediction challenges. Cow body weight is a repeated trait; however, past body weight prediction research only used data collected at a single time point. Furthermore, the utility of deep learning on body weight predictions remains unanswered. Therefore, the objectives of this study were to predict cow body weight from repeatedly measured video data and compare the performance of thresholding and Mask R-CNN deep learning approaches. We set up an Intel Realsense D435 camera at the Virginia Tech Kentland farm to collect top-view videos for 10 Holstein and 2 Jersey cows for 28 days, twice per day. Real body weight records were also collected by a walk-over-weighing system simultaneously. A total of 40,405 depth images and depth map files were obtained. We explored three approaches to segment the cow body from the background, including single thresholding, adaptive thresholding, and Mask R-CNN. Four image-derived biometric measurements, length, width, height, and volume, were estimated from segmented images and fitted in ordinary least squares. Two cross-validation designs, forecasting and leave-several-cow-out, were used to evaluate prediction performance. The correlation between image-derived biometric measurements and body weight ranged from 0.84 to 0.95. On average, the Mask-RCNN approach resulted in the best prediction coefficient of determination and mean absolute percentage error of 0.96 and 3.54% in the forecasting cross-validation, respectively. The predictive performance of the single and adaptive thresholding approaches was similar. The Mask-RCNN approach was also the best in the leave-several-cow-out cross-validation, followed by adaptive and single thresholding. The prediction coefficients of determination of Mask-RCNN for leave-one-cow-out, leave-two-cow-out, leave-three-cow-out, leave-four-cow-out were 0.05, 0.69, 0.85, and 0.90, respectively, while mean absolute percentage errors were 4.51%, 4.51%, and 4.59%, 4.70% respectively. Our results suggest predicting cow body weight from depth video data using deep learning is feasible.
54 Large-Scale Crispr Functional Characterization of Genomic Elements in Rainbow Trout (Oncorhynchus mykiss)
Authors: Phelps Michael 
Published: November 2023

Abstract
The wealth of genetic information that is now available for many agricultural animal species has opened vast potential to apply this new knowledge to improve agricultural production and sustainability. The field, however, is now faced with the daunting task of trying to understand what is arguably the most complex coded system known to mankind. In biomedical model systems, extensive efforts over decades have used single candidate gene approaches to uncover the function of many protein-coding genes and non-coding RNAs, but how this information translates to agricultural animals and their production traits is often unknown. Not only is repeating this vast effort for every agricultural species not practical, the current pace of genomic discovery is woefully inadequate for investigating animal genomes and therefore a radically new approach is needed. Using rainbow trout (RBT, Oncorhynchus mykiss) as a novel agricultural animal experimental system we have initiated a large-scale functional genomics screen that aims to characterize the function of over 1,000 genomic elements in a large animal model. The initial screen will focus on understanding how different genomic elements regulate growth and environmental resilience in these fish by combining multiplex CRISPR genome editing technology and phenotypic screening approaches to identify novel allele combinations from families of edited fish. The research is a paradigm shift in our approach to characterizing gene function, moving away from candidate-based approaches to a more genomics view of gene function. This work will shed light on the complex genetic mechanisms underlying two important production traits in fish while helping to advance our understanding of large animal functional genomics.
68 Integrating Enviromics, Genomics, and Machine Learning for Precision Breeding of Resilient Beef Cattle
Authors: Rosa Guilherme J M , Lourenco Daniela , Rowan Troy N , Brito Luiz F F , Gondro Cedric , Huang Jingyi , de Souza Simone Valle 
Published: November 2023

Abstract
Animal breeding is one of the main pillars of livestock production. Statistics, computer science, and genomics have transformed productivity in the industry. Another round of breakthroughs is expected to come from harnessing the power of big data and machine learning analytics to address the complex interaction between animal genetics and the environment. Developing methods that enable precise selection decisions for animals in diverse production environments is expected to result in significant productivity gains and a reduction in welfare issues, while also mitigating the environmental impact of suboptimal allocation of resources to ill-adapted animals. Due to climate uncertainty, an understanding of these genetic and environmental interactions will be critical for decision-making. Our multi-institutional group initiated a project to generate a comprehensive enviromics data lake to develop and apply novel methods for breeding more adapted and resilient beef cattle for varying environments. Using GIS technology and integrating various sources of environmental information from publicly available databases and satellite imaging, detailed descriptions of soil, climate, forage, and weather conditions will be created for thousands of U.S. farms, representing millions of cattle, with phenotypic and genotypic data. Farms will be comprehensively described in terms of their facilities and management practices through surveys. Machine learning and artificial intelligence techniques will then be used to predict future animal performance for precision livestock management. The proposed models will also be biologically validated by measuring direct indicators of animal resilience. Results will be communicated to beef cattle producers and industry groups to foster the implementation of environment-aware approaches into routine genetic evaluations. In this talk, we will discuss this ongoing project in detail and present some preliminary results.
69 The Bigger Picture of Phenomics: Whole-System Support for Systems Research, Models and Databases
Authors: McNamara John P 
Published: November 2023

Abstract
Phenomics started with Mendel’s elegantly simple empirical studies. Soon after, Fisher discovered purines were rich in nuclei of all cells. A century later, we have the technology to compare the actual base pair sequence of DNA and RNA with the outward expressions of phenotypes. We have sire evaluation based on genomic and phenomic measure. But, being human, it is quite common to get immersed in exciting technology and tend to forget why we developed the technology in the first place. We only refer to ensuring a stable efficient food supply of plants and animals in the reports that we expect decision makers to read and give us more money. It is, however, truly wonderful that we now have the techniques to study the questions of the ages. But the future of phenomics is built on the foundations of the past. In 1974, A. E. Freeman and others convened a meeting under the mechanism of the newly formed Regional Research Project 2 on Defining the Inefficiency of Animal Production. From that workshop came the first modern multistate genetic study in dairy cattle with the objective to compare the phenotypes (milk component production, energy and nitrogen metabolism, hormones) of dairy animals that were born from bulls of High (+795 kg PTAM) and Low (167 kg PTAM) bulls. Please remember this study was designed in the mid 1970s and the first data collected in the early 1980s, decades before efficient methods of DNA and RNA composition were available. Studies conducted at Washington State University demonstrated marked genetic and environmental differences in adipose tissues of dairy animals. Enzymes involved with catabolic reactions were highly associated with High PTAM bulls while enzymes associated with anabolic reactions in adipose tissue were more closely related to increased energy intake regardless of genetic background. With mRNA chips, we were able, after almost 40 years, to directly relate variation in mRNA expression in the adipose tissue with the genetic background of the animals. This was the first such discovery in the field, and has led to more defined and expansive phenomics studies. In the year 2023, the use of genomics, phenomics, databases, large scale, cooperative studies and team science are specific recommendations, strategies and goals of the USDA, NIFA, The National Academies, and Land-Grant Universities. The AgBioData consortium is one example of international teams working together to expand the use of genetic and genomic databases, to prepare undergraduate and graduate level educational materials on FAIR databases and to encourage and support sustainable, robustly funded public and private partnerships in the use of integrated studies and large databases, including phenomics, with the sole purpose of expanding a robust, resilient, safe and plentiful food supply.