bluuebunny/crossref_metadata_2025_split huggingface dataset:
370 Awardee Talk: Role of feed enzymes in gut health and function
Authors: Nyachoti Charles Martin 
Published: November 2020

Abstract
Exogenous enzymes are routinely added to diets for non-ruminant animals, mainly to help enhance energy and nutrient utilization, thus contributing to efficient and sustainable production systems. Also, feed enzymes allow for effective utilization of non-traditional feedstuffs (e.g. co-products) in non-ruminant diets, with potential to mitigate feed cost. In addition to increased nutrient utilization, however, feed enzymes, through their impact on the gastrointestinal environment and microbial composition, can have a profound effect on indices of gut health and function. These effects may explain the reported reductions in medication costs and variability in animal performance and mortality rates observed with dietary enzyme supplementation. By acting on their target substrates, feed enzymes reduce the availability of non-digested substrates and in the case of carbohydrate-degrading enzymes can produce short-chain oligosaccharides with potential prebiotic effects. These changes are known to modulate the gut microbiome in favor of bacteria associated with a healthy gut, while reducing the population of pathogenic bacteria, which explains the reduced incidences of diarrhea that have been attributed to enzyme supplementation. Also, feed enzymes may reduce oxidative stress and enhance the functional capacity (e.g. maintaining barrier function) of the gastrointestinal tract. Feed enzymes by themselves may never be a single solution to mitigate gut health challenges in livestock production systems, but clearly there is a mounting body of evidence to support the role of feed enzymes in this regard. Therefore, their benefits will be maximized when used as part of an integrated solution approach for containing enteric pathogens of economic importance and eliminating the negative effects of dietary components with potential to compromise intestinal integrity.
368 Protein and amino acid effects on pig microbial ecology, health and nutrition
Authors: Burkey Thomas 
Published: November 2020

Abstract
The gut microbiome is essential to animal health. Many factors, including both environmental (e.g. diet) and host-related (e.g. genetic background, sex, age), shape the intestinal microbiome. Pioneers in gut microbiology have stressed the critical importance of interactions among the diet, the gut microbiota, and the host on animal health and disease. Different protein types (e.g. plant-based vs. animal-based) have been shown to have differential effects on the gut microbiome. In addition, there is evidence of compartment-specific and amino acid-specific interactions that occur within the gastrointestinal tract. The result of these interactions must be considered to be essential as a variety of metabolites are produced and, for example, act as nutrients and modulators of physiologic processes. Our overarching goal is to discuss protein/amino acid effects on pig microbial ecology, health, and nutrition.
147 Presynchronization and delayed fixed-time artificial insemination increases pregnancy rates with sex-sorted semen in replacement beef heifers
Authors: Oosthuizen Nicky , Fontes Pedro , Filho Ramiro , Dahlen Carl R , Grieger David M , Hall John B , Lake Scott , Looney Charles , Mercadante Vitor R G , Neville Bryan W , Perry George , Powell Jeremy G , Prezotto Ligia , Seidel George , Walker Ryon , Cardoso Rodolfo C , Pohler Ky G , Lamb Graham Cliff 
Published: November 2020

Abstract
To determine effects of presynchronization and delayed fixed-time artificial insemination (TAI) on pregnancy rates to TAI (PR/AI) with sex-sorted semen, 2,855 Bos taurus beef heifers were enrolled in a completely randomized design. Within location, heifers were randomly assigned to one of eight treatments: 1 and 2), heifers were exposed to the 7-d CO-Synch+CIDR protocol wherein they received gonadotropin-releasing hormone (GnRH) and a CIDR insert on d 0, prostaglandin F2α (PGF) upon CIDR removal on d 7, and were TAI 54 ± 2 h later with conventional (CTRL54-CNV; n = 359) or sex-sorted semen (CTRL54-SEX; n = 356); 3 and 4), same as CTRL54 but were TAI at 72 ± 2 h with conventional (CTRL72-CNV; n = 366) or sex-sorted semen (CTRL72-SEX; n = 360); 5 and 6), same as CTRL54 but also received PGF 7 d prior to initial injection of GnRH (d -7) and were TAI with conventional (PRE54-CNV; n = 355) or sex-sorted semen (PRE54-SEX; n = 353); 7 and 8), same as PRE54 but had TAI delayed to 72 ± 2 h and were inseminated with conventional (PRE72-CNV; n = 351) or sex-sorted semen (PRE72-SEX; n = 355). All heifers received estrus detection patches on d 7, which were evaluated for activation at TAI. Ultrasonography was performed 30–45 d after TAI to determine PR/AI. Estrus expression was greater (P &lt; 0.01) in the CTRL72 heifers compared to CTRL54, PRE54, and PRE72 heifers (73.0 vs 55.4, 43.4, and 65.7%, respectively). Moreover, estrus expression was greater (P &lt; 0.01) in PRE72 heifers compared to CTRL54 and PRE54 heifers. Within treatment pairs, PR/AI were greater (P ≤0.04) when conventional semen was utilized compared to sex-sorted semen. Furthermore, PR/AI were greater (P = 0.02) in PRE72-SEX heifers than CTRL54-SEX heifers (45.8 vs. 37.5%). No difference (P = 0.20) was determined in PR/AI between CTRL54-CNV and PRE72-SEX heifers (50.5 vs. 45.8%). In conclusion, presynchronization in combination with delayed TAI increased estrus expression and PR/AI with sex-sorted semen in replacement beef heifers.
142 Evaluation of estrus detection patches as a tool to classify postpartum beef cows as estrous cycling or anestrous prior to the start of the breeding season
Authors: Smith Emily G , Bonacker Rachael C , Andersen Carson M , Thomas Jordan M 
Published: November 2020

Abstract
Estrus detection patches were evaluated as a tool to classify postpartum cows as estrous cycling or anestrous at the start of the breeding season. EstrotectTM Breeding Indicators were applied to 257 postpartum beef cows in three locations 25 days prior to the start of estrus synchronization. Coincident with the start of estrus synchronization, patches were scored using a 1 to 4 scale (1 = 0–25%; 2 = 25–50%; 3 = 50–75%; 4 = 75–100%) with scores of 3 or 4 considered activated. Blood samples were collected from each cow 10 days prior to, and at the start of the estrus synchronization. Serum progesterone concentrations were determined via radioimmunoassay; cows were considered estrous cycling if one or both samples exceeded a concentration of 0.5 ng/ml. Based on progesterone, 54.9% of cows were estrous cycling at the start of estrus synchronization. When missing patches were considered inconclusive results, use of patches to infer estrous cyclicity resulted in 85.0% sensitivity and 79.2% specificity with a positive predictive value of 87.2%. However, 75.1% of all cows presented with missing patches at the start of synchronization. If missing patches were considered activated, sensitivity was 95.7%, but specificity (16.4%) and PPV (58.2%) were poor, as 47.6% of cows presenting with missing patches were anestrous based on progesterone. Across all cows, pregnancy rate to AI was 58.0% (149/257). Cows with activated patches that were classified as false positives based on serum progesterone concentrations achieved a 71.4% (5/7) pregnancy rate to AI, raising questions as to whether sensitivity was underestimated due to inherent Type II errors in classification of cyclicity via progesterone. In summary, estrus detection aids are a sensitive tool to identify estrous cycling postpartum cows prior to the start of the breeding season; however, poor specificity and patch retention are limitations for this application.
156 Injectable trace mineral supplementation on grazing beef cows: effect on overall mineral status and fertility
Authors: Pancini Stefania , Currin John , Stewart Jamie L , Clark Sherrie , Redifer Tracey , Dias Nicholas W , Timlin Claire L , Sales Alvaro , Mercadante Vitor R G 
Published: November 2020

Abstract
The objective of this study was to evaluate the effect of two doses of an injectable multi-mineral complex supplementation on beef cow fertility and overall mineral status. A total of 986 Angus crossbred multiparous cows at 8 locations in Virginia were enrolled in a 7-day CO-Synch+CIDR fixed-time artificial insemination (FTAI) protocol, followed by natural service for an approximately 70 d breeding season. All cows had ad libitum access to water, mineral blocks, and diet composed of hay, grass, sudangrass, sorghum grain and commodity pellets, depending on location. Approximately 30 days before calving (d -120, day of AI is d 0) cows were randomly assigned to one of two treatments: 1) Two doses (6 mL) of Multimin® 90 (Multimin USA, Ft. Collins, CO) at d -120 and d -10 (MIN; n = 494); or 2) a negative control with no trace mineral injection (CTRL; n = 492). Body condition score was recorded on d -10, 60 and 120. Estrus detection was recorded at d 0 using estrus detection patches. Pregnancy was diagnosed on d 60 and 120 by ultrasonography. Liver biopsies were collected on d -10 and 60 in a subgroup of 60 cows equally distributed between treatments and among all locations. No differences between treatments were detected in days post-partum (P = 0.66), BCS (P = 0.24) and estrus expression (P = 0.11). Mineral status of copper, manganese, selenium or zinc was similar between treatments before (P = 0.20) and after treatment (P = 0.17). No effect of treatment was detected in FTAI pregnancy rate (50.8%, P = 0.52) or overall pregnancy rate (89.6%, P = 0.64). Two doses of injectable trace mineral administrated approximately 30 days before calving and 10 days before FTAI did not improve pregnancy rate or overall mineral status in multiparous beef cows in Virginia.
154 Evaluating the effects of prophylactic antibiotics on intestinal health in pigs given a corticotropin-releasing factor antagonist during weaning and transport
Authors: McConn Betty R , Rivier Jean E , Behan Dominic P , Richert Brian T , Radcliffe John S , Lay Donald C , Johnson Jay S 
Published: November 2020

Abstract
Prophylactic antibiotics improve intestinal health in pigs; however, it is unknown whether their efficacy interacts with a pig’s stress response following weaning and transport. The study objective was to determine whether a corticotropin-releasing factor (CRF) antagonist would impact the efficacy of prophylactic antibiotics on improving intestinal health in transported weaned pigs. Mixed-sex pigs (n = 56; 5.70 ± 1.05 kg BW) were weaned (20.49 ± 0.64 d), a blood sample was taken, pigs were given an i.p. injection of saline (SAL) or a CRF antagonist (CRFA; 30 µg/kg BW; Astressin B), and then were transported for 12 h. Pigs were given a second and third i.p. injection at 6 and 12 h of transport, respectively. Following transport, 4 SAL and 4 CRFA pigs were blood sampled and euthanized. The remaining 48 pigs were individually housed and assigned to an antibiotic [A; chlortetracycline (441 ppm) + tiamulin (38.6 ppm)] or no antibiotic (NA) diet treatment balanced by injection treatment (n = 12 pigs/treatment combination). Blood was collected at 12 h and on d 3, 7, and 14 and pigs were euthanized on d 7 (n = 24) and d 14 (n = 24) post-weaning and transport. Plasma adrenocorticotropic hormone was reduced overall (P = 0.05; 9.1%) in CRFA versus SAL pigs. On d 7, jejunal villus height was greater (P = 0.04; 33%) in A versus NA pigs. Jejunal zonula occludens-1 (58.8%) and ileal claudin-1 (100.5%) mRNA abundance tended to be increased (P = 0.09) in CRFA+NA versus SAL+NA pigs on d 7. On d 14, jejunal tumor necrosis factor-alpha mRNA abundance was reduced (P = 0.02; 29.9%) in A versus NA pigs and jejunal glucagon-like peptide-2 mRNA abundance was increased (P = 0.03) in CRFA+NA versus SAL+NA (48.4%) and SAL+A versus SAL+NA (55.8%) pigs. No intestinal health parameter differences were detected (P &gt; 0.05) between CRFA+NA and SAL+A pigs. In conclusion, CRFA and A impacted weaned pig intestinal health measures at similar levels.
168 Effects of GALR2 genotype and differing implant strategies on carcass characteristics of crossbred Angus finishing steers
Authors: Hall Jerica R , Maddock-Carlin Kasey , Dahlen Carl R , Ward Alison K 
Published: November 2020

Abstract
The objective of this study was to determine how the interaction of different implant strategies with the galanin receptor 2 (GALR2) genotype would affect carcass characteristics and meat quality in crossbred Angus finishing steers. Angus crossbred steers (n = 93) were selected for this study based on their GALR2c.-199T &gt; G genotype (GG, TT, and TG) and weaning weight with 19 GG, 38 TG, and 36 TT. Calves were blocked by initial body weight and fed a standard feedlot ration for 166 d or 202 d. Body weight data and blood samples were collected every 28 d. Steers were randomly assigned to one of two implant strategies: 1) a single Revalor-S (Merk Animal Health, NJ) on d 77 (1×), or 2) a Revalor-S on d 0 and another on d 77 (2×). Data were analyzed as a 2 × 3 factorial design of implant strategy by GALR2c.-199T &gt; G genotype using the mixed procedure in SAS, with repeated measures used for color, and significance was set at P &lt; 0.05. Hot carcass weight and back fat were directly affected by implant strategy where steers on the 2 implant strategy exhibited heavier hot carcass weights and increased back fat (P &lt; 0.05). Cook loss and shear force were influenced by the interaction of genotype by implant strategy (P &lt; 0.05). The GG steers exhibited less cook loss in comparison to all other treatments. The GG steers exhibited the highest shear force value of all treatments. The pH, water holding capacity, and color of the striploins were not affected by the genotype by implant strategy interaction or main effects of genotype and implant strategy. From this we conclude the interaction of GALR2c.-199T &gt;G genotype implant strategy does not have a negative effect on carcass characteristics or meat quality in crossbred Angus finishing steers.
174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot
Authors: Lopes Lucas S , Baes Christine F , Tulpan Dan , Chardulo Luis Artur Loyola , Neto Otavio Machado , Baldassini Welder Angelo 
Published: November 2020

Abstract
The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.
179 Open discussion of data bottlenecks in the dairy industry: The Dairy Brain Coordinated Innovation Network
Authors: Fadul Liliana , Wangen Steven , Cabrera Victor E 
Published: November 2020

Abstract
New dairy farm management technologies produce vast amounts of data. Although more data support better decisions, managing data also comes with critical challenges. In an effort to create awareness and develop guidelines for data management in the dairy industry, the University of Wisconsin Dairy Brain project is nurturing a Coordinated Innovation Network (CIN). The USDA definition of a CIN is a large “community that addresses bottlenecks [...] by bringing together experts from different disciplines and domains to identify innovative and synergistic solutions.” The Dairy Brain CIN currently has 81 members from 9 different countries. It is a growing community open to anyone who is interested in being part of the process of data collection, data engineering techniques and data usage in dairy farms. One of the objectives of the CIN is to write some design documents regarding data issues. During the first official meeting in September 2019, several CIN subcommittees were created with the purpose to write and publish opinion articles about some critical topics: 1) data security and ownership; 2) best practices of data collection and communication; 3) farm adoption; and 4) creating value from data. The idea behind these opinion articles was to generate industry-wide awareness and to start a meaningful discussion. These opinion articles have been published in Hoard’s Dairyman magazine as a series in the following dates: February 10, March 25, April 10, April 25 and May 10, 2020. In addition, an online forum was created to encourage larger industry participation. These opinion articles are the foundation of more in-depth technical articles. This presentation will focus on summarizing the content of the published opinion articles and their subsequent deliberations. The presentation will promote further discussion and invite stakeholders to join the Dairy Brain CIN and/or participate and contribute in the wider discussion.
384 Genetic and Genomic Analysis in Livestock with Increasing Datasets
Authors: Lourenco Daniela , Tsuruta Shogo , Masuda Yutaka , Misztal Ignacy 
Published: November 2020

Abstract
In animal breeding and genetics, statistical methods have been used for several decades to identify animals with the best genetic potential. One of the foundations for computing accurate estimated breeding values (EBV) is the amount of data that is used in the evaluation system — as the more data points one animal has, the more accurate its EBV is going to be. However, the animal breeding and genetics field periodically faces a big data paradox, where efficient methods have to be developed to handle the amount of data collected over time, given the computing capacity becomes the limiting factor. For instance, running genetic evaluations based on phenotypes and pedigree for a million animals was impossible in 1970. Methods and algorithms evolved to a point where using data for millions of animals was not a problem, until genomic information became available. After the development of single nucleotide polymorphism (SNP) chips for livestock in 2008, genomic information started being used in addition to phenotypes and pedigree to further improve accuracy of EBV. However, each animal is genotyped for around 50,000 SNP, which makes this data dense and difficult to work with. Over 790,000 Angus and 3.4 million Holstein cattle have been genotyped in the US as of April 2020. As the amount of new data considerably increases every week, most of the genomic evaluations are done on a weekly basis. Given that data have to be processed, the computation of EBV cannot take more than four days, which can be challenging depending on the model. In this talk we will discuss the challenges and solutions for successful genomic evaluations in large livestock populations. Finally, perspectives on the use of whole-genome sequence data and high-throughput phenotypes in genomic analysis will be summarized.
379 ASAS-EAAP Talk: Precision Phenotyping using Infrared Spectroscopy to Improve the Quality of Animal Products
Authors: Cecchinato Alessio , Pegolo Sara , Bittante Giovanni 
Published: November 2020

Abstract
There is an ever-growing interest in research oriented towards the improvement of quality of animal products. In this context, one major operational bottleneck is the possibility to collect quality indicators over the meat and dairy chains and for selective breeding purposes. The use of near-infrared (NIR) and the Fourier-transformed infrared (FTIR) spectroscopy techniques have been proven to be powerful precision phenotyping tools for high-throughput meat and milk quality assessment. Such technologies allow scoring large number of animals and/or derived-products for novel (predicted) phenotypes and indicator traits to set-up potential new payment systems and boost the genetic improvement. One important step in the use of NIR and FTIR tools is the definition of the “gold standard” as the infrared-based predictions could act only as indicators traits. Indeed, the definition of a robust calibration set, the assessment of repeatability and reproducibility of the reference (i.e., gold standard) as well as the detection of random and systematic errors are crucial steps. Once the reference phenotype has been defined, different statistical methodologies could be applied to infrared spectra data. For instance, the partial least squares regression (PLS) is a multivariate regression method commonly used to build up prediction models using NIR and FTIR spectra data. However, the implementation of advanced statistical approaches, such as Bayesian approaches and machine learning methods, might allow us to achieve more robust and accurate predictions. In this talk, we will describe and discuss some of the challenges and potentials of NIR and FTIR tools for large-scale precision phenotyping. Some examples include the use of NIR and Visible-NIR (Vis-NIR) for assessing meat quality parameters (also using portable instruments able to collect spectra directly from the muscle surface at the slaughterhouse) and the use of FTIR for predicting several traits related to fine milk composition and technological traits in dairy cattle.