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The present study investigated the potential consequences, positive or negative, that selection for favorable production-related traits may have on concentrations of vitamin B 12 and key chemical elements in dairy cow milk and serum and the possible impact on milk healthiness, and associated benefits, for the dairy product consumer. Milk and serum samples (950 and 755, respectively) were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period, generating 34,258 individual records, and analyzed for concentrations of key trace and quantity elements, heavy metals, and milk vitamin B 12 . These data were then matched to economically important production data (milk, fat, and protein yield) and management data (dry matter intake, liveweight, and body condition score). Multivariate animal models, including full pedigree information, were used to analyze data and investigate relationships between traits of interest. Results highlighted negative genetic correlations between many quantity and trace elements in both milk and serum with production and management traits. Milk yield was strongly negatively correlated with the milk quantity elements Mg and Ca (genetic correlation between traits, r a = -0.58 and -0.63, respectively) as well as the trace elements Mn, Fe, Ni, Cu, Zn, and Mo (r a = -0.32, -0.58, -0.52, -0.40, -0.34, and -0.96, respectively); and in serum, Mg, Ca, Co, Fe, and Zn (r a = -0.50, -0.36, -0.68, -0.54, and -0.90, respectively). Strong genetic correlations were noted between dry matter intake with V (r a = 0.97), Fe (r a = -0.69), Ni (r a = -0.81), and Zn (r a = -0.75), and in serum, strong negative genetic correlations were observed between dry matter intake with Ca and Se (r a = -0.95 and -0.88, respectively). Body condition score was negatively correlated with serum P, Cu, Se, and Pb (r a = -0.45, -0.35, -0.51, and -0.64, respectively) and positively correlated with Mn, Fe, and Zn (r a = 0.40, 0.71, and 0.55, respectively). Our results suggest that breeding strategies aimed at improving economically important production-related traits would most likely result in a negative impact on levels of beneficial nutrients within milk for human consumption (such as Mg, Ca, Fe, Zn, and Se)., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To improve both these qualities, the world is leveraging artificial intelligence and machine learning against this challenge. Most recently, deep learning has successfully been applied to the medical field to improve detection accuracies and speed for conditions including cancer and COVID-19. In this study, we applied deep neural networks, in the form of a generative adversarial network (GAN), to perform image-to-image processing steps needed for ovine phenotype analysis from CT scans of sheep. Key phenotypes such as gigot geometry and tissue distribution were determined using a computer vision (CV) pipeline. The results of the image processing using a trained GAN are strikingly similar (a similarity index of 98%) when used on unseen test images. The combined GAN-CV pipeline was able to process and determine the phenotypes at a speed of 0.11 s per medical image compared to approximately 30 min for manual processing. We hope this pipeline represents the first step towards automated phenotype extraction for ovine genetic breeding programmes.

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Enhancing micronutrient (i.e., mineral and vitamin) concentrations within milk and serum from dairy cows is important for both the health of the cow and the nutritive value of the milk for human consumption. However, a good understanding of the genetics underlying the micronutrient content in dairy cattle is needed to facilitate such enhancements through feeding or breeding practices. In this study, milk (n = 950) and serum (n = 766) samples were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period and analyzed for concentrations of important elements. Additionally, a subset of 256 milk samples was analyzed for concentrations of vitamin B 12 . Cows belonged to 2 genetic lines (average and highest genetic merit for milk fat plus protein yield) and were assigned to 1 of 2 diets based on either a by-product or homegrown ration. Univariate models accounting for repeated records were used to analyze element and vitamin B 12 data and investigate the effect of genotype and feeding system as well as derive estimates of variance components and genetic parameters. Bivariate models were used to study correlations both within and between milk and serum. Only concentrations of Hg in milk were seen to be affected by genotype, with higher concentrations in cows with high genetic merit. In contrast, element concentrations were influenced by feeding system such that cows fed the homegrown diet had increased milk concentrations of Ca, Cu, I, Mn, Mo, P, and K and increased serum concentrations of Cd, Cu, Fe, Mo, and V. Cows on the by-product diet had increased milk concentrations of Mg, Se, and Na and increased serum concentrations of P and Se. Heritability (h 2 ) estimates were obtained for 6 milk and 4 serum elements, including Mg (h 2 milk = 0.30), K (h 2 serum = 0.18), Ca (h 2 milk = 0.20; h 2 serum = 0.12), Mn (h 2 milk = 0.14), Cu (h 2 serum = 0.22), Zn (h 2 milk = 0.24), Se (h 2 milk = 0.15; h 2 serum = 0.10), and Mo (h 2 milk = 0.19). Significant estimates of repeatability were observed in all milk and serum quantity elements (Na, Mg, P, K, and Ca) as well as 5 milk and 7 serum trace elements. Only K in milk and serum was found to have a significant positive genetic and phenotypic correlation (0.52 and 0.22, respectively). Significant phenotypic associations were noted between milk and serum Ca (0.17), Mo (0.19), and Na (-0.79). Additional multivariate analyses between measures within sample type (i.e., milk or serum) revealed significant positive associations, both phenotypic and genetic, between some of the elements. In milk, Se was genetically correlated with Ca (0.63), Mg (0.59), Mn (0.40), P (0.53), and Zn (0.52), whereas in serum, V showed strong genetic associations with Cd (0.71), Ca (0.53), Mn (0.63), Mo (0.57), P (0.42), K (0.45), and Hg (-0.44). These results provide evidence that element concentrations in milk and blood of dairy cows are significantly influenced by both diet and genetics and demonstrate the potential for genetic selection and dietary manipulation to alter nutrient concentration to improve both cow health and the healthfulness of milk for human consumption., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Previous work has highlighted that immune-associated (IA) traits measurable in blood are associated with health, productivity, and reproduction in dairy cows. The aim of the present study was to determine relationships between IA traits measured in blood serum and those simultaneously measured in milk as well as their association with disease phenotypes. All animals were Holstein-Friesian cows from the Langhill research herd (n = 546) housed at the SRUC Dairy Research Centre in Scotland. Milk and serum samples were collected on 20 separate occasions between July 2010 and March 2015 and analyzed by ELISA for haptoglobin (Hp), tumor necrosis factor-α (TNF-α), and natural antibodies binding keyhole limpet hemocyanin (NAb KLH ) and lipopolysaccharide (NAb LPS ). Data were analyzed using mixed linear models that included pedigree information. Analyses revealed positive phenotypic correlations between milk and serum NAb (0.59 ≤ r ≤ 0.77), Hp (r = 0.37), and TNF-α (r = 0.12). Milk and serum NAb were also found to have a strong genetic correlation (0.81 ≤ r ≤ 0.94) and were genetically correlated with cow lameness (0.66 and 0.79 for milk NAb KLH and serum NAb LPS , respectively). Clinical mastitis was found to be phenotypically correlated with both milk and serum Hp (0.09 ≤ r ≤ 0.23). Serum Hp was also strongly genetically correlated with other cellular IA traits such as percent NKp46 + (a natural killer cell marker; 0.35) and percent peripheral blood mononuclear cells (PBMC; -0.90). Similarly, genetic correlations were found to exist between serum TNF-α and percent NKp46 + (0.22), percent PBMC (0.41), and percent lymphocytes (0.47). Excluding serum Hp, all milk and serum IA traits were repeatable, ranging from 0.11 (milk Hp) to 0.43 (serum NAb LPS ). Between-animal variation was highest in milk and serum NAb (0.34-0.43) and significant estimates of heritability were also observed in milk and serum NAb (0.17-0.37). Our findings show that certain IA traits, such as NAb KLH and NAb LPS , found in milk and serum are strongly correlated and highlight the potential of using routinely collected milk samples as a less invasive and cost-effective source of informative data for predictive modeling of animal IA traits., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Data collected from an experimental Holstein-Friesian research herd were used to determine genetic and phenotypic parameters of innate and adaptive cellular immune-associated traits. Relationships between immune-associated traits and production, health, and fertility traits were also investigated. Repeated blood leukocyte records were analyzed in 546 cows for 9 cellular immune-associated traits, including percent T cell subsets, B cells, NK cells, and granulocytes. Variance components were estimated by univariate analysis. Heritability estimates were obtained for all 9 traits, the highest of which were observed in the T cell subsets percent CD4 + , percent CD8 + , CD4 + :CD8 + ratio, and percent NKp46 + cells (0.46, 0.41, 0.43 and 0.42, respectively), with between-individual variation accounting for 59 to 81% of total phenotypic variance. Associations between immune-associated traits and production, health, and fertility traits were investigated with bivariate analyses. Strong genetic correlations were observed between percent NKp46 + and stillbirth rate (0.61), and lameness episodes and percent CD8 + (-0.51). Regarding production traits, the strongest relationships were between CD4 + :CD8 + ratio and weight phenotypes (-0.52 for live weight; -0.51 for empty body weight). Associations between feed conversion traits and immune-associated traits were also observed. Our results provide evidence that cellular immune-associated traits are heritable and repeatable, and the noticeable variation between animals would permit selection for altered trait values, particularly in the case of the T cell subsets. The associations we observed between immune-associated, health, fertility, and production traits suggest that genetic selection for cellular immune-associated traits could provide a useful tool in improving animal health, fitness, and fertility., (The Authors. Published by the Federation of Animal Science Societies and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY 2.0 license (http://creativecommons.org/licenses/by/2.0/).)

Gyrodactylus salaris (Monogenea, Platyhelminthes) is a notifiable freshwater pathogen responsible for causing catastrophic damage to wild Atlantic salmon stocks, most notably in Norway. In some strains of Baltic salmon (e.g., from the river Neva) however, the impact is greatly reduced due to some form of innate resistance that regulates parasite numbers, resulting in fewer host mortalities. Gyrodactylus salaris is known from 17 European states; its status in a further 35 states remains unknown; the UK, the Republic of Ireland and certain watersheds in Finland are free of the parasite. Thus, the parasite poses a serious threat if it emerges in Atlantic salmon rearing regions throughout Europe. At present, infections are generally controlled via extreme measures such as the treatment of entire river catchments with the biocide rotenone, in order to remove all hosts, before restocking with the original genetic stock. The use of rotenone in this way in EU countries is unlikely as it would be in contravention of the Water Framework Directive. Not only are such treatments economically and environmentally costly, they also eradicate the potential for any host/parasite evolutionary process to occur. Based on previous studies, UK salmon stocks have been shown to be highly susceptible to infection, analogous to Norwegian stocks. The present study investigates the impact of a G. salaris outbreak within a naïve salmon population in order to determine long-term consequences of infection and the likelihood of coexistence. Simulation of the salmon/ G. salaris system was carried out via a deterministic mathematical modelling approach to examine the dynamics of host-pathogen interactions. Results indicated that in order for highly susceptible Atlantic strains to evolve a resistance, both a moderate-strong deceleratingly costly trade-off on birth rate and a lower overall cost of the immune response are required. The present study provides insights into the potential long term impact of G. salaris if introduced into G. salaris-free territories and suggests that in the absence of external controls salmon populations are likely to recover to high densities nearing 90% of that observed pre-infection., Competing Interests: The authors have declared that no competing interests exist.

Background: Gyrodactylus salaris Malmberg, 1957 has had a devastating impact on wild Norwegian stocks of Atlantic salmon Salmo salar L., and it is the only Office International des Epizooties (OIE) listed parasitic pathogen of fish. The UK is presently recognised as G. salaris-free, and management plans for its containment and control are currently based on Scandinavian studies. The current study investigates the susceptibility of British salmonids to G. salaris, and determines whether, given the host isolation since the last glaciation and potential genetic differences, the populations under test would exhibit different levels of susceptibility, as illustrated by the parasite infection trajectory over time, from their Scandinavian counterparts., Methods: Populations of S. salar, brown trout Salmo trutta L., and grayling Thymallus thymallus (L.), raised from wild stock in UK government hatcheries, were flown to Norway and experimentally challenged with a known pathogenic strain of G. salaris. Each fish was lightly anaesthetised and marked with a unique tattoo for individual parasite counting. A single Norwegian population of S. salar from the River Lærdalselva was used as a control. Parasite numbers were assessed every seven days until day 48 and then every 14 days., Results: Gyrodactylus salaris regularly leads to high mortalities on infected juveniles S. salar. The number of G. salaris on British S. salar rose exponentially until the experiment was terminated at 33 days due to fish welfare concerns. The numbers of parasites on S. trutta and T. thymallus increased sharply, reaching a peak of infection on days 12 and 19 post-infection respectively, before declining to a constant low level of infection until the termination of the experiment at 110 days., Conclusions: The ability of S. trutta and T. thymallus to carry an infection for long periods increases the window of exposure for these two hosts and the potential transfer of G. salaris to other susceptible hosts. This study demonstrates that G. salaris can persist on S. trutta for longer periods than previously thought, and that the role that S. trutta could play in disseminating G. salaris needs to be considered carefully and factored into management plans and epidemics across Europe.

Gyrodactylus salaris is a notifiable freshwater ectoparasite of salmonids. Its primary host is Atlantic salmon (Salmo salar), upon which infections can cause death, and have led to massive declines in salmon numbers in Norway, where the parasite is widespread. Different strains of S. salar vary in their susceptibility, with Atlantic strains (such as those found in Norway) exhibiting no resistance to the parasite, and Baltic strains demonstrating an innate resistance sufficient to regulate parasite numbers on the host causing it to either die out or persist at a low level. In this study, Leslie matrix and compartmental models were used to generate data that demonstrated the population growth of G. salaris on an individual host is dependent on the total number of offspring per parasite, its longevity and the timing of its births. The data demonstrated that the key factor determining the rate of G. salaris population growth is the time at which the parasite first gives birth, with rapid birth rate giving rise to large population size. Furthermore, it was shown that though the parasite can give birth up to four times, only two births are required for the population to persist as long as the first birth occurs before a parasite is three days old. As temperature is known to influence the timing of the parasite's first birth, greater impact may be predicted if introduced to countries with warmer climates than Norway, such as the UK and Ireland which are currently recognised to be free of G. salaris. However, the outputs from the models developed in this study suggest that temperature induced trade-offs between the total number of offspring the parasite gives birth to and the first birth timing may prevent increased population growth rates over those observed in Norway.