Novel prediction models for hyperketonemia using bovine milk Fourier-transform infrared spectroscopy.

Metabolic diseases driven by negative energy balance in dairy cattle contribute to reduced milk production, increased disease incidence, culling, and death. Cow side tests for negative energy balance markers are available but are labor-intensive. Milk sample analysis using Fourier transform infrared spectroscopy (FTIR) allows for sampling numerous cows simultaneously. FTIR prediction models have moderate accuracy for hyperketonemia diagnosis (beta-hydroxybutyrate (BHB) ≥ 1.2 mmol/L). Most research using FTIR has focused on homogenous datasets and conventional prediction models, including partial least squares, linear discriminant analysis, and ElasticNet. Our objective was to evaluate more diverse modeling options, such as deep learning, gradient boosting machine models, and model ensembles for hyperketonemia classification. We compiled a sizable, heterogeneous dataset including milk FTIR and concurrent blood samples. Blood samples were tested for blood BHB, and wavenumber data was obtained from milk FTIR analysis. Using this dataset, we trained conventional prediction models and other options listed above. We demonstrate prediction model performance is similar for convolutional neural networks and ensemble models to simpler algorithm options. Results obtained from this study indicate that deep learning and model ensembles are potential algorithm options for predicting hyperketonemia in dairy cattle. Additionally, our results indicate hyperketonemia prediction models can be developed using heterogeneous datasets.

Dynamics of metabolic characteristics in dairy cows and their impact on disease-free survival time.

Dairy cows are at a greater risk of disease due to increased energy demand during the transition period. Blood biomarkers including beta-hydroxybutyrate (BHBA)1 and non-esterified fatty acids (NEFA)2 are routinely used to identify animals in a state of negative energy balance (NEB)3. Recent research demonstrates cattle have varied response to NEB, that requires multiple blood biomarkers to characterize. This research identified five subcategories (cowtypes) of metabolic responses in transition dairy cows: Healthy, Athlete, Clever, Hyperketonemia, and Poor Metabolic Adaptation Syndrome (PMAS)4. The data set used in this study was collected in Germany by VIT - Vereinigte Informationssysteme Tierhaltung w.V. from 2016 to 2020. Health issues with time of diagnostic were included in the dataset. Using previously reported prediction models for blood BHB and blood NEFA and milk Fourier-transform infrared spectroscopy (FTIR)5 data, the cowtypes in our dataset were predicted. The objective of this study is to evaluate the association of the cowtypes with the disease-free survival time in dairy cows during early post calving using an accelerated failure time regression model. Additionally, transition probabilities of the population dynamics between cowtypes are studied by means of a Markov chain model. Using Healthy cowtype as reference level, Athlete, Clever, and PMAS cowtypes were found to be significant for the disease-free survival probability (P < 0.01). Conversely, Hyperketonemia cowtype was not significant (P = 0.182). Compared to the Healthy cowtype, all other cowtypes had a negative effect on the survival probabilities, which was higher for PMAS cows. Furthermore, after computing the estimated population transition probabilities among cowtypes, the stationary distribution of the Markov chain, along with bootstrap confidence intervals were computed. The results showed 0.091 (95% CI:0.089,0.092), 0.077 (95 % CI:0.074,0.078), 0.684 (95 % CI:0.067,0.069), 0.138 (95 % CI:0.136,0.139), and 0.009 (95% CI:0.008,0.010) of probability of being in Healthy, Athlete, Clever, Hyperketonemia, and PMAS cowtype, respectively. These estimates represent the proportion of cows belonging to the different cowtypes in a herd; information which may prove useful for herd management. The application of blood biomarker predictions using milk FTIR allows us to investigate differences between predicted cowtype and movements between these states and the association with time to disease. Further research will improve our understanding of the dynamic nature of the transition period.

Developing a predictive model for beta-hydroxybutyrate and non-esterified fatty acids using milk fourier-transform infrared spectroscopy in dairy cows.

Negative energy balance following parturition predisposes dairy cattle to numerous metabolic disorders. Current diagnostics are limited by their labor requirements and inability to measure multiple metabolic markers simultaneously. Fourier-transform Infrared spectroscopy (FTIR) data, measured from milk samples, could improve the detection of metabolic disorders using routine milk samples from dairy farms. The objective of this study was to develop a predictive model for numeric values of blood beta-hydroxybutyrate (BHB) and blood non-esterified fatty acids (NEFA). The study utilized a dataset comprised of 622 observations with known blood BHB and blood NEFA values measured concurrently with the milk FTIR data. Using ElasticNet regression on milk FTIR data and production information, we built regression models for numeric blood BHB and blood NEFA prediction and classification models for blood BHB values greater than 1.2 mmol/L and blood NEFA values greater than 0.7 mmol/L. The R2 of the best fitting model was 0.56 and 0.51 for log-transformed BHB and log-transformed NEFA, respectively. The BHB classification model had a 90 % sensitivity and 83 % specificity and the NEFA classification model achieved a sensitivity of 73 % and specificity of 74 %. We applied our numeric prediction models to an external dataset (n = 9660) with known blood metabolites to validate the prediction accuracy of log-transformed blood BHB and log-transformed blood NEFA models. Log-transformed BHB root mean square error (RMSE) was 0.4018 and log-transformed NEFA RMSE was 0.4043. The second objective of this study was to develop a categorization for cows as either metabolically disordered or healthy. Responses to negative energy balance in transition cows are related to blood levels of BHB and NEFA. Cows suffering from metabolic disorders without elevated blood BHB values remain unidentified when detection is focused on blood BHB alone. To account for this differentiated metabolic response, we categorized cows as either 'metabolically healthy' or suffering a 'metabolic disorder' by using a combination of blood BHB, blood NEFA, and milk fat to protein quotient. We obtained a balanced accuracy of 94 % for the prediction of cow metabolic status. Direct prediction of metabolic status can be used to identify hyperketonemic cows in addition to cows exhibiting metabolic response patterns not detected by elevated blood BHB alone.

Full model selection using regression trees for numeric predictions of biomarkers for metabolic challenges in dairy cows.

Dairy cows suffer poor metabolic adaptation syndrome (PMAS)1 during early post-calving periods caused by negative energy balance. Measurement of blood beta-hydroxy butyric acid (BHBA)2 and blood non-esterified fatty acids (NEFA)3 allow early and accurate detection of negative energy balance. Machine learning prediction of blood BHBA and blood NEFA using milk testing samples represents an opportunity to identify at-risk animals, using less labor than direct blood testing methods. Routine milk testing on modern dairies and computer record keeping provide an immense amount of data which can then be used in machine learning models. Previous research for predicting blood metabolites using Fourier-transform infrared spectroscopy (FTIR)4 milk data has focused mainly on individual models rather than a comparison among the models. Full model selection is the process of comparing different combinations of pre-processing methods, variable selection, and statistical learning algorithms to determine which model results in the lowest prediction error for a given dataset. For this project we used a full model selection approach with regression trees (rtFMS)5 . rtFMS uses the cross-validated performance of different model configurations to feed a regression tree for selecting a final model. A total of 384 possible model configurations (algorithms, predictors and data preprocessing options) for each outcome (blood BHBA and blood NEFA) were considered in the rtFMS technique. rtFMS allows direct comparison of multiple modeling approaches reducing bias due to empirical knowledge, modeling habits, or preferences, identifying the model with minimal root mean squared prediction error (RMSE)6 . An elastic net regression model was selected as the best performing model for both biomarkers. The input data for blood BHBA predictions were FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers, obtaining RMSE = 0.354 (0.328-0.392). The best performing model for blood NEFA had input data of FTIR milk spectra, with a second derivative pre-processing, and a filter with 212 wave numbers filter along with the time of milking, obtaining RMSE = 0.601 (0.564-0.654). The comparison of multiple modeling strategies, conducted by rtFMS, present an option for improved FTIR prediction models of blood BHBA and blood NEFA by reducing error due to human bias. The implementation of rtFMS to design future prediction models can guide model inputs and features. Our prediction models have the potential to increase early detection of metabolic disorders in dairy cows during the transition period.

Evaluation of the prevalence of digital dermatitis and the effects on performance in beef feedlot cattle under organic trace mineral supplementation.

Bovine digital dermatitis (DD) is a contagious and multifactorial disease that leads to painful, ulcerative lesions of the skin near the heel-horn border of the foot, most commonly in dairy cattle. With regard to beef cattle, the pathogenesis and etiology of DD has not been widely reported or studied over the past several decades. A longitudinal field trial in a commercial feedlot was conducted to compare prevalence and effects of DD in beef steers provided a diet supplemented with a novel formulation of inorganic and organic trace mineral sources (OTM diet) compared to a diet provided with similar levels of trace minerals solely from inorganic sources (CON diet). A secondary objective was to evaluate the prevalence of DD and the potential effects on growth performance and carcass yield and quality. One thousand seventy-seven steers were assigned to 1 of the 2 treatment groups (CON diet or OTM diet) based on location of their home pens which were situated in 1 of 2 barns. All pens in the B barn (group B) were assigned to the OTM diet, and all pens in the A barn (group A) were assigned to the CON diet. The study was conducted in 2 phases: adaptation phase (AP) comprising the initial 60 d of feeding CON and OTM diets and postadaptation phase (PAP) which lasted until cattle were sent to harvest. In the AP, pens in group B had a greater proportion of steers (54.03%) with DD lesions compared to pens in group A (26.72%). During the PAP, the relative risk of observing an increased DD prevalence was significantly ( < 0.05) higher in CON group compared to OTM group. Growth performance, final live weight, and hot carcass weight were negatively impacted when steers were observed to have active DD lesions (M2 lesions) compared to steers with no M2 lesions over the study period. For ADG, a calculated loss per steer of 0.08 kg/d from type I (no M2 lesions) to type II (one M2 lesion; SE = 0.028; = 0.003) and loss of 0.14 kg/d from type I to type III (multiple M2 lesions; SE = 0.038; = 0.0003) were observed. A significant BW loss of approximately 10.06 kg (SE = 4.18; = 0.022) and a mean reduction of 5.5 kg per steer in HCW (SE = 2.74; = 0.043) were also found between type I and type II steers.

Immune response against Treponema spp. and ELISA detection of digital dermatitis.

The objective of this longitudinal study was to evaluate the immune response against Treponema spp. infection in dairy heifers affected with digital dermatitis (DD). In addition, the accuracy of an indirect ELISA detecting anti-Treponema IgG antibodies in identifying clinical DD status has been assessed. A cohort of 688 pregnant Holstein heifers was evaluated at least 3 times before calving during a period of 6 mo. Complete clinical assessment of DD presence on the back feet of each heifer and blood extraction were performed in a stand-up chute. Digital dermatitis cases were characterized by the M-stage classification system and size and level of skin proliferation. An ELISA was performed on blood serum samples obtained from a subcohort of 130 heifers. For description purposes, the animals were classified by the number of clinical cases experienced during the study period as type I (no clinical cases were observed), type II (only 1 acute clinical case diagnosed), and type III (at least 2 acute clinical cases diagnosed). Multivariable repeated-measures models were used to evaluate the immune response against Treponema spp. infection. A binormal Bayesian model for the ELISA data without cut-point values was used to assess the accuracy of the ELISA as a diagnostic tool. Animals that never experienced a DD event throughout the study kept a constant low level of antibody titer. A 56% increase in mean ELISA titer was observed in heifers upon a first clinical DD case diagnosis. After topical treatment of an acute DD case with oxytetracycline, the antibody titer decreased progressively in type II heifers, achieving mean levels of those observed in healthy cows after a mean of 223 d. Surprisingly, antibody titer was not increased in the presence of M1 (DD lesion <20mm in diameter surrounded by healthy skin) and M4.1 (DD lesion <20mm in diameter embedded in a circumscribed dyskeratotic or proliferative skin alteration) DD stages. Type III cows showed a slight increase in antibody levels. The presence of skin proliferation at first DD diagnosis was found to be associated with an odds ratio of 2.04 of becoming a type III heifer in relation to heifers presenting first lesions without skin proliferation. The ELISA validity was estimated by an area under the curve of 0.88. Predicted probabilities of infection are provided for a range of ELISA values and prevalence of infection. Early detection and treatment is essential to control DD and the ELISA can be used in understanding the immunopathology of DD and shows great promise for prescreening purposes during DD management programs in combination with traditional clinical inspection.

Short communication: minimum bactericidal concentration of disinfectants evaluated for bovine digital dermatitis-associated Treponema phagedenis-like spirochetes.

The bacterial spirochetes, Treponema spp., are thought to be a major contributor to the etiology of bovine digital dermatitis (DD), a skin disease with worldwide economic impact. Hoofbath strategies are commonly used in an attempt to control and prevent the development of DD and continuing research has been done to develop an optimal hoofbath strategy for this purpose. The aim of this study was to develop a protocol that can be used as part of the screening process for candidate hoofbath disinfectants. This protocol allows an accurate determination of the in vitro minimum inhibitory concentration and minimum bactericidal concentration of a series of disinfectants for Treponema microorganisms. Assays were performed in triplicate for each of the disinfectants at 30-s and 10-min exposure times and exposed to 10 and 20% manure (vol/vol). The results of this study can be used to categorize disinfectants based on the effect of exposure and manure concentration regarding their ability to inhibit Treponema growth. This information can then aid in optimizing strategies for hoofbath-based control of DD development and spread.

Growth curves and morphology of three Treponema subtypes isolated from digital dermatitis in cattle.

Digital dermatitis (DD) is an infectious claw disease of cattle that causes painful lesions, principally along the coronary band of the claws. In the US alone, the estimated economic impact of DD is estimated to be $190 million. The etiology of DD remains unclear and there is no reliable laboratory test, so DD is most often diagnosed clinically. Spirochetal bacteria of the genera Treponema have been implicated in DD infections following their isolation using culture techniques, serological detection of bovine antibodies against treponemes, and amplification of treponemal 16s DNA sequences by PCR. During in vitro growth of spirochetes and treponemes isolated from DD, morphological changes have been observed indicating the presence of a spiral form and an encysted form. It is not known why encysted forms appear or what role they have in the progression of DD. The current study established growth curves for three subtypes of treponemes, Treponema denticola-like, Treponema phagedenis-like, and Treponema medium-like, while photographically monitoring changes in morphology. In addition to observing spiral and encysted forms, two intermediate forms were also observed. These appeared as either spiral forms with spherical bodies or as enveloped clusters of granules. The observation of encysted forms adds further support to the theory that treponemes causing recurrent infections deep in bovine skin have mechanisms to facilitate persistence and the chronic character of DD.