Rule Discovery in Milk Content towards Mastitis Diagnosis: Dealing with Farm Heterogeneity over Multiple Years through Classification Based on Associations

Exploring the Action Mechanism of the Active Ingredient of Quercetin in Ligustrum lucidum on the Mouse Mastitis Model Based on Network Pharmacology and Molecular Biology Validation

Aim

The aim of this study is to explore the mechanism of action of quercetin, the main active anti-inflammatory component of Ligustrum lucidum, in the prevention and treatment of mastitis.

Methods

Prediction of the main active ingredients and key anti-inflammatory targets of Ligustrum lucidum using a network pharmacology platform and molecular biology validation of the results. Observation of histopathological changes in the mouse mammary gland by hematoxylin-eosin staining(H&E) method, quantitative real-time PCR(qPCR), and Western blot (WB) to detect the expression levels of relevant inflammatory factors mRNA and protein.

Results

A total of 7 active ingredients and 42 key targets were obtained from the network pharmacological analysis of Ligustrum lucidum, with quercetin as the main core ingredient and tumor necrosis factor(TNF), serine threonine protein kinase1(AKT1), and interleukin6(IL6) as the core targets; H&E results showed that pathological changes were reduced to different degrees in the dose group compared to the model group. The qPCR results showed that the relative expression of TNF and IL6 mRNA in the high dose group on day 3 and the high and medium dose groups on day 7 were not significantly different compared with the blank group (P > 0.05), and the difference between the dose groups on day 5 was significant (P < 0.05). WB results showed that the difference in nuclear factor kappa-B(NF-κB) protein expression in the medium and low dose groups on day 7 was significant compared with the blank group (P < 0.05), the difference in 5 and 7 days, significant differences in AKT1 protein expression between the middle and low dose groups (P < 0.05), nonsignificant differences in the TNF protein expression between the high dose groups on day 7 (P > 0.05), and significant differences in the IL6 protein expression between the middle and low dose groups on days 3 and 7 (P < 0.05).

Conclusion

Quercetin, the main active ingredient of Ligustrum lucidum, may act in the prevention and treatment of mastitis by inhibiting the expression of inflammatory factors in phosphoinositol 3-kinase(PI3K)-AKT and NF-κB signaling pathways and showa a significant dose-dependent effect. This study provides theoretical basis and clues for the control of mastitis in dairy cows.

Prevalence and Risk Factors of Staphylococcal Subclinical Mastitis in Dairy Animals of Chitwan, Nepal

Subclinical mastitis (SCM), the asymptomatic inflammation of breast tissue, is the most common form of mastitis in livestock. SCM prevalence and risk factors in dairy animals in Western Chitwan, Bagmati Province, Nepal, were the primary objectives of this study. Out of 243 dairy animals, 104 representing 42.8% were positive for SCM in the California mastitis test (CMT) in the study area. At the quarter level, out of 972 active quarters tested for SCM, 188 (19.3%) were positive to CMT test. The prevalence of Staphylococcal SCM was 39.92% (97/243) and 18.21% (177/972) at animal level and quarter level, respectively. Coagulase Negative Staphylococcus (CNS) (46.33%) was the most prevalent Staphylococcus to cause SCM at quarter level. While at animal level, SCM due to occurrence of both S. aureus and CNS (36.08%) in an individual was more common. High susceptibility towards Amikacin, Ceftriaxone and Gentamicin was seen against both isolates. Low resistance against Amikacin and Ceftriaxone was seen against both isolates. Ciprofloxacin (41.2% vs 27.4%) and Gentamicin (37.8% vs 23.2%) were more resistance towards CNS whereas Enrofloxacin (41.1% vs 25.2%) and Tetracycline (36.8% vs 15.1%) were more resistant towards S. aureus. Older aged, multiparous and late lactating animals had the highest prevalence of staphylococcal SCM i.e., 58.1%, 56% and 52.6%, respectively. The study concludes that there is a high prevalence of Staphylococcal SCM in Western Chitwan, Nepal. CNS is the most common mastitis pathogen. Increased antimicrobial resistance to S. aureus and CNS could be the result of the indiscriminate use of antibiotic drugs without an antibiotic susceptibility test (AST). This study emphasizes the importance of ongoing antibiotic surveillance, excellent farm and animal hygiene, and suitable housing and feeding management.

Correlation between Polymerase Chain Reaction Identification of Iron Acquisition Genes and an Iron-Deficient Incubation Test for Klebsiella pneumoniae Isolates from Bovine Mastitis

We investigated the correlation between the polymerase chain reaction (PCR) identification of six virulence genes associated with siderophore activation and the iron-uptake system (iron-acquisition genes; iucA, entB, fepA, ybtS, psn, and kfu) in mastitis-associated Klebsiella pneumoniae (K. pneumoniae). The growth of 37 K. pneumoniae isolates from the milk of cows with mild mastitis reared on Japanese dairy farms between October 2012 and December 2014 was examined by incubation in an iron-deficient medium. entB-, fepA-, or ybtS-positive isolates grew significantly better than entB-, fepA-, or ybtS-negative isolates after incubating in an iron-deficient medium for three days. Interestingly, the growth of isolates with 0 and ≥4 PCR-positive iron-acquisition genes in the iron-deficient medium were significantly different by day 2, while isolates with 2, 3, and ≥4 PCR-positive iron-acquisition genes grew significantly better than those with no PCR-positive iron-acquisition genes by day 3. Based on the correlation between the results of PCR and iron-deficient incubation tests, iron-deficient incubation for three days can be used to estimate the presence or absence of iron-acquisition genes in mastitis-associated K. pneumoniae.

Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study

Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.

Evaluating Alternatives to Locomotion Scoring for Lameness Detection in Pasture-Based Dairy Cows in New Zealand: Infra-Red Thermography

Simple Summary

Early detection accompanied by effective treatment is vital to minimise the negative impacts of lameness in dairy cows. Locomotion scoring is commonly used for detecting lameness but can be challenging to implement effectively in cows at pasture-based systems. One potential alternative detection is measuring foot skin temperature using an infrared camera. Data were collected from a 940-cow dairy farm in New Zealand with cows observed at two consecutive afternoon milkings. Locomotion scoring was undertaken at the first milking and thermal imaging of the hind feet at the second milking. As the locomotion score increased, mean foot skin temperature increased, showing that measuring temperature could be a useful alternative to locomotion scoring. However, the process needs to be speeded up and automated if it is to be used widely.

Abstract

Lameness in cattle is a complex condition with huge impacts on welfare, and its detection is challenging for the dairy industry. The present study aimed to evaluate the association between foot skin temperature (FST) measured using infrared thermography (IRT) and locomotion scoring (LS) in dairy cattle kept at pasture. Data were collected from a 940-cow dairy farm in New Zealand. Cows were observed at two consecutive afternoon milkings where LS was undertaken at the first milking (4-point scale (0–3), DairyNZ). The next day, cows were thermally imaged from the plantar aspect of the hind feet using a handheld T650sc forward-looking infrared camera (IRT). The association between FST and locomotion score was analysed using a generalised linear model with an identity link function and robust estimators. ROC curves were performed to determine optimal threshold temperature cut-off values by maximising sensitivity and specificity for detecting locomotion score ≥ 2. There was a linear association between individual locomotion scores and FST. For mean temperature (MT), each one-unit locomotion score increase was associated with a 0.944 °C rise in MT. Using MT at a cut-off point of 34.5 °C produced a sensitivity of 80.0% and a specificity of 92.4% for identifying cows with a locomotion score ≥ 2 (lame). Thus, IRT has a substantial potential to be used on-farm for lameness detection. However, automation of the process will likely be necessary for IRT to be used without interfering with farm operations.

Predicting Subclinical Ketosis in Dairy Cows Using Machine Learning Techniques

Simple Summary

The maintenance of cows in good health and physical condition is an important component of dairy cattle management. One of the major metabolic disorders in dairy cows is subclinical ketosis. Due to financial and organizational reasons it is often impossible to test all cows in a herd for ketosis using standard blood examination method. Using milk data from test-day records, obtained without additional costs for breeders, we found diagnostic models identifying cows-at-risk of subclinical ketosis. In addition, to select the best models, we present a general scoring approach for various machine learning models. With our models, breeders can identify dairy cows-at-risk of subclinical ketosis and implement appropriate management strategies and prevent losses in milk production.

Abstract

The diagnosis of subclinical ketosis in dairy cows based on blood ketone bodies is a challenging and costly procedure. Scientists are searching for tools based on results of milk performance assessment that would allow monitoring the risk of subclinical ketosis. The objective of the study was (1) to design a scoring system that would allow choosing the best machine learning models for the identification of cows-at-risk of subclinical ketosis, (2) to select the best performing models, and (3) to validate them using a testing dataset containing unseen data. The scoring system was developed using two machine learning modeling pipelines, one for regression and one for classification. As part of the system, different feature selections, outlier detection, data scaling and oversampling methods were used. Various linear and non-linear models were fit using training datasets and evaluated on holdout, testing the datasets. For the assessment of suitability of individual models for predicting subclinical ketosis, three β-hydroxybutyrate concentration in blood (bBHB) thresholds were defined: 1.0, 1.2 and 1.4 mmol/L. Considering the thresholds of 1.2 and 1.4, the logistic regression model was found to be the best fitted model, which included independent variables such as fat-to-protein ratio, acetone and β-hydroxybutyrate concentrations in milk, lactose percentage, lactation number and days in milk. In the cross-validation, this model showed an average sensitivity of 0.74 or 0.75 and specificity of 0.76 or 0.78, at the pre-defined bBHB threshold 1.2 or 1.4 mmol/L, respectively. The values of these metrics were also similar in the external validation on the testing dataset (0.72 or 0.74 for sensitivity and 0.80 or 0.81 for specificity). For the bBHB threshold at 1.0 mmol/L, the best classification model was the model based on the SVC (Support Vector Classification) machine learning method, for which the sensitivity in the cross-validation was 0.74 and the specificity was 0.73. These metrics had lower values for the testing dataset (0.57 and 0.72 respectively). Regression models were characterized by poor fitness to data (R² < 0.4). The study results suggest that the prediction of subclinical ketosis based on data from test-day records using classification methods and machine learning algorithms can be a useful tool for monitoring the incidence of this metabolic disorder in dairy cattle herds.