Principal component analysis for the early detection of mastitis and lameness in dairy cows

Acute phase proteins and total antioxidant capacity in free-roaming cats infected by pathogenic leptospires

BACKGROUND

Leptospirosis is a neglected but widespread zoonotic disease throughout the world. Most mammals are hosts of Leptospira spp., including domestic cats, species in which no consensus has been reached on the clinical presentation or diagnosis of the disease. The study of acute-phase proteins (APPs) and biomarkers of oxidative status would contribute to knowledge about the disease in cats. This report evaluated four APPs: Serum amyloid A-SAA, Haptoglobin-Hp, albumin and Paraoxonase 1-PON1 and the antioxidant response through Total Antioxidant Capacity-TAC, in 32 free-roaming cats. Cats were classified as seroreactive for anti-leptospiral antibodies (group 1, n = 8), infected with Leptospira spp (group 2, n = 5) and leptospires-free cats (group 3, n = 19).

RESULTS

SAA differences were observed between groups 1 and 2 (p-value = 0.01) and between groups 2 and 3 (p-value = 0.0001). Hp concentration differences were only detected between groups 2 and 3 (p-value = 0.001). Albumin concentrations only differed between groups 1 and 3 (p-value = 0.017) and 2 and 3 (p-value < 0.005). Cats in groups 1 (p-value < 0.005) and 2 (p-value < 0.005) had lower PON1 concentrations than group 3. No statistically significant differences between pairs of groups were detected for TAC concentrations. The principal component analysis (PCA) retained two principal components, (PC1 and PC2), explaining 60.1% of the observed variability of the inflammatory proteins and the antioxidant TAC.

CONCLUSIONS

Increases in Serum SAA, Hp, and decreases in PON1 activity may indicate an active inflammatory state in infected cats (currently or recently infected).

Mastitis in Dairy Cattle: On-Farm Diagnostics and Future Perspectives

Mastitis is one of the most important diseases in dairy cattle farms, and it can affect the health status of the udder and the quantity and quality of milk yielded. The correct management of mastitis is based both on preventive and treatment action. With the increasing concern for antimicrobial resistance, it is strongly recommended to treat only the mammary quarters presenting intramammary infection. For this reason, a timely and accurate diagnosis is fundamental. The possibility to detect and characterize mastitis directly on farm would be very useful to choose the correct management protocol. Some on-field diagnostic tools are already routinely applied to detect mastitis, such as the California Mastitis Test and on-farm culture. Other instruments are emerging to perform a timely diagnosis and to characterize mastitis, such as Infra-Red Thermography, mammary ultrasound evaluation and blood gas analysis, even if their application still needs to be improved. The main purpose of this article is to present an overview of the methods currently used to control, detect, and characterize mastitis in dairy cows, in order to perform a timely diagnosis and to choose the most appropriate management protocol, with a specific focus on on-farm diagnostic tools.

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Simple Summary

Cattle lameness detection as well as behaviour recognition are the two main objectives in the applications of precision livestock farming (PLF). Over the last five years, the development of smart sensors, big data, and artificial intelligence has offered more automatic tools. In this review, we discuss over 100 papers that used automated techniques to detect cattle lameness and to recognise animal behaviours. To assist researchers and policy-makers in promoting various livestock technologies for monitoring cattle welfare and productivity, we conducted a comprehensive investigation of intelligent perception for cattle lameness detection and behaviour analysis in the PLF domain. Based on the literature review, we anticipate that PLF will develop in an objective, autonomous, and real-time direction. Additionally, we suggest that further research should be dedicated to improving the data quality, modeling accuracy, and commercial availability.

Abstract

The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

Novel ways to use sensor data to improve mastitis management

Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.

Invited review: Toward a common language in data-driven mastitis detection research

Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data-based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that within approaches several different methods and terminologies for similar methods were used. Not all publications provided a clear description of the method used, and therefore it seemed that different methods were used between publications, whereas in fact just a different terminology was used, or the other way around. This paper is intended to serve as a reference for people from various research disciplines who need to collaborate and communicate efficiently about the topic of sensor-based mastitis detection and the methods used in this context. The framework used in this paper can support future research to correctly classify approaches and methods, which can improve the understanding of scientific publication. We encourage future research on sensor-based animal disease detection, including that of mastitis detection, to use a more coherent terminology for methods, and clearly state which technique (e.g., filtering) and approach (e.g., moving average) are used. This paper, therefore, can serve as a starting point and further stimulates the interdisciplinary cooperation in sensor-based mastitis research.

Variable selection for monitoring sickness behavior in lactating dairy cattle with the application of control charts

The present observational study investigated the application of multivariate cumulative sum (MCUSUM) control charts by including variables selected by principal component analysis and partial least squares (PLS) regression to identify sickness behavior in dairy cattle. Therefore, sensor information (24 variables) was collected from 480 milking cows on a German dairy farm between September 2018 and December 2019. These variables were gathered in potentially different scenarios on farm. In total, data from 749 animals were available for evaluation. Variables were chosen based on the information of 499 cows (62 healthy; 437 sick) with 93,598 observations. The available diagnoses were collected together to form 1,025 sickness events. Hence, the different numbers of selected variables were included into the MCUSUM control charts. The performance of the MCUSUM control charts was evaluated by a 10-fold cross validation; hence, 90% of the original data set (749 cows) represented the training data, and the remaining 10% was used to test the training results. On average, the 10 training data sets included 124,871 observations with 1,392 sickness events, and the 10 testing data sets included, on average, 13,704 observations with 153 sickness events. The MCUSUM generated from the variables selected by principal component analysis showed comparable results in training and testing in all scenarios; therefore, 70.0 to 97.4% of the sickness events were detected. The false-positive rates ranged from 8.5 to 29.6%, and thus they created at least 2.6 false-positive alerts per day in testing. The variables selected by the PLS regression approach showed comparable sickness detection rates (70.0-99.9%) as well as false-positive rates (8.2-62.8%) in most scenarios. The best performing scenario produced 2.5 false-positive alerts in testing. Summarizing, both approaches showed potential for practical implementation; however, the PLS variable selection approach showed fewer false positives. Therefore, the PLS regression approach could generate a more reliable sickness detection algorithm, if combined with MCUSUM control charts, and considered for practical implementation.

The Importance of Low Daily Risk for the Prediction of Treatment Events of Individual Dairy Cows with Sensor Systems

The prediction of health disorders is the goal of many sensor systems in dairy farming. Although mastitis and lameness are the most common health disorders in dairy cows, these diseases or treatments are a rare event related to a single day and cow. A number of studies already developed and evaluated models for classifying cows in need of treatment for mastitis and lameness with machine learning methods, but few have illustrated the effects of the positive predictive value (PPV) on practical application. The objective of this study was to investigate the importance of low-frequency treatments of mastitis or lameness for the applicability of these classification models in practice. Data from three German dairy farms contained animal individual sensor data (milkings, activity, feed intake) and were classified using machine learning models developed in a previous study. Subsequently, different risk criteria (previous treatments, information from milk recording, early lactation) were designed to isolate high-risk groups. Restricting selection to cows with previous mastitis or hoof treatment achieved the highest increase in PPV from 0.07 to 0.20 and 0.15, respectively. However, the known low daily risk of a treatment per cow remains the critical factor that prevents the reduction of daily false-positive alarms to a satisfactory level. Sensor systems should be seen as additional decision-support aid to the farmers’ expert knowledge.

Using Sensor Data to Detect Lameness and Mastitis Treatment Events in Dairy Cows: A Comparison of Classification Models

The aim of this study was to develop classification models for mastitis and lameness treatments in Holstein dairy cows as the target variables based on continuous data from herd management software with modern machine learning methods. Data was collected over a period of 40 months from a total of 167 different cows with daily individual sensor information containing milking parameters, pedometer activity, feed and water intake, and body weight (in the form of differently aggregated data) as well as the entered treatment data. To identify the most important predictors for mastitis and lameness treatments, respectively, Random Forest feature importance, Pearson’s correlation and sequential forward feature selection were applied. With the selected predictors, various machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), Extra Trees Classifier (ET) and different ensemble methods such as Random Forest (RF) were trained. Their performance was compared using the receiver operator characteristic (ROC) area-under-curve (AUC), as well as sensitivity, block sensitivity and specificity. In addition, sampling methods were compared: Over- and undersampling as compensation for the expected unbalanced training data had a high impact on the ratio of sensitivity and specificity in the classification of the test data, but with regard to AUC, random oversampling and SMOTE (Synthetic Minority Over-sampling) even showed significantly lower values than with non-sampled data. The best model, ET, obtained a mean AUC of 0.79 for mastitis and 0.71 for lameness, respectively, based on testing data from practical conditions and is recommended by us for this type of data, but GNB, LR and RF were only marginally worse, and random oversampling and SMOTE even showed significantly lower values than without sampling. We recommend the use of these models as a benchmark for similar self-learning classification tasks. The classification models presented here retain their interpretability with the ability to present feature importances to the farmer in contrast to the “black box” models of Deep Learning methods.

Recognition of Somatic Cells in Bovine Milk Using Fusion Feature

Mastitis is the major cause of loss in dairy farming. Somatic cells are one of most important standards to detect this infection. This paper proposes a novel image processing algorithm to recognize four types of somatic cells in bovine milk automatically. First, cloud model uses to segment cell images. Second, a variety of features are extracted from regions of interest. Finally, most differential features are selected using ReliefF algorithm and performances of two classifiers, Back propagation networks (BPN) and support vector machine (SVM), are compared. The experimental results are obtained using a large set of images from different sources. The results of our proposed method is not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Neuroendocrine immunomodulation network dysfunction in SAMP8 mice and PrP-hAβPPswe/PS1ΔE9 mice: potential mechanism underlying cognitive impairment

Senescence-accelerated mouse prone 8 strain (SAMP8) and PrP-hAβPPswe/PS1ΔE9 (APP/PS1) mice are classic animal models of sporadic Alzheimer's disease and familial AD respectively. Our study showed that object recognition memory, spatial learning and memory, active and passive avoidance were deteriorated and neuroendocrine immunomodulation (NIM) network was imbalance in SAMP8 and APP/PS1 mice. SAMP8 and APP/PS1 mice had their own specific phenotype of cognition, neuroendocrine, immune and NIM molecular network. The endocrine hormone corticosterone, luteinizing hormone and follicle-stimulating hormone, chemotactic factor monocyte chemotactic protein-1, macrophage inflammatory protein-1β, regulated upon activation normal T cell expressed and secreted factor and eotaxin, pro-inflammatory factor interleukin-23, and the Th1 cell acting as cell immunity accounted for cognitive deficiencies in SAMP8 mice, while adrenocorticotropic hormone and gonadotropin-releasing hormone, colony stimulating factor granulocyte colony stimulating factor, and Th2 cell acting as humoral immunity in APP/PS1 mice. On the pathway level, chemokine signaling and T cell receptor signaling pathway played the key role in cognition impairments of two models, while cytokine-cytokine receptor interaction and natural killer cell mediated cytotoxicity were more important in cognitive deterioration of SAMP8 mice than APP/PS1 mice. This mechanisms of NIM network underlying cognitive impairment is significant for further understanding the pathogenesis of AD and can provide useful information for development of AD therapeutic drug.

Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

Association of claw disorders with subclinical intramammary infections in Egyptian dairy cows

AIM

Bovine mastitis and lameness are the most common production diseases affecting dairy farms worldwide resulting in huge economic impact and impaired animal welfare. The objective of this field study was to investigate the association of infectious and non-infectious claw disorders with the occurrence of subclinical intramammary infections (IMIs) diagnosed by California mastitis test (CMT) in dairy cows under Egyptian conditions.

MATERIALS AND METHODS

A total of 43 dairy cows were included in this field study. Subclinical IMI was diagnosed by CMT on all lactating quarters of cows. A cow was considered to have subclinical IMI if it had at least one subclinically infected quarter (≥3). Cows were inspected carefully for claw disorders that recorded based on type and site. Locomotion and body condition scores were also recorded for each cow in addition to the limb affected. The association between the CMT and other explanatory variables was tested by Fisher's exact test.

RESULTS

The prevalence of infectious and non-infectious claw disorders was 81.4% (35/43) and 32.6% (14/43), respectively. Digital dermatitis (DD) and heel horn erosion were the most prevalent infectious type with 79% (34/43) and 58% (25/43), respectively, while wall fissure was the most identified non-infectious one 11.6% (5/43). The prevalence of claw disorders in hind limbs was 88.4% (38/43) and 11.6% (5/43) in the forelimbs. Infectious claw disorders were significantly associated with the subclinical IMI diagnosed by CMT (p<0.05). Non-infectious claw affections, locomotion score, body condition score, and the affected limb had no association with the occurrence of subclinical IMI.

CONCLUSION

DD is the highest prevalent claw disorder observed in dairy cows in Egypt. The hind limbs are more susceptible to claw disorders than the forelimbs. Infectious type of claw disorders is significantly associated with subclinical IMI diagnosed by CMT in dairy cows under Egyptian conditions indicating that the infectious types of claw affections may influence the udder health.

The anti-aging effects of LW-AFC via correcting immune dysfunctions in senescence accelerated mouse resistant 1 (SAMR1) strain

Although there were considerable advances in the anti-aging medical field, it is short of therapeutic drug for anti-aging. Mounting evidence indicates that the immunosenescence is the key physiopathological mechanism of aging. This study showed the treatment of LW-AFC, an herbal medicine, decreased the grading score of senescence, increased weight, prolonged average life span and ameliorated spatial memory impairment in 12- and 24-month-old senescence accelerated mouse resistant 1 (SAMR1) strain. And these anti-aging effects of LW-AFC were more excellent than melatonin. The administration of LW-AFC enhanced ConA- and LPS-induced splenocyte proliferation in aged SAMR1 mice. The treatment of LW-AFC not only reversed the decreased the proportions of helper T cells, suppressor T cells and B cells, the increased regulatory T cells in the peripheral blood of old SAMR1 mice, but also could modulate the abnormal secretion of IL-1β, IL-2, IL-6, IL-17, IL-23, GM-CSF, IFN-γ, TNF-α, TNF-β, RANTES, eotaxin, MCP-1, IL-4, IL-5, IL-10 and G-CSF. These data indicated that LW-AFC reversed the immunosenescence status by restoring immunodeficiency and decreasing chronic inflammation and suggested LW-AFC may be an effective anti-aging agent.