Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows.

The objective of this study was to determine if a 3-dimensional computer vision automatic locomotion scoring (3D-ALS) method was able to outperform human observers for classifying cows as lame or nonlame and for detecting cows affected and nonaffected by specific type(s) of hoof lesion. Data collection was carried out in 2 experimental sessions (5 mo apart). In every session all cows were assessed for (1) locomotion by 2 observers (Obs1 and Obs2) and by a 3D-ALS; and (2) identification of different types of hoof lesions during hoof trimming (i.e., skin and horn lesions and combinations of skin/horn lesions and skin/hyperplasia). Performances of observers and 3D-ALS for classifying cows as lame or nonlame and for detecting cows affected or nonaffected by types of lesion were estimated using the percentage of agreement (PA), kappa coefficient (κ), sensitivity (SEN), and specificity (SPE). Observers and 3D-ALS showed similar SENlame values for classifying lame cows as lame (SENlame comparison Obs1-Obs2 = 74.2%; comparison observers-3D-ALS = 73.9-71.8%). Specificity values for classifying nonlame cows as nonlame were lower for 3D-ALS when compared with observers (SPEnonlame comparison Obs1-Obs2 = 88.5%; comparison observers-3D-ALS = 65.3-67.8%). Accordingly, overall performance of 3D-ALS for classifying cows as lame and nonlame was lower than observers (Obs1-Obs2 comparison PAlame/nonlame = 84.2% and κlame/nonlame = 0.63; observers-3D-ALS comparisons PAlame/nonlame = 67.7-69.2% and κlame/nonlame = 0.33-0.36). Similarly, observers and 3D-ALS had comparable and moderate SENlesion values for detecting horn (SENlesion Obs1 = 68.6%; Obs2 = 71.4%; 3D-ALS = 75.0%) and combinations of skin/horn lesions (SENlesion Obs1 = 51.1%; Obs2 = 64.5%; 3D-ALS = 53.3%). The SPEnonlesion values for detecting cows without lesions when classified as nonlame were lower for 3D-ALS than for observers (SPEnonlesion Obs1 = 83.9%; Obs2 = 80.2%; 3D-ALS = 60.2%). This was translated into a poor overall performance of 3D-ALS for detecting cows affected and nonaffected by horn lesions (PAlesion/nonlesion Obs1 = 80.6%; Obs2 = 78.3%; 3D-ALS = 63.5% and κlesion/nonlesion Obs1 = 0.48; Obs2 = 0.44; 3D-ALS = 0.25) and skin/horn lesions (PAlesion/nonlesion Obs1 = 75.1%; Obs2 = 75.9%; 3D-ALS = 58.6% and κlesion/nonlesion Obs1 = 0.35; Obs2 = 0.42; 3D-ALS = 0.10), when compared with observers. Performance of observers and 3D-ALS for detecting skin lesions was poor (SENlesion for Obs1, Obs2, and 3D-ALS <40%). Comparable SENlame and SENlesion values for observers and 3D-ALS are explained by an overestimation of lameness by 3D-ALS when compared with observers. Thus, comparable SENlame and SENlesion were reached at the expense high number of false positives and low SPEnonlame and SPEnonlesion. Considering that observers and 3D-ALS showed similar performance for classifying cows as lame and for detecting horn and combinations of skin/horn lesions, the 3D-ALS could be a useful tool for supporting dairy farmers in their hoof health management.

Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis.

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield.

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.

Early recognition of bovine respiratory disease in calves using automated continuous monitoring of cough sounds.

Bovine respiratory disease (BRD) complex in calves impairs health and welfare and causes severe economic losses for the Stockperson. Early recognition of BRD should lead to earlier veterinary (antibiotic/anti-inflammatory) treatment interventions thereby reducing the severity of the disease and associated costs. Coughing is one of the clinical manifestations of BRD. It is believed that by automatically and continuously monitoring the sounds within calf houses, and analysing the coughing frequency, early recognition of BRD in calves is possible. Therefore, the objective of the present study was to develop an automated calf cough monitor and examine its potential as an early warning system for BRD in artificially reared dairy calves. The coughing sounds of 62 calves were continuously recorded by a microphone over a three-month period. A sound analysis algorithm was developed to distinguish calf coughs from other sounds (e.g. mechanical sounds). During the sound recording period the health of the calves was assessed and scored periodically per week by a trained human observer. Calves presenting with BRD received antibiotic and/or anti-inflammatory treatment and the dates of treatment were recorded. This treatment date reference served as a comparison for the investigation of whether an increase in coughing frequency could be related to calves developing BRD. The calf cough detection algorithm achieved 50.3% sensitivity, 99.2% specificity and 87.5% precision. Four out of five periods, where coughing frequency was observed to be increased, coincided with the development of BRD in more than one calf. This period of increased coughing frequency was always observed before the calves were treated. Therefore, the calf cough monitor has the potential to identify early onset of BRD in calves.

Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior.

Despite the research on opportunities to automatically measure lameness in cattle, lameness detection systems are not widely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat them properly and in a timely fashion. Many papers have focused on the automated measurement of gait or behavioral cow characteristics related to lameness. In order for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cows need to be developed and validated. Few studies have reached this latter stage of the development process. Also, comparison between the different approaches is impeded by the wide range of practical settings used to measure the gait or behavioral characteristic (e.g., measurements during normal farming routine or during experiments; cows guided or walking at their own speed) and by the different definitions of lame cows. In the majority of the publications, mildly lame cows are included in the non-lame cow group, which limits the possibility of also detecting early lameness cases. In this review, studies that used sensor technology to measure changes in gait or behavior of cows related to lameness are discussed together with practical considerations when conducting lameness research. In addition, other prerequisites for any lameness detection system on farms (e.g., need for early detection, real-time measurements) are discussed.

Relation between observed locomotion traits and locomotion score in dairy cows.

Lameness is still an important problem in modern dairy farming. Human observation of locomotion, by looking at different traits in one go, is used in practice to assess locomotion. The objectives of this article were to determine which individual locomotion traits are most related to locomotion scores in dairy cows, and whether experienced raters are capable of scoring these individual traits consistently. Locomotion and 5 individual locomotion traits (arched back, asymmetric gait, head bobbing, reluctance to bear weight, and tracking up) were scored independently on a 5-level scale for 58 videos of different cows. Videos were shown to 10 experienced raters in 2 different scoring sessions. Relations between locomotion score and traits were estimated by 3 logistic regression models aiming to calculate the size of the fixed effects on the probability of scoring a cow in 1 of the 5 levels of the scale (model 1) and the probability of classifying a cow as lame (locomotion score ≥3; model 2) or as severely lame (locomotion score ≥4; model 3). Fixed effects were rater, session, traits, and interactions among fixed effects. Odds ratios were calculated to estimate the relative probability to classify a cow as lame when an altered (trait score ≥3) or severely altered trait (trait score ≥4) was present. Overall intrarater and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement, respectively. Specific intrarater and interrater agreement for individual levels within a 5-level scale were calculated. All traits were significantly related to the locomotion score when scored with a 5-level scale and when classified as (severely) lame or nonlame. Odds ratios for altered and severely altered traits were 10.8 and 14.5 for reluctance to bear weight, 6.5 and 7.2 for asymmetric gait, and 4.8 and 3.2 for arched back, respectively. Raters showed substantial variation in reliability and agreement values when scoring traits. The acceptance threshold for overall intrarater reliability (κw ≥0.60) was exceeded by locomotion scoring and all traits. Overall interrater reliability values ranged from κw=0.53 for tracking up to κw=0.61 for reluctance to bear weight. Intrarater and interrater agreement were below the acceptance threshold (percentage of agreement <75%). Most traits tended to have lower specific intrarater and interrater agreement in level 3 and 5 of the scale. In conclusion, raters had difficulties in scoring locomotion traits consistently, especially slight alterations were difficult to detect by experienced raters. Yet, the locomotion traits reluctance to bear weight, asymmetric gait, and arched back had the strongest relation with the locomotion score. These traits should have priority in locomotion-scoring-system guidelines and are the best to be used for the development of automated locomotion scoring systems.

Effect of merging levels of locomotion scores for dairy cows on intra- and interrater reliability and agreement.

Locomotion scores are used for lameness detection in dairy cows. In research, locomotion scores with 5 levels are used most often. Analysis of scores, however, is done after transformation of the original 5-level scale into a 4-, 3-, or 2-level scale to improve reliability and agreement. The objective of this study was to evaluate different ways of merging levels to optimize resolution, reliability, and agreement of locomotion scores for dairy cows. Locomotion scoring was done by using a 5-level scale and 10 experienced raters in 2 different scoring sessions from videos from 58 cows. Intra- and interrater reliability and agreement were calculated as weighted kappa coefficient (κw) and percentage of agreement (PA), respectively. Overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were determined for the 5-level scale and after transformation into 4-, 3-, and 2-level scales by merging different combinations of adjacent levels. Intrarater reliability (κw) ranged from 0.63 to 0.86, whereas intrarater agreement (PA) ranged from 60.3 to 82.8% for the 5-level scale. Interrater κw=0.28 to 0.84 and interrater PA=22.6 to 81.8% for the 5-level scale. The specific intrarater agreement was 76.4% for locomotion level 1, 68.5% for level 2, 65% for level 3, 77.2% for level 4, and 80% for level 5. Specific interrater agreement was 64.7% for locomotion level 1, 57.5% for level 2, 50.8% for level 3, 60% for level 4, and 45.2% for level 5. Specific intra- and interrater agreement suggested that levels 2 and 3 were more difficult to score consistently compared with other levels in the 5-level scale. The acceptance threshold for overall intra- and interrater reliability (κw and κ ≥0.6) and agreement (PA ≥75%) and specific intra- and interrater agreement (≥75% for all levels within locomotion score) was exceeded only for the 2-level scale when the 5 levels were merged as (12)(345) or (123)(45). In conclusion, when locomotion scoring is performed by experienced raters without further training together, the lowest specific intra- and interrater agreement was obtained in levels 2 and 3 of the 5-level scale. Acceptance thresholds for overall intra- and interrater reliability and agreement and specific intra- and interrater agreement were exceeded only in the 2-level scale.

Performance of an image analysis processing system for hen tracking in an environmental preference chamber.

Image processing systems have been widely used in monitoring livestock for many applications, including identification, tracking, behavior analysis, occupancy rates, and activity calculations. The primary goal of this work was to quantify image processing performance when monitoring laying hens by comparing length of stay in each compartment as detected by the image processing system with the actual occurrences registered by human observations. In this work, an image processing system was implemented and evaluated for use in an environmental animal preference chamber to detect hen navigation between 4 compartments of the chamber. One camera was installed above each compartment to produce top-view images of the whole compartment. An ellipse-fitting model was applied to captured images to detect whether the hen was present in a compartment. During a choice-test study, mean ± SD success detection rates of 95.9 ± 2.6% were achieved when considering total duration of compartment occupancy. These results suggest that the image processing system is currently suitable for determining the response measures for assessing environmental choices. Moreover, the image processing system offered a comprehensive analysis of occupancy while substantially reducing data processing time compared with the time-intensive alternative of manual video analysis. The above technique was used to monitor ammonia aversion in the chamber. As a preliminary pilot study, different levels of ammonia were applied to different compartments while hens were allowed to navigate between compartments. Using the automated monitor tool to assess occupancy, a negative trend of compartment occupancy with ammonia level was revealed, though further examination is needed.

Manual and automatic locomotion scoring systems in dairy cows: a review.

The objective of this review was to describe, compare and evaluate agreement, reliability, and validity of manual and automatic locomotion scoring systems (MLSSs and ALSSs, respectively) used in dairy cattle lameness research. There are many different types of MLSSs and ALSSs. Twenty-five MLSSs were found in 244 articles. MLSSs use different types of scale (ordinal or continuous) and different gait and posture traits need to be observed. The most used MLSS (used in 28% of the references) is based on asymmetric gait, reluctance to bear weight, and arched back, and is scored on a five-level scale. Fifteen ALSSs were found that could be categorized according to three approaches: (a) the kinetic approach measures forces involved in locomotion, (b) the kinematic approach measures time and distance of variables associated to limb movement and some specific posture variables, and (c) the indirect approach uses behavioural variables or production variables as indicators for impaired locomotion. Agreement and reliability estimates were scarcely reported in articles related to MLSSs. When reported, inappropriate statistical methods such as PABAK and Pearson and Spearman correlation coefficients were commonly used. Some of the most frequently used MLSSs were poorly evaluated for agreement and reliability. Agreement and reliability estimates for the original four-, five- or nine-level MLSS, expressed in percentage of agreement, kappa and weighted kappa, showed large ranges among and sometimes also within articles. After the transformation into a two-level scale, agreement and reliability estimates showed acceptable estimates (percentage of agreement ≥ 75%; kappa and weighted kappa ≥ 0.6), but still estimates showed a large variation between articles. Agreement and reliability estimates for ALSSs were not reported in any article. Several ALSSs use MLSSs as a reference for model calibration and validation. However, varying agreement and reliability estimates of MLSSs make a clear definition of a lameness case difficult, and thus affect the validity of ALSSs. MLSSs and ALSSs showed limited validity for hoof lesion detection and pain assessment. The utilization of MLSSs and ALSSs should aim to the prevention and efficient management of conditions that induce impaired locomotion. Long-term studies comparing MLSSs and ALSSs while applying various strategies to detect and control unfavourable conditions leading to impaired locomotion are required to determine the usefulness of MLSSs and ALSSs for securing optimal production and animal welfare in practice.

Cognitive enrichment in piglet rearing: an approach to enhance animal welfare and to reduce aggressive behaviour.

It is known that pigs raised in enriched environments express less aggressive behaviour. For this reason, a new method of cognitive environmental enrichment was experimented at the University of Veterinary Medicine Hannover, Germany. In the first phase, 78 suckling piglets were trained to learn the link between a sound given by an electronic feeder and a feed reward in the form of chocolate candies during a period of 8 days. In the second phase, the same piglets were used in resident-intruder tests to verify the potential of the feeding system to interrupt aggressive behaviour. The analysis of all training rounds revealed that piglets learned the commands during 8 days of training and the interest of the piglets increased within training days (P < 0.05). In the resident-intruder test, 79.5% of aggressive interactions were broken by feeder activation. In interactions where either the aggressor or the receiver reacted, a high number of fights were stopped (96.7% versus 93.1%) indicating that it was not relevant if the aggressor or the receiver responded to the feeder activation. We conclude that the electronic feeding system has the potential to be used as cognitive enrichment for piglets, being suitable for reducing aggressive behaviour in resident-intruder situations.

Tail biting in pigs--causes and management intervention strategies to reduce the behavioural disorder. A review.

One of the largest animal welfare problems in modern pig production is tail biting. This abnormal behaviour compromises the well-being of the animals, can seriously impair animal health and can cause considerable economic losses. Tail biting has a multifactorial origin and occurs mainly in fattening pigs. High stocking densities, poor environment and bad air quality are seen as important factors. However, it is presumed that a plurality of internal and external motivators in intensive pig production can trigger this behaviour which is not reported in sounders of wild boars. The aim of this review is to summarize the causes and the effects of tail biting in pigs and present management strategies that are likely to reduce its incidence. In particular, management strategies by applying Precision Livestock Farming (PLF) technologies to monitor and control the behaviour of the pigs may be suitable to detect the outbreaks of tail biting at an early stage so that counter measures can be taken in time.

How do pigs behave before starting an aggressive interaction? Identification of typical body positions in the early stage of aggression using video labelling techniques.

The aim of this study was to identify, quantify, and describe pre-signs of aggression in pigs and the early stages of aggressive interactions. The experiment was carried out at a commercial farm on a group of 11 male pigs weighing on average 23 kg and kept in a pen of4 m x 2.5 m. In total 8 hours were videorecorded during the first 3 days after mixing. As a result, 177 aggressive interactions were identified and labelled to find pre-sign body positions before aggressive interactions, attack positions and aggressive acts performed from these positions. A total of 12 positions were classified as pre-signs (P1-P12) and 7 of them were identified immediately at the start of aggressive interactions (P6-P12). Most common pre-sign positions were P3-pigs approaching and facing each other (24%) and P2-initiator pigs approaching from the lateral side (18%). In 80% of the cases the duration of pre-signs was 1-2 sec 72% of all aggressive interactions were short (1 to 10 sec). The most frequent attack positions were P12-inverse parallel (39.5%), P7-nose to nose, 90 degrees (19.77%) and P9-nose to head (13.5%). The most frequent aggressive acts from attack positions were head knocking (34.4%), pressing (34.4%) and biting of different body parts (29.4%). Head knocking was mostly observed in relation to P7 and P2 positions and biting was common in the P7 position. In conclusion, pigs adopt specific pre-signs and body positions before the escalation of aggressive interactions. This could be used as potential sign to identify a beginning aggression.

The xylosyltransferase I gene polymorphism c.343G>T (p.A125S) is a risk factor for diabetic nephropathy in type 1 diabetes.

OBJECTIVE: Xylosyltransferase I (XT-I) is the chain-initiating enzyme in the biosynthesis of proteoglycans in basement membranes. It catalyzes the transfer of xylose to selected serine residues in the core protein. The XYLT-II gene codes for a protein highly homologous to XT-I. Proteoglycans are important components of basement membranes and are responsible for their permeability properties. Type 1 diabetic patients have an altered proteoglycan metabolism, which results in microvascular complications. Thus, genetic variations in the xylosyltransferase genes might be implicated in the initiation and progression of these complications. RESEARCH DESIGN AND METHODS: Genotyping of four genetic variations in the genes XYLT-I and XYLT-II was performed in 912 type 1 diabetic patients (453 with and 459 without diabetic nephropathy) using restriction fragment-length polymorphism. RESULTS: The distribution of the c.343G>T polymorphism in XYLT-I is significantly different between patients with and without diabetic nephropathy (P = 0.03). T-alleles were more frequent in patients with diabetic nephropathy (odds ratio 2.47 [95% CI 1.04-5.83]). The allelic frequencies of the other investigated XYLT-I and XYLT-II variations (XYLT-I: c.1989T>C in exon 9; XYLT-II: IVS6-9T>C and IVS6-14_IVS6-13insG in intron 5; and c.2402C>G: p.T801R in exon 11) were not different between patients with and without diabetic nephropathy. CONCLUSIONS: The XYLT-I c.343G>T polymorphism contributes to the genetic susceptibility to development of diabetic nephropathy in type 1 diabetic patients.

Cloning and recombinant expression of active full-length xylosyltransferase I (XT-I) and characterization of subcellular localization of XT-I and XT-II.

Xylosyltransferase I (XT-I) catalyzes the transfer of xylose from UDP-xylose to serine residues in proteoglycan core proteins. This is the first and apparently rate-limiting step in the biosynthesis of the tetrasaccharide linkage region in glycosaminoglycan-containing proteoglycans. The XYLT-II gene codes for a highly homologous protein, but its physiological function is not yet known. Here we present for the first time the construction of a vector encoding the full-length GFP-tagged human XT-I and the recombinant expression of the active enzyme in mammalian cells. We expressed XT-I-GFP and various GFP-tagged XT-I and XT-II mutants with C-terminal truncations and deletions in HEK-293 and SaOS-2 cells in order to investigate the intracellular localization of XT-I and XT-II. Immunofluorescence analysis showed a distinct perinuclear pattern of XT-I-GFP and XT-II-GFP similar to that of alpha-mannosidase II, which is a known enzyme of the Golgi cisternae. Furthermore, a co-localization of native human XT-I and alpha-mannosidase II could also be demonstrated in untransfected cells. Using brefeldin A, we could also show that both xylosyltransferases are resident in the early cisternae of the Golgi apparatus. For its complete Golgi retention, XT-I requires the N-terminal 214 amino acids. Unlike XT-I, for XT-II, the first 45 amino acids are sufficient to target and retain the GFP reporter in the Golgi compartment. Here we show evidence that the stem regions were indispensable for Golgi localization of XT-I and XT-II.