Lameness detection challenges in automated milking systems addressed with partial least squares discriminant analysis

AbstractLameness causes decreased animal welfare and leads to higher production costs. This study explored data from an automatic milking system (AMS) to model on-farm gait scoring from a commercial farm. A total of 88 cows were gait scored once per week, for 2 5-wk periods. Eighty variables retrieved from AMS were summarized week-wise and used to predict 2 defined classes: nonlame and clinically lame cows. Variables were represented with 2 transformations of the week summarized variables, using 2-wk data blocks before gait scoring, totaling 320 variables (2×2×80). The reference gait scoring error was estimated in the first week of the study and was, on average, 15%. Two partial least squares discriminant analysis models were fitted to parity 1 and parity 2 groups, respectively, to assign the lameness class according to the predicted probability of being lame (score 3 or 4/4) or not lame (score 1/4). Both models achieved sensitivity and specificity values around 80%, both in calibration and cross-validation. At the optimum values in the receiver operating characteristic curve, the false-positive rate was 28% in the parity 1 model, whereas in the parity 2 model it was about half (16%), which makes it more suitable for practical application; the model error rates were, 23 and 19%, respectively. Based on data registered automatically from one AMS farm, we were able to discriminate nonlame and lame cows, where partial least squares discriminant analysis achieved similar performance to the reference method

Experienced and inexperienced observers achieved relatively high within-observer agreement on video mobility scoring of dairy cows

AbstractAssessment of lameness prevalence and severity requires visual evaluation of thelocomotion of a cow. Welfare schemes including locomotion assessments are increasingly being adopted, and more farmers and their veterinarians might implement a locomotion-scoring routine together. However, high within-observer agreement is a prerequisite for obtaining valid mobility scorings, and within-observer agreement cannot be estimated in a barn, because the gait of cows is dynamic and may change between 2 occasions. The objective of this study was to estimate the within-observer agreement according to the observers’ educational background and experience with cattle, based on video recordings with very diverse types of gait. Groups of farmers, bovine veterinarians, first- and fourth-year veterinary students, researchers, and cattle-inexperienced sensory assessors evaluated mobility using a 5-point mobility score system developed specifically for walking cows (n=102 observers). The evaluation sessions were similar for all groups, lasted 75 min, and were organized as follows: introduction, test A, short training session, break, and test B. In total, video recordings of 22 cows were displayed twice in a random order (11 cows in each test × 2 replicates). Data were analyzed applying kappa coefficient, logistic regression, and testing for random effects of observers. The crude estimates of 95% confidence interval for weighted kappa in test A and B ranged, respectively, from 0.76 to 0.80 and 0.70 to 0.75. When adjusting for the fixed effects of video sample and gait scoring preferences, the probability of assigning the same mobility score twice to the same cow varied from 55% (sensory assessors) to 72% (fourth-year veterinary students). The random effect of the individual observers was negligible. That is, in general observers could categorize the mobility characteristics of cows quite well. Observers who preferred to assess the attributes back arch or the overall mobility score (based on uneven gait) had the highest agreement, respectively, 69 or 68%. The training session seemed insufficient to improve agreement. Nonetheless, even novice observers were able to achieve perfect agreement up to 60% of the 22 scorings with merely the experience obtained during the study (introduction and training session). The relatively small differences between groups, together with a high agreement, demonstrate that the new system is easy to follow compared with previously described scoring systems. The mobility score achieves sufficiently high within-observer repeatability to allow between-observer agreement estimates, which are reliable compared with other more-complex scoring systems. Consequently, the new scoring scale seems feasible for on-farm applications as a tool to monitor mobility within and between cows, for communication between farmers and veterinarians with diverse educational background, and for lamenessbenchmarking of herds

Reply to the comment on "Imaging of the Hydrogen Subsurface Site in Rutile TiO2''

A reply to the Comment by M. Calatayud et al. on "Imaging of the Hydrogen Subsurface Site in Rutile TiO2" (Physical Review Letters, Volume 102, Issue 13). DOI: 10.1103/physrevlett.102.136103.Peer reviewe

Imaging of the Hydrogen Subsurface Site in Rutile TiO2

From an interplay between simultaneously recorded noncontact atomic force microscopy and scanning tunneling microscopy images and simulations based on density functional theory, we reveal the location of single hydrogen species in the surface and subsurface layers of rutile TiO2. Subsurface hydrogen atoms (Hsub) are found to reside in a stable interstitial site as subsurface OH groups detectable in scanning tunneling microscopy as a characteristic electronic state but imperceptible to atomic force microscopy. The combined atomic force microscopy, scanning tunneling microscopy, and density functional theory study demonstrates a general scheme to reveal near surface defects and interstitials in poorly conducting materials.Peer reviewe