Subacute ruminal acidosis (SARA) is usually characterized by abnormal and intermittent drops in rumen pH. Nevertheless, high individual animal variability in rumen pH and the difference in measurement methods for pH data acquisition decrease the sensitivity and accuracy of pH indicators for detecting SARA in ruminants. The aim of this study was to refine rumen pH indicators in long-term SARA based on individual dairy cow reticulo-rumen pH kinetics. Animal performances and rumen parameters were studied weekly in order to validate SARA syndrome and rumen pH was continuously measured using reticulo-rumen sensors. In total, 11 primiparous dairy cows were consecutively fed two different diets for 12 successive weeks: a control diet as low-starch diet (LSD; 13% starch for 4 weeks in period 1), an acidotic diet as high-starch diet (HSD; 32% starch for 4 weeks in period 2), and again the LSD diet (3 weeks in period 3). There was a 1-week dietary transition between LSD and HSD. Commonly used absolute SARA pH indicators such as daily average, area under the curve (AUC) and time spent below pH<5.8 and pH<6 were processed from absolute (raw) daily kinetics. Then signal processing was applied to raw pH values in order to calculate relative pH indicators by filtering and normalizing data to remove inter-individual variability, sensor drift and sensor noise. Normalized AUC, times spent below NpH<-0.3 and NpH<-0.5, NpH range and NpH standard deviation were calculated. Those relative pH indicators were compared with commonly used pH indicators to assess their ability to detect SARA. This syndrome induced by HSD was confirmed by consistent expected changes in milk quality, dry matter intake and acetate : propionate ratio in the rumen, whereas the ruminal concentration of lipopolysaccharide was increased. Commonly used pH SARA indicators were not able to discriminate SARA syndrome due to high animal variability and sensor drift and noise, whereas relative pH indicators developed in this study appeared more relevant for SARA detection as assessed by receiver operating characteristic tests. This work shows that absolute pH kinetics should be corrected for drift, noise and animal variability to produce relative pH indicators that are more robust for SARA detection. These relative pH indicators could be more relevant for identifying affected animals in a herd and also for comparing SARA risk among studies.

High-starch diets (HSDs) fed to high-producing ruminants are often responsible for rumen dysfunction and could impair animal health and production. Feeding HSDs are often characterized by transient rumen pH depression, accurate monitoring of which requires costly or invasive methods. Numerous clinical signs can be followed to monitor such diet changes but no specific indicator is able to make a statement at animal level on-farm. The aim of this pilot study was to assess a combination of non-invasive indicators in dairy cows able to monitor a HSD in experimental conditions. A longitudinal study was conducted in 11 primiparous dairy cows fed with two different diets during three successive periods: a 4-week control period (P1) with a lowstarch diet (LSD; 13% starch), a 4-week period with an HSD (P2, 35% starch) and a 3-week recovery period (P3) again with the LSD. Animal behaviour was monitored throughout the experiment, and faeces, urine, saliva, milk and blood were sampled simultaneously in each animal at least once a week for analysis. A total of 136 variables were screened by successive statistical approaches including: partial least squares-discriminant analysis, multivariate analysis and mixed-effect models. Finally, 16 indicators were selected as the most representative of a HSD challenge. A generalized linear mixed model analysis was applied to highlight parsimonious combinations of indicators able to identify animals under our experimental conditions. Eighteen models were established and the combination of milk urea nitrogen, blood bicarbonate and feed intake was the best to detect the different periods of the challenge with both 100% of specificity and sensitivity. Other indicators such as the number of drinking acts, fat:protein ratio in milk, urine, and faecal pH, were the most frequently used in the proposed models. Finally, the established models highlight the necessity for animals to have more than 1 week of recovery diet to return to their initial control state after a HSD challenge. This pilot study demonstrates the interest of using combinations of non-invasive indicators to monitor feed changes from a LSD to a HSD to dairy cows in order to improve prevention of rumen dysfunction on-farm. However, the adjustment and robustness of the proposed combinations of indicators need to be challenged using a greater number of animals as well as different acidogenic conditions before being applied on-farm. ImplicationsHigh-starch diets can cause digestive disorders that negatively impact dairy cow welfare and result in large economic loss to farmers. The decrease in rumen pH is mainly responsible for the subsequent digestive and metabolic disorders, but no specific clinical sign reflects animal's health status. This pilot study proposes combinations of non-rumen indicators in several models able to detect animals affected by nutritional highstarch diet challenge in experimental conditions. Hence, we built the proof of concept that simultaneous analyses of non-rumen indicators could be used for on-farm detect...

Breed-specific growth curves (GCs) are needed for neonatal puppies, but breed-specific data may be insufficient. We investigated an unsupervised clustering methodology for modeling GCs by augmenting breed-specific data with data from breeds having similar growth profiles. Puppy breeds were grouped by median growth profiles (bodyweights between birth and Day 20) using hierarchical clustering on principal components. Median bodyweights for breeds in a cluster were centered to that cluster’s median and used to model cluster GCs by Generalized Additive Models for Location, Shape and Scale. These were centered back to breed growth profiles to produce cluster-scale breed GCs. The accuracy of breed-scale GCs modeled with breed-specific data only and cluster-scale breed GCs were compared when modeled from diminishing sample sizes. A complete dataset of Labrador Retriever bodyweights (birth to Day 20) was split into training (410 puppies) and test (460 puppies) datasets. Cluster-scale breed and breed-scale GCs were modelled from defined sample sizes from the training dataset. Quality criteria were the percentages of observed data in the test dataset outside the target growth centiles of simulations. Accuracy of cluster-scale breed GCs remained consistently high down to sampling sizes of three. They slightly overestimated breed variability, but centile curves were smooth and consistent with breed-scale GCs modeled from the complete Labrador Retriever dataset. At sampling sizes ≤ 20, the quality of breed-scale GCs reduced notably. In conclusion, GCs for neonatal puppies generated using a breed-cluster hybrid methodology can be more satisfactory than GCs at purely the breed level when sample sizes are small.

Bioequivalence testing is an essential step during the development of generic drugs. Regulatory agencies have drafted recommendations and guidelines to frame this step but without finding any consensus. Different methodologies are applied depending on the geographical region. For instance, in the EU, EMA recommends using average bioequivalence test (ABE), while in the USA, FDA recommends using population bioequivalence (PBE) test. Both methods present some limitations (e.g., when batch variability is non-negligible) making it difficult to conclude to equivalence without subsequently increasing the sample size. This article proposes an alternative method to evaluate bioequivalence: between-batch bioequivalence (BBE). It is based on the comparison between the mean difference (Reference − Test) and the Reference between-batch variability. After presenting the theoretical concepts, BBE relevance is evaluated through simulation and real case (nasal spray) studies. Simulation results showed high performance of the method based on false positive and false negative rate estimations (type I and type II errors respectively). Especially, BBE has shown significantly greater true positive rates than ABE and PBE when the Reference residual standard deviation is higher than 15%, depending on the between-batch variability and the number of batches. Finally, real case applications revealed that BBE is more efficient than ABE and PBE to demonstrate equivalence, in some well-known situations where the between-batch variability is not negligible. These results suggest that BBE could be considered as an alternative to the state-of-the-art methods allowing costless development.