Adiponectin and intracellular 5'adenosine monophosphate-activated protein kinase (AMPK) are important modulators of glucose and fat metabolism. Cinnamon exerts beneficial effects by improving insulin sensitivity and blood lipids, e.g., through increasing adiponectin concentrations and AMPK activation. The underlying mechanism is unknown. The G_i/G_o-protein-coupled receptor (GPR) 109A stimulates adiponectin secretion after binding its ligand niacin. Trans-cinnamic acid (tCA), a compound of cinnamon is another ligand. We hypothesize whether AMPK activation and adiponectin secretion by tCA is transmitted by GPR signaling. Differentiated 3T3-L1 cells were incubated with pertussis toxin (PTX), an inhibitor of G_i/G_o-protein-coupling, and treated with different tCA concentrations. Treatment with tCA increased adiponectin and the pAMPK/AMPK ratio (p ≤ 0.001). PTX incubation abolished the increased pAMPK/AMPK ratio and adiponectin secretion. The latter remained increased compared to controls (p ≤ 0.002). tCA treatment stimulated adiponectin secretion and AMPK activation; the inhibitory effect of PTX suggests GPR is involved in tCA stimulated signaling.

The prevalence of Chlamydophila spp. was determined in a cross-sectional study carried out in 2007 using 100 randomly selected dairy herds in the western part of Germany. Ten dairy cows per herd were sampled in herds with fewer than 100 cows; in bigger herds, 10% of the cows were sampled. For the detection of Chlamydophila spp., vaginal swabs from early lactating dairy cows were analyzed using an established highly sensitive genus-specific real-time PCR. In consideration of the discontinuous shedding of the pathogen, a herd was classified as positive if at least 1 animal per herd tested positive for Chlamydophila spp. By use of these methods and definitions, 61% of the dairy herds and 13.5% of the cows were detected as PCR-positive for Chlamydophila spp., which is indicative for ongoing infections. To compare herd health and herd performance between herds testing positive or negative and to identify risk factors for the presence of Chlamydophila spp., a questionnaire was designed to evaluate farm characteristics and management practices. In addition, the performance recordings of the state dairy recording organization were used for these purposes. Milk yield, number of lactations, and calving to first-service interval were lower in herds testing positive for Chlamydophila spp. compared with negative herds. For all these variables, there was no interaction between Chlamydophila status and lactation number. Replacement of animals from outside sources, use of breeding bulls, lack of separate calving pens, and low scores for cleanliness of beddings, walkways, and cows were identified as the main risk factors for Chlamydophila spp.

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.

In the last decades, there has been an increasing interest in animal protection and welfare issues. Heart rate variability (HRV) measurement with portable heart rate monitors on cows has established itself as a suitable method for assessing physiological states. However, more forward-looking technologies, already successfully applied to evaluate HRV data, are pushing the market. This study examines the validity and usability of collecting HRV data by exchanging the Polar watch V800 as a receiving unit of the data compared to a custom smartphone application on cows. Therefore, both receivers tap one signal sent by the Polar H7 transmitter simultaneously. Furthermore, there is a lack of suitable methods for the preparation and calculation of HRV parameters, especially for livestock. A method is presented for calculating more robust time domain HRV parameters via median formation. The comparisons of the respective simultaneous recordings were conducted after artifact correction for time domain HRV parameters. High correlations (r = 0.82–0.98) for cows as well as for control data set in human being (r = 0.98–0.99) were found. The utilization of smart devices and the robust method to determine time domain HRV parameters may be suitable to generate valid HRV data on cows in field-based settings.

A route towards phenylene-bithiophene macrocyclesvia oxidative thiophene coupling under pseudo-high dilution conditions is reported. This method is applied to the synthesis of a shape-persistent thiophene macrocycle with extraannularly attached perylenebisimide moieties that forms supramolecular aggregates at the solid/liquid interface.

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.