Abundance of selected bacterial groups in healthy calves and calves developing diarrhea during the first week of life: Are there differences before the manifestation of clinical symptoms?

Background: Diarrhea is still the most common and economically most significant disease of newborn calves. Objective: Analysis of the development of selected bacterial groups in the feces of neonatal calves and its significance regarding diarrhea. Animals: A total of 150 newborn Simmental calves reared in 13 Bavarian farms were included in the study. Methods: Fecal samples of calves taken at 0/6/12/24/48/72/168 hours (h) since birth were analyzed qualitatively and quantitatively for aerobic and anaerobic bacteria, such as Enterobacteriaceae, E. coli, enterococci, and lactobacilli, using cultural, biochemical, and molecular-biological methods. Concurrently, the health status of the animals was recorded. The bacterial levels of healthy and diarrheic animals were compared using statistical methods. In addition, feces samples from calves that developed diarrhea were examined by ELISA for the presence of rotaviruses, coronaviruses, E. coli F5, and Cryptosporidium (Cr.) parvum. Results: Fifty-seven out of 150 calves (37.3 %) that were examined developed diarrhea within the first week of life. In the feces of calves with diarrhea on day 1 of life, the levels of aerobes, Enterobacteriaceae, and E. coli were significantly increased (p < 0.05), while no significant differences in enterococci and lactobacilli were found. In animals with the onset of diarrhea on day 2 after birth, the load of lactobacilli was significantly reduced up to 24 h before the manifestation of clinical symptoms compared to healthy calves. For enterococci, this was only the case on the day of the onset of diarrhea. In addition, the ratios of aerobic and anaerobic bacteria, Enterobacteriaceae or E. coli to lactobacilli, of calves with diarrhea starting on day 2 after birth are significantly higher than those of healthy calves. The detection frequency of specific pathogens in diarrheic calves increased over the first week of life. Conclusion: The results suggest that the incidence of neonatal diarrhea in calves is favored by low levels of lactobacilli in the feces. From this, the hypothesis can be derived that, in addition to an optimal supply of colostrum, the earliest possible administration of lactobacilli might reduce neonatal diarrhea in calves. However, this must be verified in a subsequent feeding experiment.

An open-source framework for fast-yet-accurate calculation of quantum mechanical features.

We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities, the predictive power of the presented method competes against second-order perturbation theory in a converged atomic-orbital basis set at a fraction of its computational costs. The calculation of isotropic molecular polarizabilities is robust for a data set of more than 80 000 molecules. We present furthermore a generally applicable van der Waals radius model that is rooted on atomic static polarizabilites. Efficiency tests show that such radii can even be calculated for small- to medium-size proteins where the largest system (SARS-CoV-2 spike protein) has 42 539 atoms. Following the work of Domingo-Alemenara et al. [Domingo-Alemenara et al., Nat. Commun., 2019, 10, 5811], we present computational predictions for retention times for different chromatographic methods and describe how physicochemical features improve the predictive power of machine-learning models that otherwise only rely on two-dimensional features like molecular fingerprints. Additionally, we developed an internal benchmark set of experimental super-critical fluid chromatography retention times. For those methods, improvements of up to 10.6% are obtained when combining molecular fingerprints with physicochemical descriptors. Shapley additive explanation values show furthermore that the physical nature of the applied features can be retained within the final machine-learning models. We generally recommend the kallisto framework as a robust, low-cost, and physically motivated featurizer for upcoming state-of-the-art machine-learning studies.