Zhao XJ, et al. J Vet Sci. 2015 Dec;16(4):439-446. https://doi.org/10.4142/jvs.2015.16.4.439

The mechanistic target of rapamycin complex 1 (mTORC1) integrates various types of signal inputs, such as energy, growth factors, and amino acids to regulate cell growth and proliferation mainly through the 2 direct downstream targets, eukaryotic translation initiation factor 4E-binding protein 1 (4EBP1) and ribosomal protein S6 kinase 1 (S6K1). Most of the signal arms upstream of mTORC1 including energy status, stress signals, and growth factors converge on the tuberous sclerosis complex (TSC) − Ras homologue enriched in brain (Rheb) axis. Amino acids, however, are distinct from other signals and modulate mTORC1 using a unique pathway. In recent years, the transmission mechanism of amino acid signals upstream of mTORC1 has been gradually elucidated, and some sensors or signal transmission pathways for individual amino acids have also been discovered. With the help of these findings, we propose a general picture of recent advances, which demonstrates that various amino acids from lysosomes, cytoplasm, and Golgi are sensed by their respective sensors. These signals converge on mTORC1 and form a huge and complicated signal network with multiple synergies, antagonisms, and feedback mechanisms.

The feeding behaviour of cows is an essential sign of their health in dairy farming. For the impression of cow health status, precise and quick assessment of cow feeding behaviour is critical. This research presents a method for monitoring dairy cow feeding behaviour utilizing edge computing and deep learning algorithms based on the characteristics of dairy cow feeding behaviour. Images of cow feeding behaviour were captured and processed in real time using an edge computing device. A DenseResNet-You Only Look Once (DRN-YOLO) deep learning method was presented to address the difficulties of existing cow feeding behaviour detection algorithms’ low accuracy and sensitivity to the open farm environment. The deep learning and feature extraction enhancement of the model was improved by replacing the CSPDarknet backbone network with the self-designed DRNet backbone network based on the YOLOv4 algorithm using multiple feature scales and the Spatial Pyramid Pooling (SPP) structure to enrich the scale semantic feature interactions, finally achieving the recognition of cow feeding behaviour in the farm feeding environment. The experimental results showed that DRN-YOLO improved the accuracy, recall, and mAP by 1.70%, 1.82%, and 0.97%, respectively, compared to YOLOv4. The research results can effectively solve the problems of low recognition accuracy and insufficient feature extraction in the analysis of dairy cow feeding behaviour by traditional methods in complex breeding environments, and at the same time provide an important reference for the realization of intelligent animal husbandry and precision breeding.

Our aim was to simultaneously investigate the gut bacteria typical characteristic and conduct rumen metabolites profiling of high production dairy cows when compared to low‐production dairy cows. The bacterial differences in rumen fluid and feces were identified by 16S rDNA gene sequencing. The metabolite differences were identified by metabolomics profiling with liquid chromatography mass spectrometry ( LC ‐ MS ). The results indicated that the high‐production dairy cows presented a lower rumen bacterial richness and species evenness when compared to low‐production dairy cows. At the phylum level, the high‐production cows increased the abundance of Proteobacteria and decreased the abundance of Bacteroidetes, SR 1, Verrucomicrobia, Euryarchaeota, Planctomycetes, Synergistetes, and Chloroflexi significantly ( p < 0.05). At the genus level, the rumen fluid of the high‐production group was significantly enriched for Butyrivibrio , Lachnospira , and Dialister ( p < 0.05). Meanwhile, rumen fluid of high‐production group was depleted for Prevotella, Succiniclasticum, Ruminococcu, Coprococcus, YRC 22, CF 231, 02d06, Anaeroplasma, Selenomonas , and Ruminobacter significantly ( p < 0.05). A total of 92 discriminant metabolites were identified between high‐production cows and low‐production cows. Compared to rumen fluid of low‐production dairy cows, 10 differential metabolites were found up‐regulated in rumen fluid of high‐production dairy cows, including 6alpha‐Fluoropregn‐4‐ene‐3,20‐dione, 3‐Octaprenyl‐4‐hydroxybenzoate, disopyramide, compound III (S), 1,2‐Dimyristyl‐sn‐glycerol, 7,10,13,16‐Docosatetraenoic acid, ferrous lactate, 6‐Deoxyerythronolide B, vitamin D2, L‐Olivosyl‐oleandolide. The remaining differential metabolites were found down‐regulated obviously in high‐production cows. Metabolic pathway analyses indicated that most increased abundances of rumen fluid metabolites of high‐yield cows were related to metabolic pathways involving biosynthesis of unsaturated fatty acids, steroid biosynthesis, ubiquinone and other terpenoid‐quinone biosynthesis. Most down‐regulated metabolic pathways were relevant to nucleotide metabolism, energy metabolism, lipid metabolism and biosynthesis of some antibiotics.

Increasing dietary roughage level is a commonly used strategy to prevent subacute ruminal acidosis. We hypothesized that high-roughage diets could promote chewing activity, saliva secretion, and hence more alkaline to buffer rumen pH. To verify the hypothesis, 12 multiparous Holstein cows in mid lactation were randomly allocated to 4 treatments in a triplicated 4 × 4 Latin square experiment with one cow in each treatment surgically fitted with a ruminal cannula. Treatments were diets containing 40, 50, 60, or 70% of roughage on a DM basis. Increasing dietary roughage level decreased DM, CP, OM, starch, and NEL intake, increased ADF intake, and decreased milk yield linearly. Intake of NDF was quite stable across treatments and ranged from 7.8 to 8.1 kg/d per cow. Daily eating time increased linearly with increased roughage level. The increase in eating time was due to increased eating time per meal but not number of meals per day, which was stable and ranged from 8.3 to 8.5 meals per day across treatments. Increasing dietary roughage level had no effect on ruminating time (min/d), the number of ruminating periods (rumination periods per d), and chewing time per ruminating period (min/ruminating period). Ruminating time per kilogram of NDF intake and total chewing time per kilogram of ADF intake were similar across treatments (57.4 and 183.8 min/kg, respectively). Increasing dietary roughage level linearly increased daily total chewing time; linearly elevated the mean, maximum, and minimum ruminal pH; and linearly decreased total VFA concentration and molar proportion of propionate in ruminal fluid. Saliva secretion during eating was increased, the secretion during rumination was unaffected, but the secretion during resting tended to decrease with increased dietary roughage level. As a result, total saliva secretion was not affected by treatments. In conclusion, the results of the present study did not support the concept that high-roughage diets elevated ruminal pH through increased salivary recycling of buffering substrates.