Effects of acute lying and sleep deprivation on metabolic and inflammatory responses of lactating dairy cows

Sleep disturbance in rodent models and its sex-specific implications

Sleep disturbances, encompassing altered sleep physiology or disorders like insomnia and sleep apnea, profoundly impact physiological functions and elevate disease risk. Despite extensive research, the underlying mechanisms and sex-specific differences in sleep disorders remain elusive. While polysomnography serves as a cornerstone for human sleep studies, animal models provide invaluable insights into sleep mechanisms. However, the availability of animal models of sleep disorders is limited, with each model often representing a specific sleep issue or mechanism. Therefore, selecting appropriate animal models for sleep research is critical. Given the significant sex differences in sleep patterns and disorders, incorporating both male and female subjects in studies is essential for uncovering sex-specific mechanisms with clinical relevance. This review provides a comprehensive overview of various rodent models of sleep disturbance, including sleep deprivation, sleep fragmentation, and circadian rhythm dysfunction. We evaluate the advantages and disadvantages of each model and discuss sex differences in sleep and sleep disorders, along with potential mechanisms. We aim to advance our understanding of sleep disorders and facilitate sex-specific interventions.

Cow Behavior Recognition Based on Wearable Nose Rings

This study introduces a novel device designed to monitor dairy cow behavior, with a particular focus on feeding, rumination, and other behaviors. This study investigates the association between the cow behaviors and acceleration data collected using a three-axis, nose-mounted accelerometer, as well as the feasibility of improving the behavioral classification accuracy through machine learning. A total of 11 cows were used. We utilized three-axis acceleration sensors that were fixed to the cow's nose, and these devices provided detailed and unique data corresponding to their activity; in particular, a recorder was installed on each nasal device to obtain acceleration data, which were then used to calculate activity levels and changes. In addition, we visually observed the behavior of the cattle. The characteristic acceleration values during feeding, rumination, and other behavior were recorded; there were significant differences in the activity levels and changes between different behaviors. The results indicated that the nose ring device had the potential to accurately differentiate between eating and rumination behaviors, thus providing an effective method for the early detection of health problems and cattle management. The eating, rumination, and other behaviors of cows were classified with high accuracy using the machine learning technique, which can be used to calculate the activity levels and changes in cattle based on the data obtained from the nose-mounted, three-axis accelerometer.

Comparison of behavioral and physiological changes in postpartum healthy and inflammatory diseased cows

During parturition, cows often experience intense pain and stress, which increases the risk of inflammatory diseases. This study aimed to compare the postpartum health status between healthy cows and those diagnoses with inflammatory diseases by examining behavioral and heart rate (HR) variability (HRV) changes, to provide information before the onset of disease. Eight Holstein cows were used in this study. HR, parameters of HRV (low-frequency power: LF; high-frequency power: HF; LF/HF ratio, and total power) and time budget of individual maintenance behaviors (standing, recumbency, feeding, rumination while standing and lying, and sleep) were continuously recorded from 0 to 168 h postpartum. Milk and blood samples were collected daily. Cows were categorized as diseases based on the positive result of California mastitis test and/or serum haptoglobin concentration that exceeded 50 μg/ml after all blood samples have been collected. Compared to healthy individuals (n = 3), diseased cows (n = 5) exhibited higher HR, LF/HF, and lower total power (p < 0.05), suggesting the dominance of the sympathetic nervous system in cows with inflammatory diseases. Additionally, diseased cows showed an increased standing time budget and reduced recumbency (p < 0.05), which may be a behavioral strategy in response to discomfort from inflammation.

High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.