Temperature-humidity index monitoring during two summer seasons in dairy cow sheds in Mugello (Tuscany).

Many studies have reported that the impact of high temperatures affects physiology, welfare, health, and productivity of farm animals, and among these, the dairy cattle farming is one of the livestock sectors that suffers the greatest effects. The temperature-humidity index (THI) represents the state of the art in the evaluation of heat stress conditions in dairy cattle but often its measurement is not carried out in sheds. For this reason, the aim of this study was the monitoring of the THI in three dairy cattle farms in Mugello (Tuscany) to understand its influence on dairy cows. THI values were calculated using meteorological data from direct observation in sheds and outdoor environments. Data relating to the animal's behavior were collected using radio collars. The Pearson test and Mann-Kendall test were used for statistical analysis. The results highlighted a significant (P < 0.001) upward trend in THImax during the last 30 years both in Low Mugello (+ 1.1 every 10 years) and in High Mugello (+ 0.9 every 10 years). In Low Mugello sheds, during the period 2020-2022, more than 70% of daytime hours during the summer period were characterized by heat risk conditions (THI > 72) for livestock. On average the animals showed a significant (P < 0.001) decrease in time spent to feeding and rumination, both during the day and the night, with a significant (P < 0.001) increase in inactivity. This study fits into the growing demand for knowledge of the micro-climatic conditions within farms in order to support resilience actions for protecting both animal welfare and farm productivity from the effects of climate change. This could also be carried out thanks to estimation models which, based on the meteorological conditions forecast, could implement the thermal stress indicator (THI) directly from the high-resolution meteorological model, allowing to get a prediction of the farm's potential productivity loss based on the expected THI.

Bioeffects of Inhaled Nanoplastics on Neurons and Alteration of Animal Behaviors through Deposition in the Brain.

The potential toxicity of nanoplastics on plants has previously been illustrated, but whether nanoplastics could cause neurotoxicity, especially to higher animals, remains unclear. We now demonstrate that nanoplastics can be deposited in the brain via nasal inhalation, triggering neuron toxicity and altering the animal behavior. Polystyrene nanoparticles (PS-NPs) of PS-COOH and PS-NH2 are used as models for nanoplastics. We designed a microfluidic chip to evaluate the PS-NPs with different concentrations, surface ligands, and sizes to interact with neurons. Smaller PS-NPs can induce more cellular uptake than larger PS-NPs. PS-NPs with a size of 80 nm can reach and deposit in the brain of mice via aerosol inhalation. Mice inhaling PS-NPs exhibit fewer activities in comparison to those inhaling water droplets. An obvious neurotoxicity of the nanoplastics could be observed from the results of the inhibition of AChE activities. Our results show the potential significance of the physiochemical properties of organic nanoplastics on depositing in mammalian brains by nasal inhalation.

A Machine Vision-Based Method for Monitoring Broiler Chicken Floor Distribution.

The proper spatial distribution of chickens is an indication of a healthy flock. Routine inspections of broiler chicken floor distribution are done manually in commercial grow-out houses every day, which is labor intensive and time consuming. This task requires an efficient and automatic system that can monitor the chicken's floor distributions. In the current study, a machine vision-based method was developed and tested in an experimental broiler house. For the new method to recognize bird distribution in the images, the pen floor was virtually defined/divided into drinking, feeding, and rest/exercise zones. As broiler chickens grew, the images collected each day were analyzed separately to avoid biases caused by changes of body weight/size over time. About 7000 chicken areas/profiles were extracted from images collected from 18 to 35 days of age to build a BP neural network model for floor distribution analysis, and another 200 images were used to validate the model. The results showed that the identification accuracies of bird distribution in the drinking and feeding zones were 0.9419 and 0.9544, respectively. The correlation coefficient (R), mean square error (MSE), and mean absolute error (MAE) of the BP model were 0.996, 0.038, and 0.178, respectively, in our analysis of broiler distribution. Missed detections were mainly caused by interference with the equipment (e.g., the feeder hanging chain and water line); studies are ongoing to address these issues. This study provides the basis for devising a real-time evaluation tool to detect broiler chicken floor distribution and behavior in commercial facilities.

Prediction of total digestible nutrient and crude protein requirements according to daily weight gain, and behavioral measurements of Hanwoo heifers.

OBJECTIVE: This study was conducted to investigate the effects of energy and protein levels in the diet of Hanwoo heifers on growth response and animal behavior. METHODS: Forty heifers were randomly allocated into three experimental groups according to the target daily weight gain in 8 pens (T-0.2, 2 replications; T-0.4 and -0.6, 3 replications) based on similar body weight (BW) and age in months. The target average daily gain (ADG) was set at 0.2 (T-0.2), 0.4 (T-0.4), and 0.6 kg/d (T-0.6), and feed was based on National Institute of Animal Science (NIAS, 2017). In order to minimize hunger stress of T-0.2 and -0.4, the feeding ratio of rice straw was set to 55%, 50%, and 45% for T-0.2, -0.4 and T-0.6, respectively, so that the dry matter (DM) intake for all treatment groups was uniform but the energy and protein levels in the diet were adjusted differently. A total of 6 items (lying, standing, eating, rumination, walking and drinking) of animal behavior were analyzed. RESULTS: During the whole period of the experiment, the ADG of the T-0.2, -0.4 and -0.6 treatments were 0.48, 0.56, and 0.65 kg/d (p<0.05), respectively, showing higher gain than the predicted value, especially for the low target ADG group. Based on these results, regression equations for the total digestible nutrient (TDN) and crude protein (CP) requirements were derived. No behavioral differences were found according to the energy and protein levels in the diet because the DM intake was kept constant by adjusting the roughage and concentration ratio. However, eating time was longer (p<0.05) at T-0.2 than T-0.6 during the whole day. CONCLUSION: Through this study, it was possible to derive regression equations for predicting TDN and CP requirements according to the target ADG and BW.

Tracking floor eggs with machine vision in cage-free hen houses.

Some of the major restaurants and grocery chains in the United States have pledged to buy cage-free (CF) eggs only by 2025 or 2030. While CF house allows hens to perform more natural behaviors (e.g., dust bathing, perching, and foraging on the litter floor), a particular challenge is floor eggs (i.e., mislaid eggs on litter floor). Floor eggs have high chances of contamination. The manual collection of eggs is laborious and time-consuming. Therefore, precision poultry farming technology is necessary to detect floor eggs. In this study, 3 new deep learning models, that is, YOLOv5s-egg, YOLOv5x-egg, and YOLOv7-egg networks, were developed, trained, and compared in tracking floor eggs in 4 research cage-free laying hen facilities. Models were verified to detect eggs by using images collected in 2 different commercial houses. Results indicate that the YOLOv5s-egg model detected floor eggs with a precision of 87.9%, recall of 86.8%, and mean average precision (mAP) of 90.9%; the YOLOv5x-egg model detected the floor eggs with a precision of 90%, recall of 87.9%, and mAP of 92.1%; and the YOLOv7-egg model detected the eggs with a precision of 89.5%, recall of 85.4%, and mAP of 88%. All models performed with over 85% detection precision; however, model performance is affected by the stocking density, varying light intensity, and images occluded by equipment like drinking lines, perches, and feeders. The YOLOv5x-egg model detected floor eggs with higher accuracy, precision, mAP, and recall than YOLOv5s-egg and YOLOv7-egg. This study provides a reference for cage-free producers that floor eggs can be monitored automatically. Future studies are guaranteed to test the system in commercial houses.

Effects of the domestic thyroid stimulating hormone receptor (TSHR) variant on the hypothalamic-pituitary-thyroid axis and behavior in chicken.

Domestic chickens are less fearful, have a faster sexual development, grow bigger, and lay more eggs than their primary ancestor, the red junglefowl. Several candidate genetic variants selected during domestication have been identified, but only a few studies have directly linked them with distinct phenotypic traits. Notably, a variant of the thyroid stimulating hormone receptor (TSHR) gene has been under strong positive selection over the past millennium, but it's function and mechanisms of action are still largely unresolved. We therefore assessed the abundance of the domestic TSHR variant and possible genomic selection signatures in an extensive data set comprising multiple commercial and village chicken populations as well as wild-living extant members of the genus Gallus. Furthermore, by mean of extensive backcrossing we introgressed the wild-type TSHR variant from red junglefowl into domestic White Leghorn chickens and investigated gene expression, hormone levels, cold adaptation, and behavior in chickens possessing either the wild-type or domestic TSHR variant. While the domestic TSHR was the most common variant in all studied domestic populations and in one of two red junglefowl population, it was not detected in the other Gallus species. Functionally, the individuals with the domestic TSHR variant had a lower expression of the TSHR in the hypothalamus and marginally higher in the thyroid gland than wild-type TSHR individuals. Expression of TSHB and DIO2, two regulators of sexual maturity and reproduction in birds, was higher in the pituitary gland of the domestic-variant chickens. Furthermore, the domestic variant was associated with higher activity in the open field test. Our findings confirm that the spread of the domestic TSHR variant is limited to domesticated chickens, and to a lesser extent, their wild counterpart, the red junglefowl. Furthermore, we showed that effects of genetic variability in TSHR mirror key differences in gene expression and behavior previously described between the red junglefowl and domestic chicken.

A Machine Vision-Based Method for Monitoring Scene-Interactive Behaviors of Dairy Calf.

Requirements for animal and dairy products are increasing gradually in emerging economic bodies. However, it is critical and challenging to maintain the health and welfare of the increasing population of dairy cattle, especially the dairy calf (up to 20% mortality in China). Animal behaviors reflect considerable information and are used to estimate animal health and welfare. In recent years, machine vision-based methods have been applied to monitor animal behaviors worldwide. Collected image or video information containing animal behaviors can be analyzed with computer languages to estimate animal welfare or health indicators. In this proposed study, a new deep learning method (i.e., an integration of background-subtraction and inter-frame difference) was developed for automatically recognizing dairy calf scene-interactive behaviors (e.g., entering or leaving the resting area, and stationary and turning behaviors in the inlet and outlet area of the resting area) based on computer vision-based technology. Results show that the recognition success rates for the calf's science-interactive behaviors of pen entering, pen leaving, staying (standing or laying static behavior), and turning were 94.38%, 92.86%, 96.85%, and 93.51%, respectively. The recognition success rates for feeding and drinking were 79.69% and 81.73%, respectively. This newly developed method provides a basis for inventing evaluation tools to monitor calves' health and welfare on dairy farms.

The Tyrosine Phosphatase STEP Is Involved in Age-Related Memory Decline.

Cognitive disabilities that occur with age represent a growing and expensive health problem. Age-associated memory deficits are observed across many species, but the underlying molecular mechanisms remain to be fully identified. Here, we report elevations in the levels and activity of the striatal-enriched phosphatase (STEP) in the hippocampus of aged memory-impaired mice and rats, in aged rhesus monkeys, and in people diagnosed with amnestic mild cognitive impairment (aMCI). The accumulation of STEP with aging is related to dysfunction of the ubiquitin-proteasome system that normally leads to the degradation of STEP. Higher level of active STEP is linked to enhanced dephosphorylation of its substrates GluN2B and ERK1/2, CREB inactivation, and a decrease in total levels of GluN2B and brain-derived neurotrophic factor (BDNF). These molecular events are reversed in aged STEP knockout and heterozygous mice, which perform similarly to young control mice in the Morris water maze (MWM) and Y-maze tasks. In addition, administration of the STEP inhibitor TC-2153 to old rats significantly improved performance in a delayed alternation T-maze memory task. In contrast, viral-mediated STEP overexpression in the hippocampus is sufficient to induce memory impairment in the MWM and Y-maze tests, and these cognitive deficits are reversed by STEP inhibition. In old LOU/C/Jall rats, a model of healthy aging with preserved memory capacities, levels of STEP and GluN2B are stable, and phosphorylation of GluN2B and ERK1/2 is unaltered. Altogether, these data suggest that elevated levels of STEP that appear with advancing age in several species contribute to the cognitive declines associated with aging.