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Due to its increasing pressure on dairy cows, studies that investigate how to cope with heat stress are needed. The heat stress affects multiple aspects of cows’ lives, among which their behavior and welfare. In this study, a survey was carried out in eight farms located in Northern Italy to monitor and evaluate the environmental aspects of the barns and the behavioral responses of dairy cows. For one year, three periods were monitored: thermoneutral (T_S), hot (H_S) and cold (C_S) seasons. Temperature and relative humidity were measured by environmental sensors, and lying vs. standing time, number of lying bouts and their average duration were collected by accelerometers. The temperature-humidity index (THI) was quantified inside and outside of the barn. Results show that at the increase of the THI, behavioral adaptations occurred in all the farms, especially with a reduction of lying time and an increase of respiration rate. Four of the eight farms need interventions for improving the cows’ welfare. Here, environmental problems should be solved by introducing or improving the efficacy of the forced ventilation or by modifying the barn structure. Monitoring dairy barns with sensors and Precision Livestock Farming techniques can be helpful for future livestock farming to alert farmers on the need for their interventions to respond immediately to unwanted barn living conditions.

The COVID-19 pandemic has profoundly impacted global health, leading to extensive research focused on developing strategies to enhance outbreak response and mitigate the disease’s severity. In the aftermath of the pandemic, attention has shifted towards understanding and addressing long-term health implications, particularly in individuals experiencing persistent symptoms, known as long COVID. Research into potential interventions to alleviate long COVID symptoms has intensified, with a focus on strategies to support immune function and mitigate inflammation. One area of interest is the gut microbiota, which plays a crucial role in regulating immune responses and maintaining overall health. Prebiotics and probiotics, known for their ability to modulate the gut microbiota, have emerged as potential therapeutic agents in bolstering immune function and reducing inflammation. This review delves into the intricate relationship between long COVID, the gut microbiota, and immune function, with a specific focus on the role of prebiotics and probiotics. We examine the immune response to long COVID, emphasizing the importance of inflammation and immune regulation in the persistence of symptoms. The potential of probiotics in modulating immune responses, including their mechanisms in combating viral infections such as COVID-19, is discussed in detail. Clinical evidence supporting the use of probiotics in managing long COVID symptoms is summarized, highlighting their role as adjunctive therapy in addressing various aspects of SARS-CoV-2 infection and its aftermath.

Devices for assessing the quality of animal environments are important for maintaining production animals, thus improving animal well-being and mitigating pollutant emissions. Therefore, an IoT system was developed and preliminarily assessed across various livestock housing types, including those for pigs, dairy cows, and rabbits. This system measures and transmits key parameters, such as ambient temperature; relative humidity; light intensity; sound pressure; levels of carbon dioxide, ammonia, and hydrogen sulfide; and particulate matter and volatile organic compound concentrations. These data are sent from the sensors to a gateway and then displayed on a dashboard for monitoring. A preliminary evaluation of the system’s performance in controlled conditions revealed that the device’s accuracy and precision were within 2.7% and 3.3% of the measured values, respectively. The system was deployed in three case studies involving rabbit, pig, and dairy cow farms. The results demonstrated its effectiveness in assessing pollutant emissions and identifying critical situations where gas concentrations exceeded threshold levels, thus posing a risk to the animals. By systematically applying this technology on livestock farms to obtain a detailed understanding of the microclimatic and air quality conditions in which the animals live, animal welfare can be significantly improved.

Animal welfare is a fundamental pillar for livestock farming, and it can be endangered by a series of aspects, among which is the presence of undesired microclimates. This condition can be monitored by measuring the temperature-humidity index (THI), an index able to inform about the emergence of heat-stressing conditions in the barns. The THI can be influenced by the external environmental conditions and the barn structure, orientation, thermal buoyancy, and roof insulating materials. In order to evaluate these structural aspects of buildings and the consequent microclimate, in this study, a survey was carried out in 8 dairy cattle barns located in the northern part of Italy that were monitored continuously during thermoneutral, warm, and cold periods. Experts observed the structural aspects ,and the environmental parameters were measured with sensors. From the results emerged that the barns had structural characteristics that considerably affect the internal microclimate, with openings, roof height, forced ventilation, and building orientation playing a significant role in estimating of the THI in the barn. The more critical period was the warm one when the structures could not mitigate the external conditions, and THI exceeded the threshold of 72 for a big share of the period in all monitored farms (range between 50-80% of observations). In the best situation, the cooling systems were able to maintain the external conditions. The results confirm the importance of the barn design and of an appropriate ventilation to improve air exchanges.

Monitoring dairy cattle behavior can improve the detection of health and welfare issues for early interventions. Often commercial sensors do not provide researchers with sufficient raw and open data; therefore, the aim of this study was to develop an open and customizable system to classify cattle behaviors. A 3D accelerometer device and host-board (i.e., sensor node) were embedded in a case and fixed on a dairy cow collar. It was developed to work in two modes: (1) acquisition mode, where a mobile application supported the raw data collection during observations; and (2) operating mode, where data was processed and sent to a gateway and on the cloud. Accelerations were sampled at 25 Hz and behaviors were classified in 10-min windows. Several algorithms were trained with the 108 h of behavioral data acquired from 32 cows on 3 farms, and after evaluating their computational/memory complexity and accuracy, the Decision Tree algorithm was selected. This model detected standing, lying, eating, and ruminating with an average accuracy of 85.12%. The open nature of this system enables for the addition of other functions (e.g., real-time localization of cows) and the integration with other information sources, e.g., microenvironment and air quality sensors, thereby enhancing data processing potential.

Smart technologies such as sensors, data analytics, and automated systems could play a crucial role in enhancing the resilience of livestock housing to climate change. By providing precise and timely data, these technologies enable better resource management, early detection of stressors, and adaptive responses to environmental changes. However, a comprehensive and critical evaluation of their potential role and current applications in improving the sustainability and adaptability of livestock systems to climate change is still lacking. This research provides a first investigation of the role of smart technologies in addressing climate change challenges within the livestock sector and identifies critical areas for future research and development. A systematic and critical review of 131 papers published between 2018 and 2024, focusing on the application of smart technologies to enhance the resilience of livestock housing to climate change, was carried out. The articles were categorised based on the main livestock farming systems (dairy cows, pigs, poultry, small-ruminants, and pasture-based systems) and the type of smart technologies used, including wearable and non-wearable devices for monitoring animal health and behaviour, as well as systems for controlling environmental conditions in livestock housing. The results revealed that while smart technologies are predominantly used to monitor animal behaviour and health, their application in controlling environmental conditions is still limited. Wearable devices are extensively used in dairy cows and small ruminants, whereas non-wearable devices are more common in pigs and poultry. Future research should focus on advancing sensor technology, integrating data-driven management, and enhancing communication between sensors and farmers.

Currently, lying behavior can be assessed using continuous observations from sensors (e.g., accelerometers). The analysis of digital data deriving from accelerometers is an effective tool for studying livestock behaviors. Despite the large interest in the lying behavior of dairy cows, no reference was found in literature regarding the prediction of lying behavior as a function of the interaction of environmental parameters. The present study aimed to evaluate the influence of climatic conditions (temperature-humidity index, solar radiation, air velocity and rainfalls) on the lying behavior of a group of primiparous dairy cows, using data from accelerometers, and develop a prediction model to identify and predict the lying behavior of dairy cows as a function of the effects of environmental conditions. Results from the. GLM Procedure (SAS) showed that the model was highly significant (p < 0.001) and the r2 was 0.84. All of the effects in the model resulted in being highly significant (p < 0.001). This model, if validated properly, could be a valid early warning system to identify any deviation from the expected behavior, and to assess the effectiveness of thermal stress mitigation strategies.

Dairy cow behavior is affected by external and endogenous factors, including time of year, barn microclimate, time of day and housing. However, little is known about the combined effects of these factors. Data were collected on eight farms in Northern Italy during summer, winter and a temperate season. The temperature-humidity index (THI) was recorded using environmental sensors, whereas cow behavior was monitored using leg accelerometers and cameras. Period, time of day and their interaction all significantly affected lying, standing and feeding behavior. However, although THI had a significant negative effect on lying and a positive effect on standing during daytime (all p < 0.001), during nighttime, it only had a significant negative effect on lying duration and mean lying bout duration (p < 0.001 for both). There was also significant variation between farms in all behavioral parameters, as well as interactions with period and time of day. For instance, farm differences in lying duration were more pronounced during daytime than during nighttime. These findings show how housing can interact with other factors, such as period of the year and time of day, and illustrate the influence of barn structure and farm management on cow behavior and, consequently, their welfare.

Management systems in modern dairy farms is an important issue in relation to animal comfort and welfare. The objective of this study was to determine the effect of feed delivery frequency on the behavior patterns, visits to an automatic milking system (AMS) and on milk production of lactating dairy cows. The study was conducted on a commercial dairy farm with automatic feeding and milking systems. Feeding treatments consisted of two different frequencies, high feed delivery frequency (11 deliveries per day) and low feed delivery frequency (six deliveries per day). Lying behavior of 20 dairy cows was electronically monitored. The results obtained showed that 11 deliveries per day feed delivery frequency decreases the number of long-duration lying bouts, which may indicate that a very high feeding frequency disturbs the cows during their resting periods and thus influences both animal comfort and milk production. High feeding frequency may disturb the duration of lying bouts and alter the pattern of lying behavior throughout the day, affecting mainly the lying time during the 60 min before and following the provision of fresh feed. Delivering feed at a low frequency allow cows to distribute more evenly their lying time over the course of the day and improve their utilization of an AMS.

Background Acute pericarditis may occur frequently after viral infections. To our knowledge, influenza B virus infection complicated by pericarditis without myocardial involvement has never been reported. We report the first case of life-threatening pericarditis caused by influenza B virus infection. Case presentation A 48-years-old woman with trisomy 21 and ostium primum atrial septal defect was transferred from Cardiology to our Internal Medicine Department for severe pericardial effusion unresponsive to ibuprofen and colchicine. Based on the recent patient history of flu-like syndrome, and presence of pleuro-pericardial effusion, a viral etiology was suspected. Laboratory evaluation and molecular assay of tracheal aspirate identified influenza B virus. Therefore, the ongoing metilprednisolone and colchicine therapy was implemented with oseltamivir with progressive patient improvement and no evidence of pericardial effusion recurrence during follow-up. Conclusions Especially in autumn and winter periods, clinicians should include Influenza B virus infection on differential diagnosis of pericarditis with large pericardial effusion.