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214 results on '"Aernouts, Ben"'

15. Inflammatory Signaling via PEIZO1 Engages and Enhances the LPS-Mediated Apoptosis during Clinical Mastitis

Author

Wang, Jingyi, Li, Ming, Wu, Wenda, Zhang, HuiJing, Yang, Yue, Usman, Muhammad, Aernouts, Ben, Loor, Juan J., and Xu, Chuang

Abstract

Bovine clinical mastitis is characterized by inflammation and immune responses, with apoptosis of mammary epithelial cells as a cellular reaction to infection. PIEZO1, identified as a mechanotransduction effector channel in nonruminant animals and sensitive to both mechanical stimuli or inflammatory signals like lipopolysaccharide (LPS). However, its role in inflammatory processes in cattle has not been well-documented. The aim of this study was to elucidate the in situ expression of PIEZO1 in bovine mammary gland and its potential involvement in clinical mastitis. We observed widespread distribution and upregulation of PIEZO1 in mammary epithelial cells in clinical mastitis cows and LPS-induced mouse models, indicating a conserved role across species. In vitro studies using mammary epithelial cells (MAC-T) revealed that LPS upregulates PIEZO1. Notably, the effects of PIEZO1 artificial activator Yoda1 increased apoptosis and NLRP3 expression, effects mitigated by PIEZO1 silencing or NLRP3 inhibition. In conclusion, the activation of the PIEZO1-NLRP3 pathway induces abnormal apoptosis in mammary epithelial cells, potentially serving as a regulatory mechanism to combat inflammatory responses to abnormal stimuli.

Published
2024
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31. Joint Models to Predict Dairy Cow Survival from Sensor Data Recorded during the First Lactation

Author

Ranzato, Giovanna, Adriaens, Ines, Lora, Isabella, Aernouts, Ben, Statham, Jonathan, Azzolina, Danila, Meuwissen, Dyan, Prosepe, Ilaria, Zidi, Ali, Cozzi, Giulio, Ranzato, Giovanna, Adriaens, Ines, Lora, Isabella, Aernouts, Ben, Statham, Jonathan, Azzolina, Danila, Meuwissen, Dyan, Prosepe, Ilaria, Zidi, Ali, and Cozzi, Giulio

Abstract

Early predictions of cows’ probability of survival to different lactations would help farmers in making successful management and breeding decisions. For this purpose, this research explored the adoption of joint models for longitudinal and survival data in the dairy field. An algorithm jointly modelled daily first-lactation sensor data (milk yield, body weight, rumination time) and survival data (i.e., time to culling) from 6 Holstein dairy farms. The algorithm was set to predict survival to the beginning of the second and third lactations (i.e., second and third calving) from sensor observations of the first 60, 150, and 240 days in milk of cows’ first lactation. Using 3-time-repeated 3-fold cross-validation, the performance was evaluated in terms of Area Under the Curve and expected error of prediction. Across the different scenarios and farms, the former varied between 45% and 76%, while the latter was between 3.5% and 26%. Significant results were obtained in terms of expected error of prediction, meaning that the method provided survival probabilities in line with the observed events in the datasets (i.e., culling). Furthermore, the performances were stable among farms. These features may justify further research on the use of joint models to predict the survival of dairy cattle.

Published
2022

41. Associations between Udder Health and Management on Robotic Dairy Farms

Author

Van Den Brulle, Igor, Piepers, Sofie, Aernouts, Ben, Adriaens, Ines, Geerinckx, Katleen, and De Vliegher, Sarne

Abstract

ispartof: Proceedings of the 59th Annual Meeting National Mastitis Council (NMC) ispartof: 59th Annual Meeting National Mastitis Council (NMC) location:Orlanda, Florida date:28 Jan - 31 Jan 2020 status: Published online

Published
2020

42. Comparison of milk analysis performance between NIR laboratory analyser and miniaturised NIR MEMS sensors

Author

Uusitalo, Sanna, Aernouts, Ben, Sumen, Juha, Hietala, Eero, Utriainen, Mikko, Frondelius, Lilli, Kajava, Sari, and Pastell, Matti

Abstract

The dairy farms have a need for on-site milk analysis to determine the fat and protein content of the milk in real-time manner during milking. Near infrared (NIR) spectroscopy has shown promise as an on-farm tool for fat and protein content determination of raw milk. However, for a successful implementation, on-site analysis requires affordable and small NIR sensors for the milk analysis. This study demonstrates the potential of using Micro-Electro-Mechanical-System (MEMS) based NIR sensors for on-farm and at-line measuring the milk fat and protein content and compares the results to golden standard analysis and commercial cooled Photo Diode Array (PDA) NIR sensor Editors: J. Kucera, P. Bucek, D. Lipovsky, X. Bourrigan and M. Burke ispartof: pages:111-115 ispartof: ICAR Technical Series no. 24: New traits and adding new value to the recording and ID services in the animal production pages:111-115 ispartof: ICAR Conference location:Prague, Czech Republic date:17 Jun - 21 Jun 2019 status: Published online

Published
2019

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