Search

Respiratory rate monitoring is essential especially for anesthetized animals in veterinary and biomedical research. Current methods often rely on invasive or wearable devices, which can stress animals, especially smaller ones like rodents. Here we present a non-invasive, environmentally integrated device that detects subtle breathing movements through waveform analyzed data via a triaxial accelerometer under a flexible fabric sheet in a trampoline-like box. The accuracy of the system was tested on anesthetized mice under varying isoflurane concentrations (1 to 3%) by comparison with a laser displacement sensor. The accelerometer data closely correlated with that from a laser displacement sensor, particularly under deeper anesthesia, with minimal deviations in respiratory rate detection. This method may provide a promising alternative for animal respiratory monitoring.

Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking specific animals, recent advances in the miniaturization of wearable devices allow for the collection of acceleration and location data to track individual animal behavior. However, for broilers, there are several challenges to address when using wearables, such as coping with (i) the large numbers of chickens in commercial farms,(ii)the impact of their rapid growth, and (iii) the small weights that the devices must have to be carried by the chickens without any impact on their health or behavior. To this end, this paper describes a pilot study in which chickens were fitted with devices containing an Inertial Measurement Unit (IMU) and an Ultra-Wideband (UWB) sensor. To establish guidelines for practitioners who want to monitor broiler welfare and activity at different scales, we first compare the attachment methods of the wearables to the broiler chickens, taking into account their effectiveness (in terms of retention time) and their impact on the broiler's welfare. Then, we establish the technical requirements to carry out such a study, and the challenges that may arise. This analysis involves aspects such as noise estimation, synergy between UWB and IMU, and the measurement of activity levels based on the monitoring of chicken activity. We show that IMU data can be used for detecting activity level differences between individual animals and environmental conditions. UWB data can be used to monitor the positions and movement patterns of up to 200 animals simultaneously with an accuracy of less than 20 cm. We also show that the accuracy depends on installation aspects and that errors are larger at the borders of the monitored area. Attachment with sutures had the longest mean retention of 19.5 days, whereas eyelash glue had the shortest mean retention of 3 days. To conclude the paper, we identify current challenges and future research lines in the field.

Objective: To assess the agreement of transpulmonary thermodilution (TPTD) and transpulmonary ultrasound dilution (TPUD) against direct measurement of main pulmonary artery flow with an ultrasound transit time flow probe (UTF) over a wide range of conditions in anesthetized cats. Additionally, the trending ability of TPTD, TPUD, and esophageal Doppler ultrasonography (EDU) was evaluated against UTF., Methods: 12 purpose-bred cats were used. One cat was used for a pilot study. The cats were instrumented according to the manufacturer's recommendation. Paired measurements were made over a wide range of cardiac output. Agreement was analyzed with Bland-Altman analysis with repeated measures, and trending ability was assessed by 4-quadrant plot and concordance rate. Interchangeable was defined as percentage error less than simplified total error. Acceptable trending was defined as concordance rate > 95%., Results: Bias and percentage error were -0.084 L·min-1 and 38.2% for TPTD and -0.041 L·min-1 and 52.9% for TPUD. The concordance rate was 100%, 95.5%, and 64% to 65% for TPTD, TPUD, and EDU, respectively., Conclusions: Both TPTD and TPUD were not interchangeable to UTF. Both TPTD and TPUD provided acceptable trending ability but not EDU., Clinical Relevance: Transpulmonary thermodilution and TPUD allow detection of the direction of cardiac output changes in anesthetized cats but not EDU. Clinical and research use of these 3 devices warrants careful consideration of the limitations presented in this study.

Objective: To assess the accuracy of a flash glucose monitoring system (FGMS; FreeStyle Libre 2) in healthy dogs during isoflurane general anesthesia., Methods: 6 purpose-bred Beagle dogs underwent 3 anesthetic events, 1 week apart, in a randomized crossover study. 1 of 3 mean arterial blood pressure (MAP) states were induced under isoflurane anesthesia (hypotension [MAP, ≤ 60 mm Hg], normotension [MAP, 80 to 120 mm Hg], and hypertension [MAP, ≥ 120 mm Hg]) for 40 minutes. Interstitial glucose was collected from an FGMS (applied to the dog's neck prior to anesthesia) at 0, 5, 10, 20, 30, and 40 minutes from stabilization within targeted MAP range. Whole-blood samples were collected from a lateral saphenous vein for plasma glucose measurement (blood glucose gold standard; BGGS) and via marginal lip mucosal puncture for point-of-care glucose measurement (blood glucose point-of-care; BGPOC). Shrout-Fleiss intraclass correlation and Bland-Altman analysis were performed. Accuracy was determined using human International Organization for Standardization criteria, including Parkes consensus error grid analysis., Results: Interstitial glucose strongly correlated with BGGS and BGPOC (intraclass correlation, 0.962 and 0.953, respectively). Interstitial glucose measured higher than BGGS (median[IQR] difference 9[-2 to 20] g/dL) and lower than BGPOC (-27[-37 to -11] g/dL). Interstitial glucose BGGS increased as MAP decreased. 62.1% and 63.3% of interstitial glucose measurements were within ± 15 mg/dL when BGGS ≤ 100 mg/dL and within ± 15% of BGGS when > 100 mg/dL, respectively. However, 100% were within zones A and B of error grid analysis., Conclusions: Despite not meeting International Organization for Standardization standards, the FreeStyle Libre 2 demonstrated clinical accuracy for monitoring glucose concentrations in anesthetized dogs., Clinical Relevance: The application of an FGMS before anesthesia may be useful for monitoring at-risk patients perioperatively.

While the pig industry is crucial in global meat consumption, accounting for 34% of total consumption, respiratory diseases in pigs can cause substantial economic losses to pig farms. To alleviate this issue, we propose an advanced audio-visual monitoring system for the early detection of coughing, a key symptom of respiratory diseases in pigs, that will enhance disease management and animal welfare. The proposed system is structured into three key modules: the cough sound detection (CSD) module, which detects coughing sounds using audio data; the pig object detection (POD) module, which identifies individual pigs in video footage; and the coughing pig detection (CPD) module, which pinpoints which pigs are coughing among the detected pigs. These modules, using a multimodal approach, detect coughs from continuous audio streams amidst background noise and accurately pinpoint specific pens or individual pigs as the source. This method enables continuous 24/7 monitoring, leading to efficient action and reduced human labor stress. It achieved a substantial detection accuracy of 0.95 on practical data, validating its feasibility and applicability. The potential to enhance farm management and animal welfare is shown through proposed early disease detection.

The aim of this study was to determine whether horses exhibiting unilateral hindlimb lameness unload (rest) the lame limb more than the contralateral limb. The resting/unloading of the hindlimbs and the time spent lying down were measured using accelerometers. Ten non-lame horses and 20 lame horses were recruited for participation and monitored for 11 h overnight with accelerometers (MSR145, sampling rate: 1 Hz, and measuring range: ±15 g) attached to the lateral metatarsal and metacarpal regions of each limb. Metatarsal and metacarpal orientation were used to determine whether the limb was unloaded (rested) or loaded, respectively, or whether the horses were lying down. The relation of resting time between non-lame and lame limbs (non-lame/lame: 0.85 ± 1.2) of the lame horses differed significantly ( p = 0.035) from that of the non-lame horses (right/left: 1.08 ± 0.47). Non-lame horses rested their hindlimbs evenly (left: 15 ± 10%; right: 17 ± 16%). Horses with unilateral hindlimb lameness unloaded the lame limb longer (lame limb: 61.8 ± 25.3%, non-lame limb: 38.2 ± 25.3%) than their contralateral limb. The lame horses (13 ± 11%) lay down longer ( p = 0.012) than the non-lame horses (3 ± 6%). The degree of lameness determined by the participating veterinarians (Vet Score) (r = -0.691, p < 0.01) and the asymmetry evaluated by the lameness locator (ALL) (r = -0.426, p = 0.019) correlated with the resting ratio (rest time ratio). Both factors were also correlated with the time spent lying down (Vet Score (r = 0.364, p = 0.048) and the ALL (r = 0.398, p = 0.03)). The ALL and VET Score were significantly correlated (r = 0.557, p = 0.01). The results of this study provide a good baseline for future research into how individual resting patterns may help to detect pain.

Precision agriculture and the increasing automation efforts in animal husbandry requires continuous and complex monitoring of the animals. Rumen bolus sensors, which are cutting-edge pieces of technology and a rapidly developing research field, present an exceptional opportunity for monitoring the health status, physiological parameters, and estrus of the animals. The objective of this paper is to provide a comprehensive overview of the development process of a new sensor development. We address the issues of conceptual design, an overview of applicable sensor modalities, mechanical design, power supply design, applicable hardware solutions, applicable communication solutions and finally the sensor detection algorithms proved in field tests. In conclusion, we present a summary of the current opportunities in the field and provide an analysis of the foreseeable trends.

Objective: To determine the accuracy of a continuous glucose monitoring system (CGMS) device by comparing glucose concentrations measured over time as determined by the CGMS to those of the chemistry analyzer (reference method)., Animals: 7 healthy goats and 7 dairy calves., Methods: A randomized, crossover design with 3 treatments: control, hypoglycemia, and hyperglycemia. The CGMS device was applied to the neck. Hypoglycemia and hyperglycemia were induced by insulin and xylazine, respectively. Glucose concentrations were measured by the chemistry analyzer CGMS, point-of-care glucometer, and intensive care unit machine at 0 (before treatment), 2, 4, 6, 8, 10, and 12 hours. Agreement between the CGMS and the chemistry analyzer was determined by Bland-Altman plots. The analytical and clinical accuracy of the CGMS was determined using the International Organization for Standardization (ISO) 15197:2013 criteria and the Parkes error grid analysis., Results: In goats, the CGMS overestimated glucose concentrations during the hypoglycemic, normoglycemia, and hyperglycemia treatments. In calves, the CGMS underestimated glucose concentrations during the hypoglycemic treatment but overestimated glucose concentrations in normoglycemia and hyperglycemic treatments. The CGMS met the ISO clinical accuracy criteria for goats and calves, with > 99% of the glucose measurements in zones A and B of the Parkes grid. However, the CGMS did not meet the ISO 15197:2013 criteria for analytical accuracy., Clinical Relevance: The CGMS evaluated in our study only met the ISO 15197:2013 clinical accuracy criteria, not the analytical accuracy. Therefore, the device might be considered for clinical use.

The sleep-wake cycle represents a crucial physiological process essential for maintaining homeostasis and promoting individual growth. In dogs, alterations in sleep patterns associated with age and dog's correlation with temperament factors, such as nervousness, have been reported, and there is an increasing demand for precise monitoring of sleep and physical activity in dogs. The present study aims to develop an analysis method for measuring sleep-wake patterns and physical activity in dogs by utilizing an accelerometer and a smartphone. By analyzing time series data collected from the accelerometer attached to the dog's collar, a comprehensive sleep and activity analysis model was constructed. This model classified the activity level into seven classes and effectively highlighted the variations in sleep-activity patterns. Two classes with lower activity levels were considered as sleep, while other five levels were regarded as wake based on the rate of occurrence. This protocol of data acquisition and analysis provides a methodology that enables accurate and extended evaluation of both sleep and physical activity in dogs.

Interstitial glucose monitoring systems are commonly being used in diabetic dogs. The aim of this study was to document the incidence of complications associated with the use of a flash glucose monitoring system (FGMS) in dogs. Medical records of dogs that had placement of a 14-day FGMS during a 1-year period were reviewed. Data retrieved included the number of days the sensor remained attached and functional, sensor detachment, sensor failure prior to the end of the 14-day monitoring period, and dermatologic changes at the sensor site. Descriptive statistics were used to characterize the data. Thirty-four dogs had FGMSs placed. Most [32/34 (94%)] sensors were placed over the dorsolateral aspect of the thorax caudal to the scapula. Twenty-four sensors (71%) remained attached for the full 14 days. Incidence of complications associated with FGMS use was 13/34 (38%). The most frequent complication was mild dermatologic changes at the sensor site [6/34 (18%)]. Erythema and crusting at the attachment site were common and could be related to contact dermatitis, hypersensitivity, or skin preparation prior to placement. Flash glucose monitoring systems are safe in dogs, although there are some potential complications that should be discussed with dog owners., (Copyright and/or publishing rights held by the Canadian Veterinary Medical Association.)

Cattle behaviour is fundamentally linked to the cows' health, (re)production, and welfare. The aim of this study was to present an efficient method to incorporate Ultra-Wideband (UWB) indoor location and accelerometer data for improved cattle behaviour monitoring systems. In total, 30 dairy cows were fitted with UWB Pozyx wearable tracking tags (Pozyx, Ghent, Belgium) on the upper (dorsal) side of the cow's neck. In addition to the location data, the Pozyx tag reports accelerometer data as well. The combination of both sensor data was performed in two steps. In the first step, the actual time spent in the different barn areas was calculated using location data. In the second step, accelerometer data were used to classify cow behaviour using the location information of step 1 (e.g., a cow located in the cubicles cannot be classified as feeding, or drinking). A total of 156 hours of video recordings were used for the validation. For each hour of data, the total time each cow spent in each area and performing which behaviours (feeding, drinking, ruminating, resting, and eating concentrates) were computed using the sensors and compared against annotated video recordings. Bland-Altman plots for the correlation and difference between the sensors and the video recording were then computed for the performance analysis. The overall performance of locating the animals into the correct functional areas was very high. The R 2 was 0.99 (P < 0.001), and the root-mean-square error (RMSE) was 1.4 min (7.5% of the total time). The best performance was obtained for the feeding and lying areas (R 2  = 0.99, P < 0.001). Performance was lower in the drinking area (R 2  = 0.90, P < 0.01) and the concentrate feeder (R 2  = 0.85, P < 0.05). For the combined location + accelerometer data, high overall performance (all behaviours) was obtained with an R 2 of 0.99 (P < 0.001) and a RMSE of 1.6 min (12% of the total time). The combination of location and accelerometer data improved the RMSE of the feeding time and ruminating time compared to the accelerometer data alone (2.6-1.4 min). Moreover, the combination of location and accelerometer enabled accurate classification of additional behaviours that are difficult to detect using the accelerometer alone, such as eating concentrates and drinking (R 2  = 0.85 and 0.90, respectively). This study demonstrates the potential of combining accelerometer and UWB location data for the design of a robust monitoring system for dairy cattle., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Monitoring cardiovascular and respiratory measurements corresponds to the precision livestock farming (PLF) objective to continuously monitor and assess dairy cows' welfare and health. Changes in heart rate, breathing rate, and oxygen saturation (SpO2) are valuable metrics in human and veterinary medicine to assess stress, pain, illness, and detect critical conditions. The common way to measure heart rate is either manually or with a stethoscope. Under research conditions, heart rate is usually measured with a sports watch chest belt. Breathing rate is obtained by counting the cow's flank movements which is a time-consuming and labor-intensive method that requires training and is prone to human error. No devices are available on the market that enable practical and easy pulse oximetry in farm animals. This study presents a wireless nose ring sensor system (NoRS) composed of thermal and photoplothysmography sensors that attach to the nostrils of four Holstein dairy cows. The NoRS's thermocouple measured the cow's nasal cavity air temperature; an optic sensor measured the IR (660 nm) and RED (660 nm) signals reflected from the cow's nasal septum. Breathing was calculated from the thermocouple signal's center frequency with a fast Fourier transformation or the signal peak count (i.e., oscillations). The breathing rate was compared to breathing observed by concurrently counting the flank movements. Heart rate and SpO2 were measured by integrated pulse oximetry and heart rate monitor module (MAX30101 TinyCircuit) assembled on the NoRS circuit. Heart rate was also measured with FFT and by counting the number of peaks from the optic sensor's raw IR and RED signals. These measures were compared to an off-the-shelf hand-held pulse oximeter's heart rate and SpO2 readings during the same time. The comparisons revealed highly significant correlations for the heart rate readings where the strength of the correlation was sensitive to the method. The correlation between breathing rate and the veterinarian's visual observations was low, albeit significant. Thus, inhale-exhale cycle counting constitutes a more precise approach than flank movement counts. The hand-held device's 96% SpO2 is compatible with near-saturation values expected in healthy cows. The mean NoRS SpO2 reading was 3% less. After further piloting under field conditions, the NoRS will require no animal restraining to automatically and continuously record cows' breathing rate, heart rate, and SpO2., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Objective: To determine the accuracy of 2 interstitial glucose-monitoring systems (GMSs) for use in horses compared with a point-of-care (POC) glucometer and standard laboratory enzymatic chemistry method (CHEM)., Animals: 8 clinically normal adult horses., Procedures: One of each GMS device (Dexcom G6 and Freestyle Libre 14-day) was placed on each horse, and blood glucose concentration was measured via POC and CHEM at 33 time points and compared with simultaneous GMS readings. An oral glucose absorption test (OGAT) was performed on day 2, and glucose concentrations were measured and compared., Results: Glucose concentrations were significantly correlated with one another between all devices on days 1 to 5. Acceptable agreement was observed between Dexcom G6 and Freestyle Libre 14-day when compared with CHEM on days 1, 3, 4, and 5 with a combined mean bias of 10.45 mg/dL and 1.53 mg/dL, respectively. During dextrose-induced hyperglycemia on day 2, mean bias values for Dexcom G6 (10.49 mg/dL) and FreeStyle Libre 14-day (0.34 mg/dL) showed good agreement with CHEM., Clinical Relevance: Serial blood glucose measurements are used to diagnose or monitor a variety of conditions in equine medicine; advances in near-continuous interstitial glucose monitoring allow for minimally invasive glucose assessment, thereby reducing stress and discomfort to patients. Data from this study support the use of the Dexcom G6 and Freestyle Libre 14-day interstitial glucose-monitoring systems to estimate blood glucose concentrations in horses.

Chemical immobilisation is essential for veterinarians to perform medical procedures in wild African ungulates. Potent opioids combined with neuroleptic drugs are most often used for this purpose. The present study aimed at comparing the quality of immobilisation and effects on physiological variables between a high (high etorphine-azaperone [HE]: 0.09 mg kg-1) and low etorphine dose (low etorphine-azaperone [LE]: 0.05 mg kg-1), both combined with azaperone (0.35 mg kg-1), in 12 adult female boma-acclimatised blesbok. It was hypothesised that a reduction in etorphine's dose in combination with azaperone would result in less cardiorespiratory impairment but likely worsen the quality of immobilisation. Both treatments resulted in rapid induction and recovery times. Overall inter-treatment differences occurred in pulse rate (HE and LE: 52 ± 15 and 44 ± 11 beats minute-1, p 0.0001), respiratory rate (HE and LE: 15 ± 4 and 17 ± 4 breaths minute-1, p 0.006), partial pressure of exhaled carbon dioxide (HE and LE: 62.0 ± 5.0 and 60.0 ± 5.6 millimetre of mercury [mmHg], p 0.028) and arterial carbon dioxide (HE and LE: 58.0 ± 4.5 and 55.0 ± 3.9 mmHg, p 0.002). Both HE and LE led to bradycardia, hypertension and marked hypoxia to a similar extent. Furthermore, quality of induction, immobilisation and recovery were similar in both treatments. The role of azaperone in the development of cardiorespiratory compromise and gas exchange impairment that occurred when these combinations were used is still unclear. Further studies are recommended to elucidate drug- and dose-specific physiological effects in immobilised antelope.

Background: Monitoring blood glucose concentrations is common in critically ill neonatal foals, especially septic foals and those receiving naso-esophageal feedings or IV parenteral nutrition. Glucose typically is measured using a point-of-care (POC) glucometer but requires repeated restraint and blood collections, which may cause irritation at venipuncture sites and increased demands on nursing staff. Continuous glucose monitoring systems (CGMS) may provide an accurate alternative for monitoring blood glucose concentration., Objectives: To determine the correlation and accuracy of a CGMS to monitor neonatal foals' blood glucose concentrations as compared to a POC glucometer and laboratory chemistry analysis (CHEM)., Animals: Samples from 4 healthy and 4 ill neonatal foals., Methods: A CGMS was placed on each foal, and glucose measurements acquired from this device were compared to simultaneous measurements of blood glucose concentration using a POC glucometer and CHEM., Results: Two-hundred matched glucose measurements were collected from 8 neonatal foals. The mean bias (95% limits of agreement) between CGMS and CHEM, CGMS and POC glucometer, and POC glucometer and CHEM was 3.97 mg/dL (-32.5 to 40.4), 18.2 mg/dL (-28.8 to 65.2), and 22.18 mg/dL (-9.3 to 53.67), respectively. The Pearson's correlation coefficient (r) was significantly correlated among all devices: GCMS and CHEM (r = 0.81), CGMS and POC glucometer (r = 0.77) and POC glucometer-CHEM (r = 0.92)., Conclusions and Clinical Importance: Within the blood glucose concentration ranges in this study (78-212 mg/dL), CGMS measurements were significantly correlated with CHEM, suggesting that it is an acceptable method to provide meaningful, immediate, and continuous glucose concentration measurements in neonatal foals while eliminating the need for repeated restraint and blood collection., (© 2021 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.)

General anesthesia induces many systemic effects, including thermoregulatory impairment and subsequent perioperative hypothermia. Due to the animals' small size, monitoring and maintaining body temperatures in laboratory rodents during anesthesia is important for successful surgical outcomes and prompt anesthetic recovery. Draping materials have the potential to aid in thermal support during surgical anesthesia. In this study, rectal and surface (infrared) temperatures were measured in C57BL/6 mice under isoflurane anesthesia every 5 min for the duration of a 35-min sham surgery. In addition to placement on a circulating water bath, mice ( n = 6/group) were draped with commercial cling film (CF; Press'n Seal, Glad, Oakland, CA), a conventional paper drape (PD), or no drape (ND) during surgery. Results demonstrated that CF-draped animals had significantly higher rectal temperatures than nondraped animals. Furthermore, surface temperatures of CF-draped mice were considerably higher than those of both paper-draped and undraped animals. The data indicate that cling film is an effective material to help minimize hypothermia in mice and potentially in other laboratory rodents requiring general anesthesia.

This study assessed the feasibility of using camera image analysis to detect behavioral changes as an indicator of the onset of calving in Japanese Black cattle. Thirty-five pregnant cattle individually housed in pens were used and were continuously monitored using a digital camera system. For the automatic determination of the x and y coordinates of a cow, trajectory analysis was conducted using thermal image and analysis software, and the distances moved were calculated using coordinate data. Further, the frequency of postural changes and the time spent tail raising per hour were measured for 14 cows using visible images. The measurement data were used to calculate hourly data for 12 h prior to amniorrhexis (first rupture of the allantoic sac). The hourly distances moved tended to increase at the time of amniorrhexis, with significantly longer distances measured 3-0 h before amniorrhexis than those at 12-8 h before amniorrhexis (P < 0.05). In all cows, amniorrhexis occurred within 11 h of hourly distances moved by more than 50% compared with distance moved the previous hour. The overall average elapsed time before amniorrhexis was 9 h 30 min (range: 5-11 h). Tail raising time and the frequency of postural changes significantly increased at 1-0 h and 2-0 h before amniorrhexis, respectively. This suggests that predicting the time of calving is possible by measuring the activity of Japanese Black cows during late pregnancy using camera image analysis as a non-invasive technique.

We aimed to determine the effectiveness of estrus detection based on continuous measurements of the ventral tail base surface temperature (ST) with supervised machine learning in cattle. ST data were obtained through 51 estrus cycles on 11 female cattle (six Holsteins and five Japanese Blacks) using the tail-attached sensor. Three estrus detection models were constructed with the training data (n = 17) using machine learning techniques (random forest, artificial neural network, and support vector machine) based on 13 features extracted from sensing data (indicative of estrus-associated ST changes). Estrus detection abilities of the three models on test data (n = 34) were not statistically different among models in terms of sensitivity and precision (range 50.0% to 58.8% and 60.6% to 73.1%, respectively). The relatively poor performance of the models might indicate the difficulty of separating estrus-associated ST changes from estrus-independent fluctuations in ST.

Background: Hibernation is a physiological and behavioural adaptation that permits survival during periods of reduced food availability and extreme environmental temperatures. This is achieved through cycles of metabolic depression and reduced body temperature (torpor) and rewarming (arousal). Rewarming from torpor is achieved through the activation of brown adipose tissue (BAT) associated with a rapid increase in ventilation frequency. Here, we studied the rate of rewarming in the European hamster (Cricetus cricetus) by measuring both BAT temperature, core body temperature and ventilation frequency., Results: Temperature was monitored in parallel in the BAT (IPTT tags) and peritoneal cavity (iButtons) during hibernation torpor-arousal cycling. We found that increases in brown fat temperature preceded core body temperature rises by approximately 48 min, with a maximum re-warming rate of 20.9℃*h -1 . Re-warming was accompanied by a significant increase in ventilation frequency. The rate of rewarming was slowed by the presence of a spontaneous thoracic mass in one of our animals. Core body temperature re-warming was reduced by 6.2℃*h -1 and BAT rewarming by 12℃*h -1 . Ventilation frequency was increased by 77% during re-warming in the affected animal compared to a healthy animal. Inspection of the position and size of the mass indicated it was obstructing the lungs and heart., Conclusions: We have used a minimally invasive method to monitor BAT temperature during arousal from hibernation illustrating BAT re-warming significantly precedes core body temperature re-warming, informing future study design on arousal from hibernation. We also showed compromised re-warming from hibernation in an animal with a mass obstructing the lungs and heart, likely leading to inefficient ventilation and circulation.

Several studies have demonstrated that the intensity of estrous expression is associated with ovulation, ovarian and uterine function, and fertility, and is dependent on social hierarchy and the housing system used. Data from recent studies involving spontaneous and induced estrus have shown that a greater relative increase and longer estrus (captured by different automated activity monitors; AAM) are both associated with improved pregnancy per artificial insemination (AI; around 10 to 14% increase) and decreased pregnancy losses. Intensity and duration of estrus were surprisingly weakly associated with preovulatory follicle diameter and concentrations of plasma estradiol at estrus, whereas ovulation failure was associated with low estrus intensity. Studies have also shown that the display of estrous behavior near AI was associated with the modification of expression of genes related to the immune system, adhesion molecules, and prostaglandin synthesis in the endometrium. Transcripts in leukocytes and in the conceptus tissue associated with maternal recognition of pregnancy as well as conceptus elongation were all associated with differences in the intensity of estrous expression. Most recently, studies from the United States and Canada have demonstrated that reproductive programs emphasizing detection of estrus using AAM can be successful and comparable to intensive timed AI protocol-based programs that incorporate GnRH and PGF 2α treatments. Further, one study concluded that the administration of GnRH at AI for spontaneous estrus events greatly improved pregnancy per AI, but only for cows with reduced intensity of estrous expression, showing the potential to use AAM data as a tool in targeted reproductive programs. Quantitative information from estrus events could be used to improve estrus detection and develop decision-making strategies at the farm level. Future studies in this field should aim to better understand ovarian, conceptus, and endometrial mechanisms associated with either the expression or the intensity of estrus, and to refine the identification of phenotypes related to estrus (relative increase, absolute increase, baseline levels, duration, and repeatability within cow) to improve data usage, estrus detection, and possibly genetic selection., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Until recently, animal behavior has been studied through close and extensive observation of individual animals and has relied on subjective assessments. Wearable technologies that allow the automation of dairy cow behavior recording currently dominate the precision dairy technology market. Wearable accelerometers provide new opportunities in animal ethology using quantitative measures of dairy cow behavior. Recent research developments indicate that quantitative measures of behavior may provide new objective on-farm measures to assist producers in predicting, diagnosing, and managing disease or injury on farms and allowing producers to monitor cow comfort and estrus behavior. These recent research developments and a large increase in the availability of wearable accelerometers have led to growing interest of both researchers and producers in this technology. This review aimed to summarize the studies that have validated lying behavior derived from accelerometers and to describe the factors that should be considered when using leg-attached accelerometers and neck-worn collars to describe lying behavior (e.g., lying time and lying bouts) in dairy cows for research purposes. Specifically, we describe accelerometer technology, including the instrument properties and methods for recording motion; the raw data output from accelerometers; and methods developed for the transformation of raw data into meaningful and interpretable information. We highlight differences in validation study outcomes for researchers to consider when developing their own experimental methodology for the use of accelerometers to record lying behaviors in dairy cows. Finally, we discuss several factors that may influence the data recorded by accelerometers and highlight gaps in the literature. We conclude that researchers using accelerometers to record lying behaviors in dairy cattle should (1) select an accelerometer device that, based on device attachment and sampling rate, is appropriate to record the behavior of interest; (2) account for cow-, farm-, and management-related factors that could affect the lying behaviors recorded; (3) determine the appropriate editing criteria for the accurate interpretation of their data; (4) support their chosen method of recording, editing, and interpreting the data by referencing an appropriately designed and accurate validation study published in the literature; and (5) report, in detail, their methodology to ensure others can decipher how the data were captured and understand potential limitations of their methodology. We recommend that standardized protocols be developed for collecting, analyzing, and reporting lying behavior data recorded using wearable accelerometers for dairy cattle., (© 2020, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The aim of the study was to evaluate the net return of the implementation of a remote calving monitoring system for obstetrical and neonatal assistance on the herd economy in a dairy farm model. A total of 680 parturitions over a 7-yr period were evaluated. Age at first calving was restricted from 23 to 27 mo for primiparous cows to be included. Among groups of cows that were ready to calve in a 15-d interval, primiparous and multiparous were randomly assigned to the experimental group and monitored through a calving alarm system, whereas the others accounted for controls. Final parturition groups were as follows: control primiparous (CPP, n = 218), control multiparous (CM, n = 345), monitored primiparous (MPP, n = 56), and monitored multiparous (MM, n = 61). Monitored groups received prompt calving assistance and first neonatal care, whereas the presence of farm personnel was discontinuous for controls. A biological model was built considering significant differences in calf loss, early culling, milk production, and days open between groups. Then, a partial budget model was used to estimate costs and net return on a simulated herd of 100 lactating cows. Incidence of calf death was greater in control groups (11.06% and 10.73% in CPP and CM, respectively) compared with monitored cows (0.00% and 1.69% in MPP and MM, respectively). Multiparous cows with calf loss had increased relative risk (relative risk = 3.487) for early culling compared with multiparous counterparts with no neonatal loss. Daily milk production in the first 2 mo was 3.79 kg greater in multiparous cows with no dead calf, compared with their counterparts. A significant difference in median days open was found in MPP and CPP (118 and 148 d, respectively). In the final economic model, different simulations were analyzed. They were created assuming different prices or hypothesizing calving monitoring only in primiparous animals. The model estimated different, but always positive, net return. In conclusion, implementing a calving alarm system led to a net return from €37 to 90 per cow per year (€1 = US$1.15 at the time of the study). However, the device alone is not sufficient: it must be supported by qualified calving monitoring and assistance. Optimized personnel presence in the calving area at the right time leads to prompt calving and neonatal calf assistance and colostrum feeding within the first hours of life, thus reducing calf death and days open, and increasing milk production., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The chicken embryo is a widely used experimental animal model in many studies, including in the field of developmental biology, of the physiological responses and adaptation to altered environments, and for cancer and neurobiology research. The embryonic heart rate is an important physiological variable used as an index reflecting the embryo's natural activity and is considered one of the most difficult parameters to measure. An acceptable measurement technique of embryonic heart rate should provide a reliable cardiac signal quality while maintaining adequate gas exchange through the eggshell during the incubation and embryonic developmental period. In this paper, we present a detailed design and methodology for a non-invasive photoplethysmography (PPG)-based prototype (Egg-PPG) for real-time and continuous monitoring of embryonic heart rate during incubation. An automatic embryonic cardiac wave detection algorithm, based on normalised spectral entropy, is described. The developed algorithm successfully estimated the embryonic heart rate with 98.7% accuracy. We believe that the system presented in this paper is a promising solution for non-invasive, real-time monitoring of the embryonic cardiac signal. The proposed system can be used in both experimental studies (e.g., developmental embryology and cardiovascular research) and in industrial incubation applications.

The objectives of the 2 studies reported herein were to validate the accuracy of an automated monitoring device (AMD) to detect side lying, resting, activity, rumination, eating, walking, and panting in nonlactating and lactating dairy cows. Additionally, we aimed to determine whether the total time per cow-state recorded by the AMD within a 30-min interval corresponds to the total time per cow-state recorded simultaneously by visual observation. Study personnel (n = 2) observed pregnant nonlactating Holstein cows (n = 10) for 30 min in the morning and 30 min in the afternoon for 6 consecutive days and recorded continuously each cow-state. In study 2, study personnel (n = 2) observed lactating Holstein cows (n = 10) for 30 min in the morning and 30 min in the afternoon for 6 consecutive days. In both studies, cow-state was recorded every second, and within 1 min, the most prevalent cow-state was considered to be the behavior presented by the cow during that interval. Using the observer as the gold standard, test characteristics were calculated for the minute-by-minute interval analyses. For the 30-min interval analyses, the concordance correlation coefficient (p c ) and the coefficient of determination (R 2 ) between the total minutes for each cow-state recorded by the observer and the AMD were calculated. In study 1, for the minute-by-minute interval analyses, test characteristics were high for rumination (≥90.1%) and eating (≥73.8%), moderate for resting (≥62.9%), but negligible for medium activity (≥17%). For the 30-min interval analyses, the correlations between the total time of visual observations compared with the total time recorded by AMD for rumination (R 2 = 0.97, p c = 0.98) and eating (R 2 = 0.91, p c = 0.94) were very high, for resting (R 2 = 0.77, p c = 0.79) was high, and for medium activity (R 2 = 0.41, p c = 0.41) was low. In study 2, for the minute-by-minute interval analyses, test characteristics were high for rumination (≥79.4%), eating (≥74.2%), and resting (≥73.0%), but they were low for panting (≥31.3%) and negligible for medium activity (≥22.2%). For the 30-min interval analyses, the correlations were similar to study 1 (rumination: R 2 = 0.85, p c = 0.91; eating: R 2 = 0.95, p c = 0.97; resting: R 2 = 0.84, p c = 0.90; medium activity: R 2 = 0.44, p c = 0.57; and panting: R 2 = 0.21, p c = 0.42). In summary, the AMD used in this study provided accurate data regarding resting, rumination, and eating of pregnant nonlactating and lactating Holstein cows., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Osteoarthritis in cats is more prevalent because cats are living longer with advancement in veterinary medicine. Objective evaluation of behavioral changes in cats with osteoarthritis can facilitate an early diagnosis. The objective of this study was to investigate the utility of a novel activity monitor for analyzing physical activities and sleep quality in cats. First, a novel activity monitor (Plus Cycle®; JARMeC, Kanagawa, Japan), with a built-in three-directional accelerometer and an air pressure sensor specifically designed for cats, was compared to a human activity monitor (Actical®; Philips Respironics, OR, USA) previously used in cats (n = 10). Second, the validity of the measurement accuracy of the amount of physical activity, the number of vibrations, the number of jumps, and the resting and sleeping time was evaluated using Plus Cycle® in healthy cats (n = 6). Finally, the effects of gender and age of cats and time of day on the amount of physical activity, the number of vibrations, the number of jumps, and the resting and sleeping time were investigated in client-owned cats (n = 61). There were strong correlations between Plus Cycle® and Actical® in total activity (p < 0.05) and activity intensity (p < 0.05). When the physical activities were measured using Plus Cycle® in healthy cats, those data were quantified with high accuracy. In addition, it was also found to be very accurate in discriminating the resting and sleeping conditions of cats. In client-owned cats, there were no significant differences with respect to gender in any measured traits. The amount of physical activity and the number of jumps significantly decreased with the age of the cat. In contrast, the resting and sleeping times significantly increased with the age of the cat. In conclusion, Plus Cycle® can accurately and objectively assess physical activities and sleep quality with age of the cat, suggesting that this novel activity monitor can be used to manage the feline musculoskeletal health., Competing Interests: Plus Cycle® devices were lent to Nihon University from Japan Animal Referral Medical Center (JARMeC) in this study. JARMeC explained the usage of Plus Cycle® device and cooperated on the access to raw data of the server for borrowed Plus Cycle® devices. MY and HH are employees of JARMeC. The authors would like to declare the following patents/patent applications associated with this research: Japan Animal Referral Medical Center is applying for a patent (Status: Pending, Patent No. JP2017-514095). This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Feed intake and time spent eating at the feed bunk are important predictors of dairy cows' productivity and animal welfare, and deviations from normal eating behavior may indicate subclinical or clinical disease. In the current study, we developed a random forests algorithm to predict dairy cows' daily eating time (of a total mixed ration from a common feed bunk) using data from a 3-dimensional accelerometer and a radiofrequency identification (RFID) prototype device (logger) mounted on a neck collar. Models were trained on continuous focal animal observations from a total of 24 video recordings of 18 dairy cows at the Danish Cattle Research Centre (Foulum, Tjele, Denmark). Each session lasted from 21 to 48 h. The models included both the present time signal and observations several seconds back in time (lag window). These time-lagged signals were included with the purpose of capturing changes over time. Because of the high costs of installing an RFID antenna in the feed bunk, we also investigated a model based solely on 3-dimensional accelerometer data. Furthermore, to address the trade-off between prediction accuracy and reduced model complexity and its implications for battery longevity, we investigated the importance of including observations back in time using lag window sizes between 8 and 128 s. Performance was evaluated by internal leave-one-cow-out cross-validation. The results indicated that we obtained accurate predictions of daily eating time. For the most complex model (a lag window size of 128 s), the median of the balanced accuracy was 0.95 (interquartile interval: 0.93 to 0.96), and the median daily eating time deviation was 7 min 37 s (interquartile interval: -6 to 15 min). The median of the average daily eating time during sessions was 3 h 41 min with an interquartile interval of 2 h 56 min to 4 h 16 min. Exclusion of RFID data resulted in a considerable decrease in prediction accuracy, mainly due to a decreased sensitivity of locating the cow at the feed bunk (median balanced accuracy of 0.87 at a lag window size of 128 s). In contrast, prediction accuracy only slightly decreased with decreasing lag window size (median balanced accuracy of 0.94 at a lag window size of 8 s). We suggest a lag window size of 64 s for further development of the prototype logger. The methodology presented in this paper may be relevant for future automatic recordings of eating behavior in commercial dairy herds., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew's correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf.

The objective of the study was to develop a grazing algorithm for an ear tag-based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The recent trend in electrocardiogram (ECG) device development is towards wireless body sensors applied for patient monitoring. The ultimate goal is to develop a multi-functional body sensor that will provide synchronized vital bio-signs of the monitored user. In this paper, we present an ECG sensor for long-term monitoring, which measures the surface potential difference between proximal electrodes near the heart, called differential ECG lead or differential lead, in short. The sensor has been certified as a class IIa medical device and is available on the market under the trademark Savvy ECG. An improvement from the user's perspective-immediate access to the measured data-is also implemented into the design. With appropriate placement of the device on the chest, a very clear distinction of all electrocardiographic waves can be achieved, allowing for ECG recording of high quality, sufficient for medical analysis. Experimental results that elucidate the measurements from a differential lead regarding sensors' position, the impact of artifacts, and potential diagnostic value, are shown. We demonstrate the sensors' potential by presenting results from its various areas of application: medicine, sports, veterinary, and some new fields of investigation, like hearth rate variability biofeedback assessment and biometric authentication.

The non-invasive monitoring of physiological stress can provide conservation and wildlife managers with an invaluable tool for assessing animal welfare and psychological health of captive and free-ranging populations. A significant decrease in free-ranging primate populations globally and an increase in captive-housed primates have led to a need to monitor the stress and general welfare of these animals. We examined the suitability of three enzyme immunoassays (EIAs) for monitoring stress-related physiological responses in the samango monkey, Cercopithecus albogularis erythrarchus. We conducted an adrenocorticotropic hormone (ACTH) challenge on a male and female at the National Zoological Garden, Pretoria, South Africa. Individual faecal samples were collected 8 days pre- and post-ACTH administration and subsequently analysed for faecal glucocorticoid metabolite (fGCM) concentrations. During the study, biological stressors occurred for both the male and female. Two of the three EIAs tested (11-oxoetiocholanolone I and II) were able to reliably monitor fGCM alterations throughout the study period in both sexes. The 11-oxoetiocholanolone I EIA, however, had the lowest mean deviation from the calculated baseline value and was thus chosen as the preferred assay. Both the physiological activation of the stress response and the biological response to a stressor could be monitored with the chosen assay. The successful establishment of a reliable, non-invasive method for monitoring adrenocortical activity in C. albogularis erythrarchus will now allow conservationists, scientific researchers and wildlife managers to evaluate the level of stress experienced, and general welfare, by animals in captivity as well as free-ranging populations.

Wearable precision dairy monitoring (PDM) technologies currently used to detect estrus may provide insight into disease detection. However, the incorporation of PDM into farm management and its perceived usefulness for dairy producers have not been explored. As the targeted end users of these products, information is needed on how producers use generated disease alerts as well as barriers to adoption and usefulness. The objective of this research was to assess the perceived usefulness producers attributed to alerts from a daily generated alert list designed to identify sick or injured cows or cows that experienced a major management change. Data from 1,171 cows on 4 commercial farms in Kentucky were collected from October 2015 to October 2016. Each cow was equipped with 2 PDM technologies: a leg tag (measuring activity in steps/d and lying time in h/d) and a neck collar (measuring eating time in h/d). Alerts were generated based on an individual cow's decrease of ≥30% in activity, lying, and eating time compared with each cow's 10-d moving mean. Producers sorted alerts into 3 overall categories: (1) the cow alert was perceived to be true and the cow was visually checked, (2) the cow alert was perceived to be true, but the cow was not visually checked, and (3) the cow alert behavior change was doubted by the producer and the cow was not visually checked. Further subdivisions were also provided to explain the choice for an overall category. Over the year, 24,012 cow alerts were generated (eating time, n = 9,543; lying time, n = 9,777; activity, n = 1,590; or a combination of behaviors, n = 3,102). Only 8% of the alerts were doubted by the producer. Although 55% of alerts were perceived to be true, producers visually assessed cows based on only 21% of the alerts with a large variation between farms (2 to 45% of the alerts visually assessed). Producers were more likely to completely ignore alerts over time. Producers were more likely to perceive cow alerts to be true and visually check cows when ≤20 alerts occurred per day, cows were fresh or in early lactation, alerts occurred during the work week, or when cow alerts were for eating time, activity, or a combination of multiple behaviors. Behavioral disease alerts must be improved and correspond to an actionable change for producers to use them. Incorporating herd management software, creating and managing alerts by lactation stage, and focusing on behaviors producers already find useful could improve future alert utilization., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Measuring vital signs is central to medical practice, but they are difficult to monitor in awake laboratory animals. We examined the feasibility of a noninvasive device for telemetric assessment of respiration rate, heart rate, temperature and movement in pigs. Awake piglets were monitored continuously for 31 h (interquartile range, 7) before ( n = 4) and after ( n = 3) surgery. Data quality was sufficient for determination of all parameters. We conclude that continuous, noninvasive monitor- ing of pigs is possible by using the evaluated device.

Cold-stress syndrome (CSS) is one of the leading natural threats to free-ranging Florida manatees ( Trichechus manatus latirostris ). Cold water exposure below the species' acceptable physiologic range is a frequent occurrence for manatees during cold weather months causing CSS-induced systemic illness and significant annual mortality. Although CSS is a commonly presented condition at manatee rehabilitation facilities, the core body temperatures in CSS manatees are currently unknown due to the lack of clinically applicable and accurate temperature measurement methodologies. Our objective was to establish a clinically applicable measurement methodology of core body temperature in manatees. A novel, minimally invasive temperature technique to obtain esophageal temperature by placing a temperature sensor through an oro-gastric tube was compared to current oral and nasal methods in 20 clinically healthy manatees. Results identified the esophageal measurement as the best performing and most precise temperature methodology. The superior performance of esophageal temperature measurements differed significantly from both nasal and oral measurements, while nasal and oral measurements did not differ when compared with each other. The esophageal measurements were consistent with manatee core body temperature, facilitating generation of a reference interval for core body temperature in healthy manatees (35.0-35.8 C). Four CSS medical cases were evaluated with the newly validated esophageal temperature method, facilitating diagnosis of hypothermia. The application of this temperature measurement technique to CSS manatees in field or rehabilitation settings will help in understanding CSS pathophysiology, improve medical assessments during rehabilitation, and contribute to conservation efforts.

Background: A factory-calibrated flash glucose monitoring system (FGMS; FreeStyle Libre) recently was evaluated in dogs with uncomplicated diabetes mellitus. It is not known if this system is reliable during diabetic ketoacidosis (DKA)., Objectives: To assess the performance of the FGMS in dogs with DKA and to determine the effect of severity of ketosis and acidosis, lactate concentration, body condition score (BCS), and time wearing the sensor on the accuracy of the device., Animals: Fourteen client-owned dogs with DKA., Methods: The interstitial glucose (IG) measurements were compared with blood glucose (BG) measurements obtained using a validated portable glucometer. The influence of changes in metabolic variables (β-hydroxybutyrate, pH, bicarbonate, and lactate) and the effect of BCS and time wearing on sensor performance were evaluated. Accuracy was determined by fulfillment of ISO15197:2013 criteria., Results: Metabolic variables, BCS, and time wearing were not associated with the accuracy of the sensor. Good agreement between IG measurements and BG was obtained both before and after DKA resolution (r = .88 and r = .93, respectively). Analytical accuracy was not achieved, whereas clinical accuracy was demonstrated with 100% and 99.6% of results in zones A + B of the Parkes consensus error grid analysis before and after DKA resolution, respectively., Conclusions and Clinical Importance: Changes in metabolic variables, BCS, and time wearing do not seem to affect agreement between IG and BG. Despite not fulfilling the ISO requirements, the FGMS provides clinically accurate estimates of BG in dogs with DKA., (© 2019 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.)

This study evaluated the monitoring methods in asymptomatic dogs with high serum cystatin C (Cys-C) concentrations. Ten dogs with high serum Cys-C were divided into two groups based on the owner's choice; one receiving clinical pathology-based monitoring at an animal hospital specialised in chronic kidney disease, and the other receiving symptom-based monitoring at home, partly because they showed no clinical symptoms. The dogs that received the clinical pathology-based monitoring led to an early treatment intervention, resulted in a longer survival period than dogs received the symptom-based monitoring (P<0.05). It became clear that early treatment intervention by clinical pathology-based monitoring extends the renal survival period even in asymptomatic dogs with increased serum Cys-C concentrations.

The objectives of the 2 studies conducted were to validate the accuracy of an automated monitoring device (AMD; HR-LDn tags, SCR Engineers Ltd., Netanya, Israel) for different types of behaviors or cow-states (side lying, resting, medium activity, high activity, rumination, grazing, walking, and panting) in beef heifers and to determine if the total time per cow-state recorded by the AMD corresponds to the total time per cow-state recorded by instantaneous observations. Cow-state is recorded every second and, within 1 min, the most prevalent cow-state is considered to be the behavior presented by the animal during that interval. Study personnel (n = 2) observed heifers (n = 10) for 20 min from 0800 to 1140 h and 10 min from 1500 to 1640 h during 4 consecutive days and recorded continuously each cow-state at started and ended. Thus, study personnel were able to determine within a 1-min interval, which cow-state was most prevalent and represented the heifer's behavior. Because the proprietary machine learning algorithm prioritizes certain behaviors over others based on their contribution to the understanding of generalized bovine behavior patterns, we also determined the most prevalent behavior observed in 5-min intervals. Test characteristics (sensitivity, specificity, accuracy, and negative and positive predicted values) were calculated using the observer as the gold standard. In study 2, heifer behavior was scanned by observers (n = 2) every 5 min from 0800 to 1100 h and 1500 to 1800 h for 3 consecutive days. Total minutes per cow-state according to the observer were compared with the total minutes per cow-state according to the AMD during the same period to determine the correlation coefficient. In study 1, test characteristics were high (low ≤ 40%, moderate = 41 to 74%, high ≥ 75%) for rumination (≥ 89.7%), grazing (≥ 76.5%), and side lying (≥ 81.8%), and moderate for resting (≥ 48.8%). In study 2, the correlation coefficient for rumination (R2 = 0.92) and grazing (R2 = 0.90) were high and the correlation coefficient for resting (R2 = 0.66) and walking (R2 = 0.33) were moderate. We conclude that the AMD used in this study showed high accuracy when measuring rumination and grazing, but it was subpar when measuring resting and walking. The algorithms employed by the AMD used need to be improved for determination of walking and resting behaviors of beef cattle., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Automated monitoring of fertility in dairy cows using milk progesterone is based on the accurate and timely identification of luteolysis. In this way, well-adapted insemination advice can be provided to the farmer to further optimize fertility management. To properly evaluate and compare the performance of new and existing data-processing algorithms, a test data set of progesterone time-series that fully covers the desired variability in progesterone profiles is needed. Further, the data should be measured with a high frequency to allow rapid onset events, such as luteolysis, to be precisely determined. Collecting this type of data would require a lot of time, effort, and budget. In the absence of such data, an alternative was developed using simulated progesterone profiles for multiple cows and lactations, in which the different fertility statuses were represented. To these, relevant variability in terms of cycle characteristics and measurement error was added, resulting in a large cost-efficient data set of well-controlled but highly variable and farm-representative profiles. Besides the progesterone profiles, information on (the timing of) luteolysis was extracted from the modeling approach and used as a reference for the evaluation and comparison of the algorithms. In this study, 2 progesterone monitoring tools were compared: a multiprocess Kalman filter combined with a fixed threshold on the smoothed progesterone values to detect luteolysis, and a progesterone monitoring algorithm using synergistic control, PMASC, which uses a mathematical model based on the luteal dynamics and a statistical control chart to detect luteolysis. The timing of the alerts and the robustness against missing values of both algorithms were investigated using 2 different sampling schemes: one sample per cow every 8 h versus 1 sample per day. The alerts for luteolysis of the PMASC algorithm were on average 20 h earlier compared with the ones of the multiprocess Kalman filter, and their timing was less sensitive to missing values. This was shown by the fact that, when 1 sample per day was used, the Kalman filter gave its alerts on average 24 h later, and the variability in timing of the alerts compared with simulated luteolysis increased with 22%. Accordingly, we postulate that implementation of the PMASC system could improve the consistency of luteolysis detection on farm and lower the analysis costs compared with the current state of the art., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Introduction: Thermographic examination of 157 German Holstein heifer calves was performed to investigate the feasibility of this technique for on-farm recording of surface temperature of the head and to examine potential factors that affect the recordings. Baseline values were obtained from six defined locations on the head including both eyes, both horn buds, the muzzle and the mucous membrane of the muzzle using a high-end thermographic camera (ThermoPro TP8, Firma DIAS Infrared GmbH). Evaluation of the influence of various factors on thermographic measurements showed that ambient temperature had the largest effect on surface temperature of the head (regression coefficient, 0.10 to 0.32, p ≤ 0.01) whereas humidity had no effect (in t-test p ≥ 0.33 over all locations). There was a no correlation between rectal temperature and surface temperature (rp ≤ 0.05). The surface temperature decreased with increasing age of the calves (regression coefficient, - 0.42 to - 0.14, p ≤ 0.01). The agreement between double readings made shortly after one another was excellent at all locations (r ≥ 0.95). The emission of infrared energy varied among different locations; the most infrared energy was emitted by the eyes and the least by the muzzle. Paired locations (eyes and horn buds) had symmetric emission patterns of infrared energy. Measuring the surface temperature of the head of calves in their normal barn environment using a standardised protocol was feasible and thus could potentially be used for monitoring calves under field conditions.

Radio frequency identification (RFID) technology offers a real-time solution to monitor behavioral responses of individual animals to various stimuli, which provides crucial implications on farm management and animal well-being. The objectives of this study were to (1) develop and describe an ultra-high frequency radio frequency identification (UHF-RFID) system for continuously monitoring feeding and drinking behaviors of individual broilers in group settings; and (2) validate the performance of the UHF-RFID system against video analysis in determining the instantaneous bird number (IBN) and time spent (TS) at feeder and drinker. The UHF-RFID system consisted of cable-tie tags, antennas, a reader and a data acquisition (DAQ) system. The antennas generated electromagnetic fields where tags were detected and registered by the DAQ system. Electromagnetic fields of the antennas were modified to cover areas of concern (i.e. tube feeders and nipple drinkers) through a series of system evaluations and customizations including tag sensitivity test, power adjustment, radio wave shielding, and assessment of interference by add-ons (e.g. plastic wraps for protecting antennas and an empty carton box for zoning out broilers) and feed/feeder. System validation was performed in two experimental rooms, each with 60 tagged broilers. The results showed that the max reading distances of tags with an identical manufacturer's specification were markedly different, indicating large variations in sensitivity among the tags. Desired electromagnetic fields could be achieved by adjusting the power supplied to antennas and by partially shielding antennas with customized stainless steel sheets. The protection materials and fully loaded feeder had little effect on electromagnetic fields of the antennas. The accuracies of the UHF-RFID system for determining IBN and TS were, respectively, 92.5±4.2% and 99.0±1.2% by the feeder antennas and 94.7±4.2% and 93.7±6.9% by the drinker antennas. It is concluded that the UHF-RIFD system can accurately detect and record feeding and drinking behaviors of individual broilers in group settings and thus is a useful tool for investigating impacts of resource allocations and management practices on these behaviors.

The commercially available collar device MooMonitor+ was evaluated with regards to accuracy and application potential for measuring grazing behavior. These automated measurements are crucial as cows feed intake behavior at pasture is an important parameter of animal performance, health and welfare as well as being an indicator of feed availability. Compared to laborious and time-consuming visual observation, the continuous and automated measurement of grazing behavior may support and improve the grazing management of dairy cows on pasture. Therefore, there were two experiments as well as a literature analysis conducted to evaluate the MooMonitor+ under grazing conditions. The first experiment compared the automated measurement of the sensor against visual observation. In a second experiment, the MooMonitor+ was compared to a noseband sensor (RumiWatch), which also allows continuous measurement of grazing behavior. The first experiment on n = 12 cows revealed that the automated sensor MooMonitor+ and visual observation were highly correlated as indicated by the Spearman's rank correlation coefficient (rs) = 0.94 and concordance correlation coefficient (CCC) = 0.97 for grazing time. An rs-value of 0.97 and CCC = 0.98 was observed for rumination time. In a second experiment with n = 12 cows over 24-h periods, a high correlation between the MooMonitor+ and the RumiWatch was observed for grazing time as indicated by an rs-value of 0.91 and a CCC-value of 0.97. Similarly, a high correlation was observed for rumination time with an rs-value of 0.96 and a CCC-value of 0.99. While a higher level of agreement between the MooMonitor+ and both visual observation and RumiWatch was observed for rumination time compared to grazing time, the overall results showed a high level of accuracy of the collar device in measuring grazing and rumination times. Therefore, the collar device can be applied to monitor cow behavior at pasture on farms. With regards to the application potential of the collar device, it may not only be used on commercial farms but can also be applied to research questions when a data resolution of 15 min is sufficient. Thus, at farm level, the farmer can get an accurate and continuous measurement of grazing behavior of each individual cow and may then use those data for decision-making to optimize the animal management.

Background: Famotidine is sometimes administered as a continuous rate infusion (CRI) to treat gastrointestinal ulceration in critically ill dogs. However, clinical studies have not evaluated the efficacy of a famotidine CRI in dogs., Hypothesis/objectives: To evaluate the efficacy of famotidine at raising intragastric pH when it is administered as a CRI in dogs. We hypothesized that CRI treatment with famotidine would meet clinical goals for raising intragastric pH ≥3 and 4., Animals: Nine healthy Beagle dogs., Methods: Randomized 2-way crossover. All dogs received 1.0 mg/kg IV q12h famotidine or CRI famotidine at 1.0 mg/kg IV loading dose and 8.0 mg/kg/d for 3 consecutive days. Beginning on day 0 of treatment, intragastric pH monitoring was used to continuously record intragastric pH. Mean percentage times (MPTs) for which intragastric pH was ≥3 and ≥4 were compared between groups using analysis of variance., Results: There was a statistically significant difference (P < .05) in MPT ≥3 and ≥4 between the CRI and IV q12h groups on all treatment days. On days 1, 2, and 3, the MPTs ± SD for which pH was ≥3 were 92.1 ± 8.5, 96.3 ± 6.2, and 90.0 ± 15.7 for the CRI treatment group and 49.3 ± 27.3, 42.2 ± 19.6, and 45.8 ± 10.1, respectively, for the twice-daily group., Conclusions and Clinical Importance: These results suggest that a famotidine CRI, but not standard doses of famotidine, achieves the clinical goals established in people to promote healing of gastric tissue injury and offers an alternative to intravenous treatment with proton pump inhibitors in dogs., (© 2019 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.)

The objective of this study was to validate the CowManager SensOor ear-tag accelerometer (Agis Automatisering BV, Harmelen, the Netherlands) against visual observations of feeding, rumination, resting, and active behaviors of tiestall-housed dairy cows. Prior validation of the sensor has been published for freestall and grazing dairy herds. However, the behavioral differences that exist among these and a tiestall system necessitate additional validation. Lactating Holstein cows (n = 10) at different lactation stages and parities were included in the study. Cows were monitored both visually and with the sensor for 10 h/d for 4 consecutive days (10 cows × 10 h × 4 d = 400 h of observation total). A single trained observer classified each minute of visual observation into 1 of 13 behaviors and then summarized them into the 4 behavioral categories of eating, rumination, not active, or active. The sensor registered ear movements continuously and, based on a proprietary model, converted them into the behavioral categories. Multivariate mixed models were run to obtain covariance estimates, from which correlation coefficients were computed to assess agreement between visual observation and sensor data. The models included the percentage of time spent performing each behavior per day as the dependent variable and technology (visual observation versus sensor) and day as fixed effects. The models also included the random effects of technology and the repeated effects of technology and day. The correlation strength between visual observation and sensor data varied from poor to almost perfect by behavioral category (eating: r = 0.27; rumination: r = 0.69; eating-rumination: r = 0.83; not active: r = 0.95; and active: r = 0.89). The results suggest that the sensor can be used to accurately monitor active and not-active behaviors of tiestall-housed dairy cows. The results also suggest that although the sensor shows promise for identifying feeding behaviors in general, the independent classification of rumination and eating requires additional sensitivity., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three-axis neck-mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross-validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three-axis accelerometers mounted to a collar., (© 2019 Japanese Society of Animal Science.)

The objective of this study was to determine whether data from an electronic drinking system that tracks individual animal attendance with ear tag transponders could be used to detect social competition between dairy cows at the drinker. Specifically, we sought to identify the interval between one cow leaving the drinker and another cow taking her place that most accurately identified competitive replacements (when physical contact initiated by one cow causes the other to entirely remove her head from the drinker and the initiator subsequently places her head in the same drinker). The optimal interval to accurately identify replacements at the drinker was determined using a receiver operating characteristic curve and by evaluating the sensitivity (Se), specificity (Sp), and confusion matrix values (true positives, true negatives, false positives, false negatives) of different thresholds identified by 3 metrics: the Youden index, the point closest to (0,1) on the curve, and the point where Se roughly equals Sp. Lactating Holstein dairy cows (n = 20) were monitored for 4 consecutive 24-h periods by video recording and with an electronic drinking system. Two periods were used to identify the optimal interval (the baseline set), and the other 2 periods were used to validate the interval (the validation set). The occurrence of a replacement identified by video was paired with the interval between drinking events of 2 cows at the same electronic drinker to identify the interval that best predicted replacement events. Based on the low prevalence of replacement events compared with nonreplacement events, the interval with the lowest number of false positives was considered optimal. Of the 3 potential metrics, the point on the receiver operating characteristic curve where Se and Sp were roughly equal yielded the fewest false positives. The optimal interval to identify replacements at the drinker was ≤29 s, with 82% Se and 83% Sp in the baseline set. This interval was found to have 85% Se and 89% Sp when applied to the validation set. These results indicate that social competition between dairy cows at the drinker can be accurately measured with an electronic drinking system., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Precision dairy technology is important because of the possibility to continuously and accurately measure behavior, biometrics, and productivity on commercial and research dairy farms by an automated method with minimal human intervention. The behavior-monitoring collar (BMC) used in this study is a commercially available precision dairy technology (MooMonitor+, Dairymaster, Co. Kerry, Ireland), designed to measure rumination, heat detection, feeding, and resting behavior of dairy cows. The study objective was to compare cow behavior measured by the BMC with visual observations. Twenty-four lactating, group-housed, Holstein dairy cows (mean ± standard deviation; days in milk: 196 ± 101; parity: 2.0 ± 1.1; milk yield: 40.0 ± 9.8 kg/d) were randomly selected for observation at the University of Kentucky's research dairy farm, Lexington). Behavior-monitoring collars were assigned to cows as per farm protocol. Each cow was observed for 240 min within 1 d (0700 to 0900 h, and 1900 to 2100 h). Recordings of rumination, feeding, and resting time (min) by the BMC were compared with visual observation using Pearson correlation, concordance correlation coefficient (CCC), linear regression, and Bland-Altman plots for validation of precision and accuracy. Data from the BMC were considered precise if the correlation coefficient and coefficient of determination were high (>0.70), and mean bias from the Bland-Altman plots included zero with the 95% interval of agreement. The BMC was considered accurate if the slope from the linear regressions did not differ significantly from 1, and the CCC (ρ c ) were at least moderate (>0.90). We found very high Pearson correlation coefficients (0.99, 0.93, and 0.94) and coefficients of determination (0.97, 0.85, and 0.88) for rumination, feeding, and resting, respectively. Bland-Altman plots were acceptable; the plots did not show any bias. The Bland-Altman mean differences ± standard deviation (BMC - observation) were -7.57 ± 6.31, 15.81 ± 11.84, and -13.03 ± 9.37 min, respectively. The Bland-Altman plot's 95% interval of agreement encompassed 100% of the observations of resting time, and all but one cow's observations for both rumination and feeding time. The slope of the linear regression, however, was different than 1 for all behaviors, and rumination was the only behavior with moderate CCC. In summary, this study validates the high precision of rumination, resting, and feeding behaviors measured by a BMC in lactating dairy cows., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Introduction: In the present study the ante partum ruminating behavior and the later birth course were examined. Between May 2016 and April 2017 all cows, on a North German farm with 430 Holstein Friesian cows, were equipped with a transponder recording the ruminating behavior at least one week before the calculated date of birth. In total, 28 births could be monitored. Regardless of the course of birth, a decrease in ruminal activity was observed in all animals in the days before birth. The most significant decrease was on the last day before the birth. A significant difference occurred only at certain times. The present study confirmed an association between the early decline in ruminal activity and any imminent -dystocia. Further and in-depth studies are needed to create an appropriate algorithm.

The objectives of this study were to evaluate the effectiveness and accuracy of monitoring feeding behavior patterns using cumulative summation (CUSUM) procedures to predict the onset of bovine respiratory disease (BRD) in beef cattle. Growing bulls (N = 231) on a 70-d growth and efficiency trial were used in this study. Between days 28 and 38 of the study, 30 bulls were treated for BRD based on observed clinical signs and elevated rectal temperature (>39.5 °C); remaining bulls (n = 201) were considered healthy. Clinically-ill and healthy bulls were used to evaluate sensitivity and specificity of CUSUM models, with accuracy calculated as the average of sensitivity and specificity. All data were standardized prior to generating CUSUM charts in a daily accumulative manner. Eight univariate CUSUM models were evaluated including DMI, bunk visit (BV) frequency, BV duration, head down (HD) duration, eating rate, maximal nonfeeding interval (NFI Max), SD of nonfeeding interval (NFI SD), and time to bunk (TTB). Accuracies for detection of BRD were 80.1, 69.4, 72.4, 79.1, 63.7, 64.6, 73.2, and 48.7%, respectively, and average day of detection prior to observed symptoms of BRD were 1.0, 3.2, 3.2, 4.8, 10.2, 2.7, 1.5, and 0.6 d, respectively. Principal component analysis (PCA) of all 8 univariate traits (full model) was used to construct multivariate factors that were similarly monitored with CUSUM. Two reduced multivariate models were also constructed that included the 3 best performing feeding behavior traits (BV duration, HD duration, NFI SD) with (RBD) and without DMI (RB). Accuracy of the full multivariate model was similar to the best of the univariate models (75.0%). However, both of the reduced multivariate models (RB and RBD) were more accurate (84.0%) than the full multivariate model. All 3 of the multivariate models signaled (P < 0.05) 2.0 to 2.1 d prior to clinical observation. These results demonstrate that the use of PCA-derived multivariate factors in CUSUM charts was more accurate compared with univariate CUSUM charts, for pre-clinical detection of BRD. Furthermore, adding DMI to the RB model did not further improve accuracy or signal day of BRD detection. The use of PCA-based multivariate models to monitor feeding behavior traits should be more robust than relying on univariate trait models for preclinical detection of BRD. Results from this study demonstrate the value of using CUSUM procedures to monitor feeding behavior patterns to more accurately detect BRD prior to clinical symptoms in feedlot cattle., (© The Author(s) 2018. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Lying behaviour is important for the welfare of the cow. Therefore, reliable electronic devices may improve the management of the cows, and the devices can be used as a tool in research. However, accelerometer-based devices measure acceleration, and an algorithm is therefore necessary for the calculation of lying behaviour. Thus, validation of such devices is imperative prior to use. The objective of this study was to validate the use of the AfiTagII device for measurements of the lying time and frequency of lying bouts of Danish Holstein (DH) and Danish Jersey (DJ) cows in a loose-house system on two different bedding materials. The validation included correlations and linear regression analyses of data collected by the AfiTagII compared with data collected both by direct observations and recordings from a previously validated device (IceQube). In total, 40 cows were observed directly with primiparous and multiparous DJ and DH cows, equally represented. Furthermore, 21 cows were monitored with both AfiTagII and IceQube devices, and data from both devices were collected simultaneously. The devices were attached to the hind leg of the cow. The estimates of the lying time from the AfiTagII device was highly correlated with the recordings from direct observations (r=0.98), and there was a linear relation between these with an intercept equal to 0 and a slope close to 1. The estimates of the lying time from the AfiTagII device was also highly correlated with the IceQube recordings (r=0.94). However, the intercept deviated from 0. The frequency of lying bouts recorded by the AfiTAgII compared to direct observations showed a positive predictive value of 0.96 for lactating cows on the slatted floor and of 0.85 for the dry cows on the deep bedding. The correlations between frequency of lying bouts recorded with the two devices were high (r=0.94), but the intercept deviated from 0. In conclusion, the AfiTagII has a high accuracy for the measurements of lying behaviour in both DH and DJ cows kept on different bedding materials.

Background: Monitoring of intracranial pressure (ICP) is a critical component in the management of intracranial hypertension. Safety, efficacy, and optimal location of microsensor devices have not been defined in dogs., Hypothesis/objective: Assessment of ICP using a microsensor transducer is feasible in anesthetized and conscious animals and is independent of transducer location. Intraparenchymal transducer placement is associated with more adverse effects., Animals: Seven adult, bred-for-research dogs., Methods: In a prospective investigational study, microsensor ICP transducers were inserted into subdural and intraparenchymal locations at defined rostral or caudal locations within the rostrotentorial compartment under general anesthesia. Mean arterial pressure and ICP were measured continuously during physiological maneuvers, and for 20 hours after anesthesia., Results: Baseline mean ± SD values for ICP and cerebral perfusion pressure were 7.2 ± 2.3 and 78.9 ± 7.6 mm Hg, respectively. Catheter position did not have a significant effect on ICP measurements. There was significant variation from baseline ICP accompanying physiological maneuvers (P < .001) and with normal activities, especially with changes in head position (P < .001). Pathological sequelae were more evident after intraparenchymal versus subdural placement., Conclusions and Clinical Importance: Use of a microsensor ICP transducer was technically straightforward and provided ICP measurements within previously reported reference ranges. Results support the use of an accessible dorsal location and subdural positioning. Transient fluctuations in ICP are normal events in conscious dogs and large variations associated with head position should be accounted for when evaluating animals with intracranial hypertension., (Copyright © 2018 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.)

There is increasing scrutiny of the animal welfare impacts of all animal use activities, including agriculture, the keeping of companion animals, racing and entertainment, research and laboratory use, and wildlife management programs. A common objective of animal welfare monitoring is to quantify the frequency of adverse animal events (e.g., injuries or mortalities). The frequency of such events can be used to provide pass/fail grades for animal use activities relative to a defined threshold and to identify areas for improvement through research. A critical question in these situations is how many animals should be sampled? There are, however, few guidelines available for data collection or analysis, and consequently sample sizes can be highly variable. To address this question, we first evaluated the effect of sample size on precision and statistical power in reporting the frequency of adverse animal welfare outcomes. We next used these findings to assess the precision of published animal welfare investigations for a range of contentious animal use activities, including livestock transport, horse racing, and wildlife harvesting and capture. Finally, we evaluated the sample sizes required for comparing observed outcomes with specified standards through hypothesis testing. Our simulations revealed that the sample sizes required for reasonable levels of precision (i.e., proportional distance to the upper confidence interval limit (δ) of ≤ 0.50) are greater than those that have been commonly used for animal welfare assessments (i.e., >300). Larger sample sizes are required for adverse events with low frequency (i.e., <5%). For comparison with a required threshold standard, even larger samples sizes are required. We present guidelines, and an online calculator, for minimum sample sizes for use in future animal welfare assessments of animal management and research programs., Competing Interests: The authors have declared that no competing interests exist.