Detection Of Changes In Behavior Research Articles

210 Sleeping beauties: A daily time budget for individually housed research pigs at 6-d of age

Abstract The pre-wean period of development is difficult to manage and can impact the success of subsequent production stages. Behavior observations can provide insight into the health and welfare status of animals and allow for efficient and timely management interventions. A validated time budget can be utilized as a baseline for further comparisons and the detection of changes in behavior over time. In this project, 24-h of video data for 21 individually housed research pigs at 6 d of age was evaluated using a validated behavior ethogram. The behavior ethogram contained 5 main categories (inactive, consumption, exploratory, social, and other) and 23 behavior labels. The ethogram was applied using a validated sampling strategy, which included 5-min continuous sampling intervals at the start of each hour for active behaviors and 5-min instantaneous sampling intervals for inactive behaviors. The data from each behavior observation was quantified into durations and presented as percentage of observations spent performing a specific behavior label within the observation period. The daily time budget revealed the following time occupation across the 5 main behavior categories: inactive, 98.60% (lying, 85.43%; not lying, 13.14%); consumption, 0.30%; exploratory, 0.97%; social, 0.01%, and other, 0.12%. As expected for very young pigs, most of the day was inactive, with almost 21 h lying. The most performed behavior was “lying socially”, and this behavior represented 38.48% of the overall daily time budget. The remaining 46.95% of time spent lying was spent “lying sternal”, “lying lateral”, or “lying on an object” in the pen. The pigs are housed individually, but they are able to see, hear, and smell a neighboring pig through a plexiglass barrier. The behavior observation “lying socially” is defined as follows: “The piglet is lying (sternal or lateral) touching the plexiglass barrier. The piglet in the adjacent pen is lying directly on the other side of the barrier, and the piglets would be touching each other if the barrier was not in place.” This observation provides insight into the importance of social resting for young pigs.

Development of an imaging method for in vivo single-cell tracking under high-noise conditions: a proof of principle

This paper reports the development of a positron imaging method for in vivo single-cell tracking under high-noise conditions. Following biological processes spatially and temporally at a single-cell level in a living organism is desirable for inquiring into the relationships between the behaviours and properties of cells. Positron-emitting radionuclides enable detecting and following radioactivity-labelled substances deep inside living organisms. However, positron imaging has several challenges, such as the distribution of high noise in other areas close to the cell under investigation. In this work, an algorithm for locating a cell with millisecond resolution to combat the strong interference of nearby noise is developed. The feasibility of the method is verified by the demonstration of particle tracking and detection of behavioural changes in an environment with the signal-to-noise ratio of 1:9.

Optimization and calibration of behavioural tests on different species of planaria for ecotoxicological studies

Freshwater planarian are emerging as a valuable in vivo model for (eco) toxicological studies, but the lack of harmonization of procedures between laboratories remains a challenge. This study aimed to optimize, automate and select the best behavioural tests and analyse the potential of different planarian species as models for toxicological assessment. We implemented four tests: exploration, photomotor response, Tapping and Planarian Light Dark Test, on different planaria species using the DanioVision system. We conclude that the exploration assay performed in 24 well-plate at 10,000 lux is the one that is robust and reliable for toxicological studies with planaria. Dugesia japonica and Schmidtea mediterranea have proved to be sensitive models for toxicological screening of organophosphorus pesticides through behavioural analysis. Under necessary experimental conditions, the motility baseline in controls, for both species allowed the detection of behavioural changes, making both good models for behavioural testing in (eco) toxicological context.

Uncovering Patterns in Dairy Cow Behaviour: A Deep Learning Approach with Tri-Axial Accelerometer Data.

The accurate detection of behavioural changes represents a promising method of detecting the early onset of disease in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows' behaviour from accelerometry data acquired by single sensors on the cows' left flanks and compared the results with those obtained through classical machine learning (ML) from the same raw data. Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect five main behaviours: standing still, moving, feeding, ruminating and resting. For each 8 s time interval, 15 metrics were calculated, obtaining a dataset of 211,720 observation units and 15 columns. The entire dataset was randomly split into training (80%) and testing (20%) datasets. The DL accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML models. The best predictive model was an 8-layer convolutional neural network (CNN) with an overall accuracy and F1 score equal to 0.96. The precision, sensitivity/recall and F1 score of single behaviours had the following ranges: 0.93-0.99. The CNN outperformed all the classical ML algorithms. The CNN used to monitor the cows' conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer.

Characterization and Validation of a New 3D Printing Ink for Reducing Therapeutic Gap in Pediatrics through Individualized Medicines.

3D printing technology can be used to develop individualized medicines in hospitals and pharmacies, allowing a high degree of personalization and the possibility to adjust the dose of the API based on the quantity of material extruded. The main goal of incorporating this technology is to have a stock of API-load print cartridges that could be used at different storage times and for different patients. However, it is necessary to study the extrudability, stability, and buildability of these print cartridges during storage time. A paste-like formulation containing hydrochlorothiazide as a model drug was prepared and distributed in five print cartridges, each of which was studied for different storage times (0 h-72 h) and conditions, for repeated use on different days. For each print cartridge, an extrudability analysis was performed, and subsequently, 100 unit forms of 10 mg hydrochlorothiazide were printed. Finally, various dosage units containing different doses were printed, taking into account the optimized printing parameters based on the results of the extrudability analysis carried out previously. An appropriate methodology for the rapid development of appropriate SSE 3DP inks for pediatrics was established and evaluated. The extrudability analysis and several parameters allowed the detection of changes in the mechanical behavior of the printing inks, the pressure interval of the steady flow, and the selection of the volume of ink to be extruded to obtain each of the required doses. The print cartridges were stable for up to 72 h after processing, and orodispersible printlets containing 6 mg to 24 mg of hydrochlorothiazide can be produced using the same print cartridge and during the same printing process with guaranteed content and chemical stability. The proposed workflow for the development of new printing inks containing APIs will allow the optimization of feedstock material and human resources in pharmacy or hospital pharmacy services, thus speeding up their development and reducing costs.

Experimental arena size alters larval zebrafish photolocomotor behaviors and influences bioactivity responses to a model neurostimulant

Zebrafish behavior is increasingly common in biomedical and environmental studies of chemical bioactivity. Multiple experimental arena sizes have been used to measure photolocomotion in zebrafish depending on age, endpoints observed, and instrumentation, among other factors. However, the extent to which methodological parameters may influence naïve behavioral performance and detection of behavioral changes is poorly understood. Here we measured photolocomotion and behavioral profiles of naïve larval zebrafish across arena sizes. We then performed concentration response studies with the model neurostimulant caffeine, again across various arena dimensions. We found total swimming distance of unexposed fish to increase logarithmically with arena size, which as related to circumference, area, and volume. Photomotor response during light/dark transitions also increased with arena size. Following caffeine exposure, total distance travelled was significantly (p < 0.001) affected by well size, caffeine treatment (p < 0.001), and the interaction of these two experimental factors (p < 0.001). In addition, behavioral response profiles showed differences between 96 well plates and larger well sizes. Biphasic response, with stimulation at lower concentrations and refraction at the highest concentration, was observed in dark conditions for the 96 well size only, though almost no effects were identified in the light. However, swimming behavior was significantly (p < 0.1) altered in the highest studied caffeine treatment level in larger well sizes during both light and dark periods. Our results indicate zebrafish swim more in larger arenas and arena size influences behavioral response profiles to caffeine, though differences were mostly observed between very small and large arenas. Further, careful consideration should be given when choosing arena size, because small wells may lead to restriction, while larger wells may differentially reflect biologically relevant effects. These findings can improve comparability among experimental designs and demonstrates the importance of understanding confounding methodological variables.

Hybridized machine-learning for prompt prediction of rheology and filtration properties of water-based drilling fluids

Careful design and preparation of drilling fluids with appropriate rheology and filtration properties, combined with operational monitoring, is essential for successful drilling operations. Field results reveal that most drilling-fluid problems encountered are avoidable based on prompt detection of unexpected changes in fluid rheology and filtration behavior. Drilling-fluid rheology and filtration properties are typically only checked once or twice a day, whereas other drilling-fluid properties, such as fluid density (FD), solid percentage (S%), and March funnel viscosity (MFV), tend to be monitored several times per hour. Machine learning is therefore applied to estimate rheology and filtration properties with FD, S%, and MFV as input variables. A 1160-record field dataset collected from 14 wells drilled in two oil and gas fields in southwest Iran with water-based drilling fluids is used to predict the drilling fluid’s rheological and filtration characteristics. Plastic viscosity (PV), yield point (YP), and filtrate volume (FV) are the targeted prediction objectives. Of six models tested, Multilayer extreme learning machine (MELM) hybridized with the cuckoo optimization algorithm (COA) provides the best PV, YP, and FV predictions. It achieves root mean squared error (RMSE) values of 0.6357 mL (FV), 0.6086 cP (PV), and 0.6796 lb/100 ft 2 (YP). MELM-COA generates rapid and accurate estimations of rheology and filtration properties with potential for real-time monitoring during drilling operations, without recourse to time-consuming laboratory filtration and rheological tests. This work delivers, in a novel way, accurate and reliable predictions of drilling fluid filtration properties using only the more readily available FD, MFV, and S% variables as input features.

Hydrogenerator early fault detection: Sparse Dictionary Learning jointly with the Variational Autoencoder

Monitoring the continuous health status of a Hydraulic Turbine Generator Unit (HTGU) is a strategic task to prevent any unexpected downtime. In addition to the loss of energy production following a prolonged shutdown, the various maintenance costs represent an undesirable effect due to unanticipated failure. One of the main functions of a condition-based maintenance (CBM), is the early detection of any unexpected changes in a machine behavior. Indeed, being able to detect any drift or change in behavior compared to a reference behavior represents a major challenge in the monitoring of complex machines like a HTGU. In this paper, an early anomaly detection model for HTGU using Variational Autoencoders (VAEs) and Sparse Dictionary Learning (SDL) is proposed. The combined reconstruction error thus obtained from the VAE-SDL model is used as an early fault detection. Experimental tests on vibratory signals show that the two models thus joined increase the sensitivity as well as the robustness of the anomaly detection.

Detection of emotional and behavioural changes after traumatic brain injury: A comprehensive survey

AbstractTraumatic brain injury (TBI) can affect normal brain function and may be caused by a vehicle accident, falling, and so on. The purpose of this survey is to provide clear knowledge of TBI, the causes of TBI, the impacts of TBI, and the role of family members and friends in recovery. TBI affects the daily life of the patients, both physically and mentally. After TBI, the patients may experience many emotional and behavioural changes because of a lack of certain brain functions. These changes affect their personal and social relationships. On the other hand, these changes depend on the severity of the TBI (i.e. mild, moderate, or severe), which is measured using the Glasgow coma score. Generally, three processes are used for emotion recognition: preprocessing, feature extraction, and emotion recognition. Preprocessing is performed for landmark detection and pose normalisation, which improves the performance of emotion detection. Feature extraction and emotion recognition are performed by various deep learning techniques, such as convolution neural networks and long short‐term memory. These techniques recognise the behavioural and emotional changes (depression, anxiety, anger, personality changes etc.) of TBI patients using facial expressions. Family members and friends play an important role in TBI patients' recovery, the extent of which is based on the severity of the TBI. The care of family members and friends leads to quick recovery and rehabilitation of patients from TBI. Finally, testing is performed using Computed Tomography images, Magnetic Resonance Imaging images, Electroencephalography signals, and patient demographics, which together show that the deep learning methods achieve better performance in terms of accuracy, precision, recall, and F‐measure in recognising emotional and behavioural changes after TBI. The authors conclude with a summary of the future of emotional and behavioural change prediction methods for TBI patients.

Abstract P403: In Situ Physiological and Behavioral Monitoring With Digital Sensors for Cerebrovascular Disease: A Scoping Review

Background: Cerebrovascular disease is a life-threatening neurological event and a leading cause of long-term disability and death worldwide. Early detection of characteristic behavioral and physiological changes associated with cerebrovascular disease is critical to improving patient outcomes and quality of life measures. The growing prevalence of remote monitoring tools, from wearable devices to smartphone applications, that facilitate in situ observation of patients and the environments of daily life holds promise for more timely recognition and possible prevention of cerebrovascular accidents (CVA) like stroke. Objective: The goal of this scoping review is to examine and establish categories of innovation with digital sensors that monitor physiological and behavioral variables in situ to augment the current screening and diagnostic processes for patients with cerebrovascular disease. Methods: Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist, a robust search strategy for spanning multiple databases from 2012 to September 30, 2022, excluding review articles, articles including interventions, and articles not published in English, was implemented. Among the databases searched were Web of Science; Scopus; Ovid Embase; Ovid Cochrane Central Register of Controlled Trials; and Ovid MEDLINE and Epub ahead of print, in-process and other nonindexed citations, and daily. Results: This search strategy aggregated 689 articles, of which 101 (14.7%) articles met the inclusion criteria for this scoping review. Articles were divided into two categories based on their focus: physiological and behavioral. Articles with a physiological focus were sorted into one of nine subcategories according to the signal(s) monitored: motor function, heart rhythm, heart rate, kinematic analysis, physical activity, blood pressure, sensory deficit, electrodermal activity, and intracranial pressure. Articles focusing on behavioral variables were sorted into two subcategories: mood and fatigue. Most studies used an ECG-enabled smartwatch, like an Apple Watch 3, or passive smartphone sensors. Conclusions: This scoping review identified disparate methods and conclusions associated with the use of digital sensors for in situ physiological and behavioral monitoring of cerebrovascular disease patients. While most articles evaluated pilot validation and feasibility trials, the lack of randomized controlled trials is a critical literature gap specific to this evolving research area.

Targeting Underlying Inflammation in Carcinoma Is Essential for the Resolution of Depressiveness.

In modern clinical practice and research on behavioral changes in patients with oncological problems, there are several one-sided approaches to these problems. Strategies for early detection of behavioral changes are considered, but they must take into account the specifics of the localization and phase in the course and treatment of somatic oncological disease. Behavioral changes, in particular, may correlate with systemic proinflammatory changes. In the up-to-date literature, there are a lot of useful pointers on the relationship between carcinoma and inflammation and between depression and inflammation. This review is intended to provide an overview of these similar underlying inflammatory disturbances in both oncological disease and depression. The specificities of acute and chronic inflammation are considered as a basis for causal current and future therapies. Modern therapeutic oncology protocols may also cause transient behavioral changes, so assessment of the quality, quantity, and duration of behavioral symptoms is necessary to prescribe adequate therapy. Conversely, antidepressant properties could be used to ameliorate inflammation. We will attempt to provide some impetus and present some unconventional potential treatment targets related to inflammation. It is certain that only an integrative oncology approach is justifiable in modern patient treatment.

Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods.

Automated monitoring of pigs for timely detection of changes in behavior and the onset of tail biting might enable farmers to take immediate management actions, and thus decrease health and welfare issues on-farm. Our goal was to develop computer vision-based methods to detect tail biting in pigs using a convolutional neural network (CNN) to extract spatial information, combined with secondary networks accounting for temporal information. Two secondary frameworks were utilized, being a long short-term memory (LSTM) network applied to sequences of image features (CNN-LSTM), and a CNN applied to image representations of sequences (CNN-CNN). To achieve our goal, this study aimed to answer the following questions: (a) Can the methods detect tail biting from video recordings of entire pens? (b) Can we utilize principal component analyses (PCA) to reduce the dimensionality of the feature vector and only use relevant principal components (PC)? (c) Is there potential to increase performance in optimizing the threshold for class separation of the predicted probabilities of the outcome? (d) What is the performance of the methods with respect to each other? The study utilized one-hour video recordings of 10 pens with pigs prior to weaning, containing a total of 208 tail-biting events of varying lengths. The pre-trained VGG-16 was used to extract spatial features from the data, which were subsequently pre-processed and divided into train/test sets before input to the LSTM/CNN. The performance of the methods regarding data pre-processing and model building was systematically compared using cross-validation. Final models were run with optimal settings and evaluated on an independent test-set. The proposed methods detected tail biting with a major-mean accuracy (MMA) of 71.3 and 64.7% for the CNN-LSTM and the CNN-CNN network, respectively. Applying PCA and using a limited number of PCs significantly increased the performance of both methods, while optimizing the threshold for class separation did result in a consistent but not significant increase of the performance. Both methods can detect tail biting from video data, but the CNN-LSTM was superior in generalizing when evaluated on new data, i.e., data not used for training the models, compared to the CNN-CNN method.

Toward the automated detection of behavioral changes associated with the post-weaning transition in pigs.

We modified an automated method capable of quantifying behaviors which we then applied to the changes associated with the post-weaning transition in pigs. The method is data-driven and depends solely on video-captured image data without relying on sensors or additional pig markings. It was applied to video images generated from an experiment during which post-weaned piglets were subjected to treatments either containing or not containing in-feed antimicrobials (ZnO or antibiotics). These treatments were expected to affect piglet performance and health in the short-term by minimizing the risk from post-weaning enteric disorders, such as diarrhea. The method quantified total group feeding and drinking behaviors as well as posture (i.e., standing and non-standing) during the first week post-weaning, when the risk of post-weaning diarrhea is at its highest, by learning from the variations within each behavior using data manually annotated by a behavioral scientist. Automatically quantified changes in behavior were consistent with the effects of the absence of antimicrobials on pig performance and health, and manifested as reduced feed efficiency and looser feces. In these piglets both drinking and standing behaviors were increased during the first 6 days post-weaning. The correlation between fecal consistency and drinking behavior 6 days post weaning was relatively high, suggesting that these behaviors may have a diagnostic value. The presence or absence of in-feed antimicrobials had no effect on feeding behavior, which, however, increased over time. The approach developed here is capable of automatically monitoring several different behaviors of a group of pigs at the same time, and potentially this may be where its value as a diagnostic tool may lie.

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

Space-time analysis of informational properties of GPS time series recorded at the Campi Flegrei caldera (Italy)

In this work the time dynamics of GPS time series recorded at the Campi Flegrei caldera from 2000 to 2019 was investigated by using the Fisher Information Measure (FIM) and the Shannon entropy power (SEP), two informational methods that allow the detection of changes in the dynamical behavior of a complex system, like the volcanic one, quantifying respectively the order/organization and the disorder/uncertainty. By jointly analyzing the FIM and the SEP through the Fisher-Shannon Information Plane (FSIP) it was shown that the SEP discriminates quite well the vertical displacement time series, while the FIM discriminates better the horizontal ones, which are featured by a more complex behavior. Globally, the most striking change in the SEP is found in 2012, while at local scale abrupt loss of order (indicated by low values of FIM) well correlate with the occurrence of seismic swarms. A further informational quantity, the complexity C (given by the product between SEP and FIM), has been revealed to be sensitive to the baseline deformation level increases and to the small-amplitude modulation of deformation signals within the caldera.

Rapid depolymerization of poly(ethylene terephthalate) thin films by a dual-enzyme system and its impact on material properties

Enzymatic hydrolysis holds great promise for plastic waste recycling and upcycling. The interfacial catalysis mode, and the variability of polymer specimen properties under different degradation conditions, add to the complexity and difficulty of understanding polymer cleavage and engineering better biocatalysts. We present a systemic approach to studying the enzyme-catalyzed surface erosion of poly(ethylene terephthalate) (PET) while monitoring/controlling operating conditions in real time with simultaneous detection of mass loss and changes in viscoelastic behavior. PET nanofilms placed on water showed a porous morphology and a thickness-dependent glass transition temperature (Tg) between 40°C and 44°C, which is >20°C lower than the Tg of bulk amorphous PET. Hydrolysis by a dual-enzyme system containing thermostabilized variants of Ideonella sakaiensis PETase and MHETase resulted in a maximum depolymerization of 70% in 1h at 50°C. We demonstrate that increased accessible surface area, amorphization, and Tg reduction speed up PET degradation while simultaneously lowering the threshold for degradation-induced crystallization.

Neurochemical, histological, and behavioral profiling of the acute, sub-acute, and chronic MPTP mouse model of Parkinson's disease.

Parkinson's disease (PD) is a heterogeneous multi-systemic disorder unique to humans characterized by motor and non-motor symptoms. Preclinical experimental models of PD present limitations and inconsistent neurochemical, histological, and behavioral readouts. The 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model of PD is the most common in vivo screening platform for novel drug therapies; nonetheless, behavioral endpoints yielded amongst laboratories are often discordant and inconclusive. In this study, we characterized neurochemically, histologically, and behaviorally three different MPTP mouse models of PD to identify translational traits reminiscent of PD symptomatology. MPTP was intraperitoneally (i.p.) administered in three different regimens: (i) acute-four injections of 20 mg/kg of MPTP every 2 h; (ii) sub-acute-one daily injection of 30 mg/kg of MPTP for 5 consecutive days; and (iii) chronic-one daily injection of 4 mg/kg of MPTP for 28 consecutive days. A series of behavioral tests were conducted to assess motor and non-motor behavioral changes including anxiety, endurance, gait, motor deficits, cognitive impairment, circadian rhythm and food consumption. Impairments in balance and gait were confirmed in the chronic and acute models, respectively, with the latter showing significant correlation with lesion size. The sub-acute model, by contrast, presented with generalized hyperactivity. Both, motor and non-motor changes were identified in the acute and sub-acute regime where habituation to a novel environment was significantly reduced. Moreover, we report increased water and food intake across all three models. Overall, the acute model displayed the most severe lesion size, while across the three modelsstriatal dopamine content (DA) did not correlate with the behavioral performance. The present study demonstrates that detection of behavioral changes following MPTP exposure is challenging and does not correlate with the dopaminergic lesion extent.

Multi-Source Change-Point Detection over Local Observation Models

In this work, we address the problem of change-point detection (CPD) on high-dimensional, multi-source, and heterogeneous sequential data with missing values. We present a new CPD methodology based on local latent variable models and adaptive factorizations that enhances the fusion of multi-source observations with different statistical data-type and face the problem of high dimensionality. Our motivation comes from behavioral change detection in healthcare measured by smartphone monitored data and Electronic Health Records. Due to the high dimension of the observations and the differences in the relevance of each source information, other works fail in obtaining reliable estimates of the change-points location. This leads to methods that are not sensitive enough when dealing with interspersed changes of different intensity within the same sequence or partial missing components. Through the definition of local observation models (LOMs), we transfer the local CP information to homogeneous latent spaces and propose several factorizations that weight the contribution of each source to the global CPD. With the presented methods we demonstrate a reduction in both the detection delay and the number of not-detected CPs, together with robustness against the presence of missing values on a synthetic dataset. We illustrate its application on real-world data from a smartphone-based monitored study and add explainability on the degree of each source contributing to the detection.

Sensor-based detection of a Haemonchus contortus (Barber's pole worm) infection in sheep

Haemonchus contortus, or Barber's Pole worm, is considered one of the most pathogenic parasites of sheep due to the production, profitability and animal welfare implications associated with infections. However, detection of H. contortus infections by routine observation of sheep flocks is difficult, and producers are usually unaware of a problem until animals are severely infected. The aim of the current study was to explore the potential for accelerometer-based activity monitoring to detect H. contortus infections in sheep.A three-month clinical study was conducted in Central Queensland, Australia, whereby 45, 12-month-old merino wethers were randomly allocated to one of three treatment groups: control (n = 9), low H. contortus infection (n = 18) or high H. contortus infection (n = 18). Infection occurred on Day 0, and accelerometer sensors were attached to a halter and deployed on all animals from Day 21 to Day 70. Animals were sampled weekly (faecal and blood) and treated with an anthelmintic on Day 56. Following the clinical period, five study animals were observed for a total of five hours to compare direct behavioral observations against accelerometer data. This was then used to develop a machine learning (ML) model to classify the accelerometer data into active or inactive behavior, which was further summarized to determine the proportion of time spent active per day. The most valuable features in the selected ML model were movement variation (MV), maximum X-axis value (MaxX), standard deviation of the X-axis (SDX), maximum Y-axis value (MaxY) and standard deviation of the Z-axis (SDZ).Linear mixed-effects models identified a significant difference in all sheep activity pre- and post-anthelmintic drench on Day 56 (P < 0.001). Conversely, there was no significant difference between activity levels of infected animals compared to controls (P = 0.14). To further explore how the activity of sheep changed with clinical presentations of H. contortus infections, weekly faecal egg count (FEC) and blood packed cell volume (PCV) were compared to sheep activity using a Spearman's rank correlation test. There was a weak but significant negative correlation between FEC and sheep activity (P < 0.001) and a weak but significant positive correlation between PCV and sheep activity (P < 0.001).In conclusion, this study has shown that accelerometer-based activity monitoring may be able to identify changes in sheep activity associated with clinical presentations of H. contortus infections and between periods prior to and following anthelmintic treatment. However, the ability to detect activity differences between infected and control animals was less clear. Further research is needed to determine the impact of individual behavior variability and flock dynamics when using on-animal sensors for the detection of behavioral changes associated with disease.

Early Detection of Respiratory Diseases in Calves by Use of an Ear-Attached Accelerometer.

Simple SummaryBovine respiratory disease is one of the most important diseases in group-housed calves worldwide, with impacts on calf welfare and farm economics. Early detection of the disease is important for the well-being of the animals and a targeted treatment. Therefore, tools for an automated monitoring of individual calves would be a breakthrough in health management. In this study, we used an ear-attached accelerometer to evaluate its potential for the early detection of behavioral changes related to respiratory disease in calves. Our result showed that accelerometers are able to detect changes in activity and lying times that can be used to predict respiratory disease before clinical diagnosis.Accelerometers (ACL) can identify behavioral and activity changes in calves. In the present study, we examined the association between bovine respiratory disease (BRD) and behavioral changes detected by an ear-tag based ACL system in weaned dairy calves. Accelerometer data were analyzed from 7 d before to 1 d after clinical diagnosis of BRD. All calves in the study (n = 508) were checked daily by an adapted University of Wisconsin Calf Scoring System. Calves with a score ≥ 4 and fever for at least two consecutive days were categorized as diseased (DIS). The day of clinical diagnosis of BRD was defined as d 0. The data analysis showed a significant difference in high active times between DIS and healthy control calves (CON), with CON showing more high active times on every day, except d −3. Diseased calves showed significantly more inactive times on d −4, −2, and 0, as well as longer lying times on d −5, −2, and +1. These results indicate the potential of the ACL to detect BRD prior to a clinical diagnosis in group-housed calves. Furthermore, in this study, we described the ‘normal’ behavior in 428 clinically healthy weaned dairy calves obtained by the ACL system.