Bayesian nonparametric inference for the overlap of daily animal activity patterns

2018 ◽  
Vol 25 (4) ◽  
pp. 471-494 ◽  
Author(s):  
Gabriel Núñez-Antonio ◽  
Manuel Mendoza ◽  
Alberto Contreras-Cristán ◽  
Eduardo Gutiérrez-Peña ◽  
Eduardo Mendoza
Keyword(s):  
Activity Patterns ◽  
Nonparametric Inference ◽  
Bayesian Nonparametric ◽  
Animal Activity

scholarly journals Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes

Animals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1723
Author(s):  
Anne K. Schütz ◽  
Verena Schöler  ◽  
E. Tobias Krause  ◽  
Mareike Fischer  ◽  
Thomas Müller  ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Early Recognition ◽  
Activity Patterns ◽  
Automatic Detection ◽  
Animal Husbandry ◽  
Video Data ◽  
Activity Level ◽  
Red Foxes ◽  
Experimental Conditions ◽  
Animal Activity

Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry.

Bayesian nonparametric inference beyond the Gibbs-type framework

2018 ◽  
Vol 45 (4) ◽  
pp. 1062-1091 ◽  
Author(s):  
Federico Camerlenghi ◽  
Antonio Lijoi ◽  
Igor Prünster
Keyword(s):  
Nonparametric Inference ◽  
Bayesian Nonparametric

scholarly journals Animal activity around the clock with no overt circadian rhythms: patterns, mechanisms and adaptive value

2013 ◽  
Vol 280 (1765) ◽  
pp. 20130019 ◽  
Author(s):  
Guy Bloch ◽  
Brian M. Barnes ◽  
Menno P. Gerkema ◽  
Barbara Helm
Keyword(s):  
Circadian Rhythms ◽  
Activity Patterns ◽  
Polar Regions ◽  
Life History Stages ◽  
Adaptive Value ◽  
Physical Environments ◽  
Trade Offs ◽  
Animal Activity ◽  
Underlying Mechanisms ◽  
Organizational Principles

Circadian rhythms are ubiquitous in many organisms. Animals that are forced to be active around the clock typically show reduced performance, health and survival. Nevertheless, we review evidence of animals showing prolonged intervals of activity with attenuated or nil overt circadian rhythms and no apparent ill effects. We show that around-the-clock and ultradian activity patterns are more common than is generally appreciated, particularly in herbivores, in animals inhabiting polar regions and habitats with constant physical environments, in animals during specific life-history stages (such as migration or reproduction), and in highly social animals. The underlying mechanisms are diverse, but studies suggest that some circadian pacemakers continue to measure time in animals active around the clock. The prevalence of around-the-clock activity in diverse animals and habitats, and an apparent diversity of underlying mechanisms, are consistent with convergent evolution. We suggest that the basic organizational principles of the circadian system and its complexity encompass the potential for chronobiological plasticity. There may be trade-offs between benefits of persistent daily rhythms versus plasticity, which for reasons still poorly understood make overt daily arrhythmicity functionally adaptive only in selected habitats and for selected lifestyles.

scholarly journals Bayesian Nonparametric Inference – Why and How

2013 ◽  
Vol 8 (2) ◽  
pp. 269-302 ◽  
Author(s):  
Peter Müller ◽  
Riten Mitra
Keyword(s):  
Nonparametric Inference ◽  
Bayesian Nonparametric

scholarly journals A non-invasive digital biomarker for the detection of rest disturbances in the SOD1G93A mouse model of ALS

2019 ◽  
Author(s):  
E. Golini ◽  
M. Rigamonti ◽  
F. Iannello ◽  
C. De Rosa ◽  
F. Scavizzi ◽  
...  
Keyword(s):  
Body Weight ◽  
Mouse Model ◽  
Motor Neurons ◽  
Sleep Disturbances ◽  
Activity Patterns ◽  
Non Invasive ◽  
Younger Age ◽  
Animal Activity ◽  
Sod1g93a Mouse

AbstractAmyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease that affects both central and peripheral nervous system, leading to the degeneration of motor neurons, which eventually results in muscle atrophy, paralysis and death. Sleep disturbances are common in patients with ALS, leading to even further deteriorated quality of life. Investigating methods to potentially assess sleep and rest disturbances in animal models of ALS is thus of crucial interest.We used an automated home cage monitoring system (DVC®) to capture activity patterns that can potentially be associated with sleep and rest disturbances and thus to the progression of ALS in the SOD1G93A mouse model. DVC® enables non-intrusive 24/7 long term animal activity monitoring, which we assessed together with body weight decline and neuromuscular function deterioration measured by grid hanging and grip strength tests in male and female mice from 7 until 24 weeks of age.We show that as the ALS progresses over time in SOD1G93A mice, activity patterns during day time start becoming irregular, with frequent activity bouts that are neither observed in control mice nor in SOD1G93A at a younger age. The increasing irregularities of activity patterns during day time are quantitatively captured by designing a novel digital biomarker, referred to as Rest Disturbance Index (RDI). We show that RDI is a robust measure capable of detecting rest/sleep-related disturbances during the disease progression earlier than conventional methods, such as the grid hanging test. Moreover RDI highly correlates with grid hanging and body weight decline, especially in males.The non-intrusive long-term continuous monitoring of animal activity enabled by DVC® has been instrumental in discovering activity patterns potentially correlated with sleep and rest disturbances in the SOD1G93A mouse model of the ALS disease.

scholarly journals Bayesian nonparametric inference on stochastic ordering

Biometrika ◽  
2008 ◽  
Vol 95 (4) ◽  
pp. 859-874 ◽  
Author(s):  
D. B. Dunson ◽  
S. D. Peddada
Keyword(s):  
Stochastic Ordering ◽  
Nonparametric Inference ◽  
Bayesian Nonparametric
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