text:
@article{zheng-trajectory-2015,
	title = {Trajectory {Data} {Mining}: {An} {Overview}},
	url = {https://www.microsoft.com/en-us/research/publication/trajectory-data-mining-an-overview/},
	journal = {ACM Transaction on Intelligent Systems and Technology},
	author = {Zheng, Yu},
	year = {2015},
	note = {Type: Journal Article},
}

@ARTICLE{10.3389/fnsys.2019.00020,
	AUTHOR={Arac, Ahmet and Zhao, Pingping and Dobkin, Bruce H. and Carmichael, S. Thomas and Golshani, Peyman},
	TITLE={DeepBehavior: A Deep Learning Toolbox for Automated Analysis of Animal and Human Behavior Imaging Data},
	JOURNAL={Frontiers in Systems Neuroscience},
	VOLUME={13},
	PAGES={20},
	YEAR={2019},
	URL={https://www.frontiersin.org/article/10.3389/fnsys.2019.00020},
	DOI={10.3389/fnsys.2019.00020},
	ISSN={1662-5137},
	ABSTRACT={Detailed behavioral analysis is key to understanding the brain-behavior relationship. Here, we present deep learning-based methods for analysis of behavior imaging data in mice and humans. Specifically, we use three different convolutional neural network architectures and five different behavior tasks in mice and humans and provide detailed instructions for rapid implementation of these methods for the neuroscience community. We provide examples of three dimensional (3D) kinematic analysis in the food pellet reaching task in mice, three-chamber test in mice, social interaction test in freely moving mice with simultaneous miniscope calcium imaging, and 3D kinematic analysis of two upper extremity movements in humans (reaching and alternating pronation/supination). We demonstrate that the transfer learning approach accelerates the training of the network when using images from these types of behavior video recordings. We also provide code for post-processing of the data after initial analysis with deep learning. Our methods expand the repertoire of available tools using deep learning for behavior analysis by providing detailed instructions on implementation, applications in several behavior tests, and post-processing methods and annotated code for detailed behavior analysis. Moreover, our methods in human motor behavior can be used in the clinic to assess motor function during recovery after an injury such as stroke.}
}

@inproceedings{pandas,
  title={Data structures for statistical computing in python},
  author={McKinney, Wes and others},
  booktitle={Proceedings of the 9th Python in Science Conference},
  volume={445},
  pages={51--56},
  year={2010},
  doi ={10.25080/majora-92bf1922-00a},
  organization={Austin, TX}
}

  @Article{adehabitat,
    title = {The package adehabitat for the R software: tool for the
      analysis of space and habitat use by animals},
    journal = {Ecological Modelling},
    volume = {197},
    pages = {1035},
    year = {2006},
    author = {C. Calenge},
  }

  @Misc{shapely,
  author =    {Sean Gillies and others},
  organization = {toblerity.org},
  title =     {Shapely: manipulation and analysis of geometric objects},
  year =      {2007--},
  url = "https://github.com/Toblerity/Shapely"
}

@article{kilkenny_improving_2010,
	title = {Improving bioscience research reporting: {The} {ARRIVE} guidelines for reporting animal research},
	volume = {1},
	issn = {0976-500X},
	shorttitle = {Improving bioscience research reporting},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3043335/},
	doi = {10.4103/0976-500X.72351},
	number = {2},
	urldate = {2020-01-13},
	journal = {Journal of Pharmacology \& Pharmacotherapeutics},
	author = {Kilkenny, Carol and Browne, William J. and Cuthill, Innes C. and Emerson, Michael and Altman, Douglas G.},
	year = {2010},
	pmid = {21350617},
	pmcid = {PMC3043335},
	pages = {94--99}
}

@article{stroke_therapy_academic_industry_roundtable_stair_recommendations_1999,
	title = {Recommendations for standards regarding preclinical neuroprotective and restorative drug development},
	volume = {30},
	issn = {0039-2499},
	doi = {10.1161/01.str.30.12.2752},
	abstract = {The plethora of failed clinical trials with neuroprotective drugs for acute ischemic stroke have raised justifiable concerns about how best to proceed for the future development of such interventions. Preclinical testing of neuroprotective drugs is an important aspect of assessing their therapeutic potential, but guidelines concerning how to perform preclinical development of purported neuroprotective drugs for acute ischemic stroke are lacking. This conference of academicians and industry representatives was convened to suggest such guidelines for the preclinical evaluation of neuroprotective drugs and to recommend to potential clinical investigators the data they should review to reassure themselves that a particular neuroprotective drug has a reasonable chance to succeed in an appropriately designed clinical trial. Without rigorous, robust, and detailed preclinical evaluation, it is unlikely that novel neuroprotective drugs will prove to be effective when tested in large, time-consuming, and expensive clinical trials. Additionally, similar recommendations are provided for drugs with the potential to enhance recovery after acute ischemic stroke, a burgeoning new field with great potential but little currently available data. The suggestions contained in this document are meant to serve as overall guidelines that must be adapted to the individual characteristics related to particular drugs and their preclinical and clinical development needs.},
	language = {eng},
	number = {12},
	journal = {Stroke},
	author = {{Stroke Therapy Academic Industry Roundtable (STAIR)}},
	month = dec,
	year = {1999},
	pmid = {10583007},
	keywords = {Animals, Disease Models, Animal, Drug Combinations, Drug Evaluation, Preclinical, Enzymes, Guidelines as Topic, Neuroprotective Agents, Outcome Assessment, Health Care, Primates, Rats, Remission, Spontaneous, Sex Factors, Stroke},
	pages = {2752--2758}
}

@misc{noauthor_1_nodate,
	title = {(1) {Stroke} {Therapy} {Academic} {Industry} {Roundtable} ({STAIR}) {Recommendations} {For} {Extended} {Window} {Acute} {Stroke} {Therapy} {Trials} {\textbar} {Request} {PDF}},
	url = {https://www.researchgate.net/publication/26249404_Stroke_Therapy_Academic_Industry_Roundtable_STAIR_Recommendations_For_Extended_Window_Acute_Stroke_Therapy_Trials},
	abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
	language = {en},
	urldate = {2020-01-13},
	journal = {ResearchGate}
}

@article{fisher_update_2009,
	title = {Update of the {Stroke} {Therapy} {Academic} {Industry} {Roundtable} {Preclinical} {Recommendations}},
	volume = {40},
	issn = {0039-2499},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2888275/},
	doi = {10.1161/STROKEAHA.108.541128},
	abstract = {The initial Stroke Therapy Academic Industry Roundtable (STAIR) recommendations published in 1999 were intended to improve the quality of preclinical studies of purported acute stroke therapies. Although recognized as reasonable, they have not been closely followed nor rigorously validated. Substantial advances have occurred regarding the appropriate quality and breadth of preclinical testing for candidate acute stroke therapies for better clinical translation. The updated STAIR preclinical recommendations reinforce the previous suggestions that reproducibly defining dose response and time windows with both histological and functional outcomes in multiple animal species with appropriate physiological monitoring is appropriate. The updated STAIR recommendations include: the fundamentals of good scientific inquiry should be followed by eliminating randomization and assessment bias, a priori defining inclusion/exclusion criteria, performing appropriate power and sample size calculations, and disclosing potential conflicts of interest. After initial evaluations in young, healthy male animals, further studies should be performed in females, aged animals, and animals with comorbid conditions such as hypertension, diabetes, and hypercholesterolemia. Another consideration is the use of clinically relevant biomarkers in animal studies. Although the recommendations cannot be validated until effective therapies based on them emerge from clinical trials, it is hoped that adherence to them might enhance the chances for success.},
	number = {6},
	urldate = {2020-01-13},
	journal = {Stroke; a journal of cerebral circulation},
	author = {Fisher, Marc and Feuerstein, Giora and Howells, David W. and Hurn, Patricia D. and Kent, Thomas A. and Savitz, Sean I. and Lo, Eng H.},
	month = jun,
	year = {2009},
	pmid = {19246690},
	pmcid = {PMC2888275},
	pages = {2244--2250}
}

@article{savitzky_smoothing_1964,
	title = {Smoothing and {Differentiation} of {Data} by {Simplified} {Least} {Squares} {Procedures}.},
	volume = {36},
	issn = {0003-2700},
	url = {https://doi.org/10.1021/ac60214a047},
	doi = {10.1021/ac60214a047},
	number = {8},
	urldate = {2021-02-13},
	journal = {Analytical Chemistry},
	author = {Savitzky, Abraham. and Golay, M. J. E.},
	month = jul,
	year = {1964},
	note = {Publisher: American Chemical Society},
	pages = {1627--1639},
}

@misc{noauthor_hydroxytyrosol_nodate,
	title = {Hydroxytyrosol, the {Major} {Phenolic} {Compound} of {Olive} {Oil}, as an {Acute} {Therapeutic} {Strategy} after {Ischemic} {Stroke}. - {PubMed} - {NCBI}},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/31614692},
	urldate = {2020-01-13}
}

@inproceedings{kim_trajectory_2017,
	title = {Trajectory {Flow} {Map}: {Graph}-based {Approach} to {Analysing} {Temporal} {Evolution} of {Aggregated} {Traffic} {Flows} in {Large}-scale {Urban} {Networks}},
	shorttitle = {Trajectory {Flow} {Map}},
	urldate = {2019-12-02},
	author = {Kim, Jiwon and Zheng, Kai and Corcoran, Jonathan and Ahn, Sanghyung and Papamanolis, Marty},
	year = {2017}
}

@article{abbas_computer_2019,
	title = {Computer {Methods} for {Automatic} {Locomotion} and {Gesture} {Tracking} in {Mice} and {Small} {Animals} for {Neuroscience} {Applications}: {A} {Survey}},
	volume = {19},
	shorttitle = {Computer {Methods} for {Automatic} {Locomotion} and {Gesture} {Tracking} in {Mice} and {Small} {Animals} for {Neuroscience} {Applications}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696321/},
	doi = {10.3390/s19153274},
	abstract = {Neuroscience has traditionally relied on manually observing laboratory animals in controlled environments. Researchers usually record animals behaving freely or in a restrained manner and then annotate the data manually. The manual annotation is not desirable ...},
	language = {en},
	number = {15},
	urldate = {2020-01-13},
	journal = {Sensors (Basel, Switzerland)},
	author = {Abbas, Waseem and Rodo, David Masip},
	month = aug,
	year = {2019},
	pmid = {31349617}
}

@article{zinnhardt_multimodal_2015,
	title = {Multimodal imaging reveals temporal and spatial microglia and matrix metalloproteinase activity after experimental stroke},
	volume = {35},
	issn = {0271-678X},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4635244/},
	doi = {10.1038/jcbfm.2015.149},
	abstract = {Stroke is the most common cause of death and disability from neurologic disease in humans. Activation of microglia and matrix metalloproteinases (MMPs) is involved in positively and negatively affecting stroke outcome. Novel, noninvasive, multimodal imaging methods visualizing microglial and MMP alterations were employed. The spatio-temporal dynamics of these parameters were studied in relation to blood flow changes. Micro positron emission tomography (μPET) using [18F]BR-351 showed MMP activity within the first days after transient middle cerebral artery occlusion (tMCAo), followed by increased [18F]DPA-714 uptake as a marker for microglia activation with a maximum at 14 days after tMCAo. The inflammatory response was spatially located in the infarct core and in adjacent (penumbral) tissue. For the first time, multimodal imaging based on PET, single photon emission computed tomography, and magnetic resonance imaging revealed insight into the spatio-temporal distribution of critical parameters of poststroke inflammation. This allows further evaluation of novel treatment paradigms targeting the postischemic inflammation.},
	number = {11},
	urldate = {2020-01-13},
	journal = {Journal of Cerebral Blood Flow \& Metabolism},
	author = {Zinnhardt, Bastian and Viel, Thomas and Wachsmuth, Lydia and Vrachimis, Alexis and Wagner, Stefan and Breyholz, Hans-Jörg and Faust, Andreas and Hermann, Sven and Kopka, Klaus and Faber, Cornelius and Dollé, Frédéric and Pappata, Sabina and Planas, Anna M and Tavitian, Bertrand and Schäfers, Michael and Sorokin, Lydia M and Kuhlmann, Michael T and Jacobs, Andreas H},
	month = nov,
	year = {2015},
	pmid = {26126867},
	pmcid = {PMC4635244},
	pages = {1711--1721}
}

@article{fluri_animal_2015,
	title = {Animal models of ischemic stroke and their application in clinical research},
	volume = {9},
	issn = {1177-8881},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494187/},
	doi = {10.2147/DDDT.S56071},
	abstract = {This review outlines the most frequently used rodent stroke models and discusses their strengths and shortcomings. Mimicking all aspects of human stroke in one animal model is not feasible because ischemic stroke in humans is a heterogeneous disorder with a complex pathophysiology. The transient or permanent middle cerebral artery occlusion (MCAo) model is one of the models that most closely simulate human ischemic stroke. Furthermore, this model is characterized by reliable and well-reproducible infarcts. Therefore, the MCAo model has been involved in the majority of studies that address pathophysiological processes or neuroprotective agents. Another model uses thromboembolic clots and thus is more convenient for investigating thrombolytic agents and pathophysiological processes after thrombolysis. However, for many reasons, preclinical stroke research has a low translational success rate. One factor might be the choice of stroke model. Whereas the therapeutic responsiveness of permanent focal stroke in humans declines significantly within 3 hours after stroke onset, the therapeutic window in animal models with prompt reperfusion is up to 12 hours, resulting in a much longer action time of the investigated agent. Another major problem of animal stroke models is that studies are mostly conducted in young animals without any comorbidity. These models differ from human stroke, which particularly affects elderly people who have various cerebrovascular risk factors. Choosing the most appropriate stroke model and optimizing the study design of preclinical trials might increase the translational potential of animal stroke models.},
	urldate = {2020-01-13},
	journal = {Drug Design, Development and Therapy},
	author = {Fluri, Felix and Schuhmann, Michael K and Kleinschnitz, Christoph},
	month = jul,
	year = {2015},
	pmid = {26170628},
	pmcid = {PMC4494187},
	pages = {3445--3454}
}

@article{williams_identification_2017,
	title = {Identification of animal movement patterns using tri-axial magnetometry},
	volume = {5},
	issn = {2051-3933},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5367006/},
	doi = {10.1186/s40462-017-0097-x},
	abstract = {Background
Accelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers.

Methods
We calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour.

Results
Tri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading.

Conclusion
Magnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry.

Electronic supplementary material
The online version of this article (doi:10.1186/s40462-017-0097-x) contains supplementary material, which is available to authorized users.},
	urldate = {2020-01-13},
	journal = {Movement Ecology},
	author = {Williams, Hannah J. and Holton, Mark D. and Shepard, Emily L. C. and Largey, Nicola and Norman, Brad and Ryan, Peter G. and Duriez, Olivier and Scantlebury, Michael and Quintana, Flavio and Magowan, Elizabeth A. and Marks, Nikki J. and Alagaili, Abdulaziz N. and Bennett, Nigel C. and Wilson, Rory P.},
	month = mar,
	year = {2017},
	pmid = {28357113},
	pmcid = {PMC5367006}
}

@article{wiesmann_dietary_2016,
	title = {A {Dietary} {Treatment} {Improves} {Cerebral} {Blood} {Flow} and {Brain} {Connectivity} in {Aging} {apoE}4 {Mice}},
	volume = {2016},
	issn = {1687-5443},
	doi = {10.1155/2016/6846721},
	abstract = {APOE ε4 (apoE4) polymorphism is the main genetic determinant of sporadic Alzheimer's disease (AD). A dietary approach (Fortasyn) including docosahexaenoic acid, eicosapentaenoic acid, uridine, choline, phospholipids, folic acid, vitamins B12, B6, C, and E, and selenium has been proposed for dietary management of AD. We hypothesize that the diet could inhibit AD-like pathologies in apoE4 mice, specifically cerebrovascular and connectivity impairment. Moreover, we evaluated the diet effect on cerebral blood flow (CBF), functional connectivity (FC), gray/white matter integrity, and postsynaptic density in aging apoE4 mice. At 10-12 months, apoE4 mice did not display prominent pathological differences compared to wild-type (WT) mice. However, 16-18-month-old apoE4 mice revealed reduced CBF and accelerated synaptic loss. The diet increased cortical CBF and amount of synapses and improved white matter integrity and FC in both aging apoE4 and WT mice. We demonstrated that protective mechanisms on vascular and synapse health are enhanced by Fortasyn, independent of apoE genotype. We further showed the efficacy of a multimodal translational approach, including advanced MR neuroimaging, to study dietary intervention on brain structure and function in aging.},
	language = {eng},
	journal = {Neural Plasticity},
	author = {Wiesmann, Maximilian and Zerbi, Valerio and Jansen, Diane and Haast, Roy and Lütjohann, Dieter and Broersen, Laus M. and Heerschap, Arend and Kiliaan, Amanda J.},
	year = {2016},
	pmid = {27034849},
	pmcid = {PMC4806294},
	keywords = {Aging, Alzheimer Disease, Animals, Apolipoprotein E4, Apolipoproteins E, Brain, Brain Mapping, Diet, Disks Large Homolog 4 Protein, Fatty Acids, Female, Guanylate Kinases, Magnetic Resonance Imaging, Male, Membrane Proteins, Mice, Mice, Inbred C57BL, Mice, Transgenic, Neural Pathways, Sterols},
	pages = {6846721}
}

@article{balkaya_assessing_2013,
	title = {Assessing post-stroke behavior in mouse models of focal ischemia},
	volume = {33},
	issn = {1559-7016},
	doi = {10.1038/jcbfm.2012.185},
	abstract = {Experimental treatment strategies and neuroprotective drugs that showed therapeutic promise in animal models of stroke have failed to produce beneficial effects in human stroke patients. The difficulty in translating preclinical findings to humans represents a major challenge in cerebrovascular research. The reasons behind this translational road block might be explained by a number of factors, including poor quality control in various stages of the research process, the validity of experimental stroke models, and differences in drug administration and pharmacokinetics. Another major difference between animal studies and clinical trials is the choice of end point or outcome measures. Here, we discuss the necessity of poststroke behavioral testing to bridge the gap between clinical and experimental end points. We review established sensory-motor tests for outcome determination after focal ischemia based on the published literature as well as our own personal experience. Selected tests are described in more detail and good laboratory practice standards for behavioral testing are discussed. This review is intended for stroke researchers planning to use behavioral testing in mice.},
	language = {eng},
	number = {3},
	journal = {Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism},
	author = {Balkaya, Mustafa and Kröber, Jan M. and Rex, Andre and Endres, Matthias},
	month = mar,
	year = {2013},
	pmid = {23232947},
	pmcid = {PMC3587814},
	keywords = {Animals, Behavior, Animal, Brain Ischemia, Disease Models, Animal, Humans, Mice, Stroke},
	pages = {330--338}
}

@misc{noauthor_assessing_nodate,
	title = {Assessing post-stroke behavior in mouse models of focal ischemia. - {PubMed} - {NCBI}},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/23232947},
	urldate = {2020-01-09}
}

@article{zerbi_multinutrient_2014,
	title = {Multinutrient diets improve cerebral perfusion and neuroprotection in a murine model of {Alzheimer}'s disease},
	volume = {35},
	issn = {0197-4580},
	url = {http://www.sciencedirect.com/science/article/pii/S0197458013004508},
	doi = {10.1016/j.neurobiolaging.2013.09.038},
	abstract = {Nutritional intervention may retard the development of Alzheimer's disease (AD). In this study we tested the effects of 2 multi-nutrient diets in an AD mouse model (APPswe/PS1dE9). One diet contained membrane precursors such as omega-3 fatty acids and uridine monophosphate (DEU), whereas another diet contained cofactors for membrane synthesis as well (Fortasyn); the diets were developed to enhance synaptic membranes synthesis, and contain components that may improve vascular health. We measured cerebral blood flow (CBF) and water diffusivity with ultra-high-field magnetic resonance imaging, as alterations in these parameters correlate with clinical symptoms of the disease. APPswe/PS1dE9 mice on control diet showed decreased CBF and changes in brain water diffusion, in accordance with findings of hypoperfusion, axonal disconnection and neuronal loss in patients with AD. Both multinutrient diets were able to increase cortical CBF in APPswe/PS1dE9 mice and Fortasyn reduced water diffusivity, particularly in the dentate gyrus and in cortical regions. We suggest that a specific diet intervention has the potential to slow AD progression, by simultaneously improving cerebrovascular health and enhancing neuroprotective mechanisms.},
	language = {en},
	number = {3},
	urldate = {2020-01-09},
	journal = {Neurobiology of Aging},
	author = {Zerbi, Valerio and Jansen, Diane and Wiesmann, Maximilian and Fang, Xiaotian and Broersen, Laus M. and Veltien, Andor and Heerschap, Arend and Kiliaan, Amanda J.},
	month = mar,
	year = {2014},
	keywords = {AD mouse model, APPswe/Ps1de9, Alzheimer's disease, Cerebral blood flow, Diffusion tensor imaging, MRI, Mice, Nutrition, Omega-3 fatty acids},
	pages = {600--613}
}

@article{huang_hidden_2018,
	title = {Hidden {Markov} models for monitoring circadian rhythmicity in telemetric activity data},
	volume = {15},
	url = {https://royalsocietypublishing.org/doi/full/10.1098/rsif.2017.0885},
	doi = {10.1098/rsif.2017.0885},
	abstract = {Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates, based on probabilities of transitions between rest and activity, that are interpretable and of interest to circadian research.},
	number = {139},
	urldate = {2019-12-22},
	journal = {Journal of The Royal Society Interface},
	author = {Huang, Qi and Cohen, Dwayne and Komarzynski, Sandra and Li, Xiao-Mei and Innominato, Pasquale and Lévi, Francis and Finkenstädt, Bärbel},
	month = feb,
	year = {2018},
	pages = {20170885}
}

@article{patterson_migration_2018,
	title = {Migration dynamics of juvenile southern bluefin tuna},
	volume = {8},
	copyright = {2018 The Author(s)},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-018-32949-3},
	doi = {10.1038/s41598-018-32949-3},
	abstract = {Large scale migrations are a key component of the life history of many marine species. We quantified the annual migration cycle of juvenile southern bluefin tuna (Thunnus maccoyii; SBT) and spatiotemporal variability in this cycle, based on a multi-decadal electronic tagging dataset. Behaviour-switching models allowed for the identification of cohesive areas of residency and classified the temporal sequence of movements within a migration cycle from austral summer foraging grounds in the Great Australian Bight (GAB) to winter foraging grounds in the Indian Ocean and Tasman Sea and back to the GAB. Although specific regions within the Indian Ocean were frequented, individuals did not always return to the same area in consecutive years. Outward migrations from the GAB were typically longer than return migrations back to the GAB. The timing of individual arrivals to the GAB, which may be driven by seasonality in prey availability, was more cohesive than the timing of departures from the GAB, which may be subject to the physiological condition of SBT. A valuable fishery for SBT operates in the GAB, as do a number of scientific research programs designed to monitor SBT for management purposes; thus, understanding SBT migration to and from the area is of high importance to a number of stakeholders.},
	language = {en},
	number = {1},
	urldate = {2019-12-22},
	journal = {Scientific Reports},
	author = {Patterson, Toby A. and Eveson, J. Paige and Hartog, Jason R. and Evans, Karen and Cooper, Scott and Lansdell, Matt and Hobday, Alistair J. and Davies, Campbell R.},
	month = sep,
	year = {2018},
	pages = {1--10},
}

@article{patterson_statistical_2017,
	title = {Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges},
	volume = {101},
	issn = {1863-818X},
	shorttitle = {Statistical modelling of individual animal movement},
	url = {https://doi.org/10.1007/s10182-017-0302-7},
	doi = {10.1007/s10182-017-0302-7},
	abstract = {With the influx of complex and detailed tracking data gathered from electronic tracking devices, the analysis of animal movement data has recently emerged as a cottage industry among biostatisticians. New approaches of ever greater complexity are continue to be added to the literature. In this paper, we review what we believe to be some of the most popular and most useful classes of statistical models used to analyse individual animal movement data. Specifically, we consider discrete-time hidden Markov models, more general state-space models and diffusion processes. We argue that these models should be core components in the toolbox for quantitative researchers working on stochastic modelling of individual animal movement. The paper concludes by offering some general observations on the direction of statistical analysis of animal movement. There is a trend in movement ecology towards what are arguably overly complex modelling approaches which are inaccessible to ecologists, unwieldy with large data sets or not based on mainstream statistical practice. Additionally, some analysis methods developed within the ecological community ignore fundamental properties of movement data, potentially leading to misleading conclusions about animal movement. Corresponding approaches, e.g. based on Lévy walk-type models, continue to be popular despite having been largely discredited. We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but grounded in well-developed statistical practice and efficient to fit to large data sets.},
	language = {en},
	number = {4},
	urldate = {2019-12-22},
	journal = {AStA Advances in Statistical Analysis},
	author = {Patterson, Toby A. and Parton, Alison and Langrock, Roland and Blackwell, Paul G. and Thomas, Len and King, Ruth},
	month = oct,
	year = {2017},
	pages = {399--438}
}

@misc{noauthor_19_nodate,
	title = {(19) ({PDF}) {Hidden} {Markov} models for circular and linear-circular time series},
	url = {https://www.researchgate.net/publication/226694240_Hidden_Markov_models_for_circular_and_linear-circular_time_series},
	abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
	language = {en},
	urldate = {2019-12-22},
	journal = {ResearchGate}
}

@inproceedings{ibrahim_-line_2008,
	title = {On-line {Signature} {Verification} {Using} {Most} {Discriminating} {Features} and {Fisher} {Linear} {Discriminant} {Analysis} ({FLD})},
	doi = {10.1109/ISM.2008.115},
	abstract = {In this work, we employ a combination of strategies for partitioning and detecting abnormal fluctuations in the horizontal and vertical trajectories of an on-line generated signature profile. Alternative partitions of these spatial trajectories are generated by splitting each of the related angle, velocity and pressure profiles into two regions representing both high and low activity. The overall process can be thought of as one that exploits inter-feature dependencies by decomposing signature trajectories based upon angle, velocity and pressure - information quite characteristic to an individualpsilas signature. In the verification phase, distances of each partitioned trajectory of a test signature are calculated against a similarly partitioned template trajectory for a known signer. Finally, these distances become inputs to Fisherpsilas Linear Discriminant Analysis (FLD). Experimental results demonstrate the superiority of our approach in On-line signature verification in comparison with other techniques.},
	booktitle = {2008 {Tenth} {IEEE} {International} {Symposium} on {Multimedia}},
	author = {Ibrahim, Muhammad Talal and Kyan, Matthew and Guan, Ling},
	month = dec,
	year = {2008},
	note = {ISSN: null},
	keywords = {Acceleration, Cameras, Data acquisition, FLD, Fisher linear discriminant analysis, Fluctuations, Forgery, Handwriting recognition, Histograms, Linear discriminant analysis, Shape, Testing, data acquisition, feature extraction, handwriting recognition, most discriminating features, online generated signature profile, online signature verification},
	pages = {172--177}
}

@book{rasmussen_gaussian_2006,
	address = {Cambridge, MA, USA},
	series = {Adaptive {Computation} and {Machine} {Learning}},
	title = {Gaussian {Processes} for {Machine} {Learning}},
	publisher = {Biologische Kybernetik},
	author = {Rasmussen, CE. and Williams, CKI.},
	month = jan,
	year = {2006},
	note = {Backup Publisher: Max-Planck-Gesellschaft},
}

@inproceedings{jeong_linear_2016,
	title = {Linear discriminant analysis for symmetric lifting recognition of skilled logistic experts by center of pressure trajectory},
	doi = {10.1109/EMBC.2016.7591745},
	abstract = {Goal: The main purpose of the present study was to propose a recognizing method to analyze characteristics of symmetric lifting of skilled logistic experts by center of pressure (CoP) trajectories. Although it has been known that good posture helps reduce the intradiscal loads on lumbar discs, the most significant problem was that most of logistic workers did not know whether the current posture was proper or not. Methods : The experiment of lifting was performed three times under 18 kg loads with closed eyes. Six skilled logistic experts and six unskilled beginners were participated in. The linear discriminant analysis (LDA) was designed by seven indices which were derived from measured CoP trajectories with the Wii Balance Board. The strong point of experimental system was practical, reliable, and cheap. Results : As a result, it was found that the designed LDA discriminated difference of symmetric lifting between skilled experts and unskilled beginners with the error rate of 0.005. Discussion : It was discussed that not only most of unskilled beginners had mainly characteristics of poor lifting posture, but also the proposed method showed a high possibility to self-evaluate the symmetric lifting in order to check whether the current posture of logistic worker is proper or not.},
	booktitle = {2016 38th {Annual} {International} {Conference} of the {IEEE} {Engineering} in {Medicine} and {Biology} {Society} ({EMBC})},
	author = {Jeong, Hieyong and Kido, Michiko and Ohno, Yuko},
	month = aug,
	year = {2016},
	note = {ISSN: 1557-170X},
	keywords = {Biomechanical Phenomena, CoP trajectory, Discriminant Analysis, Employment, Error analysis, Hip, Humans, LDA, Lifting, Linear discriminant analysis, Logistics, Posture, Principal component analysis, Trajectory, Wii Balance Board, biomechanics, center of pressure trajectory, intradiscal loads, lifting posture, linear discriminant analysis, logistic worker, lumbar discs, mass 18 kg, mechanoception, skilled logistic experts, symmetric lifting recognition, unskilled beginners},
	pages = {4573--4576}
}

@misc{noauthor_8_nodate,
	title = {(8) ({PDF}) {Gaussian} {Process} {Regression} for {Trajectory} {Analysis} {\textbar} {George} {Kachergis} - {Academia}.edu},
	url = {https://www.academia.edu/2986743/Gaussian_Process_Regression_for_Trajectory_Analysis},
	urldate = {2019-12-01}
}

@inproceedings{cox_gaussian_2012,
	title = {Gaussian {Process} {Regression} for {Trajectory} {Analysis}},
	abstract = {Cognitive scientists have begun collecting the trajectories of hand movements as participants make decisions in experiments. These response trajectories offer a ﬁne-grained glimpse into ongoing cognitive processes. For example, difﬁcult decisions show more hesitation and deﬂection from the optimal path than easy decisions. However, many summary statistics used for trajectories throw away much information, or are correlated and thus partially redundant. To alleviate these issues, we introduce Gaussian process regression for the purpose of modeling trajectory data collected in psychology experiments. Gaussian processes are a well-developed statistical model that can ﬁnd parametric differences in trajectories and their derivatives (e.g., velocity and acceleration) rather than a summary statistic. We show how Gaussian process regression can be implemented hierarchically across conditions and subjects, and used to model the actual shape and covariance of the trajectories. Finally, we demonstrate how to construct a generative hierarchical Bayesian model of trajectories using Gaussian processes.},
	language = {en},
	author = {Cox, Gregory E and Kachergis, George and Shiffrin, Richard M},
	year = {2012},
	pages = {6},
}

@article{kareiva_analyzing_1983,
	title = {Analyzing insect movement as a correlated random walk},
	volume = {56},
	issn = {1432-1939},
	doi = {10.1007/BF00379695},
	abstract = {This paper develops a procedure for quantifying movement sequences in terms of move length and turning angle probability distributions. By assuming that movement is a correlated random walk, we derive a formula that relates expected square displacements to the number of consecutive moves. We show this displacement formula can be used to highlight the consequences of different searching behaviors (i.e. different probability distributions of turning angles or move lengths). Observations of Pieris rapae (cabbage white butterfly) flight and Battus philenor (pipe-vine swallowtail) crawling are analyzed as a correlated random walk. The formula that we derive aptly predicts that net displacements of ovipositing cabbage white butterflies. In other circumstances, however, net displacements are not well-described by our correlated random walk formula; in these examples movement must represent a more complicated process than a simple correlated random walk. We suggest that progress might be made by analyzing these more complicated cases in terms of higher order markov processes.},
	language = {eng},
	number = {2-3},
	journal = {Oecologia},
	author = {Kareiva, P. M. and Shigesada, N.},
	month = feb,
	year = {1983},
	pmid = {28310199},
	pages = {234--238}
}

@misc{noauthor_assessing_nodate-1,
	title = {Assessing post-stroke behavior in mouse models of focal ischemia. - {PubMed} - {NCBI}},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/23232947},
	urldate = {2019-11-25}
}

@misc{noauthor_functional_nodate,
	title = {Functional subdivisions of the rat somatic sensorimotor cortex - {Google} {Search}},
	url = {https://www.google.com/search?q=Functional+subdivisions+of+the+rat+somatic+sensorimotor+cortex&oq=Functional+subdivisions+of+the+rat+somatic+sensorimotor+cortex&aqs=chrome..69i57.281j0j7&sourceid=chrome&ie=UTF-8},
	urldate = {2019-11-25}
}

@misc{noauthor_17_nodate,
	title = {(17) ({PDF}) {Characterizing} {Visual} {Performance} in {Mice}: {An} {Objective} and {Automated} {System} {Based} on the {Optokinetic} {Reflex}},
	shorttitle = {(17) ({PDF}) {Characterizing} {Visual} {Performance} in {Mice}},
	url = {https://www.researchgate.net/publication/255984748_Characterizing_Visual_Performance_in_Mice_An_Objective_and_Automated_System_Based_on_the_Optokinetic_Reflex},
	abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
	language = {en},
	urldate = {2019-11-25},
	journal = {ResearchGate}
}

@misc{noauthor_17_nodate-1,
	title = {(17) ({PDF}) {Behavioural} and endocrinological responses of mature male goldfish to the sex pheromone 17alpha,20beta-dihydroxy-4-pregnen-3-one in the water},
	url = {https://www.researchgate.net/publication/13904826_Behavioural_and_endocrinological_responses_of_mature_male_goldfish_to_the_sex_pheromone_17alpha20beta-dihydroxy-4-pregnen-3-one_in_the_water},
	abstract = {ResearchGate is a network dedicated to science and research. Connect, collaborate and discover scientific publications, jobs and conferences. All for free.},
	language = {en},
	urldate = {2019-11-25},
	journal = {ResearchGate}
}

@misc{noauthor_use_nodate,
	title = {Use of the {Open} {Field} {Maze} to measure locomotor and anxiety-like behavior in mice. - {PubMed} - {NCBI}},
	url = {https://www.ncbi.nlm.nih.gov/pubmed/25742564},
	urldate = {2019-11-25}
}

@article{wang_modeling_2017,
	title = {Modeling {Trajectory} as {Image}: {Convolutional} {Neural} {Networks} for {Multi}-scale {Taxi} {Trajectory} {Prediction}},
	shorttitle = {Modeling {Trajectory} as {Image}},
	url = {https://www.academia.edu/34767293/Modeling_Trajectory_as_Image_Convolutional_Neural_Networks_for_Multi-scale_Taxi_Trajectory_Prediction},
	abstract = {Precise destination prediction of Taxi trajectories can benefit both efficient schedule of taxies and accurate advertisement for customers.In this paper, we propose T-CONV, a novel trajectory prediction algorithm, which models trajectories as},
	language = {en},
	urldate = {2019-11-23},
	author = {Wang, Xintong},
	year = {2017},
}

@article{meng_overview_2019,
	title = {An overview on trajectory outlier detection},
	volume = {52},
	issn = {1573-7462},
	url = {https://doi.org/10.1007/s10462-018-9619-1},
	doi = {10.1007/s10462-018-9619-1},
	abstract = {The task of trajectory outlier detection is to discover trajectories or their segments which differ substantially from or are inconsistent with the remaining set. In this paper, we make an overview on trajectory outlier detection algorithms from three aspects. Firstly, algorithms considering multi-attribute. In this kind of algorithms, as many key attributes as possible, such as speed, direction, position, time, are explored to represent the original trajectory and to compare with the others. Secondly, suitable distance metric. Many researches try to find or develop suitable distance metric which can measure the divergence between trajectories effectively and reliably. Thirdly, other studies attempt to improve existing algorithms to find outliers with less time and space complexity, and even more reliable. In this paper, we survey and summarize some classic trajectory outlier detection algorithms. In order to provide an overview, we analyze their features from the three dimensions above and discuss their benefits and shortcomings. It is hope that this review will serve as the steppingstone for those interested in advancing moving object outlier detection.},
	language = {en},
	number = {4},
	urldate = {2019-11-22},
	journal = {Artificial Intelligence Review},
	author = {Meng, Fanrong and Yuan, Guan and Lv, Shaoqian and Wang, Zhixiao and Xia, Shixiong},
	month = dec,
	year = {2019},
	keywords = {Moving object data mining, Outlier detection, Spatial-temporal data, Trajectory},
	pages = {2437--2456}
}

@article{graser_movingpandas_2019,
	title = {{MovingPandas}: {Efficient} {Structures} for {Movement} {Data} in {Python}},
	volume = {Volume 7,},
	copyright = {Österreichische Akademie der Wissenschaften},
	issn = {2308-1708},
	shorttitle = {{MovingPandas}},
	url = {https://www.austriaca.at?arp=0x003aba2b},
	doi = {10.1553/giscience2019_01_s54},
	abstract = {Movement data analysis is a high-interest topic in many scientific domains. Even though Python is the scripting language of choice in the GIS world, currently there is no Python library that would enable researchers and practitioners to interact with and analyse movement data efficiently. To close this gap, we present MovingPandas, a new Python library for dealing with movement data. Its development is based on an analysis of state-of-the-art conceptual frameworks and existing implementations (in PostGIS, Hermes, and the R package trajectories). We describe how MovingPandas avoids limitations of Simple Feature-based movement data models commonly used to handle trajectories in the GIS world. Finally, we present the current state of the MovingPandas implementation and demonstrate its use in stand-alone Python scripts, as well as within the context of the desktop GIS application QGIS. This work represents the first step towards a general-purpose Python library that enables researchers and practitioners in the GIS field and beyond to handle and analyse movement data more efficiently},
	language = {de},
	urldate = {2021-02-02},
	journal = {GI\_Forum 2019,},
	author = {Graser, Anita},
	month = jun,
	year = {2019},
	note = {Publisher: Verlag der Österreichischen Akademie der Wissenschaften},
	pages = {54--68},
}

@misc{word2vec,
      title={Distributed Representations of Words and Phrases and their Compositionality},
      author={Tomas Mikolov and Ilya Sutskever and Kai Chen and Greg Corrado and Jeffrey Dean},
      year={2013},
      eprint={1310.4546},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@article{huber_robust_1964,
	title = {Robust {Estimation} of a {Location} {Parameter}},
	volume = {35},
	issn = {0003-4851, 2168-8990},
	url = {https://projecteuclid.org/euclid.aoms/1177703732},
	doi = {10.1214/aoms/1177703732},
	abstract = {This paper contains a new approach toward a theory of robust estimation; it treats in detail the asymptotic theory of estimating a location parameter for contaminated normal distributions, and exhibits estimators--intermediaries between sample mean and sample median--that are asymptotically most robust (in a sense to be specified) among all translation invariant estimators. For the general background, see Tukey (1960) (p. 448 ff.) Let x1,⋯,xnx1,⋯,xnx\_1, {\textbackslash}cdots, x\_n be independent random variables with common distribution function F(t−ξ)F(t−ξ)F(t - {\textbackslash}xi). The problem is to estimate the location parameter ξξ{\textbackslash}xi, but with the complication that the prototype distribution F(t)F(t)F(t) is only approximately known. I shall primarily be concerned with the model of indeterminacy F=(1−ϵ)Φ+ϵHF=(1−ϵ)Φ+ϵHF = (1 - {\textbackslash}epsilon){\textbackslash}Phi + {\textbackslash}epsilon H, where 0≦ϵ{\textless}10≦ϵ{\textless}10 {\textbackslash}leqq {\textbackslash}epsilon {\textless} 1 is a known number, Φ(t)=(2π)−12∫t−∞exp(−12s2)dsΦ(t)=(2π)−12∫−∞texp(−12s2)ds{\textbackslash}Phi(t) = (2{\textbackslash}pi){\textasciicircum}\{-{\textbackslash}frac\{1\}\{2\}\} {\textbackslash}int{\textasciicircum}t\_\{-{\textbackslash}infty\} {\textbackslash}exp(-{\textbackslash}frac\{1\}\{2\}s{\textasciicircum}2) ds is the standard normal cumulative and HHH is an unknown contaminating distribution. This model arises for instance if the observations are assumed to be normal with variance 1, but a fraction ϵϵ{\textbackslash}epsilon of them is affected by gross errors. Later on, I shall also consider other models of indeterminacy, e.g., supt{\textbar}F(t)−Φ(t){\textbar}≦ϵsupt{\textbar}F(t)−Φ(t){\textbar}≦ϵ{\textbackslash}sup\_t {\textbar}F(t) - {\textbackslash}Phi(t){\textbar} {\textbackslash}leqq {\textbackslash}epsilon. Some inconvenience is caused by the fact that location and scale parameters are not uniquely determined: in general, for fixed ϵϵ{\textbackslash}epsilon, there will be several values of ξξ{\textbackslash}xi and σσ{\textbackslash}sigma such that supt{\textbar}F(t)−Φ((t−ξ)/σ){\textbar}≦ϵsupt{\textbar}F(t)−Φ((t−ξ)/σ){\textbar}≦ϵ{\textbackslash}sup\_t{\textbar}F(t) - {\textbackslash}Phi((t - {\textbackslash}xi)/{\textbackslash}sigma){\textbar} {\textbackslash}leqq {\textbackslash}epsilon, and similarly for the contaminated case. Although this inherent and unavoidable indeterminacy is small if ϵϵ{\textbackslash}epsilon is small and is rather irrelevant for practical purposes, it poses awkward problems for the theory, especially for optimality questions. To remove this difficulty, one may either (i) restrict attention to symmetric distributions, and estimate the location of the center of symmetry (this works for ξξ{\textbackslash}xi but not for σσ{\textbackslash}sigma); or (ii) one may define the parameter to be estimated in terms of the estimator itself, namely by its asymptotic value for sample size n→∞n→∞n {\textbackslash}rightarrow {\textbackslash}infty; or (iii) one may define the parameters by arbitrarily chosen functionals of the distribution (e.g., by the expectation, or the median of FFF). All three possibilities have unsatisfactory aspects, and I shall usually choose the variant which is mathematically most convenient. It is interesting to look back to the very origin of the theory of estimation, namely to Gauss and his theory of least squares. Gauss was fully aware that his main reason for assuming an underlying normal distribution and a quadratic loss function was mathematical, i.e., computational, convenience. In later times, this was often forgotten, partly because of the central limit theorem. However, if one wants to be honest, the central limit theorem can at most explain why many distributions occurring in practice are approximately normal. The stress is on the word "approximately." This raises a question which could have been asked already by Gauss, but which was, as far as I know, only raised a few years ago (notably by Tukey): What happens if the true distribution deviates slightly from the assumed normal one? As is now well known, the sample mean then may have a catastrophically bad performance: seemingly quite mild deviations may already explode its variance. Tukey and others proposed several more robust substitutes--trimmed means, Winsorized means, etc.--and explored their performance for a few typical violations of normality. A general theory of robust estimation is still lacking; it is hoped that the present paper will furnish the first few steps toward such a theory. At the core of the method of least squares lies the idea to minimize the sum of the squared "errors," that is, to adjust the unknown parameters such that the sum of the squares of the differences between observed and computed values is minimized. In the simplest case, with which we are concerned here, namely the estimation of a location parameter, one has to minimize the expression ∑i(xi−T)2∑i(xi−T)2{\textbackslash}sum\_i (x\_i - T){\textasciicircum}2; this is of course achieved by the sample mean T=∑ixi/nT=∑ixi/nT = {\textbackslash}sum\_i x\_i/n. I should like to emphasize that no loss function is involved here; I am only describing how the least squares estimator is defined, and neither the underlying family of distributions nor the true value of the parameter to be estimated enters so far. It is quite natural to ask whether one can obtain more robustness by minimizing another function of the errors than the sum of their squares. We shall therefore concentrate our attention to estimators that can be defined by a minimum principle of the form (for a location parameter): T=Tn(x1,⋯,xn)minimizes∑iρ(xi−T),T=Tn(x1,⋯,xn)minimizes∑iρ(xi−T),T = T\_n(x\_1, {\textbackslash}cdots, x\_n) minimizes {\textbackslash}sum\_i {\textbackslash}rho(x\_i - T), whereρisanon−constantfunction.(M)(M)whereρisanon−constantfunction.{\textbackslash}begin\{equation*\} {\textbackslash}tag\{M\} where {\textbackslash}rho is a non-constant function. {\textbackslash}end\{equation*\} Of course, this definition generalizes at once to more general least squares type problems, where several parameters have to be determined. This class of estimators contains in particular (i) the sample mean (ρ(t)=t2)(ρ(t)=t2)({\textbackslash}rho(t) = t{\textasciicircum}2), (ii) the sample median (ρ(t)={\textbar}t{\textbar})(ρ(t)={\textbar}t{\textbar})({\textbackslash}rho(t) = {\textbar}t{\textbar}), and more generally, (iii) all maximum likelihood estimators (ρ(t)=−logf(t)(ρ(t)=−logf(t)({\textbackslash}rho(t) = -{\textbackslash}log f(t), where fff is the assumed density of the untranslated distribution). These (MMM)-estimators, as I shall call them for short, have rather pleasant asymptotic properties; sufficient conditions for asymptotic normality and an explicit expression for their asymptotic variance will be given. How should one judge the robustness of an estimator Tn(x)=Tn(x1,⋯,xn)Tn(x)=Tn(x1,⋯,xn)T\_n(x) = T\_n(x\_1, {\textbackslash}cdots, x\_n)? Since ill effects from contamination are mainly felt for large sample sizes, it seems that one should primarily optimize large sample robustness properties. Therefore, a convenient measure of robustness for asymptotically normal estimators seems to be the supremum of the asymptotic variance (n→∞)(n→∞)(n {\textbackslash}rightarrow {\textbackslash}infty) when FFF ranges over some suitable set of underlying distributions, in particular over the set of all F=(1−ϵ)Φ+ϵHF=(1−ϵ)Φ+ϵHF = (1 - {\textbackslash}epsilon){\textbackslash}Phi + {\textbackslash}epsilon H for fixed ϵϵ{\textbackslash}epsilon and symmetric HHH. On second thought, it turns out that the asymptotic variance is not only easier to handle, but that even for moderate values of nnn it is a better measure of performance than the actual variance, because (i) the actual variance of an estimator depends very much on the behavior of the tails of HHH, and the supremum of the actual variance is infinite for any estimator whose value is always contained in the convex hull of the observations. (ii) If an estimator is asymptotically normal, then the important central part of its distribution and confidence intervals for moderate confidence levels can better be approximated in terms of the asymptotic variance than in terms of the actual variance. If we adopt this measure of robustness, and if we restrict attention to (MMM)-estimators, then it will be shown that the most robust estimator is uniquely determined and corresponds to the following ρ:ρ(t)=12t2ρ:ρ(t)=12t2{\textbackslash}rho:{\textbackslash}rho(t) = {\textbackslash}frac\{1\}\{2\}t{\textasciicircum}2 for {\textbar}t{\textbar}{\textless}k,ρ(t)=k{\textbar}t{\textbar}−12k2{\textbar}t{\textbar}{\textless}k,ρ(t)=k{\textbar}t{\textbar}−12k2{\textbar}t{\textbar} {\textless} k, {\textbackslash}rho(t) = k{\textbar}t{\textbar} - {\textbackslash}frac\{1\}\{2\}k{\textasciicircum}2 for {\textbar}t{\textbar}≧k{\textbar}t{\textbar}≧k{\textbar}t{\textbar} {\textbackslash}geqq k, with kkk depending on ϵϵ{\textbackslash}epsilon. This estimator is most robust even among all translation invariant estimators. Sample mean (k=∞)(k=∞)(k = {\textbackslash}infty) and sample median (k=0)(k=0)(k = 0) are limiting cases corresponding to ϵ=0ϵ=0{\textbackslash}epsilon = 0 and ϵ=1ϵ=1{\textbackslash}epsilon = 1, respectively, and the estimator is closely related and asymptotically equivalent to Winsorizing. I recall the definition of Winsorizing: assume that the observations have been ordered, x1≦x2≦⋯≦xnx1≦x2≦⋯≦xnx\_1 {\textbackslash}leqq x\_2 {\textbackslash}leqq {\textbackslash}cdots {\textbackslash}leqq x\_n, then the statistic T=n−1(gxg+1+xg+1+xg+2+⋯+xn−h+hxn−h)T=n−1(gxg+1+xg+1+xg+2+⋯+xn−h+hxn−h)T = n{\textasciicircum}\{-1\}(gx\_\{g + 1\} + x\_\{g + 1\} + x\_\{g + 2\} + {\textbackslash}cdots + x\_\{n - h\} + hx\_\{n - h\}) is called the Winsorized mean, obtained by Winsorizing the ggg leftmost and the hhh rightmost observations. The above most robust (MMM)-estimators can be described by the same formula, except that in the first and in the last summand, the factors xg+1xg+1x\_\{g + 1\} and xn−hxn−hx\_\{n - h\} have to be replaced by some numbers u,vu,vu, v satisfying xg≦u≦xg+1xg≦u≦xg+1x\_g {\textbackslash}leqq u {\textbackslash}leqq x\_\{g + 1\} and xn−h≦v≦xn−h+1xn−h≦v≦xn−h+1x\_\{n - h\} {\textbackslash}leqq v {\textbackslash}leqq x\_\{n - h + 1\}, respectively; g,h,ug,h,ug, h, u and vvv depend on the sample. In fact, this (MMM)-estimator is the maximum likelihood estimator corresponding to a unique least favorable distribution F0F0F\_0 with density f0(t)=(1−ϵ)(2π)−12e−ρ(t)f0(t)=(1−ϵ)(2π)−12e−ρ(t)f\_0(t) = (1 - {\textbackslash}epsilon)(2{\textbackslash}pi){\textasciicircum}\{-{\textbackslash}frac\{1\}\{2\}\}e{\textasciicircum}\{-{\textbackslash}rho(t)\}. This f0f0f\_0 behaves like a normal density for small ttt, like an exponential density for large ttt. At least for me, this was rather surprising--I would have expected an f0f0f\_0 with much heavier tails. This result is a particular case of a more general one that can be stated roughly as follows: Assume that FFF belongs to some convex set CCC of distribution functions. Then the most robust (MMM)-estimator for the set CCC coincides with the maximum likelihood estimator for the unique F0εCF0εCF\_0 {\textbackslash}varepsilon C which has the smallest Fisher information number I(F)=∫(f′/f)2fdtI(F)=∫(f′/f)2fdtI(F) = {\textbackslash}int (f'/f){\textasciicircum}2f dt among all FεCFεCF {\textbackslash}varepsilon C. Miscellaneous related problems will also be treated: the case of non-symmetric contaminating distributions; the most robust estimator for the model of indeterminacy supt{\textbar}F(t)−Φ(t){\textbar}≦ϵsupt{\textbar}F(t)−Φ(t){\textbar}≦ϵ{\textbackslash}sup\_t{\textbar}F(t) - {\textbackslash}Phi(t){\textbar} {\textbackslash}leqq {\textbackslash}epsilon; robust estimation of a scale parameter; how to estimate location, if scale and ϵϵ{\textbackslash}epsilon are unknown; numerical computation of the estimators; more general estimators, e.g., minimizing ∑i{\textless}jρ(xi−T,xj−T)∑i{\textless}jρ(xi−T,xj−T){\textbackslash}sum\_\{i {\textless} j\} {\textbackslash}rho(x\_i - T, x\_j - T), where ρρ{\textbackslash}rho is a function of two arguments. Questions of small sample size theory will not be touched in this paper.},
	language = {EN},
	number = {1},
	urldate = {2021-02-03},
	journal = {Annals of Mathematical Statistics},
	author = {Huber, Peter J.},
	month = mar,
	year = {1964},
	mrnumber = {MR161415},
	zmnumber = {0136.39805},
	note = {Publisher: Institute of Mathematical Statistics},
	pages = {73--101},
}

@article{franke_analysis_2004,
	title = {Analysis of movements and behavior of caribou ({Rangifer} tarandus) using hidden {Markov} models},
	volume = {173},
	issn = {0304-3800},
	url = {http://www.sciencedirect.com/science/article/pii/S0304380003003983},
	doi = {10.1016/j.ecolmodel.2003.06.004},
	abstract = {We explore how doubly stochastic, multiple-observation hidden Markov models (HMMs) may infer meaningful descriptions of woodland caribou (Rangifer tarandus) movement and behavior. Parameterized models allowed us to predict behavioral states (bedding, feeding and relocating), relative bout length and transitions, as well as most likely behavioral state sequences. Identification of state transitions and bout lengths appear specific to individuals and may identify dissimilar strategies of resource selection, behavior-specific habitats that are more important than is simply suggested by time spent there (pattern) and transitions between the same or different states that may be evidence for decision-making (process). Using only estimated model parameters, multiple-observation HMMs permitted us to successfully simulate movement and behavior representative of individual caribou through space and time.},
	language = {en},
	number = {2},
	urldate = {2019-12-22},
	journal = {Ecological Modelling},
	author = {Franke, Alastair and Caelli, Terry and Hudson, Robert J},
	month = apr,
	year = {2004},
	keywords = {Activity, Animal behavior, Animal movement, Hidden Markov model, Resource selection},
	pages = {259--270},
}

@book{hastie01statisticallearning,
  added-at = {2008-05-16T16:17:42.000+0200},
  address = {New York, NY, USA},
  author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
  biburl = {https://www.bibsonomy.org/bibtex/2f58afc5c9793fcc8ad8389824e57984c/sb3000},
  interhash = {d585aea274f2b9b228fc1629bc273644},
  intrahash = {f58afc5c9793fcc8ad8389824e57984c},
  keywords = {ml statistics},
  publisher = {Springer New York Inc.},
  series = {Springer Series in Statistics},
  timestamp = {2008-05-16T16:17:43.000+0200},
  title = {The Elements of Statistical Learning},
  year = 2001
}

@article{holzmann_hidden_2006,
	title = {Hidden {Markov} models for circular and linear-circular time series},
	volume = {13},
	issn = {1352-8505, 1573-3009},
	url = {http://link.springer.com/10.1007/s10651-006-0015-7},
	doi = {10.1007/s10651-006-0015-7},
	number = {3},
	journal = {Environmental and Ecological Statistics},
	author = {Holzmann, Hajo and Munk, Axel and Suster, Max and Zucchini, Walter},
	year = {2006},
	note = {Type: Journal Article},
	pages = {325--347},
}

@article{richardson_power_2015,
	title = {The power of automated behavioural homecage technologies in characterizing disease progression in laboratory mice: {A} review},
	volume = {163},
	issn = {0168-1591},
	shorttitle = {The power of automated behavioural homecage technologies in characterizing disease progression in laboratory mice},
	url = {http://www.sciencedirect.com/science/article/pii/S0168159114003050},
	doi = {10.1016/j.applanim.2014.11.018},
	abstract = {Behavioural changes that occur as animals become sick have been characterized in a number of species and include the less frequent occurrence of ‘luxury behaviours’ such as playing, grooming and socialization. ‘Sickness behaviours’ or behavioural changes following exposure to infectious agents, have been particularly well described; animals are typically less active, sleep more, exhibit postural changes and consume less food/water. Disease is frequently induced in laboratory mice to model pathophysiological processes and investigate potential therapies but despite what is known about behavioural changes as animals become sick, behavioural phenotyping of mice involved in disease studies is relatively rare. A detailed understanding of how behaviour changes as mice get sick could be applied to improve welfare of laboratory mice and support the underlying biomedical research. Specifically, characterizing behavioural changes in ill health could help those working with laboratory mice to recognize when refinements should be introduced, when severity limits are being approached and when humane endpoints should be implemented. Understanding how behaviour changes with illness may also help to identify compounds that have a clinical effect as well as when these agents act. There are an increasing number of automated systems to monitor the behaviour of laboratory mice in their homecages incorporating technologies such as the quantification of cage movement, automated video analysis and radiofrequency identification transponders/readers. Mouse models of neurodegenerative diseases particularly Huntington's disease have been well characterized using these systems and behavioural biomarkers of pathology, including changes in the animals’ use of environmental enrichment, changes in food/water consumption and alterations in circadian rhythms, are now monitored by laboratories worldwide and used to refine studies and develop therapies. In contrast, automated behavioural technologies have not been used to characterize the behaviour of mice with systemic diseases such as cancer and liver disease. In this review, common behavioural changes that occur in animals with declining health will be discussed with an emphasis on progressive disease studies involving mice. Automated homecage behaviour recording technologies will then be summarized, studies in which these systems have been used to characterize the behaviour of mice with progressive diseases will be reviewed and the potential to apply automated technologies to refine disease studies involving mice will be discussed.},
	language = {en},
	urldate = {2019-11-21},
	journal = {Applied Animal Behaviour Science},
	author = {Richardson, Claire A.},
	month = feb,
	year = {2015},
	keywords = {Animal welfare, Automated behavioural analysis, Disease, Humane endpoints, Mice, Replacement, reduction, refinement (3Rs)},
	pages = {19--27}
}

@software{sgoldenlab_2021_4521178,
  author       = {sgoldenlab and
                  Jia Jie Choong and
                  Simon Nilsson and
                  Aasiya Islam and
                  sophihwang26},
  title        = {sgoldenlab/simba: SimBA: release v1.3},
  month        = feb,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.3},
  doi          = {10.5281/zenodo.4521178},
  url          = {https://doi.org/10.5281/zenodo.4521178}
}

@article {Hsu770271,
	author = {Hsu, Alexander I. and Yttri, Eric A.},
	title = {B-SOiD: An Open Source Unsupervised Algorithm for Discovery of Spontaneous Behaviors},
	elocation-id = {770271},
	year = {2020},
	doi = {10.1101/770271},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Capturing the performance of naturalistic behaviors remains a prohibitively difficult objective. Recent machine learning applications have enabled localization of limb position; however, position alone does not yield behavior. To provide a bridge from positions to actions and their kinematics, we developed Behavioral Segmentation of Open-field in DeepLabCut (B-SOiD). This unsupervised learning algorithm discovers natural patterns in position and extracts their inherent statistics. The cluster statistics are then used to train a machine learning algorithm to classify behaviors with greater speed and accuracy due to improved ability to generalize from subject to subject. Through the application of a novel fram-eshift paradigm, B-SOiD provides the fast temporal resolution required for comparison with neural activity. B-SOiD also provides high-resolution behavioral measures such as stride and grooming kinematics, that are difficult but critical to obtain, particularly in the study of pain, compulsion, and other neurological disorders. This open-source platform surpasses the current state of the field in its improved analytical accessibility, objectivity, and ease of use.},
	URL = {https://www.biorxiv.org/content/early/2020/03/07/770271},
	eprint = {https://www.biorxiv.org/content/early/2020/03/07/770271.full.pdf},
	journal = {bioRxiv}
}

@software{vivek_hari_sridhar_2017_1134016,
  author       = {Vivek Hari Sridhar},
  title        = {vivekhsridhar/tracktor: Tracktor},
  month        = dec,
  year         = 2017,
  publisher    = {Zenodo},
  version      = {tracktor},
  doi          = {10.5281/zenodo.1134016},
  url          = {https://doi.org/10.5281/zenodo.1134016}
}

@article{wijeyakulasuriya_machine_2020,
	title = {Machine learning for modeling animal movement},
	volume = {15},
	url = {https://doi.org/10.1371/journal.pone.0235750},
	doi = {10.1371/journal.pone.0235750},
	abstract = {Animal movement drives important ecological processes such as migration and the spread of infectious disease. Current approaches to modeling animal tracking data focus on parametric models used to understand environmental effects on movement behavior and to fill in missing tracking data. Machine Learning and Deep learning algorithms are powerful and flexible predictive modeling tools but have rarely been applied to animal movement data. In this study we present a general framework for predicting animal movement that is a combination of two steps: first predicting movement behavioral states and second predicting the animal’s velocity. We specify this framework at the individual level as well as for collective movement. We use Random Forests, Neural and Recurrent Neural Networks to compare performance predicting one step ahead as well as long range simulations. We compare results against a custom constructed Stochastic Differential Equation (SDE) model. We apply this approach to high resolution ant movement data. We found that the individual level Machine Learning and Deep Learning methods outperformed the SDE model for one step ahead prediction. The SDE model did comparatively better at simulating long range movement behaviour. Of the Machine Learning and Deep Learning models the Long Short Term Memory (LSTM) individual level model did best at long range simulations. We also applied the Random Forest and LSTM individual level models to model gull migratory movement to demonstrate the generalizability of this framework. Machine Learning and deep learning models are easier to specify compared to traditional parametric movement models which can have restrictive assumptions. However, machine learning and deep learning models are less interpretable than parametric movement models. The type of model used should be determined by the goal of the study, if the goal is prediction, our study provides evidence that machine learning and deep learning models could be useful tools.},
	number = {7},
	journal = {PLOS ONE},
	author = {Wijeyakulasuriya, Dhanushi A. and Eisenhauer, Elizabeth W. and Shaby, Benjamin A. and Hanks, Ephraim M.},
	year = {2020},
	note = {Publisher: Public Library of Science},
	pages = {1--30},
}

@article{Mathisetal2018,
    title={DeepLabCut: markerless pose estimation of user-defined body parts with deep learning},
    author = {Alexander Mathis and Pranav Mamidanna and Kevin M. Cury and Taiga Abe  and Venkatesh N. Murthy and Mackenzie W. Mathis and Matthias Bethge},
    journal={Nature Neuroscience},
    year={2018},
	doi={10.1038/s41593-018-0209-y},
    url={https://www.nature.com/articles/s41593-018-0209-y}
}

@incollection{pytorch,
title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
booktitle = {Advances in Neural Information Processing Systems 32},
editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
pages = {8024--8035},
year = {2019},
publisher = {Curran Associates, Inc.},
url = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

@inproceedings{zhou_micro_2018,
	title = {Micro {Behaviors}: {A} {New} {Perspective} in {E}-commerce {Recommender} {Systems}},
	shorttitle = {Micro {Behaviors}},
	doi = {10.1145/3159652.3159671},
	abstract = {The explosive popularity of e-commerce sites has reshaped users» shopping habits and an increasing number of users prefer to spend more time shopping online. This evolution allows e-commerce sites to observe rich data about users. The majority of traditional recommender systems have focused on the macro interactions between users and items, i.e., the purchase history of a customer. However, within each macro interaction between a user and an item, the user actually performs a sequence of micro behaviors, which indicate how the user locates the item, what activities the user conducts on the item (e.g., reading the comments, carting, and ordering) and how long the user stays with the item. Such micro behaviors offer fine-grained and deep understandings about users and provide tremendous opportunities to advance recommender systems in e-commerce. However, exploiting micro behaviors for recommendations is rather limited, which motivates us to investigate e-commerce recommendations from a micro-behavior perspective in this paper. Particularly, we uncover the effects of micro behaviors on recommendations and propose an interpretable Recommendation framework RIB, which models inherently the sequence of mIcro Behaviors and their effects. Experimental results on datasets from a real e-commence site demonstrate the effectiveness of the proposed framework and the importance of micro behaviors for recommendations.},
	booktitle = {{WSDM}},
	author = {Zhou, Meizi and Ding, Zhuoye and Tang, Jiliang and Yin, Dawei},
	year = {2018},
	keywords = {E-commerce, E-commerce payment system, IBM Notes, Interaction, Online shopping, Recommender system}
}

@article{pedregosa_scikit-learn:_2011,
	title = {Scikit-learn: {Machine} {Learning} in {Python}},
	volume = {12},
	issn = {ISSN 1533-7928},
	shorttitle = {Scikit-learn},
	url = {http://www.jmlr.org/papers/v12/pedregosa11a.html},
	number = {Oct},
	urldate = {2019-11-06},
	journal = {Journal of Machine Learning Research},
	author = {Pedregosa, Fabian and Varoquaux, Gaël and Gramfort, Alexandre and Michel, Vincent and Thirion, Bertrand and Grisel, Olivier and Blondel, Mathieu and Prettenhofer, Peter and Weiss, Ron and Dubourg, Vincent and Vanderplas, Jake and Passos, Alexandre and Cournapeau, David and Brucher, Matthieu and Perrot, Matthieu and Duchesnay, Édouard},
	year = {2011},
	pages = {2825--2830}
}

@article{singh_low-cost_2019,
	title = {Low-cost solution for rodent home-cage behaviour monitoring},
	volume = {14},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220751},
	doi = {10.1371/journal.pone.0220751},
	abstract = {In the current research on measuring complex behaviours/phenotyping in rodents, most of the experimental design requires the experimenter to remove the animal from its home-cage environment and place it in an unfamiliar apparatus (novel environment). This interaction may influence behaviour, general well-being, and the metabolism of the animal, affecting the phenotypic outcome even if the data collection method is automated. Most of the commercially available solutions for home-cage monitoring are expensive and usually lack the flexibility to be incorporated with existing home-cages. Here we present a low-cost solution for monitoring home-cage behaviour of rodents that can be easily incorporated to practically any available rodent home-cage. To demonstrate the use of our system, we reliably predict the sleep/wake state of mice in their home-cage using only video. We validate these results using hippocampal local field potential (LFP) and electromyography (EMG) data. Our approach provides a low-cost flexible methodology for high-throughput studies of sleep, circadian rhythm and rodent behaviour with minimal experimenter interference.},
	language = {en},
	number = {8},
	urldate = {2019-11-06},
	journal = {PLOS ONE},
	author = {Singh, Surjeet and Bermudez-Contreras, Edgar and Nazari, Mojtaba and Sutherland, Robert J. and Mohajerani, Majid H.},
	month = aug,
	year = {2019},
	keywords = {Algorithms, Animal behavior, Cameras, Electromyography, Mice, Rodents, Sleep, Web-based applications},
	pages = {e0220751}
}

@misc{noauthor_m-track:_nodate,
	title = {M-{Track}: {A} {New} {Software} for {Automated} {Detection} of {Grooming} {Trajectories} in {Mice}},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005115},
	urldate = {2019-11-06},
	doi = {10.1371/journal.pcbi.1005115},
}

@article{jiang_trajectorynet:_2017,
	title = {{TrajectoryNet}: {An} {Embedded} {GPS} {Trajectory} {Representation} for {Point}-based {Classification} {Using} {Recurrent} {Neural} {Networks}},
	shorttitle = {{TrajectoryNet}},
	url = {http://arxiv.org/abs/1705.02636},
	abstract = {Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose TrajectoryNet-a neural network architecture for point-based trajectory classification to infer real world human transportation modes from GPS traces. To overcome the challenge of capturing the underlying latent factors in the low-dimensional and heterogeneous feature space imposed by GPS data, we develop a novel representation that embeds the original feature space into another space that can be understood as a form of basis expansion. We also enrich the feature space via segment-based information and use Maxout activations to improve the predictive power of Recurrent Neural Networks (RNNs). We achieve over 98\% classification accuracy when detecting four types of transportation modes, outperforming existing models without additional sensory data or location-based prior knowledge.},
	urldate = {2019-11-06},
	journal = {arXiv:1705.02636 [cs]},
	author = {Jiang, Xiang and de Souza, Erico N. and Pesaranghader, Ahmad and Hu, Baifan and Silver, Daniel L. and Matwin, Stan},
	month = aug,
	year = {2017},
	note = {arXiv: 1705.02636},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, H.2.8, I.2.1, I.2.6}
}
@article{zheng_trajectory_2015,
	title = {Trajectory {Data} {Mining}: {An} {Overview}},
	shorttitle = {Trajectory {Data} {Mining}},
	url = {https://www.microsoft.com/en-us/research/publication/trajectory-data-mining-an-overview/},
	abstract = {The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles and animals. Many techniques have been proposed for processing, managing and mining trajectory data in the past decade, fostering a broad range of applications. In this article, …},
	language = {en-US},
	urldate = {2019-11-06},
	journal = {ACM Transaction on Intelligent Systems and Technology},
	author = {Zheng, Yu},
	month = sep,
	year = {2015}
}

@article{zheng_trajectory_2015-1,
	title = {Trajectory {Data} {Mining}: {An} {Overview}},
	volume = {6},
	issn = {21576904},
	shorttitle = {Trajectory {Data} {Mining}},
	url = {http://dl.acm.org/citation.cfm?doid=2764959.2743025},
	doi = {10.1145/2743025},
	language = {en},
	number = {3},
	urldate = {2019-11-06},
	journal = {ACM Transactions on Intelligent Systems and Technology},
	author = {Zheng, Yu},
	month = may,
	year = {2015},
	pages = {1--41}
}

@article{calahorra_hydroxytyrosol_2019,
	title = {Hydroxytyrosol, the {Major} {Phenolic} {Compound} of {Olive} {Oil}, as an {Acute} {Therapeutic} {Strategy} after {Ischemic} {Stroke}},
	volume = {11},
	copyright = {http://creativecommons.org/licenses/by/3.0/},
	url = {https://www.mdpi.com/2072-6643/11/10/2430},
	doi = {10.3390/nu11102430},
	abstract = {Stroke is one of the leading causes of adult disability worldwide. After ischemic stroke, damaged tissue surrounding the irreversibly damaged core of the infarct, the penumbra, is still salvageable and is therefore a target for acute therapeutic strategies. The Mediterranean diet (MD) has been shown to lower stroke risk. MD is characterized by increased intake of extra-virgin olive oil, of which hydroxytyrosol (HT) is the foremost phenolic component. This study investigates the effect of an HT-enriched diet directly after stroke on regaining motor and cognitive functioning, MRI parameters, neuroinflammation, and neurogenesis. Stroke mice on an HT diet showed increased strength in the forepaws, as well as improved short-term recognition memory probably due to improvement in functional connectivity (FC). Moreover, mice on an HT diet showed increased cerebral blood flow (CBF) and also heightened expression of brain derived neurotrophic factor (Bdnf), indicating a novel neurogenic potential of HT. This result was additionally accompanied by an enhanced transcription of the postsynaptic marker postsynaptic density protein 95 (Psd-95) and by a decreased ionized calcium-binding adapter molecule 1 (IBA-1) level indicative of lower neuroinflammation. These results suggest that an HT-enriched diet could serve as a beneficial therapeutic approach to attenuate ischemic stroke-associated damage.},
	language = {en},
	number = {10},
	urldate = {2019-11-05},
	journal = {Nutrients},
	author = {Calahorra, Jesús and Shenk, Justin and Wielenga, Vera H. and Verweij, Vivienne and Geenen, Bram and Dederen, Pieter J. and Peinado, M. Ángeles and Siles, Eva and Wiesmann, Maximilian and Kiliaan, Amanda J.},
	month = oct,
	year = {2019},
	keywords = {MRI, animal model, cerebral blood flow, cerebral connectivity, dietary treatment, hydroxytyrosol, neuroinflammation, stroke},
	pages = {2430}
}

@article{iannello_non-intrusive_2019,
	title = {Non-intrusive high throughput automated data collection from the home cage},
	volume = {5},
	issn = {2405-8440},
	doi = {10.1016/j.heliyon.2019.e01454},
	abstract = {Automated home cage monitoring represents a key technology to collect animal activity information directly from the home cage. The availability of 24/7 cage data enables extensive and quantitative assessment of mouse behavior and activity over long periods of time than possible otherwise. When home cage monitoring is performed directly at the home cage rack, it is possible to leverage additional advantages, including, e.g., partial (or total) reduction of animal handling, no need for setting up external data collection system as well as not requiring dedicated labs and personnel to perform tests. In this work we introduce a home cage-home rack monitoring system that is capable of continuously detecting spontaneous animal activity occurring in the home cage directly from the home cage rack. The proposed system is based on an electrical capacitance sensing technology that enables non-intrusive and continuous home cage monitoring. We then present a few animal activity metrics that are validated via comparison against a video camera-based tracking system. The results show that the proposed home-cage monitoring system can provide animal activity metrics that are comparable to the ones derived via a conventional video tracking system, with the advantage of system scalability, limited amount of both data generated and computational capabilities required to derive metrics.},
	language = {eng},
	number = {4},
	journal = {Heliyon},
	author = {Iannello, Fabio},
	month = apr,
	year = {2019},
	pmid = {30997429},
	pmcid = {PMC6451168},
	keywords = {Bioengineering, Bioinformatics, Cancer research, Genetics, Neuroscience, Physiology, Toxicology},
	pages = {e01454}
}

@article{pernold_towards_2019,
	title = {Towards large scale automated cage monitoring – {Diurnal} rhythm and impact of interventions on in-cage activity of {C}57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique},
	volume = {14},
	issn = {1932-6203},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6361443/},
	doi = {10.1371/journal.pone.0211063},
	abstract = {Background and aims
Automated recording of laboratory animal’s home cage behavior is receiving increasing attention since such non-intruding surveillance will aid in the unbiased understanding of animal cage behavior potentially improving animal experimental reproducibility.

Material and methods
Here we investigate activity of group held female C57BL/6J mice (mus musculus) housed in standard Individually Ventilated Cages across three test-sites: Consiglio Nazionale delle Ricerche (CNR, Rome, Italy), The Jackson Laboratory (JAX, Bar Harbor, USA) and Karolinska Insititutet (KI, Stockholm, Sweden). Additionally, comparison of female and male C57BL/6J mice was done at KI. Activity was recorded using a capacitive-based sensor placed non-intrusively on the cage rack under the home cage collecting activity data every 250 msec, 24/7. The data collection was analyzed using non-parametric analysis of variance for longitudinal data comparing sites, weekdays and sex.

Results
The system detected an increase in activity preceding and peaking around lights-on followed by a decrease to a rest pattern. At lights off, activity increased substantially displaying a distinct temporal variation across this period. We also documented impact on mouse activity that standard animal handling procedures have, e.g. cage-changes, and show that such procedures are stressors impacting in-cage activity., These key observations replicated across the three test-sites, however, it is also clear that, apparently minor local environmental differences generate significant behavioral variances between the sites and within sites across weeks. Comparison of gender revealed differences in activity in the response to cage-change lasting for days in male but not female mice; and apparently also impacting the response to other events such as lights-on in males. Females but not males showed a larger tendency for week-to-week variance in activity possibly reflecting estrous cycling.

Conclusions
These data demonstrate that home cage monitoring is scalable and run in real time, providing complementary information for animal welfare measures, experimental design and phenotype characterization.},
	number = {2},
	urldate = {2019-11-05},
	journal = {PLoS ONE},
	author = {Pernold, Karin and Iannello, F. and Low, B. E. and Rigamonti, M. and Rosati, G. and Scavizzi, F. and Wang, J. and Raspa, M. and Wiles, M. V. and Ulfhake, B.},
	month = feb,
	year = {2019},
	pmid = {30716111},
	pmcid = {PMC6361443}
}

@article{liang_peeking_2019,
	title = {Peeking into the {Future}: {Predicting} {Future} {Person} {Activities} and {Locations} in {Videos}},
	shorttitle = {Peeking into the {Future}},
	url = {http://arxiv.org/abs/1902.03748},
	abstract = {Deciphering human behaviors to predict their future paths/trajectories and what they would do from videos is important in many applications. Motivated by this idea, this paper studies predicting a pedestrian's future path jointly with future activities. We propose an end-to-end, multi-task learning system utilizing rich visual features about human behavioral information and interaction with their surroundings. To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen. Experimental results demonstrate our state-of-the-art performance over two public benchmarks on future trajectory prediction. Moreover, our method is able to produce meaningful future activity prediction in addition to the path. The result provides the first empirical evidence that joint modeling of paths and activities benefits future path prediction.},
	urldate = {2019-11-01},
	journal = {arXiv:1902.03748 [cs]},
	author = {Liang, Junwei and Jiang, Lu and Niebles, Juan Carlos and Hauptmann, Alexander and Fei-Fei, Li},
	month = may,
	year = {2019},
	note = {arXiv: 1902.03748},
	doi = {10.1109/cvprw.2019.00358},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{amirian_social_2019,
	title = {Social {Ways}: {Learning} {Multi}-{Modal} {Distributions} of {Pedestrian} {Trajectories} with {GANs}},
	shorttitle = {Social {Ways}},
	url = {http://arxiv.org/abs/1904.09507},
	abstract = {This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs are very susceptible to mode collapsing and dropping, we show that the recently proposed Info-GAN allows dramatic improvements in multi-modal pedestrian trajectory prediction to avoid these issues. We also left out L2-loss in training the generator, unlike some previous works, because it causes serious mode collapsing though faster convergence. We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution. In particular, to prove this claim, we have designed a toy example dataset of trajectories that can be used to assess the performance of different methods in preserving the predictive distribution modes.},
	urldate = {2019-11-01},
	journal = {arXiv:1904.09507 [cs]},
	author = {Amirian, Javad and Hayet, Jean-Bernard and Pettre, Julien},
	month = apr,
	year = {2019},
	note = {arXiv: 1904.09507},
	doi = {10.1109/cvprw.2019.00359},
	keywords = {Computer Science - Computer Vision and Pattern Recognition}
}

@article{chesler_identification_2002,
	title = {Identification and ranking of genetic and laboratory environment factors influencing a behavioral trait, thermal nociception, via computational analysis of a large data archive},
	volume = {26},
	issn = {0149-7634},
	url = {http://www.sciencedirect.com/science/article/pii/S0149763402001033},
	doi = {10.1016/S0149-7634(02)00103-3},
	abstract = {Laboratory conditions in biobehavioral experiments are commonly assumed to be ‘controlled’, having little impact on the outcome. However, recent studies have illustrated that the laboratory environment has a robust effect on behavioral traits. Given that environmental factors can interact with trait-relevant genes, some have questioned the reliability and generalizability of behavior genetic research designed to identify those genes. This problem might be alleviated by the identification of the most relevant environmental factors, but the task is hindered by the large number of factors that typically vary between and within laboratories. We used a computational approach to retrospectively identify and rank sources of variability in nociceptive responses as they occurred in a typical research laboratory over several years. A machine-learning algorithm was applied to an archival data set of 8034 independent observations of baseline thermal nociceptive sensitivity. This analysis revealed that a factor even more important than mouse genotype was the experimenter performing the test, and that nociception can be affected by many additional laboratory factors including season/humidity, cage density, time of day, sex and within-cage order of testing. The results were confirmed by linear modeling in a subset of the data, and in confirmatory experiments, in which we were able to partition the variance of this complex trait among genetic (27\%), environmental (42\%) and genetic×environmental (18\%) sources.},
	language = {en},
	number = {8},
	urldate = {2019-11-01},
	journal = {Neuroscience \& Biobehavioral Reviews},
	author = {Chesler, Elissa J and Wilson, Sonya G and Lariviere, William R and Rodriguez-Zas, Sandra L and Mogil, Jeffrey S},
	month = dec,
	year = {2002},
	keywords = {CART, Data mining, Environment, Genetic, Mice, Nociception, Pain},
	pages = {907--923}
}

@article{valletta_applications_2017,
	title = {Applications of machine learning in animal behaviour studies},
	volume = {124},
	issn = {0003-3472},
	url = {http://www.sciencedirect.com/science/article/pii/S0003347216303360},
	doi = {10.1016/j.anbehav.2016.12.005},
	abstract = {In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into scientific knowledge and become a useful addition to the animal behaviourist's analytical toolkit.},
	language = {en},
	urldate = {2019-11-01},
	journal = {Animal Behaviour},
	author = {Valletta, John Joseph and Torney, Colin and Kings, Michael and Thornton, Alex and Madden, Joah},
	month = feb,
	year = {2017},
	keywords = {animal behaviour data, classification, clustering, dimensionality reduction, machine learning, predictive modelling, random forests, social networks, supervised learning, unsupervised learning},
	pages = {203--220}
}

@article{kerster_spatial_2016,
	title = {Spatial memory in foraging games},
	volume = {148},
	issn = {1873-7838},
	doi = {10.1016/j.cognition.2015.12.015},
	abstract = {Foraging and foraging-like processes are found in spatial navigation, memory, visual search, and many other search functions in human cognition and behavior. Foraging is commonly theorized using either random or correlated movements based on Lévy walks, or a series of decisions to remain or leave proximal areas known as "patches". Neither class of model makes use of spatial memory, but search performance may be enhanced when information about searched and unsearched locations is encoded. A video game was developed to test the role of human spatial memory in a canonical foraging task. Analyses of search trajectories from over 2000 human players yielded evidence that foraging movements were inherently clustered, and that clustering was facilitated by spatial memory cues and influenced by memory for spatial locations of targets found. A simple foraging model is presented in which spatial memory is used to integrate aspects of Lévy-based and patch-based foraging theories to perform a kind of area-restricted search, and thereby enhance performance as search unfolds. Using only two free parameters, the model accounts for a variety of findings that individually support competing theories, but together they argue for the integration of spatial memory into theories of foraging.},
	language = {eng},
	journal = {Cognition},
	author = {Kerster, Bryan E. and Rhodes, Theo and Kello, Christopher T.},
	month = mar,
	year = {2016},
	pmid = {26752603},
	keywords = {Area-restricted search, Cognition, Feeding Behavior, Foraging, Humans, Search model, Spatial Behavior, Spatial memory, Spatial Memory, Spatial Navigation, Video Games},
	pages = {85--96},
}

@article{ross_influence_2016,
	title = {Influence of musical groove on postural sway},
	volume = {42},
	issn = {1939-1277(Electronic),0096-1523(Print)},
	doi = {10.1037/xhp0000198},
	abstract = {Timescales of postural fluctuation reflect underlying neuromuscular processes in balance control that are influenced by sensory information and the performance of concurrent cognitive and motor tasks. An open question is how postural fluctuations entrain to complex environmental rhythms, such as in music, which also vary on multiple timescales. Musical groove describes the property of music that encourages auditory-motor synchronization and is used to study voluntary motor entrainment to rhythmic sounds. The influence of groove on balance control mechanisms remains unexplored. We recorded fluctuations in center of pressure (CoP) of standing participants (N = 40) listening to low and high groove music and during quiet stance. We found an effect of musical groove on radial sway variability, with the least amount of variability in the high groove condition. In addition, we observed that groove influenced postural sway entrainment at various temporal scales. For example, with increasing levels of groove, we observed more entrainment to shorter, local timescale rhythmic musical occurrences. In contrast, we observed more entrainment to longer, global timescale features of the music, such as periodicity, with decreasing levels of groove. Finally, musical experience influenced the amount of postural variability and entrainment at local and global timescales. We conclude that groove in music and musical experience can influence the neural mechanisms that govern balance control, and discuss implications of our findings in terms of multiscale sensorimotor coupling. (PsycINFO Database Record (c) 2016 APA, all rights reserved)},
	number = {3},
	journal = {Journal of Experimental Psychology: Human Perception and Performance},
	author = {Ross, Jessica M. and Warlaumont, Anne S. and Abney, Drew H. and Rigoli, Lillian M. and Balasubramaniam, Ramesh},
	year = {2016},
	note = {Place: US
Publisher: American Psychological Association},
	keywords = {Auditory Stimulation, Motor Processes, Music, Posture, Rhythm},
	pages = {308--319},
	file = {Snapshot:/Users/justinshenk/Zotero/storage/DNH9UD9R/2015-58954-001.html:text/html},
}

@article{ayers,
author = {Ayers, Carolyn and Armsworth, Paul and Brosi, Berry},
year = {2015},
month = {08},
pages = {},
title = {Determinism as a statistical metric for ecologically important recurrent behaviors with trapline foraging as a case study},
volume = {69},
journal = {Behavioral Ecology and Sociobiology},
doi = {10.1007/s00265-015-1948-3}
}

@article{morato_jaguar_2018,
	title = {Jaguar movement database: a {GPS}-based movement dataset of an apex predator in the {Neotropics}},
	volume = {99},
	copyright = {© 2018 The Authors. Ecology © 2018 The Ecological Society of America.},
	issn = {1939-9170},
	shorttitle = {Jaguar movement database},
	url = {https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecy.2379},
	doi = {10.1002/ecy.2379},
	abstract = {The field of movement ecology has rapidly grown during the last decade, with important advancements in tracking devices and analytical tools that have provided unprecedented insights into where, when, and why species move across a landscape. Although there has been an increasing emphasis on making animal movement data publicly available, there has also been a conspicuous dearth in the availability of such data on large carnivores. Globally, large predators are of conservation concern. However, due to their secretive behavior and low densities, obtaining movement data on apex predators is expensive and logistically challenging. Consequently, the relatively small sample sizes typical of large carnivore movement studies may limit insights into the ecology and behavior of these elusive predators. The aim of this initiative is to make available to the conservation-scientific community a dataset of 134,690 locations of jaguars (Panthera onca) collected from 117 individuals (54 males and 63 females) tracked by GPS technology. Individual jaguars were monitored in five different range countries representing a large portion of the species’ distribution. This dataset may be used to answer a variety of ecological questions including but not limited to: improved models of connectivity from local to continental scales; the use of natural or human-modified landscapes by jaguars; movement behavior of jaguars in regions not represented in this dataset; intraspecific interactions; and predator-prey interactions. In making our dataset publicly available, we hope to motivate other research groups to do the same in the near future. Specifically, we aim to help inform a better understanding of jaguar movement ecology with applications towards effective decision making and maximizing long-term conservation efforts for this ecologically important species. There are no costs, copyright, or proprietary restrictions associated with this data set. When using this data set, please cite this article to recognize the effort involved in gathering and collating the data and the willingness of the authors to make it publicly available.},
	language = {en},
	number = {7},
	urldate = {2021-06-04},
	journal = {Ecology},
	author = {Morato, Ronaldo G. and Thompson, Jeffrey J. and Paviolo, Agustin and Torre, Jesus A. de La and Lima, Fernando and McBride, Roy T. and Paula, Rogerio C. and Cullen, Laury and Silveira, Leandro and Kantek, Daniel L. Z. and Ramalho, Emiliano E. and Maranhão, Louise and Haberfeld, Mario and Sana, Denis A. and Medellin, Rodrigo A. and Carrillo, Eduardo and Montalvo, Victor and Monroy-Vilchis, Octavio and Cruz, Paula and Jacomo, Anah T. and Torres, Natalia M. and Alves, Giselle B. and Cassaigne, Ivonne and Thompson, Ron and Saens-Bolanos, Carolina and Cruz, Juan Carlos and Alfaro, Luiz D. and Hagnauer, Isabel and Silva, Xavier Marina da and Vogliotti, Alexandre and Moraes, Marcela F. D. and Miyazaki, Selma S. and Pereira, Thadeu D. C. and Araujo, Gediendson R. and Silva, Leanes Cruz da and Leuzinger, Lucas and Carvalho, Marina M. and Rampin, Lilian and Sartorello, Leonardo and Quigley, Howard and Tortato, Fernando and Hoogesteijn, Rafael and Crawshaw, Peter G. and Devlin, Allison L. and May, Joares A. and Azevedo, Fernando C. C. de and Concone, Henrique V. B. and Quiroga, Veronica A. and Costa, Sebastian A. and Arrabal, Juan P. and Vanderhoeven, Ezequiel and Blanco, Yamil E. Di and Lopes, Alexandre M. C. and Widmer, Cynthia E. and Ribeiro, Milton Cezar},
	year = {2018},
	note = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecy.2379},
	keywords = {behavior, conservation, GPS radio-collars, habitat use, landscape, movement ecology, Panthera onca},
	pages = {1691--1691},
	file = {Full Text PDF:/Users/justinshenk/Zotero/storage/FG8IDDP7/Morato et al. - 2018 - Jaguar movement database a GPS-based movement dat.pdf:application/pdf;Snapshot:/Users/justinshenk/Zotero/storage/FJT5T8D9/ecy.html:text/html},
}

@article{neves_recurrence_2017,
	title = {Recurrence analysis of ant activity patterns},
	volume = {12},
	url = {https://doi.org/10.1371/journal.pone.0185968},
	doi = {10.1371/journal.pone.0185968},
	abstract = {In this study, we used recurrence quantification analysis (RQA) and recurrence plots (RPs) to compare the movement activity of individual workers of three ant species, as well as a gregarious beetle species. RQA and RPs quantify the number and duration of recurrences of a dynamical system, including a detailed quantification of signals that could be stochastic, deterministic, or both. First, we found substantial differences between the activity dynamics of beetles and ants, with the results suggesting that the beetles have quasi-periodic dynamics and the ants do not. Second, workers from different ant species varied with respect to their dynamics, presenting degrees of predictability as well as stochastic signals. Finally, differences were found among minor and major caste of the same (dimorphic) ant species. Our results underscore the potential of RQA and RPs in the analysis of complex behavioral patterns, as well as in general inferences on animal behavior and other biological phenomena.},
	number = {10},
	journal = {PLOS ONE},
	author = {Neves, Felipe Marcel and Viana, Ricardo Luiz and Pie, Marcio Roberto},
	year = {2017},
	note = {Publisher: Public Library of Science},
	pages = {1--15},
}

@article{shockley,
author = {Shockley, Kevin and Santana, Marie-Vee and Fowler, Carol},
year = {2003},
month = {05},
pages = {326-32},
title = {Mutual Interpersonal Postural Constraints are Involved in Cooperative Conversation},
volume = {29},
journal = {Journal of experimental psychology. Human perception and performance},
doi = {10.1037/0096-1523.29.2.326}
}

@article{huette_drawing_2013,
	title = {Drawing from {Memory}: {Hand}-{Eye} {Coordination} at {Multiple} {Scales}},
	volume = {8},
	shorttitle = {Drawing from {Memory}},
	doi = {10.1371/journal.pone.0058464},
	abstract = {Eyes move to gather visual information for the purpose of guiding behavior. This guidance takes the form of perceptual-motor interactions on short timescales for behaviors like locomotion and hand-eye coordination. More complex behaviors require perceptual-motor interactions on longer timescales mediated by memory, such as navigation, or designing and building artifacts. In the present study, the task of sketching images of natural scenes from memory was used to examine and compare perceptual-motor interactions on shorter and longer timescales. Eye and pen trajectories were found to be coordinated in time on shorter timescales during drawing, and also on longer timescales spanning study and drawing periods. The latter type of coordination was found by developing a purely spatial analysis that yielded measures of similarity between images, eye trajectories, and pen trajectories. These results challenge the notion that coordination only unfolds on short timescales. Rather, the task of drawing from memory evokes perceptual-motor encodings of visual images that preserve coarse-grained spatial information over relatively long timescales as well.},
	journal = {PloS one},
	author = {Huette, Stephanie and Kello, Christopher and Rhodes, Theo and Spivey, Michael},
	month = mar,
	year = {2013},
	pages = {e58464},
	file = {Full Text PDF:/Users/justinshenk/Zotero/storage/JZ4JJ7T7/Huette et al. - 2013 - Drawing from Memory Hand-Eye Coordination at Mult.pdf:application/pdf},
}

@article{clustering_mice,
author = {Bak, Peter and Mansmann, Florian and Janetzko, Halldor and Keim, Daniel},
year = {2009},
month = {11},
pages = {913-20},
title = {Spatiotemporal Analysis of Sensor Logs using Growth Ring Maps},
volume = {15},
journal = {IEEE transactions on visualization and computer graphics},
doi = {10.1109/TVCG.2009.182}
}

@article{huang_mapping_2020,
	title = {Mapping {Mouse} {Behavior} with an {Unsupervised} {Spatio}-temporal {Sequence} {Decomposition} {Framework}},
	url = {https://www.biorxiv.org/content/early/2020/09/14/2020.09.14.295808},
	doi = {10.1101/2020.09.14.295808},
	abstract = {Objective quantification of animal behavior is crucial to understanding the relationship between brain activity and behavior. For rodents, this has remained a challenge due to the high-dimensionality and large temporal variability of their behavioral features. Inspired by the natural structure of animal behavior, the present study uses a parallel, multi-stage approach to decompose motion features and generate an objective metric for mapping rodent behavior into the animal’s feature space. Incorporating a three-dimensional (3D) motion-capture system and unsupervised clustering into this approach, we developed a framework that can automatically identify animal behavioral phenotypes from experimental monitoring. We demonstrate the efficacy of our framework by generating an “autistic-like behavior space” that can robustly characterize a transgenic mouse disease model based on motor activity without human supervision. Our results suggest that our framework features a broad range of applications, including animal disease model phenotyping and the modeling of relationships between neural circuits and behavior.Competing Interest StatementThe authors have declared no competing interest.},
	journal = {bioRxiv},
	author = {Huang, Kang and Han, Yaning and Chen, Ke and Pan, Hongli and Yi, Wenling and Li, Xiaoxi and Liu, Siyuan and Wei, Pengfei and Wang, Liping},
	year = {2020},
	note = {Publisher: Cold Spring Harbor Laboratory
\_eprint: https://www.biorxiv.org/content/early/2020/09/14/2020.09.14.295808.full.pdf},
}

@article{mclean_trajr:_2018,
	title = {trajr: {An} {R} package for characterisation of animal trajectories},
	volume = {124},
	copyright = {© 2018 The Authors. Ethology Published by Blackwell Verlag GmbH},
	issn = {1439-0310},
	shorttitle = {trajr},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/eth.12739},
	doi = {10.1111/eth.12739},
	abstract = {Quantitative characterisation of the trajectories of moving animals is an important component of many behavioural and ecological studies, however methods are complicated and varied, and sometimes require well-developed programming skills to implement. Here, we introduce trajr, an R package that serves to analyse animal paths, from unicellular organisms, through insects to whales. It makes a variety of statistical characterisations of trajectories, such as tortuosity, speed and changes in direction, available to biologists who may not have a background in programming. We discuss a range of indices that have been used by researchers, describe the package in detail, then use movement observations of whales and clearwing moths to demonstrate some of the capabilities of trajr. As an open-source R package, trajr encourages open and reproducible research. It supports the implementation of additional methods by providing access to trajectory analysis “building blocks,” allows the full suite of R statistical analysis tools to be applied to trajectory analysis, and the source code can be independently validated.},
	language = {en},
	number = {6},
	urldate = {2019-07-30},
	journal = {Ethology},
	author = {McLean, Donald James and Volponi, Marta A. Skowron},
	year = {2018},
	keywords = {behaviour, locomotor mimicry, navigation, speed, tortuosity, whales},
	pages = {440--448}
}

@article{mullner_modern_2011,
	title = {Modern hierarchical, agglomerative clustering algorithms},
	url = {http://arxiv.org/abs/1109.2378},
	abstract = {This paper presents algorithms for hierarchical, agglomerative clustering which perform most efficiently in the general-purpose setup that is given in modern standard software. Requirements are: (1) the input data is given by pairwise dissimilarities between data points, but extensions to vector data are also discussed (2) the output is a "stepwise dendrogram", a data structure which is shared by all implementations in current standard software. We present algorithms (old and new) which perform clustering in this setting efficiently, both in an asymptotic worst-case analysis and from a practical point of view. The main contributions of this paper are: (1) We present a new algorithm which is suitable for any distance update scheme and performs significantly better than the existing algorithms. (2) We prove the correctness of two algorithms by Rohlf and Murtagh, which is necessary in each case for different reasons. (3) We give well-founded recommendations for the best current algorithms for the various agglomerative clustering schemes.},
	urldate = {2019-07-29},
	journal = {arXiv:1109.2378 [cs, stat]},
	author = {Müllner, Daniel},
	month = sep,
	year = {2011},
	note = {arXiv: 1109.2378},
	keywords = {62H30, Computer Science - Data Structures and Algorithms, I.5.3, Statistics - Machine Learning}
}

@article{chaumont_live_2018,
	title = {Live {Mouse} {Tracker}: real-time behavioral analysis of groups of mice},
	copyright = {© 2018, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	shorttitle = {Live {Mouse} {Tracker}},
	url = {https://www.biorxiv.org/content/10.1101/345132v2},
	doi = {10.1101/345132},
	abstract = {{\textless}p{\textgreater}Preclinical studies of psychiatric disorders require the use of animal models to investigate the impact of environmental factors or genetic mutations on complex traits such as decision-making and social interactions. Here, we present a real-time method for behavior analysis of mice housed in groups that couples computer vision, machine learning and Triggered-RFID identification to track and monitor animals over several days in enriched environments. The system extracts a thorough list of individual and collective behavioral traits and provides a unique phenotypic profile for each animal. On mouse models, we study the impact of mutations of genes Shank2 and Shank3 involved in autism. Characterization and integration of data from behavioral profiles of mutated female mice reveals distinctive activity levels and involvement in complex social configuration.{\textless}/p{\textgreater}},
	language = {en},
	urldate = {2019-07-28},
	journal = {bioRxiv},
	author = {Chaumont, Fabrice de and Ey, Elodie and Torquet, Nicolas and Lagache, Thibault and Dallongeville, Stéphane and Imbert, Albane and Legou, Thierry and Sourd, Anne-Marie Le and Faure, Philippe and Bourgeron, Thomas and Olivo-Marin, Jean-Christophe},
	month = jun,
	year = {2018},
	pages = {345132}
}

@article{hetze_gait_2012,
	title = {Gait analysis as a method for assessing neurological outcome in a mouse model of stroke},
	volume = {206},
	issn = {1872-678X},
	doi = {10.1016/j.jneumeth.2012.02.001},
	abstract = {Ameliorating stroke induced neurological deficits is one of the most important goals of stroke therapy. In order to improve stroke outcome, novel treatment approaches as well as animal stroke models predictive for the clinical setting are of urgent need. One of the main obstacles in experimental stroke research is measuring long-term outcome, in particular in mouse models of stroke. On the other hand, assessing functional deficits in animal models of stroke is critical to improve the prediction of preclinical findings. Automated gait analysis provides a sensitive tool to examine locomotion and limb coordination in small rodents. Comparing mice before and 10 days after experimental stroke (60 min MCAo) we observed a significant decrease in maximum contact area, stride length and swing speed in the hind limbs, especially the contralateral one. Mice showed a disturbed interlimb coordination represented by changes in regularity index and phase dispersion. To assess whether gait analysis is applicable to assess improvements by neuroprotective compounds, we applied a model calculation and approached common statistical problems. In conclusion, gait analysis is a promising tool to assess mid- to long-term outcome in experimental stroke research.},
	language = {eng},
	number = {1},
	journal = {Journal of Neuroscience Methods},
	author = {Hetze, Susann and Römer, Christine and Teufelhart, Carena and Meisel, Andreas and Engel, Odilo},
	month = apr,
	year = {2012},
	pmid = {22343052},
	keywords = {Animals, Disease Models, Animal, Gait, Male, Mice, Mice, Inbred C57BL, Neurologic Examination, Predictive Value of Tests, Stroke, Treatment Outcome},
	pages = {7--14}
}

@article{encarnacion_long-term_2011,
	title = {Long-term behavioral assessment of function in an experimental model for ischemic stroke},
	volume = {196},
	issn = {01650270},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0165027011000367},
	doi = {10.1016/j.jneumeth.2011.01.010},
	abstract = {Middle cerebral artery occlusion (MCAO) in rats is a well-studied experimental model for ischemic stroke leading to brain infarction and functional deficits. Many preclinical studies have focused on a small time window after the ischemic episode to evaluate functional outcome for screening therapeutic candidates. Short evaluation periods following injury have led to significant setbacks due to lack of information on the delayed effects of treatments, as well as short-lived and reversible neuroprotection, so called false-positive results. In this report, we evaluated long-term functional deficit for 90 days after MCAO in two rat strains with two durations of ischemic insult, in order to identify the best experimental paradigm to assess injury and subsequent recovery. Behavioral outcomes were measured pre-MCAO followed by weekly assessment post-stroke. Behavioral tests included the 18-point composite neurological score, 28-point neuroscore, rearing test, vibrissae-evoked forelimb placing test, foot fault test and the CatWalk. Brain lesions were assessed to correlate injury to behavior outcomes at the end of study. Our results indicate that infarction volume in Sprague-Dawley rats was dependent on occlusion duration. In contrast, the infarction volume in Wistar rats did not correlate with the duration of ischemic episode. Functional outcomes were not dependent on occlusion time in either strain; however, measureable deficits were detectable long-term in limb asymmetry, 18- and 28-point neuroscores, forelimb placing, paw swing speed, and gait coordination. In conclusion, these behavioral assays, in combination with an extended long-term assessment period, can be used for evaluating therapeutic candidates in preclinical models of ischemic stroke.},
	language = {en},
	number = {2},
	urldate = {2019-07-28},
	journal = {Journal of Neuroscience Methods},
	author = {Encarnacion, Angelo and Horie, Nobutaka and Keren-Gill, Hadar and Bliss, Tonya M. and Steinberg, Gary K. and Shamloo, Mehrdad},
	month = mar,
	year = {2011},
	pages = {247--257}
}

@article{park_method_2014,
	title = {A {Method} for {Generate} a {Mouse} {Model} of {Stroke}: {Evaluation} of {Parameters} for {Blood} {Flow}, {Behavior}, and {Survival}},
	volume = {23},
	issn = {1226-2560},
	shorttitle = {A {Method} for {Generate} a {Mouse} {Model} of {Stroke}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984953/},
	doi = {10.5607/en.2014.23.1.104},
	abstract = {Stroke is one of the common causes of death and disability. Despite extensive efforts in stroke research, therapeutic options for improving the functional recovery remain limited in clinical practice. Experimental stroke models using genetically modified mice could aid in unraveling the complex pathophysiology triggered by ischemic brain injury. Here, we optimized the procedure for generating mouse stroke model using an intraluminal suture in the middle cerebral artery and verified the blockage of blood flow using indocyanine green coupled with near infra-red radiation. The first week after the ischemic injury was critical for survivability. The survival rate of 11\% in mice without any treatment but increased to 60\% on administering prophylactic antibiotics. During this period, mice showed severe functional impairment but recovered spontaneously starting from the second week onward. Among the various behavioral tests, the pole tests and neurological severity score tests remained reliable up to 4 weeks after ischemia, whereas the rotarod and corner tests became less sensitive for assessing the severity of ischemic injury with time. Further, loss of body weight was also observed for up 4 weeks after ischemia induction. In conclusion, we have developed an improved approach which allows us to investigate the role of the cell death-related genes in the disease progression using genetically modified mice and to evaluate the modes of action of candidate drugs.},
	number = {1},
	urldate = {2019-07-28},
	journal = {Experimental Neurobiology},
	author = {Park, Sin-Young and Marasini, Subash and Kim, Geu-Hee and Ku, Taeyun and Choi, Chulhee and Park, Min-Young and Kim, Eun-Hee and Lee, Young-Don and Suh-Kim, Haeyoung and Kim, Sung-Soo},
	month = mar,
	year = {2014},
	pmid = {24737945},
	pmcid = {PMC3984953},
	pages = {104--114}
}

@misc{noauthor_f1000workspace_nodate,
	title = {F1000Workspace},
	url = {https://f1000.com/work/#/items/553731},
	urldate = {2019-07-28}
}

@article{bailoo_precision_2010,
	title = {The precision of video and photocell tracking systems and the elimination of tracking errors with infrared backlighting},
	volume = {188},
	issn = {1872-678X},
	doi = {10.1016/j.jneumeth.2010.01.035},
	abstract = {Automated tracking offers a number of advantages over both manual and photocell tracking methodologies, including increased reliability, validity, and flexibility of application. Despite the advantages that video offers, our experience has been that video systems cannot track a mouse consistently when its coat color is in low contrast with the background. Furthermore, the local lab lighting can influence how well results are quantified. To test the effect of lighting, we built devices that provide a known path length for any given trial duration, at a velocity close to the average speed of a mouse in the open-field and the circular water maze. We found that the validity of results from two commercial video tracking systems (ANY-maze and EthoVision XT) depends greatly on the level of contrast and the quality of the lighting. A photocell detection system was immune to lighting problems but yielded a path length that deviated from the true length. Excellent precision was achieved consistently, however, with video tracking using infrared backlighting in both the open field and water maze. A high correlation (r=0.98) between the two software systems was observed when infrared backlighting was used with live mice.},
	language = {eng},
	number = {1},
	journal = {Journal of Neuroscience Methods},
	author = {Bailoo, Jeremy D. and Bohlen, Martin O. and Wahlsten, Douglas},
	month = apr,
	year = {2010},
	pmid = {20138914},
	pmcid = {PMC2847046},
	keywords = {Animals, Behavior, Animal, Electronic Data Processing, Exploratory Behavior, Image Enhancement, Image Processing, Computer-Assisted, Mice, Motor Activity, Movement, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Spatial Behavior, User-Computer Interface, Video Recording},
	pages = {45--52}
}

@article{spink_ethovision_2001,
	series = {Molecular {Behavior} {Genetics} of the {Mouse}},
	title = {The {EthoVision} video tracking system—{A} tool for behavioral phenotyping of transgenic mice},
	volume = {73},
	issn = {0031-9384},
	url = {http://www.sciencedirect.com/science/article/pii/S0031938401005303},
	doi = {10.1016/S0031-9384(01)00530-3},
	abstract = {Video tracking systems enable behavior to be studied in a reliable and consistent way, and over longer time periods than if they are manually recorded. The system takes an analog video signal, digitizes each frame, and analyses the resultant pixels to determine the location of the tracked animals (as well as other data).

repository: traja-team/traja

repo_name: traja-team/traja

gha_license_id: MIT

gha_language: Jupyter Notebook