Rowland Sillito PhD

Rowland Sillito


Publications:
2017
  Automated recording of home cage activity and temperature of individual rats housed in social groups: The Rodent Big Brother project
Redfern, WS, Tse, K, Grant, C, Keerie, A, Simpson, DJ, Pedersen, JC, Rimmer, V, Leslie, L, Klein, SK, Karp, NA, Sillito, R, Chartsias, A, Lukins, T, Heward, J, Vickers, C, Chapman, K, Armstrong, JD & Homberg, J (ed.) 2017, 'Automated recording of home cage activity and temperature of individual rats housed in social groups: The Rodent Big Brother project' PLoS One, vol 12, no. 9, e0181068, pp. 1-26. DOI: 10.1371/journal.pone.0181068
Measuring the activity and temperature of rats is commonly required in biomedical research. Conventional approaches necessitate single housing, which affects their behavior and wellbeing. We have used a subcutaneous radiofrequency identification (RFID) transponder to measure ambulatory activity and temperature of individual rats when group-housed in conventional, rack-mounted home cages. The transponder location and temperature is detected by a matrix of antennae in a baseplate under the cage. An infrared high-definition camera acquires side-view video of the cage and also enables automated detection of vertical activity. Validation studies showed that baseplate-derived ambulatory activity correlated well with manual tracking and with side-view whole-cage video pixel movement. This technology enables individual behavioral and temperature data to be acquired continuously from group-housed rats in their familiar, home cage environment. We demonstrate its ability to reliably detect naturally occurring behavioral effects, extending beyond the capabilities of routine observational tests and conventional monitoring equipment. It has numerous potential applications including safety pharmacology, toxicology, circadian biology, disease models and drug discovery.
General Information
Organisations: School of Informatics.
Authors: Redfern, William S., Tse, Karen, Grant, Claire, Keerie, Amy, Simpson, David J., Pedersen, John C., Rimmer, Victoria, Leslie, Lauren, Klein, Stephanie K., Karp, Natasha A., Sillito, Rowland, Chartsias, Agis, Lukins, Tim, Heward, James, Vickers, Catherine, Chapman, Kathryn & Armstrong, J. Douglas.
Number of pages: 26
Pages: 1-26
Publication Date: 6 Sep 2017
Publication Information
Category: Article
Journal: PLoS One
Volume: 12
Issue number: 9
ISSN: 1932-6203
Original Language: English
DOIs: 10.1371/journal.pone.0181068
  Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools
Bains, RS, Sillito, RR, Armstrong, JD, Cater, HL, Banks, G & Nolan, PM 2017, 'Assessing mouse behaviour throughout the light/dark cycle using automated in-cage analysis tools' Journal of Neuroscience Methods. DOI: 10.1016/j.jneumeth.2017.04.014
An important factor in reducing variability in mouse test outcomes has been to develop assays that can be used for continuous automated home cage assessment. Our experience has shown that this has been most evidenced in long-term assessment of wheel-running activity in mice. Historically, wheel-running in mice and other rodents has been used as a robust assay to determine, with precision, the inherent period of circadian rhythms in mice. Furthermore, this assay has been instrumental in dissecting the molecular genetic basis of mammalian circadian rhythms. In teasing out the elements of this test that have determined its robustness ? automated assessment of an unforced behaviour in the home cage over long time intervals ? we and others have been investigating whether similar test apparatus could be used to accurately discriminate differences in distinct behavioural parameters in mice. Firstly, using these systems, we explored behaviours in a number of mouse inbred strains to determine whether we could extract biologically meaningful differences. Secondly, we tested a number of relevant mutant lines to determine how discriminative these parameters were. Our findings show that, when compared to conventional out-of-cage phenotyping, a far deeper understanding of mouse mutant phenotype can be established by monitoring behavior in the home cage over one or more light:dark cycles.
General Information
Organisations: School of Informatics.
Authors: Bains, Rasneer S., Sillito, Rowland R., Armstrong, J. Douglas, Cater, Heather L., Banks, Gareth & Nolan, Patrick M..
Keywords: (Home cage, Welfare, Circadian, Motor function, Refinement, Wheel running. )
Number of pages: 32
Publication Date: 26 Apr 2017
Publication Information
Category: Article
Journal: Journal of Neuroscience Methods
ISSN: 0165-0270
Original Language: English
DOIs: 10.1016/j.jneumeth.2017.04.014
2016
  Analysis of individual mouse activity in group housed animals of different inbred strains using a novel automated home cage analysis system.
Bains, RS, Cater, HL, Sillito, RR, Chartsias, A, Sneddon, D, Concas, D, Keskivali-Bond, P, Lukins, TC, Wells, S, Acevedo Arozena, A, Nolan, PM & Armstrong, JD 2016, 'Analysis of individual mouse activity in group housed animals of different inbred strains using a novel automated home cage analysis system.' Frontiers in behavioral neuroscience, vol 10, no. 106. DOI: 10.3389/fnbeh.2016.00106
Central nervous system disorders such as autism as well as the range of neurodegenerative diseases such as Huntington’s disease are commonly investigated using genetically altered mouse models. The current system for characterizing these mice usually involves removing the animals from their home-cage environment and placing them into novel environments where they undergo a battery of tests measuring a range of behavioral and physical phenotypes. These tests are often only conducted for short periods of times in social isolation. However, human manifestations of such disorders are often characterized by multiple phenotypes, presented over long periods of time and leading to significant social impacts. Here, we have developed a system which will allow the automated monitoring of individual mice housed socially in the cage they are reared and housed in, within established social groups and over long periods of time. We demonstrate that the system accurately reports individual locomotor behavior within the group and that the measurements taken can provide unique insights into the effects of genetic background on individual and group behavior not previously recognized.
General Information
Organisations: Edinburgh Neuroscience.
Authors: Bains, Rasneer Sonia, Cater, Heather L, Sillito, Rowland Radford, Chartsias, Agisilaos, Sneddon, Duncan, Concas, Danilo, Keskivali-Bond, Piia, Lukins, Timothy C, Wells, Sara, Acevedo Arozena, Abraham, Nolan, Patrick Martin & Armstrong, J Douglas.
Number of pages: 22
Publication Date: 10 Jun 2016
Publication Information
Category: Article
Journal: Frontiers in behavioral neuroscience
Volume: 10
Issue number: 106
ISSN: 1662-5153
Original Language: English
DOIs: 10.3389/fnbeh.2016.00106
2015
  MEK Inhibitors Reverse cAMP-Mediated Anxiety in Zebrafish
Lundegaard, PR, Anastasaki, C, Grant, NJ, Sillito, RR, Zich, J, Zeng, Z, Paranthaman, K, Larsen, AP, Armstrong, JD, Porteous, DJ & Patton, EE 2015, 'MEK Inhibitors Reverse cAMP-Mediated Anxiety in Zebrafish' Chemistry and Biology. DOI: 10.1016/j.chembiol.2015.08.010

Altered phosphodiesterase (PDE)-cyclic AMP (cAMP) activity is frequently associated with anxiety disorders, but current therapies act by reducing neuronal excitability rather than targeting PDE-cAMP-mediated signaling pathways. Here, we report the novel repositioning of anti-cancer MEK inhibitors as anxiolytics in a zebrafish model of anxiety-like behaviors. PDE inhibitors or activators of adenylate cyclase cause behaviors consistent with anxiety in larvae and adult zebrafish. Small-molecule screening identifies MEK inhibitors as potent suppressors of cAMP anxiety behaviors in both larvae and adult zebrafish, while causing no anxiolytic behavioral effects on their own. The mechanism underlying cAMP-induced anxiety is via crosstalk to activation of the RAS-MAPK signaling pathway. We propose that targeting crosstalk signaling pathways can be an effective strategy for mental health disorders, and advance the repositioning of MEK inhibitors as behavior stabilizers in the context of increased cAMP.


General Information
Organisations: Edinburgh Imaging Facilities.
Authors: Lundegaard, Pia R, Anastasaki, Corina, Grant, Nicola J, Sillito, Rowland R, Zich, Judith, Zeng, Zhiqiang, Paranthaman, Karthika, Larsen, Anders Peter, Armstrong, J Douglas, Porteous, David J & Patton, E Elizabeth.
Publication Date: 22 Oct 2015
Publication Information
Category: Article
Journal: Chemistry and Biology
ISSN: 1074-5521
Original Language: English
DOIs: 10.1016/j.chembiol.2015.08.010
2010
  Improved Rodent Contour Extraction Using A Priori Shape Information
Sillito, RR, Lukins, TC & Armstrong, JD 2010, Improved Rodent Contour Extraction Using A Priori Shape Information. in Workshop on the Visual Observation and Analysis of Animal and Insect Behavior (held at ICPR2010).
We propose a method for automatically setting the foreground detection threshold implicit in background subtraction algorithms by measuring the similarity between the shape of a detected foreground region and a set of reference contours over a range of thresholds, and selecting the threshold that maximises this similarity measure. This method is shown to select appropriate thresholds for a range of unseen video frames.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Sillito, Rowland R., Lukins, T.C. & Armstrong, J. D..
Number of pages: 4
Publication Date: 2010
Publication Information
Category: Conference contribution
Original Language: English
2009
  Parametric Trajectory Representations for Behaviour Classification
Sillito, RR & Fisher, RB 2009, Parametric Trajectory Representations for Behaviour Classification. in Proceedings of the British Machine Vision Conference. BMVA Press, pp. 101.1-101.11. DOI: 10.5244/C.23.101
This paper presents an empirical comparison of strategies for representing motion trajectories with fixed-length vectors. We compare four techniques, which have all previously been adopted in the trajectory classification literature: least-squares cubic spline approximation, the Discrete Fourier Transform, Chebyshev polynomial approximation, and the Haar wavelet transform. We measure the class separability of five different trajectory datasets - ranging from vehicle trajectories to pen trajectories - when described in terms of these representations. Results obtained over a range of dimensionalities indicate that the different representations yield similar levels of class separability, with marginal improvements provided by Chebyshev and Spline representations. For the datasets considered here, each representation appears to yield better results when used in conjunction with a curve parametrisation strategy based on arc-length, rather than time. However, we illustrate a situation - pertinent to surveillance applications - where the converse is true.
General Information
Organisations: Institute of Perception, Action and Behaviour .
Authors: Sillito, Rowland R. & Fisher, Robert B..
Number of pages: 11
Pages: 101.1-101.11
Publication Date: 2009
Publication Information
Category: Conference contribution
Original Language: English
DOIs: 10.5244/C.23.101
2008
  Semi-supervised Learning for Anomalous Trajectory Detection
Sillito, RR & Fisher, B 2008, Semi-supervised Learning for Anomalous Trajectory Detection. in Proceedings British Machine Vision Conference BMVC2008. pp. 1035-1044.
A novel learning framework is proposed for anomalous behaviour detection in a video surveillance scenario, so that a classifier which distinguishes between normal and anomalous behaviour patterns can be incrementally trained with the assistance of a human operator. We consider the behaviour of pedestrians in terms of motion trajectories, and parametrise these trajectories using the control points of approximating cubic spline curves. This paper demonstrates an incremental semi-supervised one-class learning procedure in which unlabelled trajectories are combined with occasional examples of normal behaviour labelled by a human operator. This procedure is found to be effective on two different datasets, indicating that a human operator could potentially train the system to detect anomalous behaviour by providing only occasional interventions (a small percentage of the total number of observations).
General Information
Organisations: Institute of Perception, Action and Behaviour .
Authors: Sillito, Rowland R. & Fisher, Bob.
Number of pages: 10
Pages: 1035-1044
Publication Date: 2008
Publication Information
Category: Conference contribution
Original Language: English
2007
  Incremental One-Class Learning with Bounded Computational Complexity
Sillito, RR & Fisher, B 2007, Incremental One-Class Learning with Bounded Computational Complexity. in J Marques de Sa, LA Alexandre, W Duch & D Mandic (eds), Artificial Neural Networks - ICANN 2007. vol. 4668, Lecture Notes in Computer Science, Springer-Verlag GmbH, pp. 58-67. DOI: 10.1007/978-3-540-74690-4_7
An incremental one-class learning algorithm is proposed for the purpose of outlier detection. Outliers are identified by estimating - and thresholding - the probability distribution of the training data. In the early stages of training a non-parametric estimate of the training data distribution is obtained using kernel density estimation. Once the number of training examples reaches the maximum computationally feasible limit for kernel density estimation, we treat the kernel density estimate as a maximally-complex Gaussian mixture model, and keep the model complexity constant by merging a pair of components for each new kernel added. This method is shown to outperform a current state-of-the-art incremental one-class learning algorithm (Incremental SVDD [5]) on a variety of datasets, while requiring only an upper limit on model complexity to be specified.
General Information
Organisations: Institute of Perception, Action and Behaviour .
Authors: Sillito, Rowland R. & Fisher, Bob..
Number of pages: 10
Pages: 58-67
Publication Date: 2007
Publication Information
Category: Conference contribution
Original Language: English
DOIs: 10.1007/978-3-540-74690-4_7

Projects:
Attentive processing of dynamic visual information (PhD)