| 1. |
- Joda, Hamdi, et al.
(author)
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Medium-high resolution electrochemical genotyping of HLA-DQ2/DQ8 for detection of predisposition to coeliac disease
- 2014
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In: Analytical and Bioanalytical Chemistry. - : Springer Berlin/Heidelberg. - 1618-2642 .- 1618-2650. ; 406:12, s. 2757-2769
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Journal article (peer-reviewed)abstract
- Coeliac disease is a small intestinal disorder, induced by ingestion of gluten in genetically predisposed individuals. Coeliac disease has been strongly linked to human leukocyte antigens (HLA) located on chromosome 6, with almost 100 % of coeliac disease sufferers carrying either a HLA-DQ2 or HLA-DQ8 heterodimer, with the majority carrying HLA-DQ2 encoded by the DQA1*05:01/05:05, DQB1*02:01/02:02 alleles, whereas the remaining carry the HLA-DQ8 encoded by the DQA1*03:01, DQB1*03:02 alleles. In this work, we present the development of a multiplex electrochemical genosensor array of 36 electrodes, housed within a dedicated microfluidic platform and using a total of 10 sequence-specific probes for rapid medium-high resolution HLA-DQ2/DQ8 genotyping. An evaluation of the selectivity of the designed probes was carried out with the target sequences and 44 potentially interfering alleles, including single base mismatch differentiations; good selectivity was demonstrated. The performance of the electrochemical genosensor array was validated, analyzing real human samples for the presence of HLA-DQ2/DQ8 alleles, and compared with those obtained using laboratory-based HLA typing, and an excellent correlation was obtained.
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| 2. |
- Mann, Richard P., et al.
(author)
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Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection
- 2013
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 9:3
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Journal article (peer-reviewed)abstract
- Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture the observed locality of interactions. Traditional self-propelled particle models fail to capture the fine scale dynamics of the system. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics, while maintaining a biologically plausible perceptual range. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.
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| 3. |
- Mann, Richard P., et al.
(author)
-
Multi-scale Inference of Interaction Rules in Animal Groups Using Bayesian Model Selection
- 2012
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 8:1, s. e1002308-
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Journal article (peer-reviewed)abstract
- Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.
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| 4. |
- Strömbom, Daniel
(author)
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Attraction Based Models of Collective Motion
- 2013
-
Doctoral thesis (other academic/artistic)abstract
- Animal groups often exhibit highly coordinated collective motion in a variety of situations. For example, bird flocks, schools of fish, a flock of sheep being herded by a dog and highly efficient traffic on an ant trail. Although these phenomena can be observed every day all over the world our knowledge of what rules the individual's in such groups use is very limited. Questions of this type has been studied using so called self-propelled particle (SPP) models, most of which assume that collective motion arises from individuals aligning with their neighbors. Here we introduce and analyze a SPP-model based on attraction alone. We find that it produces all the typical groups seen in alignment-based models and some novel ones. In particular, a group that exhibits collective motion coupled with non-trivial internal dynamics. Groups that have this property are rarely seen in SPP-models and we show that even when a repulsion term is added to the attraction only model such groups are still present. These findings suggest that an interplay between attraction and repulsion may be the main driving force in real flocks and that the alignment rule may be superfluous.We then proceed to model two different experiments using the SPP-model approach. The first is a shepherding algorithm constructed primarily to model experiments where a sheepdog is herding a flock of sheep. We find that in addition to modeling the specific experimental situation well the algorithm has some properties which may make it useful in more general shepherding situations. The second is a traffic model for leaf-cutting ants bridges. Based on earlier experiments a set of traffic rules for ants on a very narrow bridge had been suggested. We show that these are sufficient to produce the observed traffic dynamics on the narrow bridge. And that when extended to a wider bridge by replacing 'Stop' with 'Turn' the new rules are sufficient to produce several key characteristics of the dynamics on the wide bridge, in particular three-lane formation.
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| 5. |
- Strömbom, Daniel
(author)
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Collective motion from local attraction
- 2011
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In: Journal of Theoretical Biology. - : Elsevier BV. - 0022-5193 .- 1095-8541. ; 283:1, s. 145-151
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Journal article (peer-reviewed)abstract
- Many animal groups, for example schools of fish or flocks of birds, exhibit complex dynamic patterns while moving cohesively in the same direction. These flocking patterns have been studied using self-propelled particle models, most of which assume that collective motion arises from individuals aligning with their neighbours. Here, we propose a self-propelled particle model in which the only social force between individuals is attraction. We show that this model generates three different phases: swarms, undirected mills and moving aligned groups. By studying our model in the zero noise limit, we show how these phases depend on the relative strength of attraction and individual inertia. Moreover, by restricting the field of vision of the individuals and increasing the degree of noise in the system, we find that the groups generate both directed mills and three dynamically moving, 'rotating chain' structures. A rich diversity of patterns is generated by social attraction alone, which may provide insight into the dynamics of natural flocks.
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| 6. |
- Strömbom, Daniel, et al.
(author)
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Solving the shepherding problem : Heuristics for herding autonomous, interacting agents
- 2014
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In: Journal of the Royal Society Interface. - : The Royal Society. - 1742-5689 .- 1742-5662. ; 11:100, s. 20140719-
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Journal article (peer-reviewed)abstract
- Herding of sheep by dogs is a powerful example of one individual causing many unwilling individuals to move in the same direction. Similar phenomena are central to crowd control, cleaning the environment and other engineering problems. Despite single dogs solving this 'shepherding problem' every day, it remains unknown which algorithm they employ or whether a general algorithm exists for shepherding. Here, we demonstrate such an algorithm, based on adaptive switching between collecting the agents when they are too dispersed and driving them once they are aggregated. Our algorithm reproduces key features of empirical data collected from sheep-dog interactions and suggests new ways in which robots can be designed to influence movements of living and artificial agents.
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