1,725 research outputs found

    Visual attention and cognitive performance in sheep

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    Cognitive probes are increasingly being used as an inferred measure of the emotional (and thus welfare) status of the animal. This reflects the bidirectional and interactive nature of emotional and cognitive systems. To date, cognitive paradigms have focused on how the emotional system biases expected outcome of prospective actions within goal-orientated scenarios. Evidence, however, suggests that negative affective state can also modulate attentional mechanisms. Measuring attention alongside other current tests of cognitive bias may provide greater resolution in the measurement of animal welfare. As a starting point for developing cognitive tasks of attentional control, we decided to assess the basic relationship between visual attention and cognitive performance in a farm animal species (sheep). Variation in visual attention and cognitive performance was sought through testing of four different breeds of upland and lowland sheep (Beulah, Bluefaced Leicester, Texel and Suffolk; n = 15/breed) on a visual attention task and a two-choice visual discrimination task (to measure cognitive performance). Cognitive performance and visual attention differed significantly between breeds (F 3,46 = 4.70, p = 0.006 and F3,5o = 6.05, p < 0.001 respectively). The least visually attentive breed of sheep (Blue face Leicester) had the lowest level of cognitive performance and the most visually attentive breed (Suffolk) had the highest level of cognitive performance. A weak but significant relationship between vigilance/fearfulness and visual attention was also observed (t44 = 3.91, p = < 0.001; r2 = 0.23) that appeared to adhere to the Yerkes-Dodson law, with both high and low levels of vigilance/fearfulness having a negative effect on visual attention. These results demonstrate a discernible relationship between visual attention and cognitive performance that provides a basis for further exploring attention systems in the context of changes in animal affective state and thus animal welfare.CHDI Inc

    Executive decision-making in the domestic sheep.

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    Two new large animal models of Huntington's disease (HD) have been developed recently, an old world monkey (macaque) and a sheep. Macaques, with their large brains and complex repertoire of behaviors are the 'gold-standard' laboratory animals for testing cognitive function, but there are many practical and ethical issues that must be resolved before HD macaques can be used for pre-clinical research. By contrast, despite their comparable brain size, sheep do not enjoy a reputation for intelligence, and are not used for pre-clinical cognitive testing. Given that cognitive decline is a major therapeutic target in HD, the feasibility of testing cognitive function in sheep must be explored if they are to be considered seriously as models of HD. Here we tested the ability of sheep to perform tests of executive function (discrimination learning, reversal learning and attentional set-shifting). Significantly, we found that not only could sheep perform discrimination learning and reversals, but they could also perform the intradimensional (ID) and extradimensional (ED) set-shifting tasks that are sensitive tests of cognitive dysfunction in humans. Their performance on the ID/ED shifts mirrored that seen in humans and macaques, with significantly more errors to reach criterion in the ED than the ID shift. Thus, sheep can perform 'executive' cognitive tasks that are an important part of the primate behavioral repertoire, but which have never been shown previously to exist in any other large animal. Sheep have great potential, not only for use as a large animal model of HD, but also for studying cognitive function and the evolution of complex behaviours in normal animals

    Temporal separation of aggregation and ubiquitination during early inclusion formation in transgenic mice carrying the Huntington's disease mutation.

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    Abnormal insoluble ubiqitinated protein aggregates are found in the brains of Huntington's disease (HD) patients and in mice transgenic for the HTT mutation. Here, we describe the earliest stages of visible NII formation in brains of R6/2 mice killed between 2 and 6 weeks of age. We found that huntingtin-positive aggregates formed rapidly (within 24-48 hours) in a spatiotemporal manner similar to that we described previously for ubiquitinated inclusions. However, in most neurons, aggregates are not ubiquitinated when they first form. It has always been assumed that mutant huntingtin is recognised as 'foreign' and consequently ubiquitinated and targeted for degradation by the ubiquitin-proteasome system pathway. Our data, however, suggest that aggregation and ubiquitination are separate processes, and that mutant huntingtin fragment is not recognized as 'abnormal' by the ubiquitin-proteasome system before aggregation. Rather, mutant Htt appears to aggregate before it is ubiquitinated, and then either aggregated huntingtin is ubiquitinated or ubiquitinated proteins are recruited into aggregates. Our findings have significant implications for the role of the ubiquitin-proteasome system in the formation of aggregates, as they suggest that this system is not involved until after the first aggregates form

    A Significance Test for Inferring Affiliation Networks from Spatio-Temporal Data.

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    Scientists have long been interested in studying social structures within groups of gregarious animals. However, obtaining evidence about interactions between members of a group is difficult. Recent technologies, such as Global Positioning System technology, have made it possible to obtain a vast wealth of animal movement data, but inferring the underlying (latent) social structure of the group from such data remains an important open problem. While intuitively appealing measures of social interaction exist in the literature, they typically lack formal statistical grounding. In this article, we provide a statistical approach to the problem of inferring the social structure of a group from the movement patterns of its members. By constructing an appropriate null model, we are able to construct a significance test to detect meaningful affiliations between members of the group. We demonstrate our method on large-scale real-world data sets of positional data of flocks of Merino sheep, Ovis aries
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