Modelling hair follicle growth dynamics as an excitable medium

The hair follicle system represents a tractable model for the study of stem cell behaviour in regenerative adult epithelial tissue. However, although there are numerous spatial scales of observation (molecular, cellular, follicle and multi follicle), it is not yet clear what mechanisms underpin the follicle growth cycle. In this study we seek to address this problem by describing how the growth dynamics of a large population of follicles can be treated as a classical excitable medium. Defining caricature interactions at the molecular scale and treating a single follicle as a functional unit, a minimal model is proposed in which the follicle growth cycle is an emergent phenomenon. Expressions are derived, in terms of parameters representing molecular regulation, for the time spent in the different functional phases of the cycle, a formalism that allows the model to be directly compared with a previous cellular automaton model and experimental measurements made at the single follicle scale. A multi follicle model is constructed and numerical simulations are used to demonstrate excellent qualitative agreement with a range of experimental observations. Notably, the excitable medium equations exhibit a wider family of solutions than the previous work and we demonstrate how parameter changes representing altered molecular regulation can explain perturbed patterns in Wnt over-expression and BMP down-regulation mouse models. Further experimental scenarios that could be used to test the fundamental premise of the model are suggested. The key conclusion from our work is that positive and negative regulatory interactions between activators and inhibitors can give rise to a range of experimentally observed phenomena at the follicle and multi follicle spatial scales and, as such, could represent a core mechanism underlying hair follicle growth

Evolutionary optimisation of neural network models for fish collective behaviours in mixed groups of robots and zebrafish

Animal and robot social interactions are interesting both for ethological studies and robotics. On the one hand, the robots can be tools and models to analyse animal collective behaviours, on the other hand, the robots and their artificial intelligence are directly confronted and compared to the natural animal collective intelligence. The first step is to design robots and their behavioural controllers that are capable of socially interact with animals. Designing such behavioural bio-mimetic controllers remains an important challenge as they have to reproduce the animal behaviours and have to be calibrated on experimental data. Most animal collective behavioural models are designed by modellers based on experimental data. This process is long and costly because it is difficult to identify the relevant behavioural features that are then used as a priori knowledge in model building. Here, we want to model the fish individual and collective behaviours in order to develop robot controllers. We explore the use of optimised black-box models based on artificial neural networks (ANN) to model fish behaviours. While the ANN may not be biomimetic but rather bio-inspired, they can be used to link perception to motor responses. These models are designed to be implementable as robot controllers to form mixed-groups of fish and robots, using few a priori knowledge of the fish behaviours. We present a methodology with multilayer perceptron or echo state networks that are optimised through evolutionary algorithms to model accurately the fish individual and collective behaviours in a bounded rectangular arena. We assess the biomimetism of the generated models and compare them to the fish experimental behaviours.Comment: 10 pages, 4 figure

Automated Calibration of a Biomimetic Space-Dependent Model for Zebrafish and Robot Collective Behaviour in a Structured Environment

Bio-hybrid systems made of robots and animals can be useful tools both for biology and robotics. To socially integrate robots into animal groups the robots should behave in a biomimetic manner with close loop interactions between robots and animals. Behavioural zebrafish experiments show that their individual behaviours depend on social interactions producing collective behaviour and depend on their position in the environment. Based on those observations we build a multilevel model to describe the zebrafish collective behaviours in a structured environment. Here, we present this new model segmented in spatial zones that each corresponds to different behavioural patterns. We automatically fit the model parameters for each zone to experimental data using a multi-objective evolutionary algorithm. We then evaluate how the resulting calibrated model compares to the experimental data. The model is used to drive the behaviour of a robot that has to integrate socially in a group of zebrafish. We show experimentally that a biomimetic multilevel and context-dependent model allows good social integration of fish and robots in a structured environment

Self-organization processes in field-invasion team sports

In nature, the interactions between agents in a complex system (fish schools; colonies of ants) are governed by information that is locally created. Each agent self-organizes (adjusts) its behaviour, not through a central command centre, but based on variables that emerge from the interactions with other system agents in the neighbourhood. Self-organization has been proposed as a mechanism to explain the tendencies for individual performers to interact with each other in field-invasion sports teams, displaying functional co-adaptive behaviours, without the need for central control. The relevance of self-organization as a mechanism that explains pattern-forming dynamics within attacker-defender interactions in field-invasion sports has been sustained in the literature. Nonetheless, other levels of interpersonal coordination, such as intra-team interactions, still raise important questions, particularly with reference to the role of leadership or match strategies that have been prescribed in advance by a coach. The existence of key properties of complex systems, such as system degeneracy, nonlinearity or contextual dependency, suggests that self-organization is a functional mechanism to explain the emergence of interpersonal coordination tendencies within intra-team interactions. In this opinion article we propose how leadership may act as a key constraint on the emergent, self-organizational tendencies of performers in field-invasion sports

Plant-plant interactions and environmental change

Natural systems are being subjected to unprecedented rates of change and unique pressures from a combination of anthropogenic environmental change drivers. Plant–plant interactions are an important part of the mechanisms governing the response of plant species and communities to these drivers. For example, competition plays a central role in mediating the impacts of atmospheric nitrogen deposition, increased atmospheric carbon dioxide concentrations, climate change and invasive nonnative species. Other plant–plant interaction processes are also being recognized as important factors in determining the impacts of environmental change, including facilitation and evolutionary processes associated with plant–plant interactions. However, plant–plant interactions are not the only factors determining the response of species and communities to environmental change drivers – their activity must be placed within the context of the wide range of factors that regulate species, communities and ecosystems. A major research challenge is to understand when plant–plant interactions play a key role in regulating the impact of environmental change drivers, and the type of role that plant–plant interactions play. Although this is a considerable challenge, some areas of current research may provide the starting point to achieving these goals, and should be pursued through large-scale, integrated, multisite experiments

From aggregation to dispersion: how habitat fragmentation prevents the emergence of consensual decision making in a group.

In fragmented landscape, individuals have to cope with the fragmentation level in order to aggregate in the same patch and take advantage of group-living. Aggregation results from responses to environmental heterogeneities and/or positive influence of the presence of congeners. In this context, the fragmentation of resting sites highlights how individuals make a compromise between two individual preferences: (1) being aggregated with conspecifics and (2) having access to these resting sites. As in previous studies, when the carrying capacity of available resting sites is large enough to contain the entire group, a single aggregation site is collectively selected. In this study, we have uncoupled fragmentation and habitat loss: the population size and total surface of the resting sites are maintained at a constant value, an increase in fragmentation implies a decrease in the carrying capacity of each shelter. For our model organism, Blattella germanica, our experimental and theoretical approach shows that, for low fragmentation level, a single resting site is collectively selected. However, for higher level of fragmentation, individuals are randomly distributed between fragments and the total sheltered population decreases. In the latter case, social amplification process is not activated and consequently, consensual decision making cannot emerge and the distribution of individuals among sites is only driven by their individual propensity to find a site. This intimate relation between aggregation pattern and landscape patchiness described in our theoretical model is generic for several gregarious species. We expect that any group-living species showing the same structure of interactions should present the same type of dispersion-aggregation response to fragmentation regardless of their level of social complexity.Journal ArticleSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Use of Computed Tomography and Histopathologic Review for Lung Lesions Produced by the Interaction Between Mycoplasma hyopneumoniae and Fumonisin Mycotoxins in Pigs

Mycoplasma hyopneumoniae has a primary role in the porcine respiratory disease complex (PRDC). The objective of this study was to determine whether fumonisin mycotoxins influence the character and/or the severity of pathological processes induced in the lungs of pigs by Mycoplasma hyopneumoniae. Four groups of pigs (n ¼ 7/group) were used, one fed 20 ppm fumonisin B1 (FB1) from 16 days of age (group F), one only infected with M. hyopneumoniae on study day 30 (group M), and a group fed FB1 and infected with M. hyopneumoniae (group MF), along with an untreated control group (group C). Computed tomography (CT) scans of infected pigs (M and MF) on study day 44 demonstrated lesions extending to the cranial and middle or in the cranial third of the caudal lobe of the lungs. The CT images obtained on study day 58 showed similar but milder lesions in 5 animals from group M, whereas lungs from 2 pigs in group MF appeared progressively worse. The evolution of average pulmonary density calculated from combined pixel frequency values, as measured by quantitative CT, was significantly influenced by the treatment and the age of the animals. The most characteristic histopathologic lesion in FB1-treated pigs was pulmonary edema, whereas the pathomorphological changes in Mycoplasma-infected pigs were consistent with catarrhal bronchointerstitial pneumonia. FB1 aggravated the progression of infection, as demonstrated by severe illness requiring euthanasia observed in 1 pig and evidence of progressive pathology in 2 pigs (group MF) between study days 44 and 58

Sensitivity of density-dependent threshold to species composition in arthropod aggregates

How mixed-species groups perform collective behaviours provides unique insights into the mechanisms that drive social interactions. Herein, we followed the aggregation process of two isopod species under monospecific and heterospecific conditions at three population densities. Our experimental results show that the formation of both the monospecific and heterospecific groups responds to a similar threshold function. Furthermore, the two species contribute equally to the mixed-species aggregate growth and are not spatiotemporally segregated. However, we show that the cohesion is weaker and the probability of forming aggregations is lower in heterospecific groups than in monospecific populations. Thus, our results show that amplification processes are shared between species, but that the weighting given to conspecific and heterospecific information differs. We develop a theoretical model to test this hypothesis. The model reproduces our experimental data and shows that a relatively low level of inter-attractions between species is able to generate mixed-species aggregates. Moreover the greater the total population, the lower this parameter value is needed to observe aggregation in both species. This highlights the importance to study not only qualitatively but also quantitatively the heterospecific interactions in mixed-species groups. Finally, the patterns observed could be biologically relevant in favouring the association between species.SCOPUS: ar.jinfo:eu-repo/semantics/publishe