Antonio Murciano

Interkinetic nuclear movement may provide spatial clues to the regulation of neurogenesis

During the transition from S phase to mitosis, vertebrate neuroepithelial cells displace their nuclei and subsequently migrate from the basal membrane to the apical surface of the neuroepithelium, a phenomenon termed interkinetic nuclear movement (INM). Here we provide evidence that cycling neuroepithelial cells pass through a neurogenic state in which they are situated apically, as defined by the capacity to express Notch1, Delta1, and Neurogenin2 (Ngn2). Based on this scenario, we have developed a mathematical model to analyze the influence of INM on neurogenesis. In the absence of INM, the model predicted an increase in the rate of neurogenesis due to the reduction in the influence of inhibitory signals on cells in the neurogenic state. This exacerbation in neurogenesis led to the diminished growth of the neuroepithelium and a reduction in the later production of neurons. Pharmacological perturbation of the stereotypical distribution of precursors along the orthogonal axis of the neuroepithelium resulted in an excess of neurogenesis, as seen by the expression of Ngn2, and of the neuronal marker RA4 in the retina. These findings suggest that INM might be important for the efficient and continued production of neurons in G0, since it is involved in defining a proneural cluster in the ventricular part of the neuroepithelium that contains precursors at stages of the mitotic cycle compatible with neuronal differentiation.

Specialization in multi-agent systems through learning

Abstract. Specialization is a common feature in animal societies that leads to an improvement in the fitness of the team members and to an increase in the resources obtained by the team. In this paper we propose a simple reinforcement learning approach to specialization in an artificial multi-agent system. The system is composed of homogeneous and non-communicating agents. Because there is no communication, the number of agents in the team can easily scale up. Agents have the same initial functionalities, but they learn to specialize and so cooperate to achieve a complex gathering task efficiently. Simulation experiments show how the multi-agent system specializes appropriately so as to reach optimal (or near-to-optimal) performance in unknown and changing environments.

Learning Signaling Behaviors and Specialization in Cooperative Agents

In this article, we present a learning mechanism that allows a multiagent system to cooperate to achieve a gathering task efficiently in unknown and changing environments. The multiagent system is a team of autonomous behavior-based agents with limited communication capabilities. Cooperation is based on the acquisition of signaling behaviors and on the specialization of the agents into different types. Every agent has the same collection of built-in reactive behaviors. Some of the built-in behaviors are fixed, whereas others can be modified through reinforcement learning. The reinforcement signal is delayed until a trial is completed and assesses the collective performance of the team. Each agent uses this common signal to learn what individual behaviors are more suitable for the team. Simulation results, and the corresponding statistical analysis, show that the multiagent system always achieves near-optimal performances.

Unravelling the interactions among microbial populations found in activated sludge during biofilm formation.

Microorganisms colonize surfaces and develop biofilms through interactions that are not yet thoroughly understood, with important implications for water and wastewater systems. This study investigated the interactions between N-acyl homoserine lactone (AHL)-producing bacteria, yeasts and protists, and their contribution to biofilm development. Sixty-one bacterial strains were isolated from activated sludge and screened for AHL production, with Aeromonas sp. found to be the dominant AHL producer. Shewanella xiamenensis, Aeromonas allosaccharophila, Acinetobacter junii and Pseudomonas aeruginosa recorded the highest adherence capabilities, with S. xiamenensis being the most effective in surface colonization. Additionally, highly significant interactions (i.e. synergic or antagonistic) were described for dual and multistrain mixtures of bacterial strains (P. aeruginosa, S. xiamenensis, A. junii and Pseudomonas stutzeri), as well as for strongly adherent bacteria co-cultured with yeasts. In this last case, the adhered biomass in co-cultures was lower than the monospecific biofilms of bacteria and yeast, with biofilm observations by microscopy suggesting that bacteria had an antagonist effect on the whole or part of the yeast population. Finally, protist predation by Euplotes sp. and Paramecium sp. on Aeromonas hydrophila biofilms not only failed to reduce biofilm formation, but also recorded unexpected results leading to the development of aggregates of high density and complexity.

Learning and stabilization of altruistic behaviors in multi-agent systems by reciprocity.

Abstract. Optimization of performance in collective systems often requires altruism. The emergence and stabilization of altruistic behaviors are difficult to achieve because the agents incur a cost when behaving altruistically. In this paper, we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. This strategy in conjunction with learning capabilities make altruistic agents cooperate only between themselves, thus preventing their exploitation by selfish agents, if future benefits are greater than the current cost of altruistic acts. Our multi-agent system is made up of agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so achieving optimal (or near-to-optimal) performances in unknown and changing environments.

Learning and stabilization of altruistic behaviors in multi-agent systems

Optimization of performance in collective systems often requires altruism. Emergence and stabilization of altruistic behaviors are difficult because the agents incur a cost when behaving altruistically. In this paper we propose a biologically inspired strategy to learn stable altruistic behaviors in artificial multi-agent systems, namely reciprocal altruism. Our multi-agent system is made up of autonomous agents with a behavior-based architecture. Agents learn the most suitable cooperative strategy for different environments by means of a reinforcement learning algorithm. Each agent receives a reinforcement signal that only measures its individual performance. Simulation results show how the multi-agent system learns stable altruistic behaviors, so reaching optimal (or near-to-optimal) performances in unknown and changing environments.

A Model for Centering Visual Stimuli Through Adaptive Value Learning

Learning through adaptive value leads to environment-dependent behaviors. This paper introduces a model for stimuli centering within a visual field following the principles of this kind of learning. The neurons responsible for the eye movement execute a mapping of the visual field by means of the adaptive value of each movement. This value is the result of the interaction of biologically inspired layers, chosen in an evolutionarily and without any planning or supervision, throughout the learning process. To check the stabilization and learning abilities of the model three measurements have been used: an energy function representing weight variations; a measurement of the increment of the distances through the chosen trajectories; and, a discriminant linear analysis of the model behavior.

Germination fitness of two temperate epiphytic ferns shifts under increasing temperatures and forest fragmentation

Ferns are an important component of ecosystems around the world. Studies of the impacts that global changes may have on ferns are scarce, yet emerging studies indicate that some species may be particularly sensitive to climate change. The lack of research in this subject is much more aggravated in the case of epiphytes, and especially those that live under temperate climates. A mathematical model was developed for two temperate epiphytic ferns in order to predict potential impacts on spore germination kinetics, in response to different scenarios of global change, coming from increasing temperature and forest fragmentation. Our results show that an increasing temperature will have a negative impact over the populations of these temperate epiphytic ferns. Under unfragmented forests the germination percentage was comparatively less influenced than in fragmented patches. This study highlight that, in the long term, populations of the studied epiphytic temperate ferns may decline due to climate change. Overall, epiphytic fern communities will suffer changes in diversity, richness and dominance. Our study draws attention to the role of ferns in epiphytic communities of temperate forests, emphasizing the importance of considering these plants in any conservation strategy, specifically forest conservation. From a methodological point of view, the model we propose could be easily used to dynamically monitor the status of ecosystems, allowing the quick prediction of possible future scenarios, which is a crucial issue in biodiversity conservation decision-making.

Influencia de la temperatura sobre la asimetría de pilancones en ambiente granítico. Aplicación de un modelo de regresión lineal

Las pilas representan un tipo de forma presente en casi todos los ambientes climaticos. El trabajo estudia el papel de los ciclos termicos como un agente importante del modelado y asimetria que presentan las paredes de las pilas en un clima Mediterra- neo templado - frio. El estudio se ha realizado en el macizo granitico de la Pedriza de Manzanares, zona protegida de gran valor ambiental, incluida en el Parque Nacional de la Sierra de Guadarrama (Madrid, Espana). El analisis de la variabilidad termica mediante modelos de regresion perio- dica multiple, pone de manifiesto la influencia de los ciclos diario y anual en funcion de las orientaciones norte y sur de las paredes de las pilas. Un modelo matematico de regresion lineal muestra como la variabilidad termica diaria influye en la alteracion de las paredes de las pilas, generando superficies planas o de concavidad mas o menos pronunciada. El trabajo tambien establece relaciones entre el grado de alteracion de las diferentes partes de las pilas, con la presencia de liquenes y dureza relativa de la roca. Los resultados avalan la hipotesis de relacion causal entre la variabilidad termica y alteracion de las paredes de las pilas segun su orientacion norte o sur.

Approximating an expectation related to cooperative robots

In this paper we develop an approximation to the expectation of a random variable implied in cooperation stability, presented in a previous work. This approximation is obtained by means of a continuous monotonous function that upper bounds the expectation. Finally, we analyze the quality of this approximation.