Aquatic community patterns across environmental gradients in a Mediterranean floodplain and their application to ecosystem restoration

El presente estudio tiene como objetivo proporcionar una base de conocimiento sólida para la restauración ecológica de ríos, basada en la respuesta de comunidades acuáticas a cambios en la conectividad hídrica, factores medioambientales y presión antrópica. La conectividad hídrica lateral resultó ser el factor principal que estructura hábitats y comunidades acuáticas en el Ebro; mientras que la turbidez, salinidad y concentración de nutrientes fueron factores secundarios. La combinación de estos factores establece un marco ecológico que permite realizar predicciones acerca de los patrones taxonómicos y funcionales con más probabilidades de ocurrir en la llanura del Ebro. La posibilidad de que se creen nuevos humedales de forma natural en el Ebro es muy baja, mientras los que quedan están amenazados por una baja renovación del agua. El objetivo de la restauración ecológica debe por tanto consistir en re-establecer un amplio rango de condiciones hídricas, de acuerdo con el potencial sostenible del ecosistema.

Revealing ensemble state transition patterns in multi-electrode neuronal recordings using hidden Markov models

In order to harness the computational capacity of dissociated cultured neuronal networks, it is necessary to understand neuronal dynamics and connectivity on a mesoscopic scale. To this end, this paper uncovers dynamic spatiotemporal patterns emerging from electrically stimulated neuronal cultures using hidden Markov models (HMMs) to characterize multi-channel spike trains as a progression of patterns of underlying states of neuronal activity. However, experimentation aimed at optimal choice of parameters for such models is essential and results are reported in detail. Results derived from ensemble neuronal data revealed highly repeatable patterns of state transitions in the order of milliseconds in response to probing stimuli.

Multiscale evolving complex network model of functional connectivity in neuronal cultures

Cultures of cortical neurons grown on multielectrode arrays exhibit spontaneous, robust and recurrent patterns of highly synchronous activity called bursts. These bursts play a crucial role in the development and topological selforganization of neuronal networks. Thus, understanding the evolution of synchrony within these bursts could give insight into network growth and the functional processes involved in learning and memory. Functional connectivity networks can be constructed by observing patterns of synchrony that evolve during bursts. To capture this evolution, a modelling approach is adopted using a framework of emergent evolving complex networks and, through taking advantage of the multiple time scales of the system, aims to show the importance of sequential and ordered synchronization in network function.

Anhedonia and reward-circuit connectivity distinguish nonresponders from responders to dorsomedial prefrontal rTMS in major depression

Background Depression is a heterogeneous mental illness. Neurostimulation treatments, by targeting specific nodes within the brain’s emotion-regulation network, may be useful both as therapies and as probes for identifying clinically relevant depression subtypes. Methods Here, we applied 20 sessions of magnetic resonance imaging-guided repetitive transcranial magnetic stimulation (rTMS) to the dorsomedial prefrontal cortex in 47 unipolar or bipolar patients with a medication-resistant major depressive episode. Results Treatment response was strongly bimodal, with individual patients showing either minimal or marked improvement. Compared with responders, nonresponders showed markedly higher baseline anhedonia symptomatology (including pessimism, loss of pleasure, and loss of interest in previously enjoyed activities) on item-by-item examination of Beck Depression Inventory-II and Quick Inventory of Depressive Symptomatology ratings. Congruently, on baseline functional magnetic resonance imaging, nonresponders showed significantly lower connectivity through a classical reward pathway comprising ventral tegmental area, striatum, and a region in ventromedial prefrontal cortex. Responders and nonresponders also showed opposite patterns of hemispheric lateralization in the connectivity of dorsomedial and dorsolateral regions to this same ventromedial region. Conclusions The results suggest distinct depression subtypes, one with preserved hedonic function and responsive to dorsomedial rTMS and another with disrupted hedonic function, abnormally lateralized connectivity through ventromedial prefrontal cortex, and unresponsive to dorsomedial rTMS. Future research directly comparing the effects of rTMS at different targets, guided by neuroimaging and clinical presentation, may clarify whether hedonia/reward circuit integrity is a reliable marker for optimizing rTMS target selection.

Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest

Tropical rainforests are becoming increasingly fragmented and understanding the genetic consequences of fragmentation is crucial for conservation of their flora and fauna. We examined populations of the toad Rhinella ornata, a species endemic to Atlantic Coastal Forest in Brazil, and compared genetic diversity among small and medium forest fragments that were either isolated or connected to large forest areas by corridors. Genetic differentiation, as measured by F(ST), was not related to geographic distance among study sites and the size of the fragments did not significantly alter patterns of genetic connectivity. However, population genetic diversity was positively related to fragment size, thus haplotype diversity was lowest in the smallest fragments, likely due to decreases in population sizes. Spatial analyses of genetic discontinuities among groups of populations showed a higher proportion of barriers to gene flow among small and medium fragments than between populations in continuous forest. Our results underscore that even species with relatively high dispersal capacities may, over time, suffer the negative genetic effects of fragmentation, possibly leading to reduced fitness of population and cases of localized extinction. (C) 2008 Elsevier Ltd. All rights reserved.

Single Synapse Information Coding in Intraburst Spike Patterns of Central Pattern Generator Motor Neurons

Burst firing is ubiquitous in nervous systems and has been intensively studied in central pattern generators (CPGs). Previous works have described subtle intraburst spike patterns (IBSPs) that, despite being traditionally neglected for their lack of relation to CPG motor function, were shown to be cell-type specific and sensitive to CPG connectivity. Here we address this matter by investigating how a bursting motor neuron expresses information about other neurons in the network. We performed experiments on the crustacean stomatogastric pyloric CPG, both in control conditions and interacting in real-time with computer model neurons. The sensitivity of postsynaptic to presynaptic IBSPs was inferred by computing their average mutual information along each neuron burst. We found that details of input patterns are nonlinearly and inhomogeneously coded through a single synapse into the fine IBSPs structure of the postsynaptic neuron following burst. In this way, motor neurons are able to use different time scales to convey two types of information simultaneously: muscle contraction (related to bursting rhythm) and the behavior of other CPG neurons (at a much shorter timescale by using IBSPs as information carriers). Moreover, the analysis revealed that the coding mechanism described takes part in a previously unsuspected information pathway from a CPG motor neuron to a nerve that projects to sensory brain areas, thus providing evidence of the general physiological role of information coding through IBSPs in the regulation of neuronal firing patterns in remote circuits by the CNS.

Contemporary pollen flow, mating patterns and effective population size inferred from paternity analysis in a small fragmented population of the Neotropical tree Copaifera langsdorffii Desf. (Leguminosae-Caesalpinioideae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Patterns of penetration of ICT goods and services in Latin American and Caribbean households: analysis based on data from national population and housing censuses, round 2000

Includes bibliography

Spatial and seasonal patterns in fish assemblage in Córrego Rico, upper Paraná river Basin

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Rock outcrop orchids reveal the genetic connectivity and diversity of inselbergs of northeastern Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Environment and dispersal paths override life strategies and residence time in determining regional patterns of invasion by alien plants

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Species delimitation, patterns of diversification and historical biogeography of the Neotropical frog genus Adenomera (Anura, Leptodactylidae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.