What can ecosystems learn? Expanding evolutionary ecology with learning theory.

BACKGROUND: The structure and organisation of ecological interactions within an ecosystem is modified by the evolution and coevolution of the individual species it contains. Understanding how historical conditions have shaped this architecture is vital for understanding system responses to change at scales from the microbial upwards. However, in the absence of a group selection process, the collective behaviours and ecosystem functions exhibited by the whole community cannot be organised or adapted in a Darwinian sense. A long-standing open question thus persists: Are there alternative organising principles that enable us to understand and predict how the coevolution of the component species creates and maintains complex collective behaviours exhibited by the ecosystem as a whole? RESULTS: Here we answer this question by incorporating principles from connectionist learning, a previously unrelated discipline already using well-developed theories on how emergent behaviours arise in simple networks. Specifically, we show conditions where natural selection on ecological interactions is functionally equivalent to a simple type of connectionist learning, 'unsupervised learning', well-known in neural-network models of cognitive systems to produce many non-trivial collective behaviours. Accordingly, we find that a community can self-organise in a well-defined and non-trivial sense without selection at the community level; its organisation can be conditioned by past experience in the same sense as connectionist learning models habituate to stimuli. This conditioning drives the community to form a distributed ecological memory of multiple past states, causing the community to: a) converge to these states from any random initial composition; b) accurately restore historical compositions from small fragments; c) recover a state composition following disturbance; and d) to correctly classify ambiguous initial compositions according to their similarity to learned compositions. We examine how the formation of alternative stable states alters the community's response to changing environmental forcing, and we identify conditions under which the ecosystem exhibits hysteresis with potential for catastrophic regime shifts. CONCLUSIONS: This work highlights the potential of connectionist theory to expand our understanding of evo-eco dynamics and collective ecological behaviours. Within this framework we find that, despite not being a Darwinian unit, ecological communities can behave like connectionist learning systems, creating internal conditions that habituate to past environmental conditions and actively recalling those conditions. REVIEWERS: This article was reviewed by Prof. Ricard V Solé, Universitat Pompeu Fabra, Barcelona and Prof. Rob Knight, University of Colorado, Boulder.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Neural synchrony indexes impaired motor slowing after errors and novelty following white matter damage.

In humans, action errors and perceptual novelty elicit activity in a shared frontostriatal brain network, allowing them to adapt their ongoing behavior to such unexpected action outcomes. Healthy and pathologic aging reduces the integrity of white matter pathways that connect individual hubs of such networks and can impair the associated cognitive functions. Here, we investigated whether structural disconnection within this network because of small-vessel disease impairs the neural processes that subserve motor slowing after errors and novelty (post-error slowing, PES; post-novel slowing, PNS). Participants with intact frontostriatal circuitry showed increased right-lateralized beta-band (12-24 Hz) synchrony between frontocentral and frontolateral electrode sites in the electroencephalogram after errors and novelty, indexing increased neural communication. Importantly, this synchrony correlated with PES and PNS across participants. Furthermore, such synchrony was reduced in participants with frontostriatal white matter damage, in line with reduced PES and PNS. The results demonstrate that behavioral change after errors and novelty result from coordinated neural activity across a frontostriatal brain network and that such cognitive control is impaired by reduced white matter integrity.

IMP2 provides an alternative to LIN28B for LET-7 target gene protection and glioma stem cell maintenance

Glioblastoma multiforme (GBM) is the most frequent and lethal primary brain tumor in adults. Accumulating evidence suggests that tumors comprise a hierarchical organization that is, at least partially, not genetically driven. Cells that reside at the apex of this hierarchy are commonly referred to as cancer stem cells (CSCs) and are believed to largely contribute to recurrence and therapeutic failure. Although the complexity of epigenetic regulation of the genome precludes prediction as to which epigenetic changes dominate CSC specification in different cancer types, the ability of microRNAs (miRNAs) to fine-tune expression of entire gene networks places them among prime candidates for establishing CSC properties. In this study we characterized the miRNA expression profile of primary GBM grown either under conditions that enrich for GSCs or their differentiated non-tumorigenic progeny (DGCs). Although, we identified a subset of miRNAs that was strongly differentially expressed between GSCs and DGCs, we observed that in GSCs both let-7 and, paradoxically, their target genes are highly expressed, suggesting protection against let-7 action. Using PAR-CLIP we show that insulin-like growth factor-2 mRNA-binding protein 2 (IMP2) provides a mechanism for let-7 target gene protection that represents an alternative to LIN28A/B, which abrogates let-7 biogenesis in normal embryonic and certain malignant stem cells. By direct binding to miRNA recognition elements, IMP2 protects its targets from let-7 mediated decay. Importantly, depletion of IMP2 in GSCs strongly impairs their self- renewal properties and tumorigenicity in vivo, a phenotype that can be rescued by expression of LIN28B, suggesting that IMP2 mainly contributes to GSC maintenance by protecting let-7 target genes from silencing. Using mouse models, we show that depletion of IMP2 in neural stem cells (NSCs) induces let-7 target gene down-regulation, impairs their clonogenic capacity, and affects differentiation. Taken together, our observations describe a novel regulatory function of IMP2 in the let-7 axis whereby it supports GSC and NSC specification. Résumé (Français) Le glioblastome (GBM) est la tumeur primaire maligne du cerveau la plus fréquente. De nombreuses études ont démontré l'existence d'une organisation hiérarchique des cellules cancéreuses liée à des mécanismes épigénétiques. Les cellules qui se trouvent au sommet de cette hiérarchie sont appelées cellules souches cancéreuses (CSC), et contribuent à l'échec thérapeutique. Bien que la complexité des régulateurs épigénétiques permette difficilement de prédire quel mécanisme contribue le plus aux propriétés des CSC, la capacité des microRNAs (miRNAs) de réguler des réseaux entiers de gènes, les placent comme des candidats de premiers choix. Ici, nous avons caractérisé le profil d'expression des miRNAs dans des tumeurs primaires de GBM cultivées dans des conditions qui enrichissent soit pour les CSC, soit pour leur contrepartie de cellules cancéreuses différences (CCD). De manière surprenante et paradoxale la famille de miRNA let-7 et leurs gènes cibles étaient hautement exprimés dans les CSC, suggérant un mécanisme de protection contre l'action des let-7. Avec l'aide de la technologie PAR-CLIP, nous démontrons que la protéine IMP2, protège les mRNAs de l'action des let-7 et représente une alternative à Lin28A/B, qui d'ordinaire réprime fortement la maturation des let-7 dans les cellules souches embryonnaires et divers cancers. En se liant à la région ciblée par les let-7, IMP2 protège ses transcrits de l'action de cette classe de microRNA qui est tumoro-supressive. La déplétion d'IMP2 dans des CSC de GBM réduit fortement leur clonogénicité in vitro et leur tumorigénicité in vivo. Ceci peut être reversé en introduisant Lin28B dans des CSC de GBM, suggérant qu'IMP2 exerce ses fonctions pro-tumorigéniques en modulant l'axe let-7. Avec l'aide de modèles murins, nous observons que la déplétion de IMP2 dans les cellules souches neurales (CSN) induit une baisse de leur clonogénicité et des cibles des miRNAs let-7, suggérant une conservation de ce mécanisme entre les CSC de GBM et les CSN. En résumé, nos observations définissent une nouvelle fonction de IMP2 dans l'axe let-7 par lequel il contribue au maintien des propriétés des CSC et des CSN.

Security, Privacy and Anonymity in Legal Distribution of Copyrighted Multimedia Content over Peer-to-Peer Networks: A Brief Overview

Peer-reviewed

Development of research networks : the case of social capital

The aim of this study is to explore how a new concept appears inscientific discussion and research, how it diffuses to other fields and out of the scientific communities, and how the networks are formed around the concept. Text and terminology take the interest of a reader in the digital environment. Texts create networks where the terminology used is dependent on the ideas, viewsand paradigms of the field. This study is based mainly on bibliographic data. Materials for bibliometric studies have been collected from different databases. The databases are also evaluated and their quality and coverage are discussed. The thesauri of those databases that have been selected for a more in depth study have also been evaluated. The material selected has been used to study how long and in which ways an innovative publication, which can be seen as a milestone in a specific field, influences the research. The concept that has been chosen as a topic for this research is Social Capital, because it has been a popular concept in different scientific fields as well as in everyday speech and the media. It seemed to be a `fashion concept´ that appeared in different situations at the Millennium. The growth and diffusion of social capital publications has been studied. The terms connected with social capital in different fields and different stages of the development have also been analyzed. The methods that have been used in this study are growth and diffusion analysis, content analysis, citation analysis, coword analysis and cocitation analysis. One method that can be used tounderstand and to interpret results of these bibliometric studies is to interview some key persons, who are known to have a gatekeeper position in the diffusion of the concept. Thematic interviews with some Finnish researchers and specialists that have influenced the diffusion of social capital into Finnish scientificand social discussions provide background information. iv The Milestone Publications on social capital have been chosen and studied. They give answers to the question "What is Social Capital?" By comparing citations to Milestone Publications with the growth of all social capital publications in a database, we can drawconclusions about the point at which social capital became generally approved `tacit knowledge´. The contribution of the present study lies foremost in understanding the development of network structures around a new concept that has diffused in scientific communities and also outside them. The network means both networks of researchers, networks of publications and networks of concepts that describe the research field. The emphasis has been on the digital environment and onthe socalled information society that we are now living in, but in this transitional stage, the printed publications are still important and widely used in social sciences and humanities. The network formation is affected by social relations and informal contacts that push new ideas. This study also gives new information about using different research methods, like bibliometric methods supported by interviews and content analyses. It is evident that interpretation of bibliometric maps presupposes qualitative information and understanding of the phenomena under study.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Communication Solutions for LVDC Island Networks

Production and generation of electrical power is evolving to more environmental friendly technologies and schemes. Pushed by the increasing cost of fossil fuels, the operational costs of producing electrical power with fossil fuels and the effect in the environment, like pollution and global warming, renewable energy sources gain con-stant impulse into the global energy economy. In consequence, the introduction of distributed energy sources has brought a new complexity to the electrical networks. In the new concept of smart grids and decen-tralized power generation; control, protection and measurement are also distributed and requiring, among other things, a new scheme of communication to operate with each other in balance and improve performance. In this research, an analysis of different communication technologies (power line communication, Ethernet over unshielded twisted pair (UTP), optic fiber, Wi-Fi, Wi-MAX, and Long Term Evolution) and their respective characteristics will be carried out. With the objective of pointing out strengths and weaknesses from different points of view (technical, economical, deployment, etc.) to establish a richer context on which a decision for communication approach can be done depending on the specific application scenario of a new smart grid deployment. As a result, a description of possible optimal deployment solutions for communication will be shown considering different options for technologies, and a mention of different important considerations to be taken into account will be made for some of the possible network implementation scenarios.

Aligning Regional Repository Networks

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Agonistic-like responses from the torus semicircularis dorsalis elicited by GABA A blockade in the weakly electric fish Gymnotus carapo

Findings by our group have shown that the dorsolateral telencephalon of Gymnotus carapo sends efferents to the mesencephalic torus semicircularis dorsalis (TSd) and that presumably this connection is involved in the changes in electric organ discharge (EOD) and in skeletomotor responses observed following microinjections of GABA A antagonist bicuculline into this telencephalic region. Other studies have implicated the TSd or its mammalian homologue, the inferior colliculus, in defensive responses. In the present study, we explore the possible involvement of the TSd and of the GABA-ergic system in the modulation of the electric and skeletomotor displays. For this purpose, different doses of bicuculline (0.98, 0.49, 0.245, and 0.015 mM) and muscimol (15.35 mM) were microinjected (0.1 µL) in the TSd of the awake G. carapo. Microinjection of bicuculline induced dose-dependent interruptions of EOD and increased skeletomotor activity resembling defense displays. The effects of the two highest doses showed maximum values at 5 min (4.3 ± 2.7 and 3.8 ± 2.0 Hz, P < 0.05) and persisted until 10 min (11 ± 5.7 and 8.7 ± 5.2 Hz, P < 0.05). Microinjections of muscimol were ineffective. During the interruptions of EOD, the novelty response (increased frequency in response to sensory novelties) induced by an electric stimulus delivered by a pair of electrodes placed in the water of the experimental cuvette was reduced or abolished. These data suggest that the GABA-ergic mechanisms of the TSd inhibit the neural substrate of the defense reaction at this midbrain level.

HYBRID ARTIFICIAL NEURAL NETWORK APPLIEDTO MODELING SCFE OF BASIL AND ROSEMARY OILS

This work presents the results of a Hybrid Neural Network (HNN) technique as applied to modeling SCFE curves obtained from two Brazilian vegetable matrices. A series Hybrid Neural Network was employed to estimate the parameters of the phenomenological model. A small set of SCFE data of each vegetable was used to generate an extended data set, sufficient to train the network. Afterwards, other sets of experimental data, not used in the network training, were used to validate the present approach. The series HNN correlates well the experimental data and it is shown that the predictions accomplished with this technique may be promising for SCFE purposes.

An artificial neural network model for prediction of quality characteristics of apples during convective dehydration

In this study, the effects of hot-air drying conditions on color, water holding capacity, and total phenolic content of dried apple were investigated using artificial neural network as an intelligent modeling system. After that, a genetic algorithm was used to optimize the drying conditions. Apples were dried at different temperatures (40, 60, and 80 °C) and at three air flow-rates (0.5, 1, and 1.5 m/s). Applying the leave-one-out cross validation methodology, simulated and experimental data were in good agreement presenting an error < 2.4 %. Quality index optimal values were found at 62.9 °C and 1.0 m/s using genetic algorithm.

Behavioural and neural correlates of operant conditioning in Lymnaea stagnalis : role of previous experience during development /

The freshwater mollusc Lymnaea stagnalis was utilized in this study to further the understanding of how network properties change as a result of associative learning, and to determine whether or not this plasticity is dependent on previous experience during development. The respiratory and neural correlates of operant conditioning were first determined in normally reared Lymnaea. The same procedure was then applied to differentially reared Lymnaea, that is, animals that had never experienced aerial respiration during their development. The aim was to determine whether these animals would demonstrate the same responses to the training paradigm. In normally reared animals, a behavioural reduction in aerial respiration was accompanied by numerous changes within the neural network. Specifically, I provide evidence of changes at the level of the respiratory central pattern generator and the motor output. In the differentially reared animals, there was little behavioural data to suggest learning and memory. There were, however, significant differences in the network parameters, similar to those observed in normally reared animals. This demonstrated an effect of operant conditioning on differentially reared animals. In this thesis, I have identified additional correlates of operant conditioning in normally reared animals and provide evidence of associative learning in differentially reared animals. I conclude plasticity is not dependent on previous experience, but is rather ontogenetically programmed within the neural network.

Comparison of classification ability of hyperball algorithms to neural network and k-nearest neighbour algorithms

The main focus of this thesis is to evaluate and compare Hyperbalilearning algorithm (HBL) to other learning algorithms. In this work HBL is compared to feed forward artificial neural networks using back propagation learning, K-nearest neighbor and 103 algorithms. In order to evaluate the similarity of these algorithms, we carried out three experiments using nine benchmark data sets from UCI machine learning repository. The first experiment compares HBL to other algorithms when sample size of dataset is changing. The second experiment compares HBL to other algorithms when dimensionality of data changes. The last experiment compares HBL to other algorithms according to the level of agreement to data target values. Our observations in general showed, considering classification accuracy as a measure, HBL is performing as good as most ANn variants. Additionally, we also deduced that HBL.:s classification accuracy outperforms 103's and K-nearest neighbour's for the selected data sets.