Stochastic models and numerical algorithms for a class of regulatory gene networks.

Regulatory gene networks contain generic modules, like those involving feedback loops, which are essential for the regulation of many biological functions (Guido et al. in Nature 439:856-860, 2006). We consider a class of self-regulated genes which are the building blocks of many regulatory gene networks, and study the steady-state distribution of the associated Gillespie algorithm by providing efficient numerical algorithms. We also study a regulatory gene network of interest in gene therapy, using mean-field models with time delays. Convergence of the related time-nonhomogeneous Markov chain is established for a class of linear catalytic networks with feedback loops.

The long-term survival of in vitro engineered nervous tissue derived from the specific neural differentiation of mouse embryonic stem cells.

Embryonic stem cells (ESCs) offer attractive prospective as potential source of neurons for cell replacement therapy in human neurodegenerative diseases. Besides, ESCs neural differentiation enables in vitro tissue engineering for fundamental research and drug discovery aimed at the nervous system. We have established stable and long-term three-dimensional (3D) culture conditions which can be used to model long latency and complex neurodegenerative diseases. Mouse ESCs-derived neural progenitor cells generated by MS5 stromal cells induction, result in strictly neural 3D cultures of about 120-mum thick, whose cells expressed mature neuronal, astrocytes and myelin markers. Neurons were from the glutamatergic and gabaergic lineages. This nervous tissue was spatially organized in specific layers resembling brain sub-ependymal (SE) nervous tissue, and was maintained in vitro for at least 3.5 months with great stability. Electron microscopy showed the presence of mature synapses and myelinated axons, suggesting functional maturation. Electrophysiological activity revealed biological signals involving action potential propagation along neuronal fibres and synaptic-like release of neurotransmitters. The rapid development and stabilization of this 3D cultures model result in an abundant and long-lasting production that is compatible with multiple and productive investigations for neurodegenerative diseases modeling, drug and toxicology screening, stress and aging research.

Extrasynaptic neurotransmission in the modulation of brain function. Focus on the striatal neuronal glial networks

Extrasynaptic neurotransmission is an important short distance form of volume transmission (VT) and describes the extracellular diffusion of transmitters and modulators after synaptic spillover or extrasynaptic release in the local circuit regions binding to and activating mainly extrasynaptic neuronal and glial receptors in the neuroglial networks of the brain. Receptor-receptor interactions in G protein-coupled receptor (GPCR) heteromers play a major role, on dendritic spines and nerve terminals including glutamate synapses, in the integrative processes of the extrasynaptic signaling. Heteromeric complexes between GPCR and ion-channel receptors play a special role in the integration of the synaptic and extrasynaptic signals. Changes in extracellular concentrations of the classical synaptic neurotransmitters glutamate and GABA found with microdialysis is likely an expression of the activity of the neuron-astrocyte unit of the brain and can be used as an index of VT-mediated actions of these two neurotransmitters in the brain. Thus, the activity of neurons may be functionally linked to the activity of astrocytes, which may release glutamate and GABA to the extracellular space where extrasynaptic glutamate and GABA receptors do exist. Wiring transmission (WT) and VT are fundamental properties of all neurons of the CNS but the balance between WT and VT varies from one nerve cell population to the other. The focus is on the striatal cellular networks, and the WT and VT and their integration via receptor heteromers are described in the GABA projection neurons, the glutamate, dopamine, 5-hydroxytryptamine (5-HT) and histamine striatal afferents, the cholinergic interneurons, and different types of GABA interneurons. In addition, the role in these networks of VT signaling of the energy-dependent modulator adenosine and of endocannabinoids mainly formed in the striatal projection neurons will be underlined to understand the communication in the striatal cellular networks

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

Glucagon-like peptide-1 and control of insulin secretion.

Although glucose is the major regulator of insulin secretion by pancreatic beta cells, its action is modulated by several neural and hormonal stimuli. In particular, hormones secreted by intestinal endocrine cells stimulate glucose-induced insulin secretion very potently after nutrient absorption. These hormones, called gluco-incretins or insulinotropic hormones, are major regulators of postprandial glucose homeostasis. The main gluco-incretins are GIP (gastric inhibitory polypeptide or glucose-dependent insulinotropic polypeptide) and GLP-1 (glucagon-like polypeptide-1). The secretion of GIP, a 42 amino acid polypeptide secreted by duodenal K cells, is triggered by fat and glucose. GIP stimulation of insulin secretion depends on the presence of specific beta-cell receptors and requires glucose at a concentration at least equal to or higher than the normoglycaemic level of approximately 5 mM. GIP accounts for about 50% of incretin activity, and the rest may be due to GLP-1 which is produced by proteolytic processing of the preproglucagon molecule in intestinal L cells. GLP-1 is the most potent gluco-incretin characterized so far. As with GIP, its stimulatory action requires a specific membrane receptor and normal or elevated glucose concentrations. Contrary to GIP, the incretin effect of GLP-1 is maintained in non-insulin-dependent diabetic patients. This peptide or agonists of its beta-cell receptor could provide new therapeutic tools for the treatment of Type II diabetic hyperglycaemia.

Neural substrates of social emotion regulation: a FMRI study on imitation and expressive suppression to dynamic facial signals.

Emotion regulation is crucial for successfully engaging in social interactions. Yet, little is known about the neural mechanisms controlling behavioral responses to emotional expressions perceived in the face of other people, which constitute a key element of interpersonal communication. Here, we investigated brain systems involved in social emotion perception and regulation, using functional magnetic resonance imaging (fMRI) in 20 healthy participants. The latter saw dynamic facial expressions of either happiness or sadness, and were asked to either imitate the expression or to suppress any expression on their own face (in addition to a gender judgment control task). fMRI results revealed higher activity in regions associated with emotion (e.g., the insula), motor function (e.g., motor cortex), and theory of mind (e.g., [pre]cuneus) during imitation. Activity in dorsal cingulate cortex was also increased during imitation, possibly reflecting greater action monitoring or conflict with own feeling states. In addition, premotor regions were more strongly activated during both imitation and suppression, suggesting a recruitment of motor control for both the production and inhibition of emotion expressions. Expressive suppression (eSUP) produced increases in dorsolateral and lateral prefrontal cortex typically related to cognitive control. These results suggest that voluntary imitation and eSUP modulate brain responses to emotional signals perceived from faces, by up- and down-regulating activity in distributed subcortical and cortical networks that are particularly involved in emotion, action monitoring, and cognitive control.

Neural plasticity associated with recently versus often heard objects.

In natural settings the same sound source is often heard repeatedly, with variations in spectro-temporal and spatial characteristics. We investigated how such repetitions influence sound representations and in particular how auditory cortices keep track of recently vs. often heard objects. A set of 40 environmental sounds was presented twice, i.e. as prime and as repeat, while subjects categorized the corresponding sound sources as living vs. non-living. Electrical neuroimaging analyses were applied to auditory evoked potentials (AEPs) comparing primes vs. repeats (effect of presentation) and the four experimental sections. Dynamic analysis of distributed source estimations revealed i) a significant main effect of presentation within the left temporal convexity at 164-215ms post-stimulus onset; and ii) a significant main effect of section in the right temporo-parietal junction at 166-213ms. A 3-way repeated measures ANOVA (hemisphere×presentation×section) applied to neural activity of the above clusters during the common time window confirmed the specificity of the left hemisphere for the effect of presentation, but not that of the right hemisphere for the effect of section. In conclusion, spatio-temporal dynamics of neural activity encode the temporal history of exposure to sound objects. Rapidly occurring plastic changes within the semantic representations of the left hemisphere keep track of objects heard a few seconds before, independent of the more general sound exposure history. Progressively occurring and more long-lasting plastic changes occurring predominantly within right hemispheric networks, which are known to code for perceptual, semantic and spatial aspects of sound objects, keep track of multiple exposures.

On the differential evolution algorithm and its appliation to training radial basis function networks

The parameter setting of a differential evolution algorithm must meet several requirements: efficiency, effectiveness, and reliability. Problems vary. The solution of a particular problem can be represented in different ways. An algorithm most efficient in dealing with a particular representation may be less efficient in dealing with other representations. The development of differential evolution-based methods contributes substantially to research on evolutionary computing and global optimization in general. The objective of this study is to investigatethe differential evolution algorithm, the intelligent adjustment of its controlparameters, and its application. In the thesis, the differential evolution algorithm is first examined using different parameter settings and test functions. Fuzzy control is then employed to make control parameters adaptive based on an optimization process and expert knowledge. The developed algorithms are applied to training radial basis function networks for function approximation with possible variables including centers, widths, and weights of basis functions and both having control parameters kept fixed and adjusted by fuzzy controller. After the influence of control variables on the performance of the differential evolution algorithm was explored, an adaptive version of the differential evolution algorithm was developed and the differential evolution-based radial basis function network training approaches were proposed. Experimental results showed that the performance of the differential evolution algorithm is sensitive to parameter setting, and the best setting was found to be problem dependent. The fuzzy adaptive differential evolution algorithm releases the user load of parameter setting and performs better than those using all fixedparameters. Differential evolution-based approaches are effective for training Gaussian radial basis function networks.

Protective effects of social networks on disability among older adults in Spain

The loss of autonomy at advanced ages is not only associated with ageing, but also with the characteristics of the physical and social environment. Recent investigations have shown that social networks, social engagement and participation act like predictors of disability among the elderly. The aim of this study is to determine whether social networks are related to the development and progression of disability in the early years of old age. The source of data is the first wave of the survey "Processes of Vulnerability among Spanish Elderly", carried out in 2005 to a sample of 1 244 individuals. The population object of study is the cohort aged 70 to 74 years in metropolitan areas (Madrid and Barcelona) and not institutionalized. Disability is measured by the development of basic activities of daily life (ADL), and instrumental activities of daily life (IADL). The structural aspects of the social relationships are measured through the diversity of social networks and participation. We used the social network index (SNI). For each point over the SNI, the risk of developing any type of disability decreased by 49% (HR = 0.51, 95%CI = 0.31-0.82). The SNI was a decisive factor in all forecasting models constructed with some hazard ratios (HR) that ranged from 0.29 (95%CI = 0.14-0.59) in the first model to 0.43 (95%CI 0.20-0.90) in the full model. The results of the present study showed a strong association between an active social life, emotional support provided by friends and confidents and disability. These findings suggest a protective effect of social networks on disability. Also, these results indicate that some family and emotional ties have a significant effect on both the prevalence and the incidence of disability.

Percolation of spatially constrained Erdos-R enyi networks with degree correlations

Motivated by experiments on activity in neuronal cultures [J. Soriano, M. Rodr ́ıguez Mart́ınez, T. Tlusty, and E. Moses, Proc. Natl. Acad. Sci. 105, 13758 (2008)], we investigate the percolation transition and critical exponents of spatially embedded Erd̋os-Ŕenyi networks with degree correlations. In our model networks, nodes are randomly distributed in a two-dimensional spatial domain, and the connection probability depends on Euclidian link length by a power law as well as on the degrees of linked nodes. Generally, spatial constraints lead to higher percolation thresholds in the sense that more links are needed to achieve global connectivity. However, degree correlations favor or do not favor percolation depending on the connectivity rules. We employ two construction methods to introduce degree correlations. In the first one, nodes stay homogeneously distributed and are connected via a distance- and degree-dependent probability. We observe that assortativity in the resulting network leads to a decrease of the percolation threshold. In the second construction methods, nodes are first spatially segregated depending on their degree and afterwards connected with a distance-dependent probability. In this segregated model, we find a threshold increase that accompanies the rising assortativity. Additionally, when the network is constructed in a disassortative way, we observe that this property has little effect on the percolation transition.

Implementing telephony signalling over IP networks using SIGTRAN standards

Diplomityössä tutkitaan yhteiskanavamerkinantoverkon signalointiliikenteen siirtoa IP-pohjaisiin verkkoihin käyttäen IETF:n aliorganisaation, SIGTRAN:in, määrittelemiä standardeja. Tutkimuksen lisäksi työssä suoritetaan myös toteutus, joka mahdollistaa em. verkkojen yhdistymisen. Ensin työssä käsitellään yhteiskanavamerkinantoverkon tulevaisuus ja alan yleiset näkymät, jotka luovat pohjan työn toteutuksen ymmärtämiseen. Toiseksi työssä esitellään toteutuksessa käytetyt teknologiat. Esiteltäviin teknologioihin kuuluvat yhteiskanavamerkinantoverkko, IP-pohjainen verkko, SIGTRAN:in määrittelemät standardit, toteutustyökalut sekä ympäristö, johon toteutus liitetään. Toteutukseen olennaisesti liittyvät asiat ovat painotettuina teknologioiden esittelyssä. Diplomityön loppuosassa kuvataan merkinantoyhdyskäytävän toteutus suunnittelun ja toteutettujen toiminnallisuuksien osalta. Diplomityön tuloksena saatiin uusi testattu merkinantosovellus, joka täyttää yhteiskanavamerkinantoverkolle asetetut vaatimukset. Toteutus toimii osana Intellitel(TM) ONE palvelualustaa. Toteutuksen kehitys tulee jatkumaan.

Application of Neural Network Methods in the Prediction of Stock Markets

The purpose of the research is to define practical profit which can be achieved using neural network methods as a prediction instrument. The thesis investigates the ability of neural networks to forecast future events. This capability is checked on the example of price prediction during intraday trading on stock market. The executed experiments show predictions of average 1, 2, 5 and 10 minutes’ prices based on data of one day and made by two different types of forecasting systems. These systems are based on the recurrent neural networks and back propagation neural nets. The precision of the predictions is controlled by the absolute error and the error of market direction. The economical effectiveness is estimated by a special trading system. In conclusion, the best structures of neural nets are tested with data of 31 days’ interval. The best results of the average percent of profit from one transaction (buying + selling) are 0.06668654, 0.188299453, 0.349854787 and 0.453178626, they were achieved for prediction periods 1, 2, 5 and 10 minutes. The investigation can be interesting for the investors who have access to a fast information channel with a possibility of every-minute data refreshment.

Text Classification and Indexing in IP Networks

Internet on elektronisen postin perusrakenne ja ollut tärkeä tiedonlähde akateemisille käyttäjille jo pitkään. Siitä on tullut merkittävä tietolähde kaupallisille yrityksille niiden pyrkiessä pitämään yhteyttä asiakkaisiinsa ja seuraamaan kilpailijoitansa. WWW:n kasvu sekä määrällisesti että sen moninaisuus on luonut kasvavan kysynnän kehittyneille tiedonhallintapalveluille. Tällaisia palveluja ovet ryhmittely ja luokittelu, tiedon löytäminen ja suodattaminen sekä lähteiden käytön personointi ja seuranta. Vaikka WWW:stä saatavan tieteellisen ja kaupallisesti arvokkaan tiedon määrä on huomattavasti kasvanut viime vuosina sen etsiminen ja löytyminen on edelleen tavanomaisen Internet hakukoneen varassa. Tietojen hakuun kohdistuvien kasvavien ja muuttuvien tarpeiden tyydyttämisestä on tullut monimutkainen tehtävä Internet hakukoneille. Luokittelu ja indeksointi ovat merkittävä osa luotettavan ja täsmällisen tiedon etsimisessä ja löytämisessä. Tämä diplomityö esittelee luokittelussa ja indeksoinnissa käytettävät yleisimmät menetelmät ja niitä käyttäviä sovelluksia ja projekteja, joissa tiedon hakuun liittyvät ongelmat on pyritty ratkaisemaan.

Nature of the Epidemic Threshold for the Susceptible-Infected-Susceptible Dynamics in Networks

We develop an analytical approach to the susceptible-infected-susceptible epidemic model that allows us to unravel the true origin of the absence of an epidemic threshold in heterogeneous networks. We find that a delicate balance between the number of high degree nodes in the network and the topological distance between them dictates the existence or absence of such a threshold. In particular, small-world random networks with a degree distribution decaying slower than an exponential have a vanishing epidemic threshold in the thermodynamic limit.

Structural Brain Connectivity in School-Age Preterm Infants Provides Evidence for Impaired Networks Relevant for Higher Order Cognitive Skills and Social Cognition.

Extreme prematurity and pregnancy conditions leading to intrauterine growth restriction (IUGR) affect thousands of newborns every year and increase their risk for poor higher order cognitive and social skills at school age. However, little is known about the brain structural basis of these disabilities. To compare the structural integrity of neural circuits between prematurely born controls and children born extreme preterm (EP) or with IUGR at school age, long-ranging and short-ranging connections were noninvasively mapped across cortical hemispheres by connection matrices derived from diffusion tensor tractography. Brain connectivity was modeled along fiber bundles connecting 83 brain regions by a weighted characterization of structural connectivity (SC). EP and IUGR subjects, when compared with controls, had decreased fractional anisotropy-weighted SC (FAw-SC) of cortico-basal ganglia-thalamo-cortical loop connections while cortico-cortical association connections showed both decreased and increased FAw-SC. FAw-SC strength of these connections was associated with poorer socio-cognitive performance in both EP and IUGR children.