Voltage-gated sodium channel expression in mouse DRG after SNI leads to re-evaluation of projections of injured fibers.

BACKGROUND: Dysregulation of voltage-gated sodium channels (Na(v)s) is believed to play a major role in nerve fiber hyperexcitability associated with neuropathic pain. A complete transcriptional characterization of the different isoforms of Na(v)s under normal and pathological conditions had never been performed on mice, despite their widespread use in pain research. Na(v)s mRNA levels in mouse dorsal root ganglia (DRG) were studied in the spared nerve injury (SNI) and spinal nerve ligation (SNL) models of neuropathic pain. In the SNI model, injured and non-injured neurons were intermingled in lumbar DRG, which were pooled to increase the tissue available for experiments. RESULTS: A strong downregulation was observed for every Na(v)s isoform expressed except for Na(v)1.2; even Na(v)1.3, known to be upregulated in rat neuropathic pain models, was lower in the SNI mouse model. This suggests differences between these two species. In the SNL model, where the cell bodies of injured and non-injured fibers are anatomically separated between different DRG, most Na(v)s were observed to be downregulated in the L5 DRG receiving axotomized fibers. Transcription was then investigated independently in the L3, L4 and L5 DRG in the SNI model, and an important downregulation of many Na(v)s isoforms was observed in the L3 DRG, suggesting the presence of numerous injured neurons there after SNI. Consequently, the proportion of axotomized neurons in the L3, L4 and L5 DRG after SNI was characterized by studying the expression of activating transcription factor 3 (ATF3). Using this marker of nerve injury confirmed that most injured fibers find their cell bodies in the L3 and L4 DRG after SNI in C57BL/6 J mice; this contrasts with their L4 and L5 DRG localization in rats. The spared sural nerve, through which pain hypersensitivity is measured in behavioral studies, mostly projects into the L4 and L5 DRG. CONCLUSIONS: The complex regulation of Na(v)s, together with the anatomical rostral shift of the DRG harboring injured fibers in C57BL/6 J mice, emphasize that caution is necessary and preliminary anatomical experiments should be carried out for gene and protein expression studies after SNI in mouse strains.

Hydrogen-induced changes in the breakdown voltage of InP HEMTs

In this work, electrical measurements show that the breakdown voltage,BVDG, of InP HEMTs increases following exposure to H2. This BVDG shift is nonrecoverable. The increase in BVDG is found to be due to a decrease in the carrier concentration in the extrinsic portion of the device.We provide evidence that H2 reacts with the exposed InAlAs surface in the extrinsic region next to the gate, changing the underlying carrier concentration. Hall measurements of capped and uncapped HEMT samples show that the decrease in sheet carrier concentration can be attributed to a modification of the exposed InAlAs surface. Consistent with this, XPS experiments on uncapped heterostructures give evidence of As loss from the InAlAs surface upon exposure to hydrogen.

A current-mode interface circuit for a piezoresistive pressure sensor

An interfacing circuit for piezoresistive pressure sensors based on CMOS current conveyors is presented. The main advantages of the proposed interfacing circuit include the use of a single piezoresistor, the capability of offset compensation, and a versatile current-mode configuration, with current output and current or voltage input. Experimental tests confirm linear relation of output voltage versus piezoresistance variation.

Resting-state temporal synchronization networks emerge from connectivity topology and heterogeneity.

Spatial patterns of coherent activity across different brain areas have been identified during the resting-state fluctuations of the brain. However, recent studies indicate that resting-state activity is not stationary, but shows complex temporal dynamics. We were interested in the spatiotemporal dynamics of the phase interactions among resting-state fMRI BOLD signals from human subjects. We found that the global phase synchrony of the BOLD signals evolves on a characteristic ultra-slow (<0.01Hz) time scale, and that its temporal variations reflect the transient formation and dissolution of multiple communities of synchronized brain regions. Synchronized communities reoccurred intermittently in time and across scanning sessions. We found that the synchronization communities relate to previously defined functional networks known to be engaged in sensory-motor or cognitive function, called resting-state networks (RSNs), including the default mode network, the somato-motor network, the visual network, the auditory network, the cognitive control networks, the self-referential network, and combinations of these and other RSNs. We studied the mechanism originating the observed spatiotemporal synchronization dynamics by using a network model of phase oscillators connected through the brain's anatomical connectivity estimated using diffusion imaging human data. The model consistently approximates the temporal and spatial synchronization patterns of the empirical data, and reveals that multiple clusters that transiently synchronize and desynchronize emerge from the complex topology of anatomical connections, provided that oscillators are heterogeneous.

Networks of people in specialty production: family firms in the iron and steel Wire industries in Spain (1870-2000)

Capital intensive industries in specialized niches of production have constituted solid ground for family firms in Spain , as evidenced by the experience of the iron and steel wire industries between 1870 and 2000. The embeddedness of these firms in their local and regional environments have allowed the creation of networks that, together with favourable institutional conditions, significantly explain the dominance of family entrepreneurship in iron and steel wire manufacturing in Spain, until the end of the 20 th century. Dominance of family firms at the regional level has not been not an obstacle for innovation in wire manufacturing in Spain, which has taken place even when institutional conditions blocked innovation and traditional networking. Therefore, economic theories about the difficulties dynastic family firms may have to perform appropriately in science-based industries must be questioned

Synchronizability of EEG-Based Functional Networks in Early Alzheimer's Disease.

Recently graph theory and complex networks have been widely used as a mean to model functionality of the brain. Among different neuroimaging techniques available for constructing the brain functional networks, electroencephalography (EEG) with its high temporal resolution is a useful instrument of the analysis of functional interdependencies between different brain regions. Alzheimer's disease (AD) is a neurodegenerative disease, which leads to substantial cognitive decline, and eventually, dementia in aged people. To achieve a deeper insight into the behavior of functional cerebral networks in AD, here we study their synchronizability in 17 newly diagnosed AD patients compared to 17 healthy control subjects at no-task, eyes-closed condition. The cross-correlation of artifact-free EEGs was used to construct brain functional networks. The extracted networks were then tested for their synchronization properties by calculating the eigenratio of the Laplacian matrix of the connection graph, i.e., the largest eigenvalue divided by the second smallest one. In AD patients, we found an increase in the eigenratio, i.e., a decrease in the synchronizability of brain networks across delta, alpha, beta, and gamma EEG frequencies within the wide range of network costs. The finding indicates the destruction of functional brain networks in early AD.

Testing Spatial Autocorrelation in Weighted Networks : the Modes Permutation Test

In a weighted spatial network, as specified by an exchange matrix, the variances of the spatial values are inversely proportional to the size of the regions. Spatial values are no more exchangeable under independence, thus weakening the rationale for ordinary permutation and bootstrap tests of spatial autocorrelation. We propose an alternative permutation test for spatial autocorrelation, based upon exchangeable spatial modes, constructed as linear orthogonal combinations of spatial values. The coefficients obtain as eigenvectors of the standardised exchange matrix appearing in spectral clustering, and generalise to the weighted case the concept of spatial filtering for connectivity matrices. Also, two proposals aimed at transforming an acessibility matrix into a exchange matrix with with a priori fixed margins are presented. Two examples (inter-regional migratory flows and binary adjacency networks) illustrate the formalism, rooted in the theory of spectral decomposition for reversible Markov chains.

Validation of network communicability metrics for the analysis of brain structural networks.

Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Transition to parenthood: The role of social interaction and endogenous networks

Empirical studies indicate that the transition to parenthood is influenced by an individual's peer group. To study the mechanisms creating interdepen- dencies across individuals' transition to parenthood and its timing we apply an agent-based simulation model. We build a one-sex model and provide agents with three different characteristics regarding age, intended education and parity. Agents endogenously form their network based on social closeness. Network members then may influence the agents' transition to higher parity levels. Our numerical simulations indicate that accounting for social inter- actions can explain the shift of first-birth probabilities in Austria over the period 1984 to 2004. Moreover, we apply our model to forecast age-specific fertility rates up to 2016.

Modelling non-industrial private forest landowners' strategic decision making by using logistic regression and neural networks: case of predicting the choice of forest taxation basis

Summary

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Regulation of Nav1.7 and Nav1.8 voltage-gated sodium channels by Nedd4-2 and β-subunits and its implication in neuropathic pain

In mammals, the presence of excitable cells in muscles, heart and nervous system is crucial and allows fast conduction of numerous biological information over long distances through the generation of action potentials (AP). Voltage-gated sodium channels (Navs) are key players in the generation and propagation of AP as they are responsible for the rising phase of the AP. Navs are heteromeric proteins composed of a large pore-forming a-subunit (Nav) and smaller ß-auxiliary subunits. There are ten genes encoding for Navl.l to Nav1.9 and NaX channels, each possessing its own specific biophysical properties. The excitable cells express differential combinations of Navs isoforms, generating a distinct electrophysiological signature. Noteworthy, only when anchored at the membrane are Navs functional and are participating in sodium conductance. In addition to the intrinsic properties of Navs, numerous regulatory proteins influence the sodium current. Some proteins will enhance stabilization of membrane Navs while others will favour internalization. Maintaining equilibrium between the two is of crucial importance for controlling cellular excitability. The E3 ubiquitin ligase Nedd4-2 is a well-characterized enzyme that negatively regulates the turnover of many membrane proteins including Navs. On the other hand, ß-subunits are known since long to stabilize Navs membrane anchoring. Peripheral neuropathic pain is a disabling condition resulting from nerve injury. It is characterized by the dysregulation of Navs expressed in dorsal root ganglion (DRG) sensory neurons as highlighted in different animal models of neuropathic pain. Among Navs, Nav1.7 and Nav1.8 are abundantly and specifically expressed in DRG sensory neurons and have been recurrently incriminated in nociception and neuropathic pain development. Using the spared nerve injury (SNI) experimental model of neuropathic pain in mice, I observed a specific reduction of Nedd4-2 in DRG sensory neurons. This decrease subsequently led to an upregulation of Nav1.7 and Nav1.8 protein and current, in the axon and the DRG neurons, respectively, and was sufficient to generate neuropathic pain-associated hyperexcitability. Knocking out Nedd4-2 specifically in nociceptive neurons led to the same increase of Nav1.7 and Nav1.8 concomitantly with an increased thermal sensitivity in mice. Conversely, rescuing Nedd4-2 downregulation using viral vector transfer attenuated neuropathic pain mechanical hypersensitivity. This study demonstrates the significant role of Nedd4-2 in regulating cellular excitability in vivo and its involvement in neuropathic pain development. The role of ß-subunits in neuropathic pain was already demonstrated in our research group. Because of their stabilization role, the increase of ßl, ß2 and ß3 subunits in DRGs after SNI led to increased Navs anchored at the membrane. Here, I report a novel mechanism of regulation of a-subunits by ß- subunits in vitro; ßl and ß3-subunits modulate the glycosylation pattern of Nav1.7, which might account for stabilization of its membrane expression. This opens new perspectives for investigation Navs state of glycosylation in ß-subunits dependent diseases, such as in neuropathic pain. - Chez les mammifères, la présence de cellules excitables dans les muscles, le coeur et le système nerveux est cruciale; elle permet la conduction rapide de nombreuses informations sur de longues distances grâce à la génération de potentiels d'action (PA). Les canaux sodiques voltage-dépendants (Navs) sont des participants importants dans la génération et la propagation des PA car ils sont responsables de la phase initiale de dépolarisation du PA. Les Navs sont des protéines hétéromériques composées d'une grande sous-unité a (formant le pore du canal) et de petites sous-unités ß accompagnatrices. Il existe dix gènes qui codent pour les canaux sodiques, du Nav 1.1 au Nav 1.9 ainsi que NaX, chacun possédant des propriétés biophysiques spécifiques. Les cellules excitables expriment différentes combinaisons des différents isoformes de Navs, qui engendrent une signature électrophysiologique distincte. Les Navs ne sont fonctionnels et ne participent à la conductibilité du Na+, que s'ils sont ancrés à la membrane plasmique. En plus des propriétés intrinsèques des Navs, de nombreuses protéines régulatrices influencent également le courant sodique. Certaines protéines vont favoriser l'ancrage et la stabilisation des Navs exprimés à la membrane, alors que d'autres vont plutôt favoriser leur internalisation. Maintenir l'équilibre des deux processus est crucial pour contrôler l'excitabilité cellulaire. Dans ce contexte, Nedd4-2, de la famille des E3 ubiquitin ligase, est une enzyme bien caractérisée qui régule l'internalisation de nombreuses protéines, notamment celle des Navs. Inversement, les sous-unités ß sont connues depuis longtemps pour stabiliser l'ancrage des Navs à la membrane. La douleur neuropathique périphérique est une condition débilitante résultant d'une atteinte à un nerf. Elle est caractérisée par la dérégulation des Navs exprimés dans les neurones sensoriels du ganglion spinal (DRG). Ceci a été démontré à de multiples occasions dans divers modèles animaux de douleur neuropathique. Parmi les Navs, Nav1.7 et Nav1.8 sont abondamment et spécifiquement exprimés dans les neurones sensoriels des DRG et ont été impliqués de façon récurrente dans le développement de la douleur neuropathique. En utilisant le modèle animal de douleur neuropathique d'épargne du nerf sural (spared nerve injury, SNI) chez la souris, j'ai observé une réduction spécifique des Nedd4-2 dans les neurones sensoriels du DRG. Cette diminution avait pour conséquence l'augmentation de l'expression des protéines et des courants de Nav 1.7 et Nav 1.8, respectivement dans l'axone et les neurones du DRG, et était donc suffisante pour créer l'hyperexcitabilité associée à la douleur neuropathique. L'invalidation pour le gène codant pour Nedd4-2 dans une lignée de souris génétiquement modifiées a conduit à de similaires augmentations de Nav1.7 et Nav1.8, parallèlement à une augmentation à la sensibilité thermique. A l'opposé, rétablir une expression normale de Nedd4-2 en utilisant un vecteur viral a eu pour effet de contrecarrer le développement de l'hypersensibilité mécanique lié à ce modèle de douleur neuropathique. Cette étude démontre le rôle important de Nedd4-2 dans la régulation de l'excitabilité cellulaire in vivo et son implication dans le développement des douleurs neuropathiques. Le rôle des sous-unités ß dans les douleurs neuropathiques a déjà été démontré dans notre groupe de recherche. A cause de leur rôle stabilisateur, l'augmentation des sous-unités ßl, ß2 et ß3 dans les DRG après SNI, conduit à une augmentation des Navs ancrés à la membrane. Dans mon travail de thèse, j'ai observé un nouveau mécanisme de régulation des sous-unités a par les sous-unités ß in vitro. Les sous-unités ßl et ß3 régulent l'état de glycosylation du canal Nav1.7, et stabilisent son expression membranaire. Ceci ouvre de nouvelles perspectives dans l'investigation de l'état de glycosylation des Navs dans des maladies impliquant les sous-unités ß, notamment les douleurs neuropathiques.

Communication in networks with hierarchical branching

We present a simple model of communication in networks with hierarchical branching. We analyze the behavior of the model from the viewpoint of critical systems under different situations. For certain values of the parameters, a continuous phase transition between a sparse and a congested regime is observed and accurately described by an order parameter and the power spectra. At the critical point the behavior of the model is totally independent of the number of hierarchical levels. Also scaling properties are observed when the size of the system varies. The presence of noise in the communication is shown to break the transition. The analytical results are a useful guide to forecasting the main features of real networks.

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar¹

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.