Arbuscular mycorrhizal fungi: genetics of multigenomic, clonal networks and its ecological consequences

Arbuscular mycorrhizal fungi are thought to have remained asexual for 400 million years although recent studies have suggested that considerable genetic and phenotypic variation could potentially exist in populations. A brief discussion of these multigenomic organisms is presented. (C) 2003 The Linnean Society of London.

Myosin motors and not actin comets are mediators of the actin-based Golgi-to-endoplasmic reticulum protein transport

We have previously reported that actin filaments are involved in protein transport from the Golgi complex to the endoplasmic reticulum. Herein, we examined whether myosin motors or actin comets mediate this transport. To address this issue we have used, on one hand, a combination of specific inhibitors such as 2,3-butanedione monoxime (BDM) and 1-[5-isoquinoline sulfonyl]-2-methyl piperazine (ML7), which inhibit myosin and the phosphorylation of myosin II by the myosin light chain kinase, respectively; and a mutant of the nonmuscle myosin II regulatory light chain, which cannot be phosphorylated (MRLC2AA). On the other hand, actin comet tails were induced by the overexpression of phosphatidylinositol phosphate 5-kinase. Cells treated with BDM/ML7 or those that express the MRLC2AA mutant revealed a significant reduction in the brefeldin A (BFA)-induced fusion of Golgi enzymes with the endoplasmic reticulum (ER). This delay was not caused by an alteration in the formation of the BFA-induced tubules from the Golgi complex. In addition, the Shiga toxin fragment B transport from the Golgi complex to the ER was also altered. This impairment in the retrograde protein transport was not due to depletion of intracellular calcium stores or to the activation of Rho kinase. Neither the reassembly of the Golgi complex after BFA removal nor VSV-G transport from ER to the Golgi was altered in cells treated with BDM/ML7 or expressing MRLC2AA. Finally, transport carriers containing Shiga toxin did not move into the cytosol at the tips of comet tails of polymerizing actin. Collectively, the results indicate that 1) myosin motors move to transport carriers from the Golgi complex to the ER along actin filaments; 2) nonmuscle myosin II mediates in this process; and 3) actin comets are not involved in retrograde transport.

Clearance of a hirano body-like F-actin aggresome generated by jasplakinolide

We have reported in a variety of mammalian cells the reversible formation of a filamentous actin (F-actin)-enriched aggresome generated by the actin toxin jasplakinolide (Lázaro-Diéguez et al., J Cell Sci 2008; 121:1415-25). Notably, this F-actin aggresome (FAG) resembles in many aspects the pathological Hirano body, which frequently appears in some diseases such as Alzheimer's and alcoholism. Using selective inhibitors, we examined the molecular and subcellular mechanisms that participate in the clearance of the FAG. Chaperones, microtubules, proteasomes and autophagosomes all actively participate to eliminate the FAG. Here we compile and compare these results and discuss the involvement of each process. Because of its simplicity and high reproducibility, our cellular model could help to test pharmacological agents designed to interfere with the mechanisms involved in the clearance of intracellular bodies and, in particular, of those enriched in F-actin.

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

Shaping fission yeast cells by rerouting actin-based transport on microtubules.

Kinesins and myosins transport cargos to specific locations along microtubules and actin filaments, respectively. The relative contribution of the two transport systems for cell polarization varies extensively in different cell types, with some cells relying exclusively on actin-based transport while others mainly use microtubules. Using fission yeast, we asked whether one transport system can substitute for the other. In this organism, microtubules and actin cables both contribute to polarized growth by transporting cargos to cell poles, but with distinct roles: microtubules transport landmarks to label cell poles for growth and actin assembly but do not directly contribute to the growth process [1]. Actin cables serve as tracks for myosin V delivery of growth vesicles to cell poles [2-4]. We engineered a chimera between the motor domain of the kinesin 7 Tea2 and the globular tail of the myosin V Myo52, which we show transports Ypt3, a myosin cargo receptor, to cell poles along microtubules. Remarkably, this chimera restores polarized growth and viability to cells lacking actin cables. It also bypasses the normal microtubule-dependent marking of cell poles for polarized growth, but not for other functions. Thus, a synthetic motor protein successfully redirects cargos along a distinct cytoskeletal route.

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.