Reduced synchronization persistence in neural networks derived from atm-deficient mice

Many neurodegenerative diseases are characterized by malfunction of the DNA damage response. Therefore, it is important to understand the connection between system level neural network behavior and DNA. Neural networks drawn from genetically engineered animals, interfaced with micro-electrode arrays allowed us to unveil connections between networks’ system level activity properties and such genome instability. We discovered that Atm protein deficiency, which in humans leads to progressive motor impairment, leads to a reduced synchronization persistence compared to wild type synchronization, after chemically imposed DNA damage. Not only do these results suggest a role for DNA stability in neural network activity, they also establish an experimental paradigm for empirically determining the role a gene plays on the behavior of a neural network.

Housing plans and urban rehabilitation in Spain, 1992-2012

This paper explores the urban rehabilitation projects promoted by the Spanish Government between 1992 and 2012 through housing plans. The analysis is based on the comparison of programmes and estimations gathered in these plans with actual housing production within this period in order to find the connection between sectoral housing planning and real estate cycles in these last twenty years. During the period under review, six state housing plans, that were mainly focused on the promotion of newly-constructed state-subsidised housing, were developed, including the Areas of Integrated Rehabilitation programmes (ARI programmes). In spite of the relevance and growing complexity of these programmes, these played a subsidiary role in the government housing policy and were insignificant regarding the whole real estate production in this period.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Evolución de las políticas de rehabilitación en Áreas de Rehabilitación Integrada en España (1978-2012)

El artículo estudia la evolución de las políticas de intervención en Áreas de Rehabilitación Integral (ARI) desarrolladas en España entre 1978 y 2012. Se analiza la aparición y consolidación del concepto en la política estatal de vivienda, desde el Real Decreto 3148/1978 hasta el Plan de Vivienda 2009-2012. Por otro lado se analiza la «integralidad» de las intervenciones en áreas urbanas, mediante el estudio de veinte casos representativos, entendiendo como integral aquella intervención que actúa en la ordenación urbana, el diseño urbano y el medioambiente local, la edificación y la dimensión socioeconómica. El trabajo procede de un estudio realizado para el Ministerio de Fomento mediante convenio suscrito con el Departamento de Urbanística y Ordenación del Territorio (ETSAM, UPM) para el Análisis de las políticas estatales y europeas en materia de regeneración urbana y rehabilitación de barrios.