Conflito e trauma: XVI Colóquio de Outono

It has been the main concern of CEHUM, as a Research Centre within the Humanities which operates in an inter and transdisciplinary structure to listen attentively to the “noise of the world” and attempt a global interpretation of the signs of the times issuing from the world around us, as vibrant echoes of many social and cultural pressing issues. Every year each new Colóquio de Outono attempts to give evidence of that concern through the topic chosen for debate, ample enough and challenging enough to trigger a lively multidisciplinary dialogue amongst the diff erent research groups that compose this centre, the participants and our invited guest speakers. Throughout the three days of this 16th Colóquio de Outono we had the privilege to debate the propositions of a vast number of national and international specialists in the manifold fi elds of inquiry here represented, engaging keynote speakers, project advisors, members of research teams and external researchers attached to the various research projects currently running in CEHUM, in the fi elds of literature, linguistics, philosophy, ethics, visual arts, cultural studies, music and performance. Each specifi c fi eld of studies was however never seen isolated, but always embodied in a geo-cultural context and within the scope of a wide variety of critical debates and current theories of knowledge, as a signal of our understanding of the Humanities as a rich and plural territory which engages us all, scholars, researchers, students.

Verificación del modelado geotécnico del subsuelo de Córdoba

El subsuelo de la ciudad de Córdoba ha sido objeto de estudios por parte de nuestro grupo de investigación que han permitido desarrollar: * Un esquema geotécnico basado en la definición de unidades de mapeo de diferentes grados de detalle. * Bases de datos provenientes de estudios de suelos efectuados en sitios de construcción de obras de ingeniería. * Un sistema de procesamiento de datos implementado en Geobase (software específico). (...) Objetivos generales y específicos: El objetivo final de este programa es el de poder contar con un sistema que permita conocer los suelos presentes en cada lugar de la ciudad y su comportamiento geotécnico, con alto grado de confiabilidad. (...) Como objetivos específicos de esta etapa de trabajo se planea: 1. La retroalimentación del modelo generado a nivel de detalle. Se verificará la delimitación de las zonas que fueran dibujadas en los mapas como consecuencia de un análisis regional y la presencia de diversas unidades de mapeo geotécnico en cada perfil de suelo. Este es un objetivo permanente y es el que permite aumentar la dimensión de la Base[de datos]. (...) 2. Análisis estadístico de las variables geotérmicas acumuladas puntualmente en la Base de datos mediante el uso de técnicas geo-estadísticas de manera de obtener los valores probables de cada propiedad físico-mecánica del terreno de la ciudad y la homogeneidad estadística de las zonas trazadas. 3. Chequeo in situ de los valores probables: se ha planificado la verificación en el terreno de los parámetros obtenidos previamente. Para ello se realizarán tareas de campaña y ensayos de laboratorio. La comunidad de Córdoba podrá valerse del conocimiento de la distribución y del comportamiento geotécnico de los suelos para la planificación y racionalización del uso de la tierra, de los servicios y las construcciones públicas y privadas.

Радиационные риск-факторы и безопасность населения

В брошуре вы ознакомитесь с основныме понятиями о радиации на природу и организм человека и, что самое главное, с основными принципами радиационной безопасности.

Multilayer perceptron with local constraint as an emerging method in spatial data analysis

The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

Nuclear Factor I genomic binding associates with chromatin boundaries.

BACKGROUND: The Nuclear Factor I (NFI) family of DNA binding proteins (also called CCAAT box transcription factors or CTF) is involved in both DNA replication and gene expression regulation. Using chromatin immuno-precipitation and high throughput sequencing (ChIP-Seq), we performed a genome-wide mapping of NFI DNA binding sites in primary mouse embryonic fibroblasts. RESULTS: We found that in vivo and in vitro NFI DNA binding specificities are indistinguishable, as in vivo ChIP-Seq NFI binding sites matched predictions based on previously established position weight matrix models of its in vitro binding specificity. Combining ChIP-Seq with mRNA profiling data, we found that NFI preferentially associates with highly expressed genes that it up-regulates, while binding sites were under-represented at expressed but unregulated genes. Genomic binding also correlated with markers of transcribed genes such as histone modifications H3K4me3 and H3K36me3, even outside of annotated transcribed loci, implying NFI in the control of the deposition of these modifications. Positional correlation between + and - strand ChIP-Seq tags revealed that, in contrast to other transcription factors, NFI associates with a nucleosomal length of cleavage-resistant DNA, suggesting an interaction with positioned nucleosomes. In addition, NFI binding prominently occurred at boundaries displaying discontinuities in histone modifications specific of expressed and silent chromatin, such as loci submitted to parental allele-specific imprinted expression. CONCLUSIONS: Our data thus suggest that NFI nucleosomal interaction may contribute to the partitioning of distinct chromatin domains and to epigenetic gene expression regulation.NFI ChIP-Seq and input control DNA data were deposited at Gene Expression Omnibus (GEO) repository under accession number GSE15844. Gene expression microarray data for mouse embryonic fibroblasts are on GEO accession number GSE15871.

Evaluating the influence of spatial uncertainty in locality points for species distributional modeling

1. Species distribution modelling is used increasingly in both applied and theoretical research to predict how species are distributed and to understand attributes of species' environmental requirements. In species distribution modelling, various statistical methods are used that combine species occurrence data with environmental spatial data layers to predict the suitability of any site for that species. While the number of data sharing initiatives involving species' occurrences in the scientific community has increased dramatically over the past few years, various data quality and methodological concerns related to using these data for species distribution modelling have not been addressed adequately. 2. We evaluated how uncertainty in georeferences and associated locational error in occurrences influence species distribution modelling using two treatments: (1) a control treatment where models were calibrated with original, accurate data and (2) an error treatment where data were first degraded spatially to simulate locational error. To incorporate error into the coordinates, we moved each coordinate with a random number drawn from the normal distribution with a mean of zero and a standard deviation of 5 km. We evaluated the influence of error on the performance of 10 commonly used distributional modelling techniques applied to 40 species in four distinct geographical regions. 3. Locational error in occurrences reduced model performance in three of these regions; relatively accurate predictions of species distributions were possible for most species, even with degraded occurrences. Two species distribution modelling techniques, boosted regression trees and maximum entropy, were the best performing models in the face of locational errors. The results obtained with boosted regression trees were only slightly degraded by errors in location, and the results obtained with the maximum entropy approach were not affected by such errors. 4. Synthesis and applications. To use the vast array of occurrence data that exists currently for research and management relating to the geographical ranges of species, modellers need to know the influence of locational error on model quality and whether some modelling techniques are particularly robust to error. We show that certain modelling techniques are particularly robust to a moderate level of locational error and that useful predictions of species distributions can be made even when occurrence data include some error.

La figura di Nikola Tesla (1856-1943) in Tajna Nikole Tesle (1980)

Nikola Tesla è considerato, dai divulgatori scientifici che di lui si occupano, una figura chiave nella storia della scienza moderna e contemporanea, per l’importanza che hanno avuto le sue ricerche nello sviluppo di tecnologie del XX e XXI secolo. La sua attuale scarsa fama nel piano accademico contrasta con un’abbondante presenza di un Nikola Tesla come personaggio di finzione nelle arti popolari. Questa ricezione ambigua di Tesla potrebbe essere di fatto relazionata con il carattere utopico e visionario delle sue speranze nella scienza come mezzo di conoscenza per il miglioramento della società umana. Questo breve saggio si propone di analizzare come venga presentato il personaggio di Nikola Tesla nel lungometraggio jugoslavo Tajna Nikole Tesle, contribuendo così alla comprensione di come la narrativa di finzione possa sfruttare l’immagine di uno scienziato relativamente agli interessi politici legati a specifici contesti socio-storici.

Uncovering hidden duplicated content in public transcriptomics data.

As part of the development of the database Bgee (a dataBase for Gene Expression Evolution), we annotate and analyse expression data from different types and different sources, notably Affymetrix data from GEO and ArrayExpress, and RNA-Seq data from SRA. During our quality control procedure, we have identified duplicated content in GEO and ArrayExpress, affecting ∼14% of our data: fully or partially duplicated experiments from independent data submissions, Affymetrix chips reused in several experiments, or reused within an experiment. We present here the procedure that we have established to filter such duplicates from Affymetrix data, and our procedure to identify future potential duplicates in RNA-Seq data. Database URL: http://bgee.unil.ch/

Comparación de datos de expresión génica del servidor local con datos de una base de datos remota

En la presente memoria se detalla con precisión las diversas fases del trabajo para construir una aplicación web en el servidor http://revolutionresearch.uab.es que permite enriquecer los clusters de la microarray del usuario con información biomédica de una base de datos remota. Los clusters de origen estadístico (o no) de la microarray del usuario se enriquecen a partir de cruzar sus genes marcadores con la base de datos de genes marcadores de microarrays (base de datos remota) con clusters basados en información biomédica. La base de datos de genes marcadores de microarrays ha sido obtenida a partir de la base de datos de GEO Profiles del NCBI.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Urban malaria in the Brazilian Western Amazon Region I: high prevalence of asymptomatic carriers in an urban riverside district is associated with a high level of clinical malaria

Cross sectional studies on malaria prevalence was performed in 2001, 2002, and 2004 in Vila Candelária, an urban riverside area of Porto Velho, Rondônia, in the Brazilian Western Amazon, followed by longitudinal surveys on malaria incidence. Vila Candelária is a working class district, provided with electricity, water supply, and basic sanitation. Previous preliminary surveys indicated high malaria incidence in this community. At the end of year 2000 regular diagnostic and treatment measures for malaria were introduced, with active search of febrile cases among residents. Despite of both rapid treatment of cases and relative good sanitary and housing conditions, the malaria incidence persisted at high levels during the following years with an annual parasite index of 150 to 300/1000 inhabitants. Parasite surveys in 2001, 2002, and 2004 achieved through microscopy and polymerase chain reaction to diagnose malaria showed a constant high prevalence of asymptomatic carriers for both Plasmodium falciparum and P. vivax parasites. It was concluded that asymptomatic carriers represent an important reservoirs of parasites and that the carriers might contribute to maintaining the high level of transmission. Comparing our findings to similar geo-demographic situations found in other important urban communities of the Brazilian Amazon, we propose that asymptomatic carriers could explain malaria's outbreaks like the one recently observed in Manaus.

Developing new approaches for detecting and preventing Aedes aegypti population outbreaks: basis for surveillance, alert and control system

A new approach to dengue vector surveillance based on permanent egg-collection using a modified ovitrap and Bacillus thuringiensis israelensis(Bti) was evaluated in different urban landscapes in Recife, Northeast Brazil. From April 2004 to April 2005, 13 egg-collection cycles of four weeks were carried out. Geo-referenced ovitraps containing grass infusion, Bti and three paddles were placed at fixed sampling stations distributed over five selected sites. Continuous egg-collections yielded more than four million eggs laid into 464 sentinel-ovitraps over one year. The overall positive ovitrap index was 98.5% (over 5,616 trap observations). The egg density index ranged from 100 to 2,500 eggs per trap-cycle, indicating a wide spread and high density of Aedes aegypti (Diptera: Culicidae) breeding populations in all sites. Fluctuations in population density over time were observed, particularly a marked increase from January on, or later, according to site. Massive egg-collection carried out at one of the sites prevented such a population outbreak. At intra-site level, egg counts made it possible to identify spots where the vector population is consistently concentrated over the time, pinpointing areas that should be considered high priority for control activities. The results indicate that these could be promising strategies for detecting and preventing Ae. aegypti population outbreaks.

Cluster recognition in spatial-temporal sequences: The case of forest fires

Forest fire sequences can be modelled as a stochastic point process where events are characterized by their spatial locations and occurrence in time. Cluster analysis permits the detection of the space/time pattern distribution of forest fires. These analyses are useful to assist fire-managers in identifying risk areas, implementing preventive measures and conducting strategies for an efficient distribution of the firefighting resources. This paper aims to identify hot spots in forest fire sequences by means of the space-time scan statistics permutation model (STSSP) and a geographical information system (GIS) for data and results visualization. The scan statistical methodology uses a scanning window, which moves across space and time, detecting local excesses of events in specific areas over a certain period of time. Finally, the statistical significance of each cluster is evaluated through Monte Carlo hypothesis testing. The case study is the forest fires registered by the Forest Service in Canton Ticino (Switzerland) from 1969 to 2008. This dataset consists of geo-referenced single events including the location of the ignition points and additional information. The data were aggregated into three sub-periods (considering important preventive legal dispositions) and two main ignition-causes (lightning and anthropogenic causes). Results revealed that forest fire events in Ticino are mainly clustered in the southern region where most of the population is settled. Our analysis uncovered local hot spots arising from extemporaneous arson activities. Results regarding the naturally-caused fires (lightning fires) disclosed two clusters detected in the northern mountainous area.

Seeds of distrust: conflict in Uganda

We study the effect of civil conflict on social capital, focusing on Uganda's experience during the last decade. Using individual and county-level data, we document large causal effects on trust and ethnic identity of an exogenous outburst of ethnic conflicts in 2002-2005. We exploit two waves of survey data from Afrobarometer (Round 4 Afrobarometer Survey in Uganda, 2000, 2008), including information on socioeconomic characteristics at the individual level, and geo-referenced measures of fighting events from ACLED. Our identification strategy exploits variations in the both the spatial and ethnic intensity of fighting. We find that more intense fighting decreases generalized trust and increases ethnic identity. The effects are quantitatively large and robust to a number of control variables, alternative measures of violence, and different statistical techniques involving ethnic and spatial fixed effects and instrumental variables. Controlling for the intensity of violence during the conflict, we also document that post-conflict economic recovery is slower in ethnically fractionalized counties. Our findings are consistent with the existence of a self-reinforcing process between conflicts and ethnic cleavages.

Genetic clusters and sex-biased gene flow in a unicolonial Formica ant.

BACKGROUND: Animal societies are diverse, ranging from small family-based groups to extraordinarily large social networks in which many unrelated individuals interact. At the extreme of this continuum, some ant species form unicolonial populations in which workers and queens can move among multiple interconnected nests without eliciting aggression. Although unicoloniality has been mostly studied in invasive ants, it also occurs in some native non-invasive species. Unicoloniality is commonly associated with very high queen number, which may result in levels of relatedness among nestmates being so low as to raise the question of the maintenance of altruism by kin selection in such systems. However, the actual relatedness among cooperating individuals critically depends on effective dispersal and the ensuing pattern of genetic structuring. In order to better understand the evolution of unicoloniality in native non-invasive ants, we investigated the fine-scale population genetic structure and gene flow in three unicolonial populations of the wood ant F. paralugubris. RESULTS: The analysis of geo-referenced microsatellite genotypes and mitochondrial haplotypes revealed the presence of cryptic clusters of genetically-differentiated nests in the three populations of F. paralugubris. Because of this spatial genetic heterogeneity, members of the same clusters were moderately but significantly related. The comparison of nuclear (microsatellite) and mitochondrial differentiation indicated that effective gene flow was male-biased in all populations. CONCLUSION: The three unicolonial populations exhibited male-biased and mostly local gene flow. The high number of queens per nest, exchanges among neighbouring nests and restricted long-distance gene flow resulted in large clusters of genetically similar nests. The positive relatedness among clustermates suggests that kin selection may still contribute to the maintenance of altruism in unicolonial populations if competition occurs among clusters.