Do Airports green cities? : Von der Airport City zur nachhaltigen Region ; Flughafenstrategien und Regionalentwicklung

Multivariate pattern recognition approaches have become a prominent tool in neuroimaging data analysis. These methods enable the classification of groups of participants (e.g. controls and patients) on the basis of subtly different patterns across the whole brain. This study demonstrates that these methods can be used, in combination with automated morphometric analysis of structural MRI, to determine with great accuracy whether a single subject has been engaged in regular mental training or not. The proposed approach allowed us to identify with 94.87% accuracy (p<0.001) if a given participant is a regular meditator (from a sample of 19 regular meditators and 20 non-meditators). Neuroimaging has been a relevant tool for diagnosing neurological and psychiatric impairments. This study may suggest a novel step forward: the emergence of a new field in brain imaging applications, in which participants could be identified based on their mental experience

Modeling gene expression regulatory networks with the sparse vector autoregressive model

<p>Abstract</p> <p>Background</p> <p>To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.</p> <p>Results</p> <p>We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets.</p> <p>Conclusion</p> <p>The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any <it>a priori </it>information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.</p

Controle cognitivo associado à indução de irritabilidade: um estudo de RMf usando recordações autobiográficas

OBJECTIVE: Despite the relevance of irritability emotions to the treatment, prognosis and classification of psychiatric disorders, the neurobiological basis of this emotional state has been rarely investigated to date. We assessed the brain circuitry underlying personal script-driven irritability in healthy subjects (n = 11) using functional magnetic resonance imaging. METHOD: Blood oxygen level-dependent signal changes were recorded during auditory presentation of personal scripts of irritability in contrast to scripts of happiness or neutral emotional content. Self-rated emotional measurements and skin conductance recordings were also obtained. Images were acquired using a 1,5T magnetic resonance scanner. Brain activation maps were constructed from individual images, and between-condition differences in the mean power of experimental response were identified by using cluster-wise nonparametric tests. RESULTS: Compared to neutral scripts, increased blood oxygen level-dependent signal during irritability scripts was detected in the left subgenual anterior cingulate cortex, and in the left medial, anterolateral and posterolateral dorsal prefrontal cortex (cluster-wise p-value < 0.05). While the involvement of the subgenual cingulate and dorsal anterolateral prefrontal cortices was unique to the irritability state, increased blood oxygen level-dependent signal in dorsomedial and dorsal posterolateral prefrontal regions were also present during happiness induction. CONCLUSION: Irritability induction is associated with functional changes in a limited set of brain regions previously implicated in the mediation of emotional states. Changes in prefrontal and cingulate areas may be related to effortful cognitive control aspects that gain salience during the emergence of irritability.OBJETIVO: Apesar da relevância de emoções de irritabilidade para o tratamento, prognóstico e classificação dos transtornos psiquiátricos, as bases neurobiológicas deste tipo de estado emocional foram raramente investigadas até hoje. Este estudo avaliou os circuitos cerebrais subjacentes à irritabilidade induzida por scripts pessoais em voluntários saudáveis (n = 11) usando ressonância magnética funcional. MÉTODO: Mudanças no sinal dependente do nível de oxigenação sanguínea (blood-oxygen level dependent signal) foram registradas durante a apresentação por via auditiva de scripts pessoais de irritabilidade em contraste com scripts de felicidade ou de conteúdo emocional neutro. Escores em escalas de autoavaliação emocional e medidas de condutância da pele também foram obtidos. A aquisição de imagens foi realizada em aparelho de ressonância magnética de 1,5 T. Os mapas de ativação cerebral foram construídos a partir das imagens individuais, e as diferenças entre as condições experimentais foram investigadas utilizando testes não-paramétricos baseados em permutações. RESULTADOS: Em comparação com scripts neutros, a apresentação de scripts de irritabilidade levou a aumentos de sinal dependente do nível de oxigenação sanguínea na porção subgenual do giro do cíngulo anterior esquerdo e nas porções medial, ântero-lateral e póstero-lateral do córtex pré-frontal dorsal (cluster-wise p-valor < 0,05). Enquanto o envolvimento do cíngulo anterior subgenual e do córtex pré-frontal dorsal antero-lateral surgiu apenas em associação com o estado de irritabilidade, aumentos do sinal dependente do nível de oxigenação sanguínea nas porções dorso-medial e dorsal póstero-lateral do córtex pré-frontal também estiveram presentes durante indução de felicidade. CONCLUSÃO: Indução de irritabilidade está associada a mudanças de atividade funcional num conjunto restrito de regiões cerebrais previamente implicadas na mediação de estados emocionais. Mudanças na atividade de porções do giro do cíngulo e pré-frontais podem estar relacionadas a esforço de controle cognitivo associado à expressão de emoções de irritabilidade

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

Abstract\ud \ud \ud \ud Background\ud \ud Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.\ud \ud \ud \ud Results\ud \ud Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.\ud \ud \ud \ud Conclusion\ud \ud GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.This research was supported by FAPESP, CAPES, CNPq, FINEP and PRP-USP.This research was supported by FAPESP, CAPES, CNPq, FINEP and PRPUSP

Spectral Lag Relations in GRB Pulses Detected with HETE-2

Using a pulse-fit method, we investigate the spectral lags between the traditional gamma-ray band (50-400 keV) and the X-ray band (6-25 keV) for 8 GRBs with known redshifts (GRB 010921, GRB 020124, GRB 020127, GRB 021211, GRB 030528, GRB 040924, GRB 041006, GRB 050408) detected with the WXM and FREGATE instruments aboard the HETE-2 satellite. We find several relations for the individual GRB pulses between the spectral lag and other observables, such as the luminosity, pulse duration, and peak energy (Epeak). The obtained results are consistent with those for BATSE, indicating that the BATSE correlations are still valid at lower energies (6-25 keV). Furthermore, we find that the photon energy dependence for the spectral lags can reconcile the simple curvature effect model. We discuss the implication of these results from various points of view.Comment: 13 pages, 9 figures, accepted for the publication in PASJ (minor corrections

Structural covariance of neostriatal and limbic regions in patients with obsessive-compulsive disorder

Background: Frontostriatal and frontoamygdalar connectivity alterations in patients with obsessive-compulsive disorder (OCD) have been typically described in functional neuroimaging studies. However, structural covariance, or volumetric correlations across distant brain regions, also provides network-level information. Altered structural covariance has been described in patients with different psychiatric disorders, including OCD, but to our knowledge, alterations within frontostriatal and frontoamygdalar circuits have not been explored. Methods: We performed a mega-analysis pooling structural MRI scans from the Obsessive-compulsive Brain Imaging Consortium and assessed whole-brain voxel-wise structural covariance of 4 striatal regions (dorsal and ventral caudate nucleus, and dorsal-caudal and ventral-rostral putamen) and 2 amygdalar nuclei (basolateral and centromedial-superficial). Images were preprocessed with the standard pipeline of voxel-based morphometry studies using Statistical Parametric Mapping software. Results: Our analyses involved 329 patients with OCD and 316 healthy controls. Patients showed increased structural covariance between the left ventral-rostral putamen and the left inferior frontal gyrus/frontal operculum region. This finding had a significant interaction with age; the association held only in the subgroup of older participants. Patients with OCD also showed increased structural covariance between the right centromedial-superficial amygdala and the ventromedial prefrontal cortex. Limitations: This was a cross-sectional study. Because this is a multisite data set analysis, participant recruitment and image acquisition were performed in different centres. Most patients were taking medication, and treatment protocols differed across centres. Conclusion: Our results provide evidence for structural network-level alterations in patients with OCD involving 2 frontosubcortical circuits of relevance for the disorder and indicate that structural covariance contributes to fully characterizing brain alterations in patients with psychiatric disorders

The impact of measurement errors in the identification of regulatory networks

<p>Abstract</p> <p>Background</p> <p>There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise.</p> <p>Results</p> <p>This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data.</p> <p>Conclusions</p> <p>Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.</p