38 resultados para Network pattern language

em BORIS: Bern Open Repository and Information System - Berna - Suiça


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OBJECTIVE There is increasing evidence that epileptic activity involves widespread brain networks rather than single sources and that these networks contribute to interictal brain dysfunction. We investigated the fast-varying behavior of epileptic networks during interictal spikes in right and left temporal lobe epilepsy (RTLE and LTLE) at a whole-brain scale using directed connectivity. METHODS In 16 patients, 8 with LTLE and 8 with RTLE, we estimated the electrical source activity in 82 cortical regions of interest (ROIs) using high-density electroencephalography (EEG), individual head models, and a distributed linear inverse solution. A multivariate, time-varying, and frequency-resolved Granger-causal modeling (weighted Partial Directed Coherence) was applied to the source signal of all ROIs. A nonparametric statistical test assessed differences between spike and baseline epochs. Connectivity results between RTLE and LTLE were compared between RTLE and LTLE and with neuropsychological impairments. RESULTS Ipsilateral anterior temporal structures were identified as key drivers for both groups, concordant with the epileptogenic zone estimated invasively. We observed an increase in outflow from the key driver already before the spike. There were also important temporal and extratemporal ipsilateral drivers in both conditions, and contralateral only in RTLE. A different network pattern between LTLE and RTLE was found: in RTLE there was a much more prominent ipsilateral to contralateral pattern than in LTLE. Half of the RTLE patients but none of the LTLE patients had neuropsychological deficits consistent with contralateral temporal lobe dysfunction, suggesting a relationship between connectivity changes and cognitive deficits. SIGNIFICANCE The different patterns of time-varying connectivity in LTLE and RTLE suggest that they are not symmetrical entities, in line with our neuropsychological results. The highest outflow region was concordant with invasive validation of the epileptogenic zone. This enhanced characterization of dynamic connectivity patterns could better explain cognitive deficits and help the management of epilepsy surgery candidates.

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OBJECTIVE In patients with epilepsy, seizure relapse and behavioral impairments can be observed despite the absence of interictal epileptiform discharges (IEDs). Therefore, the characterization of pathologic networks when IEDs are not present could have an important clinical value. Using Granger-causal modeling, we investigated whether directed functional connectivity was altered in electroencephalography (EEG) epochs free of IED in left and right temporal lobe epilepsy (LTLE and RTLE) compared to healthy controls. METHODS Twenty LTLE, 20 RTLE, and 20 healthy controls underwent a resting-state high-density EEG recording. Source activity was obtained for 82 regions of interest (ROIs) using an individual head model and a distributed linear inverse solution. Granger-causal modeling was applied to the source signals of all ROIs. The directed functional connectivity results were compared between groups and correlated with clinical parameters (duration of the disease, age of onset, age, and learning and mood impairments). RESULTS We found that: (1) patients had significantly reduced connectivity from regions concordant with the default-mode network; (2) there was a different network pattern in patients versus controls: the strongest connections arose from the ipsilateral hippocampus in patients and from the posterior cingulate cortex in controls; (3) longer disease duration was associated with lower driving from contralateral and ipsilateral mediolimbic regions in RTLE; (4) aging was associated with a lower driving from regions in or close to the piriform cortex only in patients; and (5) outflow from the anterior cingulate cortex was lower in patients with learning deficits or depression compared to patients without impairments and to controls. SIGNIFICANCE Resting-state network reorganization in the absence of IEDs strengthens the view of chronic and progressive network changes in TLE. These resting-state connectivity alterations could constitute an important biomarker of TLE, and hold promise for using EEG recordings without IEDs for diagnosis or prognosis of this disorder.

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A recent analysis of more than 100 countries found that the extent to which their languages grammatically allowed for an asymmetric treatment of men and women correlated with socio-economic indices of gender inequality (Prewitt-Freilino, Caswell, & Laakso, 2012). In a set of four studies we examine whether the availability of feminine forms as indicated by the most recent dictionaries (1) predicts the actual percentage of women and gender wage gap for all professions registered in Poland; (2) predicts the longitudinal pattern of use of the occupational job-titles; (3) relates to social perception of the sample of 150 professions.

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Intravital imaging has revealed that T cells change their migratory behavior during physiological activation inside lymphoid tissue. Yet, it remains less well investigated how the intrinsic migratory capacity of activated T cells is regulated by chemokine receptor levels or other regulatory elements. Here, we used an adjuvant-driven inflammation model to examine how motility patterns corresponded with CCR7, CXCR4, and CXCR5 expression levels on ovalbumin-specific DO11.10 CD4(+) T cells in draining lymph nodes. We found that while CCR7 and CXCR4 surface levels remained essentially unaltered during the first 48-72 h after activation of CD4(+) T cells, their in vitro chemokinetic and directed migratory capacity to the respective ligands, CCL19, CCL21, and CXCL12, was substantially reduced during this time window. Activated T cells recovered from this temporary decrease in motility on day 6 post immunization, coinciding with increased migration to the CXCR5 ligand CXCL13. The transiently impaired CD4(+) T cell motility pattern correlated with increased LFA-1 expression and augmented phosphorylation of the microtubule regulator Stathmin on day 3 post immunization, yet neither microtubule destabilization nor integrin blocking could reverse TCR-imprinted unresponsiveness. Furthermore, protein kinase C (PKC) inhibition did not restore chemotactic activity, ruling out PKC-mediated receptor desensitization as mechanism for reduced migration in activated T cells. Thus, we identify a cell-intrinsic, chemokine receptor level-uncoupled decrease in motility in CD4(+) T cells shortly after activation, coinciding with clonal expansion. The transiently reduced ability to react to chemokinetic and chemotactic stimuli may contribute to the sequestering of activated CD4(+) T cells in reactive peripheral lymph nodes, allowing for integration of costimulatory signals required for full activation.

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Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

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Guidelines for a gender-fair use of the languages represented in the ITN LCG network were analyzed comparatively for specific criteria. All institutional or governmental guidelines aim at attenuating male-biased representations that are brought about by certain grammatical structures of the respective language. These guidelines primarily focus on the use of masculine forms as generics because they reduce the visibility of women in language. The comparison shows that guidelines for English, a language without grammatical gender, emphasize neutralization as a means of referring to both sexes. This differs from grammatical gender languages, such as German and Italian, in which feminine-masculine word-pairs are recommended in order to avoid the masculine bias. The guidelines all aim to promote the formulation of comprehensive and readable texts that are free of discrimination.

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The pattern of contact sensitization to the supposedly most important allergens assembled in the baseline series differs between countries, presumably at least partly because of exposure differences. Objectives. To describe the prevalence of contact sensitization to allergens tested in consecutive patients in the years 2007 and 2008, and to discuss possible differences.

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This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

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BACKGROUND: The role of the language network in the pathophysiology of formal thought disorder has yet to be elucidated. AIMS: To investigate whether specific grey-matter deficits in schizophrenic formal thought disorder correlate with resting perfusion in the left-sided language network. METHOD: We investigated 13 right-handed patients with schizophrenia and formal thought disorder of varying severity and 13 matched healthy controls, using voxel-based morphometry and magnetic resonance imaging perfusion measurement (arterial spin labelling). RESULTS: We found positive correlations between perfusion and the severity of formal thought disorder in the left frontal and left temporoparietal language areas. We also observed bilateral deficits in grey-matter volume, positively correlated with the severity of thought disorder in temporoparietal areas and other brain regions. The results of the voxel-based morphometry and the arterial spin labelling measurements overlapped in the left posterior superior temporal gyrus and left angular gyrus. CONCLUSIONS: Specific grey-matter deficits may be a risk factor for state-related dysfunctions of the left-sided language system, leading to local hyperperfusion and formal thought disorder.

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PURPOSE: The aim of this study was to assess the outcome of patients with primary spinal myxopapillary ependymoma (MPE). MATERIALS AND METHODS: Data from a series of 85 (35 females, 50 males) patients with spinal MPE were collected in this retrospective multicenter study. Thirty-eight (45%) underwent surgery only and 47 (55%) received postoperative radiotherapy (RT). Median administered radiation dose was 50.4 Gy (range, 22.2-59.4). Median follow-up of the surviving patients was 60.0 months (range, 0.2-316.6). RESULTS: The 5-year progression-free survival (PFS) was 50.4% and 74.8% for surgery only and surgery with postoperative low- (<50.4 Gy) or high-dose (>or=50.4 Gy) RT, respectively. Treatment failure was observed in 24 (28%) patients. Fifteen patients presented treatment failure at the primary site only, whereas 2 and 1 patients presented with brain and distant spinal failure only. Three and 2 patients with local failure presented with concomitant spinal distant seeding and brain failure, respectively. One patient failed simultaneously in the brain and spine. Age greater than 36 years (p = 0.01), absence of neurologic symptoms at diagnosis (p = 0.01), tumor size >or=25 mm (p = 0.04), and postoperative high-dose RT (p = 0.05) were variables predictive of improved PFS on univariate analysis. In multivariate analysis, only postoperative high-dose RT was independent predictors of PFS (p = 0.04). CONCLUSIONS: The observed pattern of failure was mainly local, but one fifth of the patients presented with a concomitant spinal or brain component. Postoperative high-dose RT appears to significantly reduce the rate of tumor progression.