A newly recognized syndrome of marfanoid habitus; Long face; Hypotelorism; Long, thin nose; Long, thin hands and feet; and a specific pattern of language and learning disabilities

Here, we report on a newly recognized syndrome in a Brazilian family with three affected women, who had a Marfanoid habitus; long face; hypotelorism; long, thin nose; long, thin hands and feet; and language and learning disabilities. The disorder is compatible with autosomal dominant inheritance. (C) 2007 Wiley-Liss, Inc.

Complex networks analysis of language complexity

Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability. Copyright (c) EPLA, 2012

The macro level effects of gender - (un)fair language in Polish

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.

In vivo TCR Signaling in CD4(+) T Cells Imprints a Cell-Intrinsic, Transient Low-Motility Pattern Independent of Chemokine Receptor Expression Levels, or Microtubular Network, Integrin, and Protein Kinase C Activity

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.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

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.

Comparative Analysis of Existing Guidelines for Gender-Fair Language within the ITN LCG Network. ITN-LCG, Workpackage B: Report on Milestone 1

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.

A universal language for continuous network simulation /

Typescript.

Common command language for file manipulation and network job execution : an example /

Includes bibliographical references (p. 27).

On Logical Correction of Neural Network Algorithms for Pattern Recognition

The paper is devoted to the description of hybrid pattern recognition method developed by research groups from Russia, Armenia and Spain. The method is based upon logical correction over the set of conventional neural networks. Output matrices of neural networks are processed according to the potentiality principle which allows increasing of recognition reliability.

Practical, Computation Efficient High-Order Neural Network for Rotation and Shift Invariant Pattern Recognition

In this paper, a modification for the high-order neural network (HONN) is presented. Third order networks are considered for achieving translation, rotation and scale invariant pattern recognition. They require however much storage and computation power for the task. The proposed modified HONN takes into account a priori knowledge of the binary patterns that have to be learned, achieving significant gain in computation time and memory requirements. This modification enables the efficient computation of HONNs for image fields of greater that 100 × 100 pixels without any loss of pattern information.

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

Biologically Plausible Connectionist Prediction of Natural Language Thematic Relations

In Natural Language Processing (NLP) symbolic systems, several linguistic phenomena, for instance, the thematic role relationships between sentence constituents, such as AGENT, PATIENT, and LOCATION, can be accounted for by the employment of a rule-based grammar. Another approach to NLP concerns the use of the connectionist model, which has the benefits of learning, generalization and fault tolerance, among others. A third option merges the two previous approaches into a hybrid one: a symbolic thematic theory is used to supply the connectionist network with initial knowledge. Inspired on neuroscience, it is proposed a symbolic-connectionist hybrid system called BIO theta PRED (BIOlogically plausible thematic (theta) symbolic-connectionist PREDictor), designed to reveal the thematic grid assigned to a sentence. Its connectionist architecture comprises, as input, a featural representation of the words (based on the verb/noun WordNet classification and on the classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. BIO theta PRED is designed to ""predict"" thematic (semantic) roles assigned to words in a sentence context, employing biologically inspired training algorithm and architecture, and adopting a psycholinguistic view of thematic theory.