Order and disorder in fiction labyrinths: the librarians` representations in some contemporary cultural productions

This paper, first result of a larger research, proposes a query about some aspects of social representation of libraries and librarians, as they appear in literary and cinematographic productions. Little by little, this query, which arose from purposes of organizing catalogues, revealed elements that established different series, in which the narrative genre (literary or cinematographic) has no relevance to either libraries or librarians` representations. The presence of these elements seems to show some expectations and utopias in relation to the common knowledge, independently from narratives being located in the past, in the present or in the future, stimulating reflection on some medieval and baroque traditions about the library universe and its main characters, the librarians. The cinematographic material selected for research was The time machine, Farenheit 451, The day after tomorrow, Star Wars - episode II and the novels Martin Eden, The man without qualities, The time machine and La sombra del viento.

Maintenance and differentiation of neural stem cells

The adult mammalian brain contains self-renewable, multipotent neural stem cells (NSCs) that are responsible for neurogenesis and plasticity in specific regions of the adult brain. Extracellular matrix, vasculature, glial cells, and other neurons are components of the niche where NSCs are located. This surrounding environment is the source of extrinsic signals that instruct NSCs to either self-renew or differentiate. Additionally, factors such as the intracellular epigenetics state and retrotransposition events can influence the decision of NSC`s fate into neurons or glia. Extrinsic and intrinsic factors form an intricate signaling network, which is not completely understood. These factors altogether reflect a few of the key players characterized so far in the new field of NSC research and are covered in this review. (C) 2010 John Wiley & Sons, Inc. WIREs Syst Biol Med 2011 3 107-114 DOI:10.1002/wsbm:100

Samba, a Xenopus hnRNP Expressed in Neural and Neural Crest Tissues

RNA binding proteins regulate gene expression at the posttranscriptional level and play important roles in embryonic development. Here, we report the cloning and expression of Samba, a Xenopus hnRNP that is maternally expressed and persists at least until tail bud stages. During gastrula stages, Samba is enriched in the dorsal regions. Subsequently, its expression is elevated only in neural and neural crest tissues. In the latter, Samba expression overlaps with that of Slug in migratory neural crest cells. Thereafter, Samba is maintained in the neural crest derivatives, as well as other neural tissues, including the anterior and posterior neural tube and the eyes. Overexpression of Samba in the animal pole leads to defects in neural crest migration and cranial cartilage development. Thus, Samba encodes a Xenopus hnRNP that is expressed early in neural and neural crest derivatives and may regulate crest cells migratory behavior. Developmental Dynamics 238:204-209, 2009. (C) 2008 Wiley-Liss, Inc.

Enhanced Neural Progenitor/Stem Cells Self-Renewal via the Interaction of Stress-Inducible Protein 1 with the Prion Protein

Prion protein (PrPC), when associated with the secreted form of the stress-inducible protein 1 (STI1), plays an important role in neural survival, neuritogenesis, and memory formation. However, the role of the PrP(C)-STI1 complex in the physiology of neural progenitor/stem cells is unknown. In this article, we observed that neurospheres cultured from fetal forebrain of wild-type (Prnp(+/+)) and PrP(C)-null (Prnp(0/0)) mice were maintained for several passages without the loss of self-renewal or multipotentiality, as assessed by their continued capacity to generate neurons, astrocytes, and oligodendrocytes. The homogeneous expression and colocalization of STI1 and PrP(C) suggest that they may associate and function as a complex in neurosphere-derived stem cells. The formation of neurospheres from Prnp(0/0) mice was reduced significantly when compared with their wild-type counterparts. In addition, blockade of secreted STI1, and its cell surface ligand, PrP(C), with specific antibodies, impaired Prnp(+/+) neurosphere formation without further impairing the formation of Prnp(0/0) neurospheres. Alternatively, neurosphere formation was enhanced by recombinant STI1 application in cells expressing PrP(C) but not in cells from Prnp(0/0) mice. The STI1-PrP(C) interaction was able to stimulate cell proliferation in the neurosphere-forming assay, while no effect on cell survival or the expression of neural markers was observed. These data suggest that the STI1-PrP(C) complex may play a critical role in neural progenitor/stem cells self-renewal via the modulation of cell proliferation, leading to the control of the stemness capacity of these cells during nervous system development. STEM CELLS 2011;29:1126-1136

Administration of Neural Precursor Cells Ameliorates Renal Ischemia-Reperfusion Injury

In this study we evaluated whether administration of stem cells of neural origin (neural precursor cells, NPCs) could be protective against renal ischemia-reperfusion injury (IRI). We hypothesized that stem cell outcomes are not tissue-specific and that NPCs can improve tissue damage through paracrine mechanisms, especially due to immunomodulation. To this end, Wistar rats (200-250 g) were submitted to 1-hour ischemia and treated with NPCs (4 x 10(6) cells/animal) at 4 h of reperfusion. To serve as controls, ischemic animals were treated with cerebellum homogenate harvested from adult rat brain. All groups were sacrificed at 24 h of reperfusion. NPCs were isolated from rat fetus telencephalon and cultured until neurosphere formation (7 days). Before administration, NPCs were labeled with carboxyfluorescein diacetate succinimydylester (CFSE). Kidneys were harvested for analysis of cytokine profile and macrophage infiltration. At 24 h, NPC treatment resulted in a significant reduction in serum creatinine (IRI + NPC 1.21 + 0.18 vs. IRI 3.33 + 0.14 and IRI + cerebellum 2.95 + 0.78mg/dl, p < 0.05) and acute tubular necrosis (IRI + NPC 46.0 + 2.4% vs. IRI 79.7 + 14.2%, p < 0.05). NPC-CFSE and glial fibrillary acidic protein (GFAP)-positive cells (astrocyte marker) were found exclusively in renal parenchyma, which also presented GFAP and SOX-2 (an embryonic neural stem cell marker) mRNA expression. NPC treatment resulted in lower renal proinflammatory IL1-beta and TNF-alpha expression and higher anti-inflammatory IL-4 and IL-10 transcription. NPC-treated animals also had less macrophage infiltration and decreased serum proinflammatory cytokines (IL-1 beta, TNF-alpha and INF-gamma). Our data suggested that NPC therapy improved renal function by influencing immunological responses. Copyright (C) 2009 S. Karger AG, Basel

Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

A Road Following Approach Using Artificial Neural Networks Combinations

Navigation is a broad topic that has been receiving considerable attention from the mobile robotic community over the years. In order to execute autonomous driving in outdoor urban environments it is necessary to identify parts of the terrain that can be traversed and parts that should be avoided. This paper describes an analyses of terrain identification based on different visual information using a MLP artificial neural network and combining responses of many classifiers. Experimental tests using a vehicle and a video camera have been conducted in real scenarios to evaluate the proposed approach.

Design of associative memories using cellular neural networks

Cellular neural networks (CNNs) have locally connected neurons. This characteristic makes CNNs adequate for hardware implementation and, consequently, for their employment on a variety of applications as real-time image processing and construction of efficient associative memories. Adjustments of CNN parameters is a complex problem involved in the configuration of CNN for associative memories. This paper reviews methods of associative memory design based on CNNs, and provides comparative performance analysis of these approaches.

Pseudoclassical description of scalar particle in non-abelian background and path-integral representations

Path-integral representations for a scalar particle propagator in non-Abelian external backgrounds are derived. To this aim, we generalize the procedure proposed by Gitman and Schvartsman of path-integral construction to any representation of SU(N) given in terms of antisymmetric generators. And for arbitrary representations of SU(N), we present an alternative construction by means of fermionic coherent states. From the path-integral representations we derive pseudoclassical actions for a scalar particle placed in non-Abelian backgrounds. These actions are classically analyzed and then quantized to prove their consistency.

Path integral representations in noncommutative quantum mechanics and noncommutative version of Berezin-Marinov action

It is known that the actions of field theories on a noncommutative space-time can be written as some modified (we call them theta-modified) classical actions already on the commutative space-time (introducing a star product). Then the quantization of such modified actions reproduces both space-time noncommutativity and the usual quantum mechanical features of the corresponding field theory. In the present article, we discuss the problem of constructing theta-modified actions for relativistic QM. We construct such actions for relativistic spinless and spinning particles. The key idea is to extract theta-modified actions of the relativistic particles from path-integral representations of the corresponding noncommutative field theory propagators. We consider the Klein-Gordon and Dirac equations for the causal propagators in such theories. Then we construct for the propagators path-integral representations. Effective actions in such representations we treat as theta-modified actions of the relativistic particles. To confirm the interpretation, we canonically quantize these actions. Thus, we obtain the Klein-Gordon and Dirac equations in the noncommutative field theories. The theta-modified action of the relativistic spinning particle is just a generalization of the Berezin-Marinov pseudoclassical action for the noncommutative case.

Cross-situational learning of object-word mapping using Neural Modeling Fields

The issue of how children learn the meaning of words is fundamental to developmental psychology. The recent attempts to develop or evolve efficient communication protocols among interacting robots or Virtual agents have brought that issue to a central place in more applied research fields, such as computational linguistics and neural networks, as well. An attractive approach to learning an object-word mapping is the so-called cross-situational learning. This learning scenario is based on the intuitive notion that a learner can determine the meaning of a word by finding something in common across all observed uses of that word. Here we show how the deterministic Neural Modeling Fields (NMF) categorization mechanism can be used by the learner as an efficient algorithm to infer the correct object-word mapping. To achieve that we first reduce the original on-line learning problem to a batch learning problem where the inputs to the NMF mechanism are all possible object-word associations that Could be inferred from the cross-situational learning scenario. Since many of those associations are incorrect, they are considered as clutter or noise and discarded automatically by a clutter detector model included in our NMF implementation. With these two key ingredients - batch learning and clutter detection - the NMF mechanism was capable to infer perfectly the correct object-word mapping. (C) 2009 Elsevier Ltd. All rights reserved.

How language can help discrimination in the Neural Modelling Fields framework

The relationship between thought and language and, in particular, the issue of whether and how language influences thought is still a matter of fierce debate. Here we consider a discrimination task scenario to study language acquisition in which an agent receives linguistic input from an external teacher, in addition to sensory stimuli from the objects that exemplify the overlapping categories that make up the environment. Sensory and linguistic input signals are fused using the Neural Modelling Fields (NMF) categorization algorithm. We find that the agent with language is capable of differentiating object features that it could not distinguish without language. In this sense, the linguistic stimuli prompt the agent to redefine and refine the discrimination capacity of its sensory channels. (C) 2007 Elsevier Ltd. All rights reserved.

Polar orthogonal representations of real reductive algebraic groups

We prove that a polar orthogonal representation of a real reductive algebraic group has the same closed orbits as the isotropy representation of a pseudo-Riemannian symmetric space. We also develop a partial structural theory of polar orthogonal representations of real reductive algebraic groups which slightly generalizes some results of the structural theory of real reductive Lie algebras. (c) 2008 Elsevier Inc. All rights reserved.