VELOCITY FIELD OF COMPRESSIBLE MAGNETOHYDRODYNAMIC TURBULENCE: WAVELET DECOMPOSITION AND MODE SCALINGS

We study compressible magnetohydrodynamic turbulence, which holds the key to many astrophysical processes, including star formation and cosmic-ray propagation. To account for the variations of the magnetic field in the strongly turbulent fluid, we use wavelet decomposition of the turbulent velocity field into Alfven, slow, and fast modes, which presents an extension of the Cho & Lazarian decomposition approach based on Fourier transforms. The wavelets allow us to follow the variations of the local direction of the magnetic field and therefore improve the quality of the decomposition compared to the Fourier transforms, which are done in the mean field reference frame. For each resulting component, we calculate the spectra and two-point statistics such as longitudinal and transverse structure functions as well as higher order intermittency statistics. In addition, we perform a Helmholtz-Hodge decomposition of the velocity field into incompressible and compressible parts and analyze these components. We find that the turbulence intermittency is different for different components, and we show that the intermittency statistics depend on whether the phenomenon was studied in the global reference frame related to the mean magnetic field or in the frame defined by the local magnetic field. The dependencies of the measures we obtained are different for different components of the velocity; for instance, we show that while the Alfven mode intermittency changes marginally with the Mach number, the intermittency of the fast mode is substantially affected by the change.

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.

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.

Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.

A note on a practical relationship between filter coefficients and scaling and wavelet functions of Discrete Wavelet Transforms

In this paper, the relationship between the filter coefficients and the scaling and wavelet functions of the Discrete Wavelet Transform is presented and exemplified from a practical point-of-view. The explanations complement the wavelet theory, that is well documented in the literature, being important for researchers who work with this tool for time-frequency analysis. (c) 2011 Elsevier Ltd. All rights reserved.

Wavelet-based dynamic time warping

Dynamic Time Warping (DTW), a pattern matching technique traditionally used for restricted vocabulary speech recognition, is based on a temporal alignment of the input signal with the template models. The principal drawback of DTW is its high computational cost as the lengths of the signals increase. This paper shows extended results over our previously published conference paper, which introduces an optimized version of the DTW I hat is based on the Discrete Wavelet Transform (DWT). (C) 2008 Elsevier B.V. All rights reserved.