NMR characterization of blends of poly(hydroxybutyrate-co-hydroxyvalerate) with poly(vinyl acetate)

The extent of mixing in blends of poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV) (27% HV) and poly(vinyl acetate) (PVAc) has been measured using a number of different techniques, principally solid-state NMR. Differential scanning calorimetry DSC measurements indicated effective mixing of the polymer chains on a scale of several nanometres. The results of H-1 T-1 and H-1 T-1rho. measurements confirm intimate mixing of the chains. A change on blending in the H-1 T-1rho, and the H-1 NMR line width of the signal from the protons of PVAc was consistent with an increase in the amplitude and frequency of motion of this component. The PVAc chains reside within the inter-lamellar space, as confirmed by spin diffusion measurements after H-1 T-1rho preparation. (C) 2003 Society of Chemical Industry.

Mutations in the DLG3 gene cause nonsyndromic X-linked mental retardation

We have identified truncating mutations in the human DLG3 ( neuroendocrine dlg) gene in 4 of 329 families with moderate to severe X-linked mental retardation. DLG3 encodes synapse-associated protein 102 (SAP102), a member of the membrane-associated guanylate kinase protein family. Neuronal SAP102 is expressed during early brain development and is localized to the postsynaptic density of excitatory synapses. It is composed of three amino-terminal PDZ domains, an src homology domain, and a carboxyl-terminal guanylate kinase domain. The PDZ domains interact directly with the NR2 subunits of the NMDA glutamate receptor and with other proteins responsible for NMDA receptor localization, immobilization, and signaling. The mutations identified in this study all introduce premature stop codons within or before the third PDZ domain, and it is likely that this impairs the ability of SAP102 to interact with the NMDA receptor and/or other proteins involved in downstream NMDA receptor signaling pathways. NMDA receptors have been implicated in the induction of certain forms of synaptic plasticity, such as long-term potentiation and long-term depression, and these changes in synaptic efficacy have been proposed as neural mechanisms underlying memory and learning. The disruption of NMDA receptor targeting or signaling, as a result of the loss of SAP102, may lead to altered synaptic plasticity and may explain the intellectual impairment observed in individuals with DLG3 mutations.

Spermatophore and Gonopore Morphology of the Southwestern-Atlantic Hermit Crab Pagurus exilis (Benedict, 1892) (Anomura, Paguridae)

Marcelo A. Scelzo, Marina Z. Fantucci, and Fernando L. Mantelatto (2010) Spermatophore and gonopore morphology of the southwestern-Atlantic hermit crab Pagurus exilis (Benedict, 1892) (Anomura, Paguridae). Zoological Studies 49(3): 421-433. The form and function of the spermatophore have been used as a complementary tool in studies of the reproductive biology and systematics of hermit crabs. In this context, we describe the spermatophore and gonopore morphology of Pagurus exilis. The spermatophores were extracted from the distal part of the vas deferens of specimens collected in Argentina and Brazil. The spermatophores were composed of 3 major regions: a main ampulla (with a sperm capsule inside and an accessory ampulla at the base), a stalk, and a pedestal. Each spermatophore had a distinct dorsolateral suture line around the ampulla, where the rupture occurs to release the sperm. The spermatophore total length was 1.5 times the main ampulla length. The main ampulla was oval and slightly flattened. A triangular accessory ampulla extended from the main ampulla base to the pedestal on 1 side, and contained no to several sperm. The stalk is short and flattened, and as wide as the main ampulla. One to 3 spermatophores were found attached to each pedestal, which was almost oblong in shape. The dimensions of the spermatophore and its component parts were directly influenced by the size of the hermit crab. Gonopores of males were covered by long pappose setae, while female gonopores bore a few short cuspidate setae. Specimens from Brazil and Argentina had the same spermatophore morphology, corroborating the previously observed absence of genetic differences between the both populations. The spermatophore morphology of this species has similarities with the broad general pattern of the Paguridae, being most similar to one of the (at least) 3 patterns of spermatophore morphology described for Pa gurus. http://zoolstud.sinica.edu.tw/Journals/49.3/421.pdf

The John Dewey`s epistemology and informational literacy

The `reflexive thinking` concept is discussed in this article as a means of contextualizing John Dewey`s intellectual legacy. `Reflection` represents a fundamental element for the construction of the necessary competences to information seeking and use, and consequently to individual and collective development. Since the reflexive thinking habit in information literacy is a way of learning, some questions concerning teaching and learning processes are also investigated. The discussion is, therefore, supported by the supposition that reflexive thinking is a cognitive strategy that allows a deeper comprehension of related problems, phenomena, and processes by means of the perception of the relations and the identification of involved elements, as well as the analysis and interpretation of meanings, empowering the information literacy process.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

The impact of functional connectivity changes on support vector machines mapping of fMRI data

Functional magnetic resonance imaging (fMRI) is currently one of the most widely used methods for studying human brain function in vivo. Although many different approaches to fMRI analysis are available, the most widely used methods employ so called ""mass-univariate"" modeling of responses in a voxel-by-voxel fashion to construct activation maps. However, it is well known that many brain processes involve networks of interacting regions and for this reason multivariate analyses might seem to be attractive alternatives to univariate approaches. The current paper focuses on one multivariate application of statistical learning theory: the statistical discrimination maps (SDM) based on support vector machine, and seeks to establish some possible interpretations when the results differ from univariate `approaches. In fact, when there are changes not only on the activation level of two conditions but also on functional connectivity, SDM seems more informative. We addressed this question using both simulations and applications to real data. We have shown that the combined use of univariate approaches and SDM yields significant new insights into brain activations not available using univariate methods alone. In the application to a visual working memory fMRI data, we demonstrated that the interaction among brain regions play a role in SDM`s power to detect discriminative voxels. (C) 2008 Elsevier B.V. All rights reserved.