Perception of Social Cues of Danger in Autism Spectrum Disorders

Intuitive grasping of the meaning of subtle social cues is particularly affected in autism spectrum disorders (ASD). Despite their relevance in social communication, the effect of averted gaze in fearful faces in conveying a signal of environmental threat has not been investigated using real face stimuli in adults with ASD. Here, using functional MRI, we show that briefly presented fearful faces with averted gaze, previously shown to be a strong communicative signal of environmental danger, produce different patterns of brain activation than fearful faces with direct gaze in a group of 26 normally intelligent adults with ASD compared with 26 matched controls. While implicit cue of threat produces brain activation in attention, emotion processing and mental state attribution networks in controls, this effect is absent in individuals with ASD. Instead, individuals with ASD show activation in the subcortical face-processing system in response to direct eye contact. An effect of differences in looking behavior was excluded in a separate eye tracking experiment. Our data suggest that individuals with ASD are more sensitive to direct eye contact than to social signals of danger conveyed by averted fearful gaze

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/

A framework for evolutionary systems biology

<p>Abstract</p> <p>Background</p> <p>Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.</p> <p>Results</p> <p>Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions <it>in silico</it>. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.</p> <p>Conclusion</p> <p>EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.</p

Interference studies in slotted silica tube trap technique

Interference effects of KCl, NH4H2PO4, (NH4)(2)CO3, MgCl2, NH4NO3, NaNO3, (NH4)(2)SO4, MgSO4.7H(2)O, CaCl2, Ca(NO3)(2) and NaCl on Bi, Cd, In, Pb and Sb were investigated using the atom trapping technique with a double slotted silica tube. Analyte solutions at concentration levels of 60, 6, 300, 75, 100 ng mL(-1) for Bi, Cd, In, Pb and Sb, respectively, were aspirated into an air-acetylene flame and analyte species were collected on the silica surface. Revolatilization was effected by aspirating IBMK at the muL level. A method was proposed to classify interferences as the types of condensed phase or gas phase. In case of high NaCl content in the sample matrix, devitrification of the silica surface is a problem