Molecular modeling on some human interleukins sharing gamma c of interleukin-2 receptor, and structure-function relationships

Five models for human interleukin-7 (HIL-7), HIL-9, HIL-13, HIL-15 and HIL-17 have been generated by SYBYL software package. The primary models were optimized using molecular dynamics and molecular mechanics methods. The final models were optimized using a steepest descent algorithm and a subsequent conjugate gradient method. The complexes with these interleukins and the common gamma chain of interleukin-2 receptor (IL-2R) were constructed and subjected to energy minimization. We found residues, such as Gln127 and Tyr103, of the common gamma chain of IL-2R are very important. Other residues, e.g. Lys70, Asn128 and Glu162, are also significant. Four hydrophobic grooves and two hydrophilic sites converge at the active site triad of the gamma chain. The binding sites of these interleukins interaction with the common gamma chain exist in the first helical and/or the fourth helical domains. Therefore, we conclude that these interleukins binds to the common gamma chain of IL-2R by the first and the fourth helix domain. Especially at the binding sites of some residues (lysine, arginine, asparagine, glutamic acid and aspartic acid), with a discontinuous region of the common gamma chain of IL-2R, termed the interleukins binding sites (103-210). The study of these sites can be important for the development of new drugs. (C) 2000 Elsevier Science B.V. All rights reserved.

Structure-function relationship of three neurotoxins from the venom of Naja kaouthia: a comparison between the NMR-derived structure of NT2 with its homologues, NT1 and NT3

Three homologous short-chain neurotoxins, named NT1, NT2 and NT3, were purified from the venom of Naja kaouthia. NT1 has an identical amino acid sequence to cobrotoxin from Naja naja atra [Biochemistry 32 (1993) 2131]. NT3 shares the same sequence with cobrotoxin b [J. Biochem. (Tokyo) 122 (1997) 1252], whereas NT2 is a novel 6 1 -residue neurotoxin. Tests of their physiological functions indicate that NT1 shows a greater inhibition of muscle contraction induced by electrical stimulation of the nerve than do NT2 and NT3. Homonuclear proton two-dimensional NMR methods were utilized to study the solution tertiary structure of NT2. A homology model-building method was employed to predict the structure of NT3. Comparison of the structures of these three toxins shows that the surface conformation of NT1 facilitates the substituted base residues, Arg28, Arg30, and Arg36, to occupy the favorable spatial location in the central region of loop 11, and the cation groups of all three arginines face out of the molecular surface of NT1 This may contribute greatly to the higher binding of NT1 with AchR compared to NT2 and NT3. (C) 2002 Elsevier Science B,V. All rights reserved.

Robust F0 estimation based on a multi-microphone periodicity function for distant-talking speech

This work addresses the problem of deriving F0 from distanttalking speech signals acquired by a microphone network. The method here proposed exploits the redundancy across the channels by jointly processing the different signals. To this purpose, a multi-microphone periodicity function is derived from the magnitude spectrum of all the channels. This function allows to estimate F0 reliably, even under reverberant conditions, without the need of any post-processing or smoothing technique. Experiments, conducted on real data, showed that the proposed frequency-domain algorithm is more suitable than other time-domain based ones.

Random function priors for exchangeable arrays with applications to graphs and relational data

A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a flexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efficient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.

Function propagation through nested systems

Concepts of function are central to design but statements about a device's functions can be interpreted in different ways. This raises problems for researchers trying to clarify the foundations of design theory and for those developing design support-tools that can represent and reason about function. By showing how functions relate systems to their sub-systems and super-systems, this article illustrates some limitations of existing function terminology and some problems with existing function statements. To address these issues, a system-relative function terminology is introduced. This is used to demonstrate that systems function not only with respect to their most local super-system, but also with respect to their more global super-systems. © 2012 Elsevier Ltd. All rights reserved.

Dynamical structure function identifiability conditions enabling signal structure reconstruction

Networks of controlled dynamical systems exhibit a variety of interconnection patterns that could be interpreted as the structure of the system. One such interpretation of system structure is a system's signal structure, characterized as the open-loop causal dependencies among manifest variables and represented by its dynamical structure function. Although this notion of structure is among the weakest available, previous work has shown that if no a priori structural information is known about the system, not even the Boolean structure of the dynamical structure function is identifiable. Consequently, one method previously suggested for obtaining the necessary a priori structural information is to leverage knowledge about target specificity of the controlled inputs. This work extends these results to demonstrate precisely the a priori structural information that is both necessary and sufficient to reconstruct the network from input-output data. This extension is important because it significantly broadens the applicability of the identifiability conditions, enabling the design of network reconstruction experiments that were previously impossible due to practical constraints on the types of actuation mechanisms available to the engineer or scientist. The work is motivated by the proteomics problem of reconstructing the Per-Arnt-Sim Kinase pathway used in the metabolism of sugars. © 2012 IEEE.

Blocking central opiate function modulates hedonic impact and anterior cingulate response to rewards and losses.

Reward processing is linked to specific neuromodulatory systems with a dopaminergic contribution to reward learning and motivational drive being well established. Neuromodulatory influences on hedonic responses to actual receipt of reward, or punishment, referred to as experienced utility are less well characterized, although a link to the endogenous opioid system is suggested. Here, in a combined functional magnetic resonance imaging-psychopharmacological investigation, we used naloxone to block central opioid function while subjects performed a gambling task associated with rewards and losses of different magnitudes, in which the mean expected value was always zero. A graded influence of naloxone on reward outcome was evident in an attenuation of pleasure ratings for larger reward outcomes, an effect mirrored in attenuation of brain activity to increasing reward magnitude in rostral anterior cingulate cortex. A more striking effect was seen for losses such that under naloxone all levels of negative outcome were rated as more unpleasant. This hedonic effect was associated with enhanced activity in anterior insula and caudal anterior cingulate cortex, areas implicated in aversive processing. Our data indicate that a central opioid system contributes to both reward and loss processing in humans and directly modulates the hedonic experience of outcomes.

When will a flame describing function approach to thermoacoustics work well?

In any thermoacoustic analysis, it is important not only to predict linear frequencies and growth rates, but also the amplitude and frequencies of any limit cycles. The Flame Describing Function (FDF) approach is a quasi-linear analysis which allows the prediction of both the linear and nonlinear behaviour of a thermoacoustic system. This means that one can predict linear growth rates and frequencies, and also the amplitudes and frequencies of any limit cycles. The FDF achieves this by assuming that the acoustics are linear and that the flame, which is the only nonlinear element in the thermoacoustic system, can be adequately described by considering only its response at the frequency at which it is forced. Therefore any harmonics generated by the flame's nonlinear response are not considered. This implies that these nonlinear harmonics are small or that they are sufficiently filtered out by the linear dynamics of the system (the low-pass filter assumption). In this paper, a flame model with a simple saturation nonlinearity is coupled to simple duct acoustics, and the success of the FDF in predicting limit cycles is studied over a range of flame positions and acoustic damping parameters. Although these two parameters affect only the linear acoustics and not the nonlinear flame dynamics, they determine the validity of the low-pass filter assumption made in applying the flame describing function approach. Their importance is highlighted by studying the level of success of an FDF-based analysis as they are varied. This is achieved by comparing the FDF's prediction of limit-cycle amplitudes to the amplitudes seen in time domain simulations.

Inhibitory Synaptic Plasticity - Spike timing dependence and putative network function.

While the plasticity of excitatory synaptic connections in the brain has been widely studied, the plasticity of inhibitory connections is much less understood. Here, we present recent experimental and theoretical □ndings concerning the rules of spike timing-dependent inhibitory plasticity and their putative network function. This is a summary of a workshop at the COSYNE conference 2012.

Multilinear function factorisation for time series feature extraction

This work applies a variety of multilinear function factorisation techniques to extract appropriate features or attributes from high dimensional multivariate time series for classification. Recently, a great deal of work has centred around designing time series classifiers using more and more complex feature extraction and machine learning schemes. This paper argues that complex learners and domain specific feature extraction schemes of this type are not necessarily needed for time series classification, as excellent classification results can be obtained by simply applying a number of existing matrix factorisation or linear projection techniques, which are simple and computationally inexpensive. We highlight this using a geometric separability measure and classification accuracies obtained though experiments on four different high dimensional multivariate time series datasets. © 2013 IEEE.