Isomorphous replacement of cystine with selenocystine in endothelin: oxidative refolding, biological and conformational properties of [Sec3,Sec11,Nle7]-endothelin-1

Air re-oxidation of fully reduced human endothelin-1 under optimized conditions yields the natural isomer with parallel disulfide bridges and the non-natural isomer with crossed disulfide bridges at a ratio of 3:1. In view of the recently determined highly reducing redox potential of selenocysteine (-381 mV) in peptides, the half-cystine residues Cys3 and Cys11 of the natural isomer of endothelin-1 were replaced by selenocysteine. Taking advantage of the high stability of the diselenide group toward reducing agents for disulfides a regioselective disulfide bridging of the second cysteine pair allowed for straightforward preparation of the [Sec3,Sec11, Nle7]-endothelin-1. NMR structural analysis showed conformational preferences of this endothelin analog that were identical to those of the natural hormone. Similarly, the bioactivity data confirmed that replacement of cysteine residues with selenocysteine was without detectable effect on receptor recognition and signal transduction. Both findings strongly support that the exchange of sulfur against selenium produces a fully isomorphous molecule as recently observed for similar exchanges at the level of methionine residues in proteins. Moreover, oxidative refolding of the fully reduced [Sec3,Sec11,Nle7]-endothelin-1 fulfilled the expectation that the redox potential of the selenocysteines would dictate quantitative formation of the natural isomer. These results suggest that the selenocysteine approach, besides offering an interesting chemical tool for induction of correct oxidative folding of multiple cysteine-containing peptides, should even allow for the preparation of non-natural isomers and thus for studying conformational preferences of folding intermediates in peptides and proteins.     

On vector averaging over the unit hypersphere

Sample averaging is a commonly used way to smooth out irregularities of data and to get rid of random fluctuations in measurements analysis. In adaptive signal processing, where an adaptive system learns its own parameters in order to perform a predefined task, the learnt parameters-pattern may depend on the initial learning state and on the fluctuations of the statistical features of the input signals to the system. In adaptive system learning, averaging may be employed as a method to merge several learnt parameters-patterns in order to get a better representative pattern. Even in the case of scalar parameters, the concept of averaging is not uniquely defined as scalar parameters spaces may exhibit a rich structure to be dealt with. The case of multiple parameter patterns where single parameters are mutually constrained to each other may exhibit an even richer structure. In the present paper, we deal with the case of parameters-patterns belonging to the unit hypersphere and develop an averaging technique based on the differential geometrical structure of such a curved space. Numerical experiments illustrate the behavior of the developed averaging algorithm.     

Auto-Regressive Moving Average Models on Complex-Valued Matrix Lie Groups

The present contribution aims at extending the classical scalar autoregressive moving average (ARMA) model to generate random (as well as deterministic) paths on complex-valued matrix Lie groups. The numerical properties of the developed ARMA model are studied by recurring to a tailored version of the Z-transform on Lie groups and to statistical indicators tailored to Lie groups, such as correlation functions on tangent bundles. The numerical behavior of the devised ARMA model is also illustrated by numerical simulations.     

Identification of unbalance forces by metaheuristic search algorithms

This paper discusses the identification of parameters in rotary systems, namely, the unbalance magnitude, phase and position in the rotor system. These parameters can be identified using the measured orbits in the hydrodynamic bearings. The oil film forces are evaluated in the different positions of the orbit of the journal and are applied to the model of the shaft. The model, integrated in time domain, allows with an assumed unbalance, to simulate the orbits. The objective function is basically the difference between measured and simulated orbits, and its minimum corresponds to the identified unbalance amount, phase and position along the shaft. With respect to traditional model based identification procedures, this approach using oil film forces instead of oil film linearized stiffness and damping coefficients, and unfiltered orbits instead of 1X vibration components is suitable to deal with non-linear behaviour of the system.     

Trading genes along the silk road: mtDNA sequences and the origin of central Asian populations

Central Asia is a vast region at the crossroads of different habitats, cultures, and trade routes. Little is known about the genetics and the history of the population of this region. We present the analysis of mtDNA control-region sequences in samples of the Kazakh, the Uighurs, the lowland Kirghiz, and the highland Kirghiz, which we have used to address both the population history of the region and the possible selective pressures that high altitude has on mtDNA genes. Central Asian mtDNA sequences present features intermediate between European and eastern Asian sequences, in several parameters-such as the frequencies of certain nucleotides, the levels of nucleotide diversity, mean pairwise differences, and genetic distances. Several hypotheses could explain the intermediate position of central Asia between Europe and eastern Asia, but the most plausible would involve extensive levels of admixture between Europeans and eastern Asians in central Asia, possibly enhanced during the Silk Road trade and clearly after the eastern and western Eurasian human groups had diverged. Lowland and highland Kirghiz mtDNA sequences are very similar, and the analysis of molecular variance has revealed that the fraction of mitochondrial genetic variance due to altitude is not significantly different from zero. Thus, it seems unlikely that altitude has exerted a major selective pressure on mitochondrial genes in central Asian populations.     

Learning independent components on the orthogonal group of matrices by retractions

Neural independent component learning algorithms based on optimization on manifolds have attracted interest in the neural network community. In the past years, we have developed learning algorithms specialized for the orthogonal group of matrices as parameters manifold. Here, we sketch a view of these algorithms by the help of ‘retractions’ on manifolds.     

Comparison of Modeling Approaches for the Capacitance–Voltage and Current–Voltage Characteristics of Advanced Gate Stacks

In this paper, we compare the capacitance-voltage and current-voltage characteristics of gate stacks calculated with different simulation models developed by seven different research groups, including open and closed boundaries approaches to solve the Schroumldinger equation inside the stack. The comparison has been carried out on template device structures, including pure SiO₂ dielectrics and high-kappa stacks, forcing the use of the same physical parameters in all models. Although the models are based on different modeling assumptions, the discrepancies among results in terms of capacitance and leakage current are small. These discrepancies have been carefully investigated by analyzing the individual modeling parameters and the internal quantities (e.g., tunneling probabilities and subband energies) contributing to current and capacitance     

Blind deconvolution by spectral weighted least-squares technique

A new algorithm is presented for blind deconvolution of non-minimum phase systems. It is based on the gradient minimisation of a criterion designed as the weighted square of the difference between two statistical spectra. Its main advantages are that it does not require signal preprocessing (such as spectral prewhitening) nor source information, except for statistics up to the fourth-order, and has a high convergence speed and fairly limited computational requirements