Continental and Antarctic Lichens: isolation,identification and molecular modeling of the depside tenuiorin from the Antarctic lichen Umbilicaria antarctica as tau protein inhibitor

Abstract

Alzheimer´s disease (AD) is the most common form of dementia involving Aβ and tau protein. So far, AD cure remains elusive, but considering that AD progresses throughout tau pathology, which turns tau protein an appropriate target, besides tau is also included in other neurodegenerative disorders named as tauopathies. Here, we have isolated seventeen compounds belonging to six lichens species. Due to scarce of spectroscopic data of the compound 5,7-dihydroxy-6-methylphthalide, we explained their structural elucidation based on NMR data. In this study, we show that only tenuiorin from Umbilicaria antarctica inhibited 50% of tau 4R at 100?µM. Then, we shown that molecular interactions of tenuiorin with the steric zipper model of the hexapeptide ³⁰⁶VQIVYK³¹¹ were studied by docking calculations and the results suggested that tenuiorin forms both hydrogen bonds with lysine and glutamine side chains and forms several hydrophobic interactions with valine and lysine from ³⁰⁶VQIVYK³¹¹ motif.     

Second Order Perturbations of a Schwarzschild Black Hole: Inclusion of Odd Parity Perturbations

We consider perturbations of a Schwarzschild black hole that can be of both even and odd parity, keeping terms up to second order in perturbation theory, for the l = 2 axisymmetric case. We develop explicit formulae for the evolution equations and radiated energies and waveforms using the Regge–Wheeler–Zerilli approach. This formulation is useful, for instance, for the treatment in the "close limit approximation" of the collision of counterrotating black holes.     

Homogenization of magneto-electro-elastic multilaminated materials

In this work, based on the periodic unfolding homogenizationtechnique, the limiting equations modelling the behaviour ofthree-dimensional magneto-electro-elastic periodic structuresare rigorously established. The local problems and the correspondinghomogenized coefficients of the elastic, dielectric, magneticpermittivity, piezoelectric, piezomagnetic and magneto-electric(ME) tensors are explicitly described. The homogenization modelis exemplified for laminated composites and a unified generalformula for all effective properties of periodic multilaminatedmagneto-electro-elastic composites is obtained. This formulais applied to investigate the global behaviour for the importantcase of transversely isotropic constituents and any finite numberof layers in each periodic cell. Examples that provide theoreticalevidence of the presence of both a product property and theME effect are given.     

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.     

Computational performance and cross‐validation error precision of five PLS algorithms using designed and real data sets

An evaluation of computational performance and precision regarding the cross‐validation error of five partial least squares (PLS) algorithms (NIPALS, modified NIPALS, Kernel, SIMPLS and bidiagonal PLS), available and widely used in the literature, is presented. When dealing with large data sets, computational time is an important issue, mainly in cross‐validation and variable selection. In the present paper, the PLS algorithms are compared in terms of the run time and the relative error in the precision obtained when performing leave‐one‐out cross‐validation using simulated and real data sets. The simulated data sets were investigated through factorial and Latin square experimental designs. The evaluations were based on the number of rows, the number of columns and the number of latent variables. With respect to their performance, the results for both simulated and real data sets have shown that the differences in run time are statistically different. PLS bidiagonal is the fastest algorithm, followed by Kernel and SIMPLS. Regarding cross‐validation error, all algorithms showed similar results. However, in some situations as, for example, when many latent variables were in question, discrepancies were observed, especially with respect to SIMPLS. Copyright © 2010 John Wiley & Sons, Ltd.     

Determination of total arsenic in seawater by hydride generation atomic fluorescence spectrometry

In this work, the determination of total As in seawater by hydride generation atomic fluorescence spectrometry was studied. The influence of the chemical, flow and instrumental parameters were investigated and optimized. The pre-reduction of As(V) to As(III) was performed using KI plus ascorbic acid in 3.5 mol L^− 1 HCl medium. No multiplicative interference was present and external aqueous calibration could be used. The limit of detection was 36 ng L^− 1, while the repeatability was 2% (n = 10), at a 500 ng L^− 1 concentration level. The sample throughput was 15 h^− 1 if triplicate measurements were made. The accuracy was assessed by the analysis of a seawater certified reference material and excellent agreement between the obtained and certified values was verified. The procedure was used for the analysis of seawater offshore samples collected at the Brazilian coast and results ranging from 860 to 1200 ng L^− 1 were found.     

Asymptotic stability analysis in uncertain multi-delayed state neural networks via Lyapunov–Krasovskii theory

This paper presents a new approach to the analysis of asymptotic stability of artificial neural networks (ANN) with multiple time-varying delays subject to polytope-bounded uncertainties. This approach is based on the Lyapunov–Krasovskii stability theory for functional differential equations and the linear matrix inequality (LMI) technique with the use of a recent Leibniz–Newton model based transformation without including any additional dynamics.Three examples with numerical simulations are used to illustrate the effectiveness of the proposed method. The first example considers the neural network with multiple time-varying delays, which may be seen as a particular case of the second example where it is subject to uncertainties and multiple time-varying delays. Finally, the third example analyzes the stability of the neural network with higher numbers of neurons subject to a single time-delay. The Hopf bifurcation theory is used to verify the stability of the system when the origin falls into instability in the bifurcation point.     

On fundamental skew distributions

A new class of multivariate skew-normal distributions, fundamental skew-normal distributions and their canonical version, is developed. It contains the product of independent univariate skew-normal distributions as a special case. Stochastic representations and other main properties of the associated distribution theory of linear and quadratic forms are considered. A unified procedure for extending this class to other families of skew distributions such as the fundamental skew-symmetric, fundamental skew-elliptical, and fundamental skew-spherical class of distributions is also discussed.     

Determination of arsenic in diesel, gasoline and naphtha by graphite furnace atomic absorption spectrometry using microemulsion medium for sample stabilization

A procedure for the determination of As in diesel, gasoline and naphtha at μg L⁻¹ levels by GFAAS is proposed. Sample stabilization was achieved by the formation of three component solutions prepared by mixing appropriate volumes of the samples propan-1-ol and nitric acid aqueous solution. This mixture resulted in a one-phase medium, which was indefinitely stable. No changes in the analyte signals were observed over several days in spiked samples, proving long-term stabilization ability. The use of conventional (Pd) and permanent (Ir) modification was investigated and the former was preferred. Central composite design multivariate optimization defined the optimum microemulsion composition as well as the temperature program. In this way, calibration using aqueous analytical solutions was possible, since the same sensitivity was observed in the investigated microemulsion media and in 0.2% v/v HNO₃. Coefficients of correlation larger than 0.999 and an As characteristic mass of 22 pg were observed. Recoveries (n=4) obtained from spiked samples were 98±4, 99±3 and 103±5%, and the limits of detection in the original samples were 1.8, 1.2 and 1.5 μg L⁻¹ for diesel, gasoline and naphtha, respectively. Validation was performed by the analysis of a set of commercial samples by independent comparative procedures. No significant difference (Student’s t-test, p<0.05) was observed between comparative and proposed procedure results. The total determination cycle lasted 4 min for diesel and 3 min for gasoline and naphtha, equivalent to a sample throughput of 7 h⁻¹ for diesel and 10 h⁻¹ for gasoline and naphtha.