Histoire de la Hongrie médiévale: Des Angevins aux Habsbourgs

Écrit par trois des meilleurs historiens hongrois, le second tome de cette Histoire de la Hongrie médiévale montre comment s\u27est constituée l\u27identité hongroise dans les deux derniers siècles du Moyen Âge. Le royaume s\u27intègre alors dans la chrétienté latine et il connaît un développement rapide de la culture écrite. Ce livre est essentiel pour comprendre l\u27histoire de l\u27Europe du Centre-Est

Consistent assignment of the vibrations of symmetric and asymmetric para-disubstituted benzene molecules

We give a description of the phenyl-ring-localized vibrational modes of the ground states of the para-disubstituted benzene molecules including both symmetric and asymmetric cases. In line with others, we quickly conclude that the use of Wilson mode labels is misleading and ambiguous; we conclude the same regarding the related ones of Varsányi. Instead we label the modes consistently based upon the Mulliken (Herzberg) method for the modes of para-difluorobenzene (pDFB). Since we wish the labelling scheme to cover both symmetrically- and asymmetrically-substituted molecules, we apply the Mulliken labelling under C2v symmetry. By studying the variation of the vibrational wavenumbers with mass of the substituent, we are able to identify the corresponding modes across a wide range of molecules and hence provide consistent assignments. Particularly interesting are pairs of vibrations that evolve from in- and out-of-phase motions in pDFB to more localized modes in asymmetric molecules. We consider the para isomers of the following: the symmetric dihalobenzenes, xylene, hydroquinone, the asymmetric dihalobenzenes, halotoluenes, halophenols and cresol

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Deferred imitation and declarative memory in domestic dogs

This study demonstrates for the first time deferred imitation of novel actions in dogs (Canis familiaris) with retention intervals of 1.5 min and memory of familiar actions with intervals ranging from 0.40 to 10 min. Eight dogs were trained using the 'Do as I do' method to match their own behaviour to actions displayed by a human demonstrator. They were then trained to wait for a short interval to elapse before they were allowed to show the previously demonstrated action. The dogs were then tested for memory of the demonstrated behaviour in various conditions, also with the so-called two-action procedure and in a control condition without demonstration. Dogs were typically able to reproduce familiar actions after intervals as long as 10 min, even if distracted by different activities during the retention interval and were able to match their behaviour to the demonstration of a novel action after a delay of 1 min. In the two-action procedure, dogs were typically able to imitate the novel demonstrated behaviour after retention intervals of 1.5 min. The ability to encode and recall an action after a delay implies that facilitative processes cannot exhaustively explain the observed behavioural similarity and that dogs' imitative abilities are rather based on an enduring mental representation of the demonstration. Furthermore, the ability to imitate a novel action after a delay without previous practice suggests presence of declarative memory in dogs. © 2013 Springer-Verlag Berlin Heidelberg

Oxytocin receptor gene polymorphisms are associated with human directed social behavior in dogs (Canis familiaris)

The oxytocin system has a crucial role in human sociality; several results prove that polymorphisms of the oxytocin receptor gene are related to complex social behaviors in humans. Dogs' parallel evolution with humans and their adaptation to the human environment has made them a useful species to model human social interactions. Previous research indicates that dogs are eligible models for behavioral genetic research, as well. Based on these previous findings, our research investigated associations between human directed social behaviors and two newly described (−212AG, 19131AG) and one known (rs8679684) single nucleotide polymorphisms (SNPs) in the regulatory regions (5′ and 3′ UTR) of the oxytocin receptor gene in German Shepherd (N = 104) and Border Collie (N = 103) dogs. Dogs' behavior traits have been estimated in a newly developed test series consisting of five episodes: Greeting by a stranger, Separation from the owner, Problem solving, Threatening approach, Hiding of the owner. Buccal samples were collected and DNA was isolated using standard protocols. SNPs in the 3′ and 5′ UTR regions were analyzed by polymerase chain reaction based techniques followed by subsequent electrophoresis analysis. The gene–behavior association analysis suggests that oxytocin receptor gene polymorphisms have an impact in both breeds on (i) proximity seeking towards an unfamiliar person, as well as their owner, and on (ii) how friendly dogs behave towards strangers, although the mediating molecular regulatory mechanisms are yet unknown. Based on these results, we conclude that similarly to humans, the social behavior of dogs towards humans is influenced by the oxytocin system

QSAR studies on a number of pyrrolidin-2-one antiarrhythmic arylpiperazinyls

The activity of a number of 1-[3-(4-arylpiperazin-1-yl)propyl]pyrrolidin-2-one antiarrhythmic (AA) agents was described using the quantitative structure–activity relationship model by applying it to 33 compounds. The molecular descriptors of the AA activity were obtained by quantum chemical calculations combined with molecular modeling calculations. The resulting model explains up to 91% of the variance and it was successfully validated by four tests (LOO, LMO, external test, and Y-scrambling test). Statistical analysis shows that the AA activity of the studied compounds depends mainly on the PCR and JGI4 descriptors

Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts

Wavelets are a powerful tool for signal and image denoising. Most of the denoising applications in different fields were based on the thresholding of the discrete wavelet transform (DWT) coefficients. Nevertheless, DWT transform is not a time or shift invariant transform and results depend on the selected shift. Improvements on the denoising performance can be obtained using the stationary wavelet transform (SWT) (also called shift-invariant or undecimated wavelet transform). Denoising using SWT has previously shown a robust and usually better performance than denoising using DWT but with a higher computational cost. In this paper, wavelet shrinkage schemes are applied for reducing noise in synthetic and experimental non-destructive evaluation ultrasonic A-scans, using DWT and a cycle-spinning implementation of SWT. A new denoising procedure, which we call random partial cycle spinning (RPCS), is presented. It is based on a cycle-spinning over a limited number of shifts that are selected in a random way. Wavelet denoising based on DWT, SWT and RPCS have been applied to the same sets of ultrasonic A-scans and their performances in terms of SNR are compared. In all cases three well known threshold selection rules (Universal, Minimax and Sure), with decomposition level dependent selection, have been used. It is shown that the new procedure provides a good robust denoising performance, without the DWT fluctuating performance, and close to SWT but with a much lower computational cost.This work was partially supported by Spanish MCI Project DPI2011-22438San Emeterio Prieto, JL.; Rodríguez-Hernández, MA. (2015). Wavelet Cycle Spinning Denoising of NDE Ultrasonic Signals Using a Random Selection of Shifts. 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Interpreting linear support vector machine models with heat map molecule coloring

<p>Abstract</p> <p>Background</p> <p>Model-based virtual screening plays an important role in the early drug discovery stage. The outcomes of high-throughput screenings are a valuable source for machine learning algorithms to infer such models. Besides a strong performance, the interpretability of a machine learning model is a desired property to guide the optimization of a compound in later drug discovery stages. Linear support vector machines showed to have a convincing performance on large-scale data sets. The goal of this study is to present a heat map molecule coloring technique to interpret linear support vector machine models. Based on the weights of a linear model, the visualization approach colors each atom and bond of a compound according to its importance for activity.</p> <p>Results</p> <p>We evaluated our approach on a toxicity data set, a chromosome aberration data set, and the maximum unbiased validation data sets. The experiments show that our method sensibly visualizes structure-property and structure-activity relationships of a linear support vector machine model. The coloring of ligands in the binding pocket of several crystal structures of a maximum unbiased validation data set target indicates that our approach assists to determine the correct ligand orientation in the binding pocket. Additionally, the heat map coloring enables the identification of substructures important for the binding of an inhibitor.</p> <p>Conclusions</p> <p>In combination with heat map coloring, linear support vector machine models can help to guide the modification of a compound in later stages of drug discovery. Particularly substructures identified as important by our method might be a starting point for optimization of a lead compound. The heat map coloring should be considered as complementary to structure based modeling approaches. As such, it helps to get a better understanding of the binding mode of an inhibitor.</p