Conformational analysis of compstatin analogues with molecular dynamics simulations in explicit water

The cyclic 13-residue peptide compstatin is a potential therapeutic agent against the unregulated activation of the complement system. A thorough knowledge of its structural and dynamical properties in solution may assist the design of improved complement inhibitors. NMR studies have suggested that the 5-8 segment of free compstatin folds into a critical for activity 5-8 beta turn and the rest of the peptide is mainly disordered. Earlier computational studies of compstatin analogues with a polar-hydrogen/generalized-Born approximation reproduced the 5-8 turn, but also indicated the formation of beta-hairpin or alpha-helical elements and the existence of interactions between certain charged or aromatic sidechains. However, these features are absent or partly present in the NMR spectra, due to extensive conformational averaging. In order to check the compstatin properties with a more rigorous model of the intra- and intermolecular interactions, we conduct here 98-ns all-atom/explicit-water simulations of three compstatin analogues with variable activity; a native analogue, the more active mutant V4W/H9A and the inactive mutant Q5G. The 5-8 beta-turn population is in good accord with NMR. For the systems studied here, the simulations suggest that the 5-8 turn population does not correlate strictly with activity, in agreement with earlier mutational studies. Furthermore, they show structural differences among the analogues outside the 5-8 region. The possible role of these differences in activity is discussed. The probability of beta-hairpin or alpha-helix elements is much smaller with respect to the polar-hydrogen/GB simulations, and the persistent Trp4-Trp7 or Asp6-Arg11 sidechain interactions of the earlier GB studies are not reproduced. The present simulations extend the NMR data and improve our understanding of the properties of compstatin and related analogues.     

Determination of the method of construction of 1650 B.C. wall paintings

In this paper, a methodology of general applicability is presented for answering the question if an artist used a number of archetypes to draw a painting or if he drew it freehand. In fact, the contour line parts of the drawn objects that potentially correspond to archetypes are initially spotted. Subsequently, the exact form of these archetypes and their appearance throughout the painting is determined. The method has been applied to celebrated Thera Late Bronze Age wall paintings with full success. It has been demonstrated that the artist or group of artists has used seven geometrical archetypes and seven corresponding well-constructed stencils (four hyperbolae, two ellipses, and one Archimedes' spiral) to draw the wall painting "Gathering of Crocus" in 1650 B.C. This method of drawing seems to be unique in the history of arts and of great importance for archaeology, and the history of mathematics and sciences, as well.     

On the variable dissolution kinetics of zinc ferrite in acid media

Different magnetic fractions of narrow-sized industrial residues of zinc ferrite [(Zn_1-x, Fe)FeO₄, x ≤ 0.4] have been leached with 1 mol/L H₂SO₄ at temperatures ranging from 348 to 368 K. Fractions with excess iron, which exhibit higher magnetic susceptibility, are found to dissolve faster. It is believed that the lattice substitution of Zn by Fe²⁺ in the more magnetic ferrite fractions creates crystal defects and renders the ferrite more chemically reactive. An interfacial electrochemical reaction mechanism has been advanced to account for the effects of solid state composi tion and solution composition on the variation of the dissolution kinetics of zinc ferrite. Finally, the practical implications of these findings on the industrial leaching of zinc ferrite are briefly addressed.     

A nonparametric Bayesian approach toward robot learning by demonstration

In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose. A major limitation of GMR models concerns automatic selection of the proper number of model states, i.e., the number of model component densities. Existing methods, including likelihood- or entropy-based criteria, usually tend to yield noisy model size estimates while imposing heavy computational requirements. Recently, Dirichlet process (infinite) mixture models have emerged in the cornerstone of nonparametric Bayesian statistics as promising candidates for clustering applications where the number of clusters is unknown a priori. Under this motivation, to resolve the aforementioned issues of GMR-based methods for robot learning by demonstration, in this paper we introduce a nonparametric Bayesian formulation for the GMR model, the Dirichlet process GMR model. We derive an efficient variational Bayesian inference algorithm for the proposed model, and we experimentally investigate its efficacy as a robot learning by demonstration methodology, considering a number of demanding robot learning by demonstration scenarios.     

Enhancing ontology-based antipattern detection using Bayesian networks

Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The antipattern ontology has been recently proposed as a knowledge base for SPARSE, an intelligent system that can detect the antipatterns that exist in a software project. However, apart from the plethora of antipatterns that are inherently informal and imprecise, the information used in the antipattern ontology itself is many times imprecise or vaguely defined. For example, the certainty in which a cause, symptom or consequence of an antipattern exists in a software project. Taking into account probabilistic information would yield more realistic, intelligent and effective ontology-based applications to support the technology of antipatterns. However, ontologies are not capable of representing uncertainty and the effective detection of antipatterns taking into account the uncertainty that exists in software project antipatterns still remains an open issue. Bayesian Networks (BNs) have been previously used in order to measure, illustrate and handle antipattern uncertainty in mathematical terms. In this paper, we explore the ways in which the antipattern ontology can be enhanced using Bayesian networks in order to reinforce the existing ontology-based detection process. This approach allows software developers to quantify the existence of an antipattern using Bayesian networks, based on probabilistic knowledge contained in the antipattern ontology regarding relationships of antipatterns through their causes, symptoms and consequences. The framework is exemplified using a Bayesian network model of 13 antipattern attributes, which is constructed using BNTab, a plug-in developed for the Protege ontology editor that generates BNs based on ontological information.     

A spatially-constrained normalized Gamma process prior

In this work, we propose a novel nonparametric Bayesian method for clustering of data with spatial interdependencies. Specifically, we devise a novel normalized Gamma process, regulated by a simplified (pointwise) Markov random field (Gibbsian) distribution with a countably infinite number of states. As a result of its construction, the proposed model allows for introducing spatial dependencies in the clustering mechanics of the normalized Gamma process, thus yielding a novel nonparametric Bayesian method for spatial data clustering. We derive an efficient truncated variational Bayesian algorithm for model inference. We examine the efficacy of our approach by considering an image segmentation application using a real-world dataset. We show that our approach outperforms related methods from the field of Bayesian nonparametrics, including the infinite hidden Markov random field model, and the Dirichlet process prior.     

Studying the effect of perceived hedonic mobile device quality on user experience evaluations of mobile applications

When people interact with digital artefacts they perceive their pragmatic and hedonic qualities. In the case of interacting with mobile devices and applications, users seek utility as they try to satisfy certain needs, but at the same time they have certain feelings and emotions when, for example, they feel attached to their personal phone and/or trust its brand. Due to this strong relation between users and mobile devices a significant problem occurs when researchers want to evaluate the user experience of a mobile application in laboratory settings: the selection of an appropriate mobile device. Towards this end, this paper aims to unveil the effect of perceived hedonic quality of a mobile device on the user experience evaluation results of an application. Our results show that the perceived hedonic quality of a mobile device significantly affected the perceived pragmatic quality of the application, but not the hedonic one.     

Throughput maximization for speed scaling with agreeable deadlines

We study the following energy-efficient scheduling problem. We are given a set of n jobs which have to be scheduled by a single processor whose speed can be varied dynamically. Each job \(J_j\) is characterized by a processing requirement (work) \(p_j\), a release date \(r_j\), and a deadline \(d_j\). We are also given a budget of energy E which must not be exceeded and our objective is to maximize the throughput (i.e., the number of jobs which are completed on time). We show that the problem can be solved optimally via dynamic programming in \(O(n^4 \log n \log P)\) time when all jobs have the same release date, where P is the sum of the processing requirements of the jobs. For the more general case with agreeable deadlines where the jobs can be ordered so that, for every \(i < j\), it holds that \(r_i \le r_j\) and \(d_i \le d_j\), we propose an optimal dynamic programming algorithm which runs in \(O(n^6 \log n \log P)\) time. In addition, we consider the weighted case where every job \(J_j\) is also associated with a weight \(w_j\) and we are interested in maximizing the weighted throughput (i.e., the total weight of the jobs which are completed on time). For this case, we show that the problem becomes \(\mathcal{NP}\)-hard in the ordinary sense even when all jobs have the same release date and we propose a pseudo-polynomial time algorithm for agreeable instances.     

Online querying of d-dimensional hierarchies

In this paper we describe a distributed system designed to efficiently store, query and update multidimensional data organized into concept hierarchies and dispersed over a network. Our system employs an adaptive scheme that automatically adjusts the level of indexing according to the granularity of the incoming queries, without assuming any prior knowledge of the workload. Efficient roll-up and drill-down operations take place in order to maximize the performance by minimizing query flooding. Updates are performed on-line, with minimal communication overhead, depending on the level of consistency needed. Extensive experimental evaluation shows that, on top of the advantages that a distributed storage offers, our method answers the vast majority of incoming queries, both point and aggregate ones, without flooding the network and without causing significant storage or load imbalance. Our scheme proves to be especially efficient in cases of skewed workloads, even when these change dynamically with time. At the same time, it manages to preserve the hierarchical nature of data. To the best of our knowledge, this is the first attempt towards the support of concept hierarchies in DHTs.     

Solving genus zero Diophantine equations over number fields

Let K be a number field and F(X,Y) an absolutely irreducible polynomial of K[X,Y] such that the algebraic curve defined by the equation F(X,Y)=0 is rational. In this paper we present practical algorithms for the determination of all solutions of the Diophantine equation F(X,Y)=0 in algebraic integers of K.