A novel concentration dependent amino acid ion pair strategy to mediate drug permeation using indomethacin as a model insoluble drug

Assessment of oral drug bioavailability is an important parameter for new chemical entities (NCEs) in drug development cycle. After evaluating the pharmacological response of these new molecules, the following critical stage is to investigate their in vitro permeability. Despite the great success achieved by prodrugs, covalent linking the drug molecule with a hydrophobic moiety might result in a new entity that might be toxic or ineffective. Therefore, an alternative that would improve the drug uptake without affecting the efficacy of the drug molecule would be advantageous. The aim of the current study is to investigate the effect of ion-pairing on the permeability profile of a model drug: indomethacin (IND) to understand the mechanism behind the permeability improvement across Caco-2 monolayers. Arginine and lysine formed ion-pairs with IND at various molar ratios 1:1, 1:2, 1:4 and 1:8 as reflected by the double reciprocal graphs. The partitioning capacities of the IND were evaluated using octanol/water partitioning studies and the apparent permeabilities (P app) were measured across Caco-2 monolayers for the different formulations. Partitioning studies reflected the high hydrophobicity of IND (Log P = 3) which dropped upon increasing the concentrations of arginine/lysine in the ion pairs. Nevertheless, the prepared ion pairs improved IND permeability especially after 60 min of the start of the experiment. Coupling partitioning and permeability results suggest a decrease in the passive transcellular uptake due to the drop in IND portioning capacities and a possible involvement of active carriers. Future work will investigate which transport gene might be involved in the absorption of the ion paired formulations using molecular biology technologies. © 2014 Elsevier B.V. All rights reserved.

Coloring random graphs and maximizing local diversity

We study a variation of the graph coloring problem on random graphs of finite average connectivity. Given the number of colors, we aim to maximize the number of different colors at neighboring vertices (i.e. one edge distance) of any vertex. Two efficient algorithms, belief propagation and Walksat are adapted to carry out this task. We present experimental results based on two types of random graphs for different system sizes and identify the critical value of the connectivity for the algorithms to find a perfect solution. The problem and the suggested algorithms have practical relevance since various applications, such as distributed storage, can be mapped onto this problem.

Resource allocation in sparse graphs

Resource allocation in sparsely connected networks, a representative problem of systems with real variables, is studied using the replica and Bethe approximation methods. An efficient distributed algorithm is devised on the basis of insights gained from the analysis and is examined using numerical simulations,showing excellent performance and full agreement with the theoretical results. The physical properties of the resource allocation model are discussed.

Message passing for task redistribution on sparse graphs

The problem of resource allocation in sparse graphs with real variables is studied using methods of statistical physics. An efficient distributed algorithm is devised on the basis of insight gained from the analysis and is examined using numerical simulations, showing excellent performance and full agreement with the theoretical results.

Solvable model for distribution networks on random graphs

We propose a simple model that captures the salient properties of distribution networks, and study the possible occurrence of blackouts, i.e., sudden failings of large portions of such networks. The model is defined on a random graph of finite connectivity. The nodes of the graph represent hubs of the network, while the edges of the graph represent the links of the distribution network. Both, the nodes and the edges carry dynamical two state variables representing the functioning or dysfunctional state of the node or link in question. We describe a dynamical process in which the breakdown of a link or node is triggered when the level of maintenance it receives falls below a given threshold. This form of dynamics can lead to situations of catastrophic breakdown, if levels of maintenance are themselves dependent on the functioning of the net, once maintenance levels locally fall below a critical threshold due to fluctuations. We formulate conditions under which such systems can be analyzed in terms of thermodynamic equilibrium techniques, and under these conditions derive a phase diagram characterizing the collective behavior of the system, given its model parameters. The phase diagram is confirmed qualitatively and quantitatively by simulations on explicit realizations of the graph, thus confirming the validity of our approach. © 2007 The American Physical Society.

Inference and optimization of real edges on sparse graphs:A statistical physics perspective

Inference and optimization of real-value edge variables in sparse graphs are studied using the Bethe approximation and replica method of statistical physics. Equilibrium states of general energy functions involving a large set of real edge variables that interact at the network nodes are obtained in various cases. When applied to the representative problem of network resource allocation, efficient distributed algorithms are also devised. Scaling properties with respect to the network connectivity and the resource availability are found, and links to probabilistic Bayesian approximation methods are established. Different cost measures are considered and algorithmic solutions in the various cases are devised and examined numerically. Simulation results are in full agreement with the theory. © 2007 The American Physical Society.

Peer mentoring in Higher Education:a reciprocal route to student success

This paper provides an introduction to the findings of a large HEFCE/Paul Hamlyn-sponsored project, which aimed to investigate and analyse the value of peer mentoring in facilitating a successful transition into higher education.

Competition for shortest paths on sparse graphs

Optimal paths connecting randomly selected network nodes and fixed routers are studied analytically in the presence of a nonlinear overlap cost that penalizes congestion. Routing becomes more difficult as the number of selected nodes increases and exhibits ergodicity breaking in the case of multiple routers. The ground state of such systems reveals nonmonotonic complex behaviors in average path length and algorithmic convergence, depending on the network topology, and densities of communicating nodes and routers. A distributed linearly scalable routing algorithm is also devised. © 2012 American Physical Society.

Personality across working life:the longitudinal and reciprocal influences of personality on work

Summary: This paper focuses on the role of personality at different stages of people's working lives. We begin by reviewing the research in industrial, work, and organizational (IWO) psychology regarding the longitudinal and dynamic influences of personality as an independent variable at different career stages, structuring our review around a framework of people's working lives and careers over time. Next, we review recent studies in the personality and developmental psychology domain regarding the influence of changing life roles on personality. In this domain, personality also serves as a dependent variable. By blending these two domains, it becomes clear that the study of reciprocal effects of work and personality might open a new angle in IWO psychology's long-standing tradition of personality research. To this end, we outline various implications for conceptual development (e.g., trait stability) and empirical research (e.g., personality and work incongruence). Finally, we discuss some methodological and statistical considerations for research in this new research domain. In the end, our review should enrich the way that IWO psychologists understand personality at work, focusing away from its unidirectional predictivist influence on job performance toward a more complex longitudinal reciprocal interplay of personality and working life. © 2013 John Wiley & Sons, Ltd.

Research allocation on sparse graphs

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Algorithms for solving large random graphs

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Belief propagation on dense graphs with few strong links

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

Message-passing for inference and optimization of real variables on sparse graphs

The inference and optimization in sparse graphs with real variables is studied using methods of statistical mechanics. Efficient distributed algorithms for the resource allocation problem are devised. Numerical simulations show excellent performance and full agreement with the theoretical results. © Springer-Verlag Berlin Heidelberg 2006.

The topics they are a-changing - characterising topics with time-stamped semantic graphs

DBpedia has become one of the major sources of structured knowledge extracted from Wikipedia. Such structures gradually re-shape the representation of Topics as new events relevant to such topics emerge. Such changes make evident the continuous evolution of topic representations and introduce new challenges to supervised topic classification tasks, since labelled data can rapidly become outdated. Here we analyse topic changes in DBpedia and propose the use of semantic features as a more stable representation of a topic. Our experiments show promising results in understanding how the relevance of features to a topic changes over time.

Shortest node-disjoint paths on random graphs

A localized method to distribute paths on random graphs is devised, aimed at finding the shortest paths between given source/destination pairs while avoiding path overlaps at nodes. We propose a method based on message-passing techniques to process global information and distribute paths optimally. Statistical properties such as scaling with system size and number of paths, average path-length and the transition to the frustrated regime are analyzed. The performance of the suggested algorithm is evaluated through a comparison against a greedy algorithm. © 2014 IOP Publishing Ltd and SISSA Medialab srl.