A Bag-of-Paths Node Criticality Measure

This work compares several node (and network) criticality measures quantifying to which extend each node is critical with respect to the communication flow between nodes of the network, and introduces a new measure based on the Bag-of-Paths (BoP) framework. Network disconnection simulation experiments show that the new BoP measure outperforms all the other measures on a sample of Erdos-Renyi and Albert-Barabasi graphs. Furthermore, a faster (still O(n^3)), approximate, BoP criticality relying on the Sherman-Morrison rank-one update of a matrix is introduced for tackling larger networks. This approximate measure shows similar performances as the original, exact, one

Developments in the theory of randomized shortest paths with a comparison of graph node distances

There have lately been several suggestions for parametrized distances on a graph that generalize the shortest path distance and the commute time or resistance distance. The need for developing such distances has risen from the observation that the above-mentioned common distances in many situations fail to take into account the global structure of the graph. In this article, we develop the theory of one family of graph node distances, known as the randomized shortest path dissimilarity, which has its foundation in statistical physics. We show that the randomized shortest path dissimilarity can be easily computed in closed form for all pairs of nodes of a graph. Moreover, we come up with a new definition of a distance measure that we call the free energy distance. The free energy distance can be seen as an upgrade of the randomized shortest path dissimilarity as it defines a metric, in addition to which it satisfies the graph-geodetic property. The derivation and computation of the free energy distance are also straightforward. We then make a comparison between a set of generalized distances that interpolate between the shortest path distance and the commute time, or resistance distance. This comparison focuses on the applicability of the distances in graph node clustering and classification. The comparison, in general, shows that the parametrized distances perform well in the tasks. In particular, we see that the results obtained with the free energy distance are among the best in all the experiments.Comment: 30 pages, 4 figures, 3 table

Antibody fragments as probe in biosensor development

Today's proteomic analyses are generating increasing numbers of biomarkers, making it essential to possess highly specific probes able to recognize those targets. Antibodies are considered to be the first choice as molecular recognition units due to their target specificity and affinity, which make them excellent probes in biosensor development. However several problems such as difficult directional immobilization, unstable behavior, loss of specificity and steric hindrance, may arise from using these large molecules. Luckily, protein engineering techniques offer designed antibody formats suitable for biomarker analysis. Minimization strategies of antibodies into Fab fragments, scFv or even single-domain antibody fragments like VH, VL or VHHs are reviewed. Not only the size of the probe but also other issues like choice of immobilization tag, type of solid support and probe stability are of critical importance in assay development for biosensing. In this respect, multiple approaches to specifically orient and couple antibody fragments in a generic one-step procedure directly on a biosensor substrate are discussed

Two betweenness centrality measures based on Randomized Shortest Paths

This paper introduces two new closely related betweenness centrality measures based on the Randomized Shortest Paths (RSP) framework, which fill a gap between traditional network centrality measures based on shortest paths and more recent methods considering random walks or current flows. The framework defines Boltzmann probability distributions over paths of the network which focus on the shortest paths, but also take into account longer paths depending on an inverse temperature parameter. RSP's have previously proven to be useful in defining distance measures on networks. In this work we study their utility in quantifying the importance of the nodes of a network. The proposed RSP betweenness centralities combine, in an optimal way, the ideas of using the shortest and purely random paths for analysing the roles of network nodes, avoiding issues involving these two paradigms. We present the derivations of these measures and how they can be computed in an efficient way. In addition, we show with real world examples the potential of the RSP betweenness centralities in identifying interesting nodes of a network that more traditional methods might fail to notice.Comment: Minor updates; published in Scientific Report

Process monitoring and visualization solutions for hot-melt extrusion : a review

Objectives: Hot-melt extrusion (HME) is applied as a continuous pharmaceutical manufacturing process for the production of a variety of dosage forms and formulations. To ensure the continuity of this process, the quality of the extrudates must be assessed continuously during manufacturing. The objective of this review is to provide an overview and evaluation of the available process analytical techniques which can be applied in hot-melt extrusion. Key Findings: Pharmaceutical extruders are equipped with traditional (univariate) process monitoring tools, observing barrel and die temperatures, throughput, screw speed, torque, drive amperage, melt pressure and melt temperature. The relevance of several spectroscopic process analytical techniques for monitoring and control of pharmaceutical HME has been explored recently. Nevertheless, many other sensors visualizing HME and measuring diverse critical product and process parameters with potential use in pharmaceutical extrusion are available, and were thoroughly studied in polymer extrusion. The implementation of process analytical tools in HME serves two purposes: (1) improving process understanding by monitoring and visualizing the material behaviour and (2) monitoring and analysing critical product and process parameters for process control, allowing to maintain a desired process state and guaranteeing the quality of the end product. Summary: This review is the first to provide an evaluation of the process analytical tools applied for pharmaceutical HME monitoring and control, and discusses techniques that have been used in polymer extrusion having potential for monitoring and control of pharmaceutical HME

A maximum entropy approach to multiple classifiers combination

In this paper,we present amaximumentropy (maxent) approach to the fusion of experts opinions, or classifiers outputs, problem. Themaxent approach is quite versatile and allows us to express in a clear, rigorous,way the a priori knowledge that is available on the problem. For instance, our knowledge about the reliability of the experts and the correlations between these experts can be easily integrated: Each piece of knowledge is expressed in the form of a linear constraint. An iterative scaling algorithm is used in order to compute the maxent solution of the problem. The maximum entropy method seeks the joint probability density of a set of random variables that has maximum entropy while satisfying the constraints. It is therefore the “most honest” characterization of our knowledge given the available facts (constraints). In the case of conflicting constraints, we propose to minimise the “lack of constraints satisfaction” or to relax some constraints and recompute the maximum entropy solution. The maxent fusion rule is illustrated by some simulations