A generative model for protein contact networks

In this paper we present a generative model for protein contact networks. The soundness of the proposed model is investigated by focusing primarily on mesoscopic properties elaborated from the spectra of the graph Laplacian. To complement the analysis, we study also classical topological descriptors, such as statistics of the shortest paths and the important feature of modularity. Our experiments show that the proposed model results in a considerable improvement with respect to two suitably chosen generative mechanisms, mimicking with better approximation real protein contact networks in terms of diffusion properties elaborated from the Laplacian spectra. However, as well as the other considered models, it does not reproduce with sufficient accuracy the shortest paths structure. To compensate this drawback, we designed a second step involving a targeted edge reconfiguration process. The ensemble of reconfigured networks denotes improvements that are statistically significant. As a byproduct of our study, we demonstrate that modularity, a well-known property of proteins, does not entirely explain the actual network architecture characterizing protein contact networks. In fact, we conclude that modularity, intended as a quantification of an underlying community structure, should be considered as an emergent property of the structural organization of proteins. Interestingly, such a property is suitably optimized in protein contact networks together with the feature of path efficiency.Comment: 18 pages, 67 reference

Multifractal Characterization of Protein Contact Networks

The multifractal detrended fluctuation analysis of time series is able to reveal the presence of long-range correlations and, at the same time, to characterize the self-similarity of the series. The rich information derivable from the characteristic exponents and the multifractal spectrum can be further analyzed to discover important insights about the underlying dynamical process. In this paper, we employ multifractal analysis techniques in the study of protein contact networks. To this end, initially a network is mapped to three different time series, each of which is generated by a stationary unbiased random walk. To capture the peculiarities of the networks at different levels, we accordingly consider three observables at each vertex: the degree, the clustering coefficient, and the closeness centrality. To compare the results with suitable references, we consider also instances of three well-known network models and two typical time series with pure monofractal and multifractal properties. The first result of notable interest is that time series associated to proteins contact networks exhibit long-range correlations (strong persistence), which are consistent with signals in-between the typical monofractal and multifractal behavior. Successively, a suitable embedding of the multifractal spectra allows to focus on ensemble properties, which in turn gives us the possibility to make further observations regarding the considered networks. In particular, we highlight the different role that small and large fluctuations of the considered observables play in the characterization of the network topology

Analysis of heat kernel highlights the strongly modular and heat-preserving structure of proteins

In this paper, we study the structure and dynamical properties of protein contact networks with respect to other biological networks, together with simulated archetypal models acting as probes. We consider both classical topological descriptors, such as the modularity and statistics of the shortest paths, and different interpretations in terms of diffusion provided by the discrete heat kernel, which is elaborated from the normalized graph Laplacians. A principal component analysis shows high discrimination among the network types, either by considering the topological and heat kernel based vector characterizations. Furthermore, a canonical correlation analysis demonstrates the strong agreement among those two characterizations, providing thus an important justification in terms of interpretability for the heat kernel. Finally, and most importantly, the focused analysis of the heat kernel provides a way to yield insights on the fact that proteins have to satisfy specific structural design constraints that the other considered networks do not need to obey. Notably, the heat trace decay of an ensemble of varying-size proteins denotes subdiffusion, a peculiar property of proteins

Modelling and recognition of protein contact networks by multiple kernel learning and dissimilarity representations

Multiple kernel learning is a paradigm which employs a properly constructed chain of kernel functions able to simultaneously analyse different data or different representations of the same data. In this paper, we propose an hybrid classification system based on a linear combination of multiple kernels defined over multiple dissimilarity spaces. The core of the training procedure is the joint optimisation of kernel weights and representatives selection in the dissimilarity spaces. This equips the system with a two-fold knowledge discovery phase: by analysing the weights, it is possible to check which representations are more suitable for solving the classification problem, whereas the pivotal patterns selected as representatives can give further insights on the modelled system, possibly with the help of field-experts. The proposed classification system is tested on real proteomic data in order to predict proteins' functional role starting from their folded structure: specifically, a set of eight representations are drawn from the graph-based protein folded description. The proposed multiple kernel-based system has also been benchmarked against a clustering-based classification system also able to exploit multiple dissimilarities simultaneously. Computational results show remarkable classification capabilities and the knowledge discovery analysis is in line with current biological knowledge, suggesting the reliability of the proposed system

Education in anesthesia: three years of online logbook implementation in an Italian school

BACKGROUND: The progress of physicians through residency training in anesthesiology can be monitored using an online logbook. The aim of this investigation was to establish how residents record clinical activities in their computerized web-based logbooks during their first years of anesthesiology training. METHODS: For this retrospective observational trial, the ESSE 3(©) digital registry of the University of Modena and Reggio Emilia, Italy was used to record all anesthesia-related activities performed by three consecutive year-groups of residents (Groups A, B and C) between 2009 and 2012. The ratio of activities to sessions was chosen as a surrogate measure of compliance. RESULTS: A total of 41,348 actions were analyzed. The ratio of activities to sessions showed a statistically significant decline for all activities concerning the perioperative management of anesthesia, with a steady reduction from the first to the last year-group (Group A 23.7, Group B 14.1 and Group C 2.2; p = 0.003). CONCLUSIONS: An online activities logbook is a useful tool for recording and assessing the clinical activities undertaken by each resident during residency training in anesthesiology

Propofol: a safe anaesthetic drug in experimental cardiac surgery in rabbits

Experimental surgery needs a pharmacological approach that can interfere with cardiac function

Action of combined magnetic fields on aqueous solution of glutamic acid: the further development of investigations

In the present work the results of the known investigation of the influence of combined static (40 μT) and alternating (amplitude of 40 nT) parallel magnetic fields on the current through the aqueous solution of glutamic acid, were successfully replicated. Fourteen experiments were carried out by the application of the combined magnetic fields to the solution placed into a Plexiglas reaction vessel at application of static voltage to golden electrodes placed into the solution. Six experiments were carried out by the application of the combined magnetic fields to the solution placed in a Plexiglas reaction vessel, without electrodes, within an electric field, generated by means of a capacitor at the voltage of 27 mV. The frequency of the alternating field was scanned within the bounds of 1.0 Hz including the cyclotron frequency corresponding to a glutamic acid ion and to the applied static magnetic field. In this study the prominent peaks with half-width of ~0.5 Hz and with different heights (till 80 nA) were registered at the alternating magnetic field frequency equal to the cyclotron frequency (4.2 Hz). The general reproducibility of the investigated effects was 70% among the all solutions studied by us and they arose usually after 40–60 min. after preparation of the solution. In some made-up solutions the appearance of instability in the registered current was noted in 30–45 min after the solution preparation. This instability endured for 20–40 min. At the end of such instability period the effects of combined fields action appeared practically every time. The possible mechanisms of revealed effects were discussed on the basis of modern quantum electrodynamics

Detection of bovine alpha-S1-casein in term and preterm human colostrum with proteomic techniques

Due to increased social awareness of allergens and population hyper-sensitization, the reported incidence of allergic reactions to food allergens has increased over the past two decades. Cow's milk proteins (CMPs) are among the most common food allergens. The aim of this study was to use proteomics techniques to investigate cow's milk allergens in both full-term human colostrum and in preterm newborns' mothers - where both groups showed no prior allergen detection – in order to understand whether cow's milk allergens could be a cause of sensitization established through lactation. The most relevant finding was the detection of the intact bovine alpha-S1-casein in both term and preterm colostrum. Using techniques detailed in this paper and which allowed for direct protein identification, β-lactoglobulin was not detected in any of the colostrum samples. According to our results, bovine alpha 1 casein is considered a major cow's milk allergen, is readily secreted in human milk, and so could be considered a possible cause of sensitization in exclusively breastfed infants