Complete trails of co-authorship network evolution

The rise and fall of a research field is the cumulative outcome of its intrinsic scientific value and social coordination among scientists. The structure of the social component is quantifiable by the social network of researchers linked via co-authorship relations, which can be tracked through digital records. Here, we use such co-authorship data in theoretical physics and study their complete evolutionary trail since inception, with a particular emphasis on the early transient stages. We find that the co-authorship networks evolve through three common major processes in time: the nucleation of small isolated components, the formation of a tree-like giant component through cluster aggregation, and the entanglement of the network by large-scale loops. The giant component is constantly changing yet robust upon link degradations, forming the network's dynamic core. The observed patterns are successfully reproducible through a new network model

Flux networks in metabolic graphs

A metabolic model can be represented as bipartite graph comprising linked reaction and metabolite nodes. Here it is shown how a network of conserved fluxes can be assigned to the edges of such a graph by combining the reaction fluxes with a conserved metabolite property such as molecular weight. A similar flux network can be constructed by combining the primal and dual solutions to the linear programming problem that typically arises in constraint-based modelling. Such constructions may help with the visualisation of flux distributions in complex metabolic networks. The analysis also explains the strong correlation observed between metabolite shadow prices (the dual linear programming variables) and conserved metabolite properties. The methods were applied to recent metabolic models for Escherichia coli, Saccharomyces cerevisiae, and Methanosarcina barkeri. Detailed results are reported for E. coli; similar results were found for the other organisms.Comment: 9 pages, 4 figures, RevTeX 4.0, supplementary data available (excel

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics

Comparison of aneroid and oscillometric blood pressure measurements in children.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.Limited data exist on the comparison of blood pressure (BP) measurements using aneroid and oscillometric devices. The purpose of the study was to investigate the difference in BP obtained using oscillometric and aneroid BP monitors in 9- to 10-year-old children. A total of 979 children were divided into group O, which underwent two oscillometric BP readings followed by two aneroid readings, and group A, which had BP measured in the reverse order. No significant difference was found between the mean (±standard deviation) of the two systolic BP readings obtained using the oscillometric and aneroid devices (111.5±8.6 vs 111.3±8.1 mm Hg; P=.39), whereas the mean diastolic BP was lower with the oscillometric monitor (61.5±8.0 vs 64.5±6.8 mm Hg; P<.001). A significant downward trend in BP was observed with each consecutive measurement, and agreement between the two monitors was limited. Multiple BP measurements are, therefore, recommended before the diagnosis of elevated BP or hypertension is made with either method.Landspitali - The National University Hospital of Iceland Research Fun

Plasma 25-hydroxyvitamin D2 and D3 levels and incidence of postoperative atrial fibrillation.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.Low circulating levels of total 25-hydroxyvitamin D (25(OH)D) have been associated with an increased risk of adverse effects after cardiac surgery. The metabolites, 25(OH)D2 and 25(OH)D3, provide a good index of vitamin D status. In this study, we examined the association between preoperative plasma levels of total 25(OH)D, 25(OH)D2 and 25(OH)D3 and the risk of postoperative atrial fibrillation (POAF) following open heart surgery. The levels of plasma 25(OH)D2 and 25(OH)D3 in 118 patients, who underwent coronary artery bypass grafting and/or valvular surgery, were measured immediately prior to surgery and on postoperative day 3 by liquid chromatography-tandem mass spectrometry. Patients who developed POAF had higher median plasma levels of 25(OH)D2 than those who remained in sinus rhythm (SR) (P = 0·003), but no significant difference was noted in levels of 25(OH)D3 or total 25(OH)D between the two groups (P > 0·05). By univariate analysis, patients with total 25(OH)D and 25(OH)D2 levels above the median had higher frequency of POAF (P < 0·05) and the incidence of POAF increased significantly with each higher quartile of preoperative plasma levels of 25(OH)D2 (P = 0·001), an association that was independent of confounding factors. In both the SR and POAF groups, the median plasma levels of 25(OH)D2, 25(OH)D3 and total 25(OH)D were lower (P < 0·05) on the third postoperative day compared with preoperatively. Our findings demonstrate that higher plasma levels of 25(OH)D2 are associated with increased risk of POAF, while this is not the case for 25(OH)D3 or total 25(OH)D. The reason for these discrepant results is not clear but warrants further study

The solution space of metabolic networks: producibility, robustness and fluctuations

Flux analysis is a class of constraint-based approaches to the study of biochemical reaction networks: they are based on determining the reaction flux configurations compatible with given stoichiometric and thermodynamic constraints. One of its main areas of application is the study of cellular metabolic networks. We briefly and selectively review the main approaches to this problem and then, building on recent work, we provide a characterization of the productive capabilities of the metabolic network of the bacterium E.coli in a specified growth medium in terms of the producible biochemical species. While a robust and physiologically meaningful production profile clearly emerges (including biomass components, biomass products, waste etc.), the underlying constraints still allow for significant fluctuations even in key metabolites like ATP and, as a consequence, apparently lay the ground for very different growth scenarios.Comment: 10 pages, prepared for the Proceedings of the International Workshop on Statistical-Mechanical Informatics, March 7-10, 2010, Kyoto, Japa

Quantifying the connectivity of a network: The network correlation function method

Networks are useful for describing systems of interacting objects, where the nodes represent the objects and the edges represent the interactions between them. The applications include chemical and metabolic systems, food webs as well as social networks. Lately, it was found that many of these networks display some common topological features, such as high clustering, small average path length (small world networks) and a power-law degree distribution (scale free networks). The topological features of a network are commonly related to the network's functionality. However, the topology alone does not account for the nature of the interactions in the network and their strength. Here we introduce a method for evaluating the correlations between pairs of nodes in the network. These correlations depend both on the topology and on the functionality of the network. A network with high connectivity displays strong correlations between its interacting nodes and thus features small-world functionality. We quantify the correlations between all pairs of nodes in the network, and express them as matrix elements in the correlation matrix. From this information one can plot the correlation function for the network and to extract the correlation length. The connectivity of a network is then defined as the ratio between this correlation length and the average path length of the network. Using this method we distinguish between a topological small world and a functional small world, where the latter is characterized by long range correlations and high connectivity. Clearly, networks which share the same topology, may have different connectivities, based on the nature and strength of their interactions. The method is demonstrated on metabolic networks, but can be readily generalized to other types of networks.Comment: 10 figure

Development and pilot testing of an integrated, web-based self-management program for irritable bowel syndrome (IBS)

Although essential, many medical practices are unable to adequately support irritable bowel syndrome (IBS) patient self-management. Web-based programs can help overcome these barriers

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion

In mammalian cells, &gt;25% of synthesized proteins are exported through the secretory pathway. The pathway complexity, however, obfuscates its impact on the secretion of different proteins. Unraveling its impact on diverse proteins is particularly important for biopharmaceutical production. Here we delineate the core secretory pathway functions and integrate them with genome-scale metabolic reconstructions of human, mouse, and Chinese hamster ovary\ua0cells. The resulting reconstructions enable the computation of energetic costs and machinery demands of each secreted protein. By integrating additional omics data, we find that highly secretory cells have adapted to reduce expression and secretion of other expensive host cell proteins. Furthermore, we predict metabolic costs and maximum productivities of biotherapeutic proteins and identify protein features that most significantly impact protein secretion. Finally, the model successfully predicts the increase in secretion of a monoclonal antibody after silencing a highly expressed selection marker. This work represents a knowledgebase of the mammalian secretory pathway that serves as a novel tool for systems biotechnology