News and price returns from threshold behaviour and vice-versa: exact solution of a simple agent-based market model

Starting from an exact relationship between news, threshold and price return distributions in the stationary state, I discuss the ability of the Ghoulmie-Cont-Nadal model of traders to produce fat-tailed price returns. Under normal conditions, this model is not able to transform Gaussian news into fat-tailed price returns. When the variance of the news so small that only the players with zero threshold can possibly react to news, this model produces Levy-distributed price returns with a -1 exponent. In the special case of super-linear price impact functions, fat-tailed returns are obtained from well-behaved news.Comment: 4 pages, 3 figures. This is quite possibly the final version. To appear in J. Phys

Myocarditis and intramural coronary vasculitis in polyarteritis nodosa: an unusual treatable form of heart failure

We describe an uncommon cardiac presentation of polyarteritis nodosa. A 68-year-old woman, with a history of fatigue, weight loss, and myalgia of the lower extremities, was admitted for congestive heart failure. Coronary arteries were normal. Endomyocardial biopsy showed active lymphocytic myocarditis with associated intramural small vessels necrotizing vasculitis. The overexpression of TLR-4 and the negativity for myocardial viruses suggested an immune mediated mechanism of cardiac damage. These histologic findings associated to weight loss >4 kg not due to dieting or other factors, myalgias, and polyneuropathy, were consistent with the diagnosis of polyarteritis nodosa. Immunosuppressive treatment, consisting of cyclophosphamide and prednisolone, led to a significant improvement of cardiac function. Polyarteritis nodosa can be the cause of unexplained heart failure due to myocarditis and intramural vessels vasculitis. Its recognition is crucial to obtain a cardiac recovery with a tailored immunosuppressive treatment

po 324 interferon regulatory factor 1 irf1 regulates inflammatory and metabolic phenotypes in pancreatic ductal adenocarcinoma

Introduction Pancreatic Ductal Adenocarcinoma (PDAC) is the most frequent neoplasia of the exocrine pancreas. This tumour is and is characterised by a pervasive heterogeneity, with the coexistence of a range of histological grades, from epithelial-like to mesenchymal-like features. We previously dissected the transcriptional and epigenetic networks underlying PDAC grading. We identified the association of low grade phenotypes with a cell-autonomous interferon-related signature. Therefore, we set out to investigate the sustainment of inflammatory and interferon-related signatures in well-differentiated pancreatic cancer cells, and to determine the role of this network in PDAC biology. Material and methods We used cell-line based models of cancer differentiation, xenografts and human samples. We used CRISPR-Cas9 mediated genome editing to delete the transcription factor IRF1 (Interferon Regulatory Factor 1) in low-grade PDAC cells. RNA-seq, metabolic assays (oxygraphy, steady state metabolomics, fluxomics) and cell biology assays were carried out in IRF1 wt and knock-out cell lines. Data validation in human PDAC samples was carried out by immunohistochemistry. Results and discussions We found that IRF1 is a transcription factor differentially expressed between low- and high-grade PDACs, both in cell lines and in human tumours. IRF1 deletion in low-grade cell lines reduced the expression of genes in the antigen processing and presentation pathways, while its overexpression promoted the expression of the same genes in high-grade cells, where they are normally not expressed. Furthermore, xenografted IRF1-deficient cell lines recruited fewer immune cells in vivo . IRF1 deletion also affected epithelial phenotypes, including growth rate, cell shape, motility and collagen remodelling ability. Alongside, we unveiled a role of IRF1 in the control of the metabolism of low-grade PDAC cells, consisting in the control of mitochondrial respiration and lipogenesis as well as of the overall lipid profile of these cells. Conclusion To conclude, our results provide hints on the regulatory networks controlling cell differentiation in human PDACs. We show that IRF1 acts as a pleiotropic regulator in the low grade component of PDACs, with wide effects on immunological and metabolic features of this cancer population. Our work reinforces the body of knowledge needed for the development of those therapeutic strategies aiming at exploiting immunological or metabolic pitfalls

Comprehensive Analysis of Market Conditions in the Foreign Exchange Market: Fluctuation Scaling and Variance-Covariance Matrix

We investigate quotation and transaction activities in the foreign exchange market for every week during the period of June 2007 to December 2010. A scaling relationship between the mean values of number of quotations (or number of transactions) for various currency pairs and the corresponding standard deviations holds for a majority of the weeks. However, the scaling breaks in some time intervals, which is related to the emergence of market shocks. There is a monotonous relationship between values of scaling indices and global averages of currency pair cross-correlations when both quantities are observed for various window lengths $\Delta t$.Comment: 13 pages, 10 figure

Finding motif pairs in the interactions between heterogeneous proteins via bootstrapping and boosting

<p>Abstract</p> <p>Background</p> <p>Supervised learning and many stochastic methods for predicting protein-protein interactions require both negative and positive interactions in the training data set. Unlike positive interactions, negative interactions cannot be readily obtained from interaction data, so these must be generated. In protein-protein interactions and other molecular interactions as well, taking all non-positive interactions as negative interactions produces too many negative interactions for the positive interactions. Random selection from non-positive interactions is unsuitable, since the selected data may not reflect the original distribution of data.</p> <p>Results</p> <p>We developed a bootstrapping algorithm for generating a negative data set of arbitrary size from protein-protein interaction data. We also developed an efficient boosting algorithm for finding interacting motif pairs in human and virus proteins. The boosting algorithm showed the best performance (84.4% sensitivity and 75.9% specificity) with balanced positive and negative data sets. The boosting algorithm was also used to find potential motif pairs in complexes of human and virus proteins, for which structural data was not used to train the algorithm. Interacting motif pairs common to multiple folds of structural data for the complexes were proven to be statistically significant. The data set for interactions between human and virus proteins was extracted from BOND and is available at <url>http://virus.hpid.org/interactions.aspx</url>. The complexes of human and virus proteins were extracted from PDB and their identifiers are available at <url>http://virus.hpid.org/PDB_IDs.html</url>.</p> <p>Conclusion</p> <p>When the positive and negative training data sets are unbalanced, the result via the prediction model tends to be biased. Bootstrapping is effective for generating a negative data set, for which the size and distribution are easily controlled. Our boosting algorithm could efficiently predict interacting motif pairs from protein interaction and sequence data, which was trained with the balanced data sets generated via the bootstrapping method.</p

Antioxidant capacity of durum wheat large flour particles may be evaluated by QUENCHER_ABTS assay by adopting a proper calculation mode

Assessment of Antioxidant Capacity (AC) of foods is useful to consider cumulative/ synergistic action of all dietary antioxidants, thus providing a more integrated information than the simple sum of measurable antioxidants. Among the different AC assays, the QUENCHERABTS (QUick, Easy, New, CHEap and Reproducible) procedure is based on the direct reaction of ABTS•+ reagent with fine solid food particles without extraction of antioxidants. This assay is able to measure both soluble and insoluble antioxidants, that simultaneously come into contact with ABTS•+ molecules by either liquid–liquid or solid–liquid interactions, respectively. These interactions may change depending on the particle diameter. Usually, particles having 0.1–0.3 mm size are used. Here, AC was evaluated on whole flour (WF), derived from a mix of grains of ten durum wheat varieties, characterized by three different particle sizes: a smaller one, ≤0.2 mm (control, WF0.2), and two larger ones, ≤0.5 mm and ≤1 mm (WF0.5 and WF1, respectively). Moreover, a novel AC calculation procedure based on the slope value of the regression line of ABTS•+ response vs flour amount is presented in detail. The classical QUENCHERABTS procedure provided for WF0.2 an AC value of 42.0±2.7 μmol eq. Trolox/g d.w. A similar result was obtained for WF0.5 (38.3±0.9 μmol eq. Trolox/g d.w.), thus indicating that these large particles may be analyzed by the QUENCHERABTS assay provided that the “slope” calculation procedure is used. On the contrary, WF1 showed about half AC (20.3±0.2 μmol eq. Trolox/g d.w.), thus showing that very large particles cannot be used even adopting the “slope” calculation

iRefR: an R package to manipulate the iRefIndex consolidated protein interaction database

<p>Abstract</p> <p>Background</p> <p>The iRefIndex addresses the need to consolidate protein interaction data into a single uniform data resource. iRefR provides the user with access to this data source from an R environment.</p> <p>Results</p> <p>The iRefR package includes tools for selecting specific subsets of interest from the iRefIndex by criteria such as organism, source database, experimental method, protein accessions and publication identifier. Data may be converted between three representations (MITAB, edgeList and graph) for use with other R packages such as igraph, graph and RBGL.</p> <p>The user may choose between different methods for resolving redundancies in interaction data and how n-ary data is represented. In addition, we describe a function to identify binary interaction records that possibly represent protein complexes. We show that the user choice of data selection, redundancy resolution and n-ary data representation all have an impact on graphical analysis.</p> <p>Conclusions</p> <p>The package allows the user to control how these issues are dealt with and communicate them via an R-script written using the iRefR package - this will facilitate communication of methods, reproducibility of network analyses and further modification and comparison of methods by researchers.</p

The Homeodomain Resource: a comprehensive collection of sequence, structure, interaction, genomic and functional information on the homeodomain protein family

The Homeodomain Resource is a curated collection of sequence, structure, interaction, genomic and functional information on the homeodomain family. The current version builds upon previous versions by the addition of new, complete sets of homeodomain sequences from fully sequenced genomes, the expansion of existing curated homeodomain information and the improvement of data accessibility through better search tools and more complete data integration. This release contains 1534 full-length homeodomain-containing sequences, 93 experimentally derived homeodomain structures, 101 homeodomain protein–protein interactions, 107 homeodomain DNA-binding sites and 206 homeodomain proteins implicated in human genetic disorders

Assessing the functional structure of genomic data

Motivation: The availability of genome-scale data has enabled an abundance of novel analysis techniques for investigating a variety of systems-level biological relationships. As thousands of such datasets become available, they provide an opportunity to study high-level associations between cellular pathways and processes. This also allows the exploration of shared functional enrichments between diverse biological datasets, and it serves to direct experimenters to areas of low data coverage or with high probability of new discoveries

Validation of Agent-Based Models in Economics and Finance

Since the survey by Windrum et al. (Journal of Artificial Societies and Social Simulation 10:8, 2007), research on empirical validation of agent-based models in economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated critical review of the existing validation techniques. We sketch a simple theoretical framework that conceptualizes existing validation approaches, which we examine along three different dimensions: (i) comparison between artificial and real-world data; (ii) calibration and estimation of model parameters; and (iii) parameter space exploration. Finally, we discuss open issues in the field of ABM validation and estimation. In particular, we argue that more research efforts should be devoted toward advancing hypothesis testing in ABM, with specific emphasis on model stationarity and ergodicity