DNAReplication: a database of information and resources for the eukaryotic DNA replication community

DNAReplication (at http://www.dnareplication.net) has been set up as a freely available single resource to facilitate access to information on eukaryotic DNA replication. This database summarizes organism-sorted data on replication proteins in the categories of nomenclature, biochemical properties, motifs, interactions, modifications, structure, cell localization and expression, and general comments. Replication concepts are defined and a general model of the steps in DNA replication is presented. Links to relevant websites and homepages of replication labs are provided. The site also has an interactive section where links to recent replication papers are posted and readers are provided with the facility to post comments about each paper. The interactive and links pages are modified weekly and the whole site is updated annually

Glueball condensates as holographic duals of supersymmetric Q-balls and boson stars

We study non-spinning Q-balls and boson stars in 4-dimensional Anti-de Sitter (AdS) space-time. We use an exponential scalar field potential that appears in gauge-mediated supersymmetry (SUSY) breaking in the minimal supersymmetric extension of the Standard Model (MSSM). We investigate the dependence of the charge and mass of these non-topological solitons on the negative cosmological constant, the frequency that appears in the periodic time-dependence as well as on the ratio between the SUSY breaking scale and the Planck mass. Next to fundamental solutions without nodes in the scalar field function we also construct radially excited solutions. In the second part of the paper we put the emphasis on the holographic interpretation of these solutions in terms of Bose-Einstein condensates of scalar glueballs that are described by a strongly coupled Quantum Field Theory (QFT) on the boundary of global AdS.Comment: 17 pages including 11 figures; v2: 19 pages including 13 figures, references added, figures adde

Estimating the Empirical Lorenz Curve and Gini Coefficient in the Presence of Error

The Lorenz curve is a graphical tool that is widely used to characterize the concentration of a measure in a population, such as wealth. It is frequently the case that the measure of interest used to rank experimental units when estimating the empirical Lorenz curve, and the corresponding Gini coefficient, is subject to random error. This error can result in an incorrect ranking of experimental units which inevitably leads to a curve that exaggerates the degree of concentration (variation) in the population. We explore this bias and discuss several widely available statistical methods that have the potential to reduce or remove the bias in the empirical Lorenz curve. The properties of these methods are examined and compared in a simulation study. This work is motivated by a health outcomes application which seeks to assess the concentration of black patient visits among primary care physicians. The methods are illustrated on data from this study

Towards designing robust coupled networks

Natural and technological interdependent systems have been shown to be highly vulnerable due to cascading failures and an abrupt collapse of global connectivity under initial failure. Mitigating the risk by partial disconnection endangers their functionality. Here we propose a systematic strategy of selecting a minimum number of autonomous nodes that guarantee a smooth transition in robustness. Our method which is based on betweenness is tested on various examples including the famous 2003 electrical blackout of Italy. We show that, with this strategy, the necessary number of autonomous nodes can be reduced by a factor of five compared to a random choice. We also find that the transition to abrupt collapse follows tricritical scaling characterized by a set of exponents which is independent on the protection strategy

Knowledge Graph Completion via Complex Tensor Factorization

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs—labeled directed graphs—and predicting missing relationships—labeled edges. State-of-the-art embedding models propose different trade-offs between modeling expressiveness, and time and space complexity. We reconcile both expressiveness and complexity through the use of complex-valued embeddings and explore the link between such complex-valued embeddings and unitary diagonalization. We corroborate our approach theoretically and show that all real square matrices—thus all possible relation/adjacency matrices—are the real part of some unitarily diagonalizable matrix. This results opens the door to a lot of other applications of square matrices factorization. Our approach based on complex embeddings is arguably simple, as it only involves a Hermitian dot product, the complex counterpart of the standard dot product between real vectors, whereas other methods resort to more and more complicated composition functions to increase their expressiveness. The proposed complex embeddings are scalable to large data sets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks

First clinical application of a novel T1 mapping of the whole brain

Background: The aim of this study was to evaluate the reproducibility and clinical value of the novel single-shot T1 mapping method for rapid and accurate multi-slice coverage of the whole brain, described by Wang et al. 2015. Methods: At a field strength of 3 Tesla, T1 mappings of 139 patients (51 of them without pathologic findings) and two repeats of five volunteers were performed at 0.5 mm in-plane resolution. Mean T1 values were determined in 18 manually segmented regions-of-interest without pathologic findings. Reproducibility of the repeated scans was calculated using mean coefficient of variations. Pathologies were grouped and separately evaluated. Results: The mean age of the cohort was 49 (range 1–95 years). T1 relaxation times for ordinary brain and pathologies were in accordance with the literature values. Intra- and inter-subject reproducibility was excellent, and mean coefficient of variations were 2.4% and 3.8%, respectively. Discussion: The novel rapid T1 mapping method is a reliable magnetic resonance imaging technique for identifying and quantifying normal brain structures and may thus serve as a basis for assessing pathologies. The fast and parallel online calculation enables a comfortable use in everyday clinical practice. We see a possible clinical value in a large spectrum of diseases, which should be investigated in further studies