3 tera-basepairs as a fundamental limit for robust DNA replication

10 p.-2 tab.In order to maintain functional robustness and species integrity, organisms must ensure high fidelity of the genome duplication process. This is particularly true during early development, where cell division is often occurring both rapidly and coherently. By studying the extreme limits of suppressing DNA replication failure due to double fork stall errors, we uncover a fundamental constant that describes a trade-off between genome size and architectural complexity of the developing organism. This constant has the approximate value N_U ≈ 3×10^12 basepairs, and depends only on two highly conserved molecular properties of DNA biology. We show that our theory is successful in interpreting a diverse range of data across the Eukaryota.MAM, LA and TJN acknowledge prior support from the Scottish Universities Life Sciences Alliance. JJB acknowledges support from Cancer Research UK (grant C303/A14301) and the Wellcome Trust (grant WT096598MA). TJN acknowledges prior support from the National Institutes of Health (Physical Sciences in Oncology Centers, U54 CA143682).Peer reviewe

Bioassays for coastal water quality: an assessment using the larval development of Haliotis midae L

The United States Environmental Protection Agency (USEPA) has established a suite of methods that use coastal invertebrate species as bioassay organisms to test industrial and domestic effluent as well as coastal waters for potential toxicity. Although these methods are used globally, the potential of such toxicity tests has not been adequately explored for South African coastal waters. This study serves to describe a simple, cost-effective and relatively quick testing procedure using the development of Haliotis midae larvae as a bioassay of coastal water quality. This test is based on the sensitivity of these larvae to low concentrations of zinc (Zn). Its performance in a field trial demonstrates not only that this test has the potential to identify coastal waters of poor quality, but also that such identification could be of value in attempts to restock natural abalone populations, which are under extreme pressure from legal and illegal exploitation. Further work in this line should focus on the refinement of the methodology for this and other local species and should aim to contribute to the development of suitable criteria for the management of coastal water quality in South Africa. WaterSA Vol.28(4) 2002: 457-46

Relational Orientation versus Firm Orientation: Want versus Should

This paper provides insights into employee decision making when there is a conflict between doing what is best for the firm (firm orientation) and doing what is best for one s interpersonal relationship with an external stakeholder representative (relational orientation). We apply construal level theory (Liberman and Trope, 1998; Trope and Liberman, 2003) to propose a framework that explains the effects of psychological distance dimensions on an employee's choice to act either in the best interests of their interpersonal relationships (what they want to do), or their firm (what they should do)

Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines

Researchers' networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or co-authorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost-increase of a researcher's visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers' interdisciplinarity at individual level (diversity of topics related to the researcher) and at social level (his/her centrality in the researchers' network). We found that visibility boosts by certain research topics were positively correlated with researchers' individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers' social-level interdisciplinarity. Research topics' correlations with researchers' individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of "interdisciplinarity" of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On

Considerations about multistep community detection

The problem and implications of community detection in networks have raised a huge attention, for its important applications in both natural and social sciences. A number of algorithms has been developed to solve this problem, addressing either speed optimization or the quality of the partitions calculated. In this paper we propose a multi-step procedure bridging the fastest, but less accurate algorithms (coarse clustering), with the slowest, most effective ones (refinement). By adopting heuristic ranking of the nodes, and classifying a fraction of them as `critical', a refinement step can be restricted to this subset of the network, thus saving computational time. Preliminary numerical results are discussed, showing improvement of the final partition.Comment: 12 page

How is data science involved in policy analysis?: A bibliometric perspective

© 2018 Portland International Conference on Management of Engineering and Technology, Inc. (PICMET). What are the implications of big data in terms of big impacts? Our research focuses on the question, 'How are data analytics involved in policy analysis to create complementary values?' We address this from the perspective of bibliometrics. We initially investigate a set of articles published in Nature and Science, seeking cutting-edge knowledge to sharpen research hypotheses on what data science offers policy analysis. Based on a set of bibliometric models (e.g., topic analysis, scientific evolutionary pathways, and social network analysis), we follow up with studies addressing two aspects: (1) we examine the engagement of data science (including statistical, econometric, and computing approaches) in current policy analyses by analyzing articles published in top-level journals in the areas of political science and public administration; and (2) we examine the development of policy analysis-oriented data analytic models in top-level journals associated with computer science (including both artificial intelligence and information systems). Observations indicate that data science contribution to policy analysis is still an emerging area. Data scientists are moving further than policy analysts, due to technical difficulties in exploiting data analytic models. Integrating artificial intelligence with econometrics is identified as a particularly promising direction

Statistically validated networks in bipartite complex systems

Many complex systems present an intrinsic bipartite nature and are often described and modeled in terms of networks [1-5]. Examples include movies and actors [1, 2, 4], authors and scientific papers [6-9], email accounts and emails [10], plants and animals that pollinate them [11, 12]. Bipartite networks are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set. When one constructs a projected network with nodes from only one set, the system heterogeneity makes it very difficult to identify preferential links between the elements. Here we introduce an unsupervised method to statistically validate each link of the projected network against a null hypothesis taking into account the heterogeneity of the system. We apply our method to three different systems, namely the set of clusters of orthologous genes (COG) in completely sequenced genomes [13, 14], a set of daily returns of 500 US financial stocks, and the set of world movies of the IMDb database [15]. In all these systems, both different in size and level of heterogeneity, we find that our method is able to detect network structures which are informative about the system and are not simply expression of its heterogeneity. Specifically, our method (i) identifies the preferential relationships between the elements, (ii) naturally highlights the clustered structure of investigated systems, and (iii) allows to classify links according to the type of statistically validated relationships between the connected nodes.Comment: Main text: 13 pages, 3 figures, and 1 Table. Supplementary information: 15 pages, 3 figures, and 2 Table

Googling the brain: discovering hierarchical and asymmetric network structures, with applications in neuroscience

Hierarchical organisation is a common feature of many directed networks arising in nature and technology. For example, a well-defined message-passing framework based on managerial status typically exists in a business organisation. However, in many real-world networks such patterns of hierarchy are unlikely to be quite so transparent. Due to the nature in which empirical data is collated the nodes will often be ordered so as to obscure any underlying structure. In addition, the possibility of even a small number of links violating any overall “chain of command” makes the determination of such structures extremely challenging. Here we address the issue of how to reorder a directed network in order to reveal this type of hierarchy. In doing so we also look at the task of quantifying the level of hierarchy, given a particular node ordering. We look at a variety of approaches. Using ideas from the graph Laplacian literature, we show that a relevant discrete optimization problem leads to a natural hierarchical node ranking. We also show that this ranking arises via a maximum likelihood problem associated with a new range-dependent hierarchical random graph model. This random graph insight allows us to compute a likelihood ratio that quantifies the overall tendency for a given network to be hierarchical. We also develop a generalization of this node ordering algorithm based on the combinatorics of directed walks. In passing, we note that Google’s PageRank algorithm tackles a closely related problem, and may also be motivated from a combinatoric, walk-counting viewpoint. We illustrate the performance of the resulting algorithms on synthetic network data, and on a real-world network from neuroscience where results may be validated biologically