We define two words in a language to be connected if they express similar concepts. The network of connections among the many thousands of words that make up a language is important not only for the study of the structure and evolution of languages, but also for cognitive science. We study this issue quantitatively, by mapping out the conceptual network of the English language, with the connections being defined by the entries in a Thesaurus dictionary. We find that this network presents a small-world structure, with an amazingly small average shortest path, and appears to exhibit an asymptotic scale-free feature with algebraic connectivity distribution.PACS numbers: 87.23. Ge,89.75.Hc Any language is composed of many thousands of words linked together in an apparently fairly sophisticated way. A language can thus be regarded as a network, in the following sense: (1) the words correspond to nodes of the network, and (2) a link exists between two words if they express similar concepts. Clearly, the underlying network of a language is necessarily sparse in the sense that the average number of links per node is typically much smaller than the total number of nodes. Identifying and understanding the common network topology of languages is of great importance, not only for the study of languages themselves, but also for cognitive science where one of the most fundamental issues concerns associative memory, which is intimately related to the network topology.Recently, there has been a tremendous amount of interest in the study of large, sparse, and complex networks since the seminal papers by Watts and Strogatz [1] on the small-world characteristic and by Barabási and Albert on scale-free features [2]. The small-world concept is static in the sense that it describes the topological property of the network at a given time. Two statistical quantities characterizing a static networks are clustering C and shortest path L , where the former is the probability that any two nodes are connected to each other, given that they are both connected to a common node, and the latter measures the minimal number of links connecting two nodes in the network. Regular networks have high clusterings and small average shortest paths, with random networks at the opposite of the spectrum which have small shortest paths and low clusterings [3]. Small-world networks fall somewhere in between these two extremes. In particular, a network is small world if its clustering coefficient is almost as high as that of a regular network but its average shortest path is almost as small as that of a random network with the same parameters. Watts and Strogatz demonstrated that a small-world network can be easily constructed by adding to a regular network a few additional random links connecting otherwise distant nodes. The scale-free property, on the other hand, is defined by an algebraic behavior in the probability distribution P (k) of k, the number of links at a node in the network. This property is dynamic because it is the consequence of the natural...

This work reviews the present position of and surveys future perspectives in the physics of chaotic advection: the field that emerged three decades ago at the intersection of fluid mechanics and nonlinear dynamics, which encompasses a range of applications with length scales ranging from micrometers to hundreds of kilometers, including systems as diverse as mixing and thermal processing of viscous fluids, microfluidics, biological flows, and oceanographic and atmospheric flows.

Eukaryotic genomes are replicated from multiple DNA replication origins. We present complementary deep sequencing approaches to measure origin location and activity in Saccharomyces cerevisiae. Measuring the increase in DNA copy number during a synchronous S-phase allowed the precise determination of genome replication. To map origin locations, replication forks were stalled close to their initiation sites; therefore, copy number enrichment was limited to origins. Replication timing profiles were generated from asynchronous cultures using fluorescence-activated cell sorting. Applying this technique we show that the replication profiles of haploid and diploid cells are indistinguishable, indicating that both cell types use the same cohort of origins with the same activities. Finally, increasing sequencing depth allowed the direct measure of replication dynamics from an exponentially growing culture. This is the first time this approach, called marker frequency analysis, has been successfully applied to a eukaryote. These data provide a high-resolution resource and methodological framework for studying genome biology.