We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.

We extend the standard scale-free network model to include a "triad formation step". We analyze the geometric properties of networks generated by this algorithm both analytically and by numerical calculations, and find that our model possesses the same characteristics as the standard scale-free networks like the power-law degree distribution and the small average geodesic length, but with the high-clustering at the same time. In our model, the clustering coefficient is also shown to be tunable simply by changing a control parameter-the average number of triad formation trials per time step.PACS numbers: 89.75.Fb, 89.75.Hc, A great number of systems in many branches of science can be modeled as large sparse graphs, sharing many geometrical properties [1]. For example: social networks, computer networks, and metabolic networks of certain organisms all have a logarithmically growing average geodesic (shortest path) length ℓ and an approximately algebraically decaying distribution of vertex degree. In addition to this, social networks typically show a high clustering, or local transitivity: If person A knows B and C, then B and C are likely to know each other.Works on the geometry of social networks, which is the main focus of the present paper, have originated from Rapoport's studies of disease spreading [2], and have been further developed in Refs. [3,4]. General mathematical models for random graphs with a structural bias are called the Markov graphs and were studied in Ref. [5]. In the physics literature, networks with high clustering are commonly modeled by the small-world network model of Watts and Strogatz (WS) [6], while networks with the power-law degree distribution by the scale-free network model of Barabási and Albert (BA) [7]. Although both models have a logarithmically increasing ℓ with the network size, each model lacks the property of the other model: the WS model shows a high clustering but without the power-law degree distribution, while the BA model with the scale-free nature does not possess the high clustering. In this work, we propose a network model which has both the perfect power-law degree distribution and the high clustering. Furthermore, in our model, the degree of the clustering, measured by the clustering coefficient (see below), is shown to be tunable and thus controllable by adjusting a parameter of the model.We start from the definition of a network as a graph G = (V, E), where V is the set of vertices and E is the set of edges [8]. An edge connects pairs of vertices in V and not more than one edge may connect a specific pair of vertices. To quantify the clustering, Watts and Strogatz introduced the clustering coefficient γ ≡ γ v with the average · · · for all vertices in V. The local clus- * Electronic address: holme@tp.umu.se † Electronic address: kim@tp.umu.se

MALAT1 has previously been described as a metastasis-promoting long non-coding RNA (lncRNA). Unexpectedly, we found that targeted inactivation of the Malat1 gene without altering the expression of its adjacent genes in a transgenic mouse model of breast cancer promoted lung metastasis, and importantly, this phenotype was reversed by genetic add-back of Malat1 . Similarly, knockout of MALAT1 in human breast cancer cells induced their metastatic ability, which was reversed by Malat1 re-expression. Conversely, overexpression of Malat1 suppressed breast cancer metastasis in transgenic, xenograft, and syngeneic models. Mechanistically, MALAT1 binds and inactivates the pro-metastatic transcription factor TEAD, blocking TEAD from associating with its co-activator YAP and target gene promoters. Moreover, MALAT1 levels inversely correlate with breast cancer progression and metastatic ability. These findings demonstrate that MALAT1 is a metastasis-suppressing lncRNA rather than a metastasis promoter in breast cancer, calling for rectification of the model for a highly abundant and conserved lncRNA.

The authors examined cultural preferences for formal versus intuitive reasoning among East Asian (Chinese and Korean), Asian American, and European American university students. We investigated categorization (Studies 1 and 2), conceptual structure (Study 3), and deductive reasoning (Studies 3 and 4). In each study a cognitive conflict was activated between formal and intuitive strategies of reasoning. European Americans, more than Chinese and Koreans, set aside intuition in favor of formal reasoning. Conversely, Chinese and Koreans relied on intuitive strategies more than European Americans. Asian Americans' reasoning was either identical to that of European Americans, or intermediate. Differences emerged against a background of similar reasoning tendencies across cultures in the absence of conflict between formal and intuitive strategies.