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Proc Natl Acad Sci U S A. 2006 Jun 6; 103(23): 8573–8574.
PMCID: PMC1482620

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Rapidly identifying network communities

Members of networks are joined to other members by connections of varying types and often cluster into modules, groups, or communities. Identifying and characterizing these communities is a fundamental problem in network analysis. One way to tackle this problem is to optimize the quality function known as “modularity” over possible divisions of a network. Mark Newman reports a mathematical technique for quickly identifying and analyzing communities that form in large networks, and the algorithm may be useful for studying communities in social, computer, metabolic, and regulatory networks. Newman showed that network modularity can be expressed in terms of a “modularity matrix,” which leads to new formulas that reveal the community structure. The author tested the matrix method on classic and new networks, including social networks, the metabolic network of the worm C. elegans, coauthorships between condensed matter physicists, and networks representing the political leanings of blogs and books. The method was found to deliver more rapid, higher-quality results compared with three other published algorithm methods; in the case of the largest network, a collaboration network of 27,000 physicists, Newman's algorithm required only 20 min on a modern desktop computer to find community linkages. — P.D.

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Network of political books.

“Modularity and community structure in networks” by M. E. J. Newman (see pages 8577–8582)


Computing water's quantum properties

Despite water's centrality to life, its quantum properties are still not fully understood. Alexander Donchev et al. have developed a mathematical model that accurately describes water's properties from first principles. The model may be useful for understanding water's properties in a pure state and in biomolecular systems. Donchev et al. applied a quantum mechanical polarizable force-field model, which they had developed for studying organic systems, to the simulation of pure water. The model accurately calculated water's thermodynamic and structural properties, including its unique density changes near 0°C. The model, which is based on quantum mechanical calculations for systems of small molecules and their dimers, showed as good or better agreement with the measured properties of liquid water than previous computer models devised strictly to fit those properties. The authors say that further refinement of the equations and parameters in their model should bring it even closer to reproducing water's properties. The model may also be useful for understanding the interactions between water and organic molecules such as proteins. — P.D.

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Temperature–density relationship of water.

“Water properties from first principles: Simulations by a general-purpose quantum mechanical polarizable force field” by A. G. Donchev, N. G. Galkin, A. A. Illarionov, O. V. Khoruzhii, M. A. Olevanov, V. D. Ozrin, M. V. Subbotin, and V. I. Tarasov (see pages 8613–8617)


Neuropeptides suppress insect molting

In insects, the molting cycle is regulated by hormones such as ecdysone, which is produced by the prothoracic gland. Recent research has shown that a family of diverse neuropeptides called FMRFamides, which mediate insect muscle development, may also influence the prothoracic gland and regulate molting. Naoki Yamanaka et al. demonstrate that four FMRFamides found in the Bombyx silkworm can suppress molting by directly inhibiting the prothoracic gland. The researchers identified a 177-residue propeptide, Bommo-FMRFamide (BRFa), which matured into the four neuropeptides. These peptides inhibited both ecdysone and cAMP accumulation in the prothoracic gland of fifth-instar larvae. BRFa localized to the silkworm CNS and to two pairs of neurons in the insect's thoracic ganglia, which innervate the prothoracic gland. The neurons fired signals only when ecdysteroid titers were low, and mass spectroscopy analysis confirmed that four BRFa-like peptides were delivered to the gland's surface. These results shed light on the mechanisms controlling insect development and identify a potentially novel regulator of molting. — F.A.

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Axons innervating silkworm prothoracic gland.

“Regulation of insect steroid hormone biosynthesis by innervating peptidergic neurons” by Naoki Yamanaka, Dušan Žitňan, Young-Joon Kim, Michael E. Adams, Yue-Jin Hua, Yusuke Suzuki, Minoru Suzuki, Akemi Suzuki, Honoo Satake, Akira Mizoguchi, Kiyoshi Asaoka, Yoshiaki Tanaka, and Hiroshi Kataoka (see pages 8622–8627)


Gaseous nitrogen leaks from tropical forests

Nitrogen is a critical nutrient for determining plant growth within Earth's climate system. However, when it comes to the vast areas of tropical forests in the world, little is known about the nitrogen cycle. Benjamin Houlton et al. used isotope ratios of 15N to 14N to reveal how the tropical nitrogen cycle responds to variations in climate. Using a series of forests on the Hawaiian island of Maui, the authors investigated why the 15N-to-14N ratio curiously drops as rainfall increases. The authors debunked the most common explanations, changes in atmospheric inputs and leaching of nitrogen to stream waters, and instead showed that the nitrogen pattern originates from a group of bacteria that occur naturally in the soil. These “denitrifiers,” which obtain their energy by converting nitrate to gaseous nitrogen, caused both the high 15N-to-14N ratio of the forests and its decrease with rainfall. Houlton et al. provided evidence that bacterial denitrification expels up to half of all nitrogen that enters the ecosystems back to the atmosphere, dramatically impacting the tropical nitrogen cycle. — B.T.

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Maui tropical forest.

“Isotopic evidence for large gaseous nitrogen losses from tropical rainforests” by Benjamin Z. Houlton, Daniel M. Sigman, and Lars O. Hedin (see pages 8745–8750)


Coevolution of flax resistance and rust genes

Plants can stave off infection by expressing resistance proteins that recognize corresponding avirulence factors produced by invading pathogens. This recognition triggers defense mechanisms that limit the extent of infection, often by inducing localized necrosis. By studying the direct interaction of plant and pathogen proteins in the flax plant, Peter Dodds et al. have determined that flax resistance genes and flax rust fungus avirulence genes coevolved. From six rust strains, the researchers identified 12 avirulence variants that displayed up to a 20% difference in amino acid composition. Diversifying selection, driven by the pressure of host resistance, produced the differences in the avirulence factors. Despite the sequence differences, these factors retained their overall three-dimensional structures and bound directly to corresponding flax resistance proteins. A similar evolutionary pressure generated polymorphisms in the flax resistance genes. Dodds et al. say that this evolutionary “arms race” between plant and pathogen has created extensive diversification in both resistance and avirulence genes. — F.A.

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Flax line leaves.

“Direct protein interaction underlies gene-for-gene specificity and coevolution of the flax resistance genes and flax rust avirulence genes” by Peter N. Dodds, Gregory J. Lawrence, Ann-Maree Catanzariti, Trazel Teh, Ching-I. A. Wang, Michael A. Ayliffe, Bostjan Kobe, and Jeffrey G. Ellis (see pages 8888–8893)

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