The stellar and sub-stellar IMF of simple and composite populations

The current knowledge on the stellar IMF is documented. It appears to become top-heavy when the star-formation rate density surpasses about 0.1Msun/(yr pc^3) on a pc scale and it may become increasingly bottom-heavy with increasing metallicity and in increasingly massive early-type galaxies. It declines quite steeply below about 0.07Msun with brown dwarfs (BDs) and very low mass stars having their own IMF. The most massive star of mass mmax formed in an embedded cluster with stellar mass Mecl correlates strongly with Mecl being a result of gravitation-driven but resource-limited growth and fragmentation induced starvation. There is no convincing evidence whatsoever that massive stars do form in isolation. Various methods of discretising a stellar population are introduced: optimal sampling leads to a mass distribution that perfectly represents the exact form of the desired IMF and the mmax-to-Mecl relation, while random sampling results in statistical variations of the shape of the IMF. The observed mmax-to-Mecl correlation and the small spread of IMF power-law indices together suggest that optimally sampling the IMF may be the more realistic description of star formation than random sampling from a universal IMF with a constant upper mass limit. Composite populations on galaxy scales, which are formed from many pc scale star formation events, need to be described by the integrated galactic IMF. This IGIMF varies systematically from top-light to top-heavy in dependence of galaxy type and star formation rate, with dramatic implications for theories of galaxy formation and evolution.Comment: 167 pages, 37 figures, 3 tables, published in Stellar Systems and Galactic Structure, Vol.5, Springer. This revised version is consistent with the published version and includes additional references and minor additions to the text as well as a recomputed Table 1. ISBN 978-90-481-8817-

Tuning the electrical conductance of metalloporphyrin supramolecular wires

In contrast with conventional single-molecule junctions, in which the current flows parallel to the long axis or plane of a molecule, we investigate the transport properties of M(II)-5,15-diphenylporphyrin (M-DPP) single-molecule junctions (M=Co, Ni, Cu, or Zn divalent metal ions), in which the current flows perpendicular to the plane of the porphyrin. Novel STM-based conductance measurements combined with quantum transport calculations demonstrate that current-perpendicular-to-the-plane (CPP) junctions have three-orders-of-magnitude higher electrical conductanc than their current in-plane (CIP) counterparts, ranging from 2.10−2 G0 for Ni-DPP up to 8.10−2 G0 for Zn-DPP. The metal ion in the center of the DPP skeletons is strongly coordinated with the nitrogens of the pyridyl coated electrodes, with a binding energy that is sensitive to the choice of metal ion. We find that the binding energies of Zn-DPP and Co-DPP are significantly higher than those of Ni-DPP and Cu-DPP. Therefore when combined with its higher conductance, we identify Zn-DPP as the favoured candidate for high conductance CPP single-molecule devices

Analysis and prediction of cancerlectins using evolutionary and domain information

<p>Abstract</p> <p>Background</p> <p>Predicting the function of a protein is one of the major challenges in the post-genomic era where a large number of protein sequences of unknown function are accumulating rapidly. Lectins are the proteins that specifically recognize and bind to carbohydrate moieties present on either proteins or lipids. Cancerlectins are those lectins that play various important roles in tumor cell differentiation and metastasis. Although the two types of proteins are linked, still there is no computational method available that can distinguish cancerlectins from the large pool of non-cancerlectins. Hence, it is imperative to develop a method that can distinguish between cancer and non-cancerlectins.</p> <p>Results</p> <p>All the models developed in this study are based on a non-redundant dataset containing 178 cancerlectins and 226 non-cancerlectins in which no two sequences have more than 50% sequence similarity. We have applied the similarity search based technique, i.e. BLAST, and achieved a maximum accuracy of 43.25%. The amino acids compositional analysis have shown that certain residues (e.g. Leucine, Proline) were preferred in cancerlectins whereas some other (e.g. Asparatic acid, Asparagine) were preferred in non-cancerlectins. It has been found that the PROSITE domain "Crystalline beta gamma" was abundant in cancerlectins whereas domains like "SUEL-type lectin domain" were found mainly in non-cancerlectins. An SVM-based model has been developed to differentiate between the cancer and non-cancerlectins which achieved a maximum Matthew's correlation coefficient (MCC) value of 0.32 with an accuracy of 64.84%, using amino acid compositions. We have developed a model based on dipeptide compositions which achieved an MCC value of 0.30 with an accuracy of 64.84%. Thereafter, we have developed models based on split compositions (2 and 4 parts) and achieved an MCC value of 0.31, 0.32 with accuracies of 65.10% and 66.09%, respectively. An SVM model based on Position Specific Scoring Matrix (PSSM), generated by PSI-BLAST, was developed and achieved an MCC value of 0.36 with an accuracy of 68.34%. Finally, we have integrated the PROSITE domain information with PSSM and developed an SVM model that has achieved an MCC value of 0.38 with 69.09% accuracy.</p> <p>Conclusion</p> <p>BLAST has been found inefficient to distinguish between cancer and non-cancerlectins. We analyzed the protein sequences of cancer and non-cancerlectins and identified interesting patterns. We have been able to identify PROSITE domains that are preferred in cancer and non-cancerlectins and thus provided interesting insights into the two types of proteins. The method developed in this study will be useful for researchers studying cancerlectins, lectins and cancer biology. The web-server based on the above study, is available at <url>http://www.imtech.res.in/raghava/cancer_pred/</url></p

Metagenomic discovery of novel enzymes and biosurfactants in a slaughterhouse biofilm microbial community.

DNA derived from environmental samples is a rich source of novel bioactive molecules. The choice of the habitat to be sampled predefines the properties of the biomolecules to be discovered due to the physiological adaptation of the microbial community to the prevailing environmental conditions. We have constructed a metagenomic library in Escherichia coli DH10b with environmental DNA (eDNA) isolated from the microbial community of a slaughterhouse drain biofilm consisting mainly of species from the family Flavobacteriaceae. By functional screening of this library we have identified several lipases, proteases and two clones (SA343 and SA354) with biosurfactant and hemolytic activities. Sequence analysis of the respective eDNA fragments and subsequent structure homology modelling identified genes encoding putative N-acyl amino acid synthases with a unique two-domain organisation. The produced biosurfactants were identified by NMR spectroscopy as N-acyltyrosines with N-myristoyltyrosine as the predominant species. Critical micelle concentration and reduction of surface tension were similar to those of chemically synthesised N-myristoyltyrosine. Furthermore, we showed that the newly isolated N-acyltyrosines exhibit antibiotic activity against various bacteria. This is the first report describing the successful application of functional high-throughput screening assays for the identification of biosurfactant producing clones within a metagenomic library.peerReviewe