Review of Modern Babylon? Prostituting Children in Thailand by Heather Montgomery

Johnson reviews Modern Babylon?: Prostituting Children in Thailand by Heather Montgomery.

Lattice damage produced in GaN by swift heavy ions

Wurtzite GaN epilayers bombarded at 300 K with 200 MeV Au-197(16+) ions are studied by a combination of transmission electron microscopy (TEM) and Rutherford backscattering/channeling spectrometry (RBS/C). Results reveal the formation of near-continuous tracks propagating throughout the entire similar to1.5-mum-thick GaN film. These tracks, similar to100 Angstrom in diameter, exhibit a large degree of structural disordering but do not appear to be amorphous. Throughout the bombarded epilayer, high-resolution TEM reveals planar defects which are parallel to the basal plane of the GaN film. The gross level of lattice disorder, as measured by RBS/C, gradually increases with increasing ion fluence up to similar to10(13) cm(-2). For larger fluences, delamination of the nitride film from the sapphire substrate occurs. Based on these results, physical mechanisms of the formation of lattice disorder in GaN in such a high electronic stopping power regime are discussed. (C) 2004 American Institute of Physics.

Localization and movement of mineral oil in plants by fluorescence and confocal microscopy

Fluorescence and confocal laser scanning microscopy were explored to investigate the movement and localization of mineral oils in citrus. In a laboratory experiment, fluorescence microscopy observation indicated that when a 'narrow' distillation fraction of an nC23 horticultural mineral oil was applied to adaxial and opposing abaxial leaf surfaces of potted orange [Citrus x aurantium L. (Sapindales: Rutaceae)] trees, oil penetrated steadily into treated leaves and, subsequently, moved to untreated petioles of the leaves and adjacent untreated stems. In another experiment, confocal laser scanning microscopy was used to visualize the penetration into, and the subsequent cellular distribution of, an nC24 agricultural mineral oil in C. trifoliata L. seedlings. Oil droplets penetrated or diffused into plants via both stomata and the cuticle of leaves and stems, and then moved within intercellular spaces and into various cells including phloem and xylem. Oil accumulated in droplets in intercellular spaces and within cells near the cell membrane. Oil entered cells without visibly damaging membranes or causing cell death. In a field experiment with mature orange trees, droplets of an nC23 horticultural mineral oil were observed, by fluorescence microscopy, in phloem sieve elements in spring flush growth produced 4-5 months and 16-17 months after the trees were sprayed with oil. These results suggest that movement of mineral oil in plants is both apoplastic via intercellular spaces and symplastic via plasmodesmata. The putative pattern of the translocation of mineral oil in plants and its relevance to oil-induced chronic phytotoxicity are discussed.

LPS regulates proinflammatory gene expression in macrophages by altering histone deacetylase expression

Bacterial LPS triggers dramatic changes in gene expression in macrophages. We show here that LPS regulated several members of the histone deacetylase (HDAC) family at the mRNA level in murine bone marrow-derived macrophages (BMM). LPS transiently repressed, then induced a number of HDACs (Hdac-4, 5, 7) in BMM, whereas Hdac-1 mRNA was induced more rapidly. Treatment of BMM with trichostatin A (TSA), an inhibitor of HDACs, enhanced LPS-induced expression of the Cox-2, Cxcl2, and Ifit2 genes. In the case of Cox-2, this effect was also apparent at the promoter level. Overexpression of Hdac-8 in RAW264 murine macrophages blocked the ability of LPS to induce Cox-2 mRNA. Another class of LPS-inducible genes, which included Ccl2, Ccl7, and Edn1, was suppressed by TSA, an effect most likely mediated by PU.1 degradation. Hence, HDACs act as potent and selective negative regulators of proinflammatory gene expression and act to prevent excessive inflammatory responses in macrophages.

Predicting residue-wise contact orders in proteins by support vector regression

Background: The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results: We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. Conclusion: The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.