157 research outputs found

    Higher Dimensional Cylindrical or Kasner Type Electrovacuum Solutions

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    We consider a D dimensional Kasner type diagonal spacetime where metric functions depend only on a single coordinate and electromagnetic field shares the symmetries of spacetime. These solutions can describe static cylindrical or cosmological Einstein-Maxwell vacuum spacetimes. We mainly focus on electrovacuum solutions and four different types of solutions are obtained in which one of them has no four dimensional counterpart. We also consider the properties of the general solution corresponding to the exterior field of a charged line mass and discuss its several properties. Although it resembles the same form with four dimensional one, there is a difference on the range of the solutions for fixed signs of the parameters. General magnetic field vacuum solution are also briefly discussed, which reduces to Bonnor-Melvin magnetic universe for a special choice of the parameters. The Kasner forms of the general solution are also presented for the cylindrical or cosmological cases.Comment: 16 pages, Revtex. Text and references are extended, Published versio

    Elevated Incidence of Dental Caries in a Mouse Model of Cystic Fibrosis

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    Saliva bicarbonate constitutes the main buffering system which neutralizes the pH fall generated by the plaque bacteria during sugar metabolism. We found that the saliva pH is severely decreased in a mouse model of cystic fibrosis disease (CF). Given the close relationship between pH and caries development, we hypothesized that caries incidence might be elevated in the mouse CF model.). are enhanced at low pH values, we speculate that the decrease in the bicarbonate content and pH buffering of the saliva is at least partially responsible for the increased severity of lesions observed in the CF mouse

    High-resolution X-ray spectroscopy of rare events: a different look at local structure and chemistry

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    The combination of large-acceptance high-resolution X-ray optics with bright synchrotron sources permits quantitative analysis of rare events such as X-ray fluorescence from very dilute systems, weak fluorescence transitions or X-ray Raman scattering. Transition-metal Kβ fluorescence contains information about spin and oxidation state; examples of the characterization of the Mn oxidation states in the oxygen-evolving complex of photosystem II and Mn-consuming spores from the marine bacillus SG-1 are presented. Weaker features of the Kβ spectrum resulting from valence-level and 'interatomic' ligand to metal transitions contain detailed information on the ligand-atom type, distance and orientation. Applications of this spectral region to characterize the local structure of model compounds are presented. X-ray Raman scattering (XRS) is an extremely rare event, but also represents a unique technique to obtain bulk-sensitive low-energy

    Automatic structure classification of small proteins using random forest

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    <p>Abstract</p> <p><b>Background</b></p> <p>Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.</p> <p><b>Result</b>s</p> <p>Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP <it>Class, Fold, Super-family </it>or <it>Family </it>levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.</p> <p>Conclusions</p> <p>The utility of random forest in classifying domains from the place-holder classes of SCOP to the true <it>Class, Fold, Super-family </it>or <it>Family </it>levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.</p
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