11,601 research outputs found

    A study of physico-chemical interactions between Haemophilus influenzae type b and meningococcus group C conjugate vaccines.

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    Background: Haemophilus influenzae type b (Hib) and Meningococcal group C (MenC) conjugate vaccines, which protect against otitis media, bacteremia and invasive diseases, including pneumonia and meningitis, are attractive candidates for combination, since they are both administered to infants and children. A Hib-MenC combination booster at 12 mo has recently been introduced in the U.K. Objectives: To rule out the possibility for the individual vaccine components in a Hib-MenC combination to interact, rendering one or both of them less effective, this work assessed whether these two saccharide-protein conjugates, namely, Hib oligosaccharide-CRM197 (Cross-Reacting Material 197) and MenC-CRM197, interact in solution. Furthermore an evaluation of the size and integrity of the vaccines was also performed. Methods: HPLC Size-exclusion chromatography (SEC) with UV-adsorption and refractive index detection was performed with a phosphate and non-phosphate saline buffer to characterize the size of Hib and MenC conjugates as individual components or when combined. Results: Hib-CRM197 eluted significantly earlier than MenC-CRM197 in both phosphate-saline and MOPS-saline buffers on a TSK5000 PWXL column. When combined, there was no significant change in their elution. Refractive index monitoring showed no evidence of significant free saccharide or free protein. Conclusions: By size-exclusion chromatography and refractive index detection methods, there was no indication of degradation, and no evidence of significant associative interactions between Hib-CRM197 and MenC-CRM197 in saline-based buffers, pH 7.2. African Health Sciences Vol. 7 (4) 2007: pp.190-19

    Recombination dramatically speeds up evolution of finite populations

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    We study the role of recombination, as practiced by genetically-competent bacteria, in speeding up Darwinian evolution. This is done by adding a new process to a previously-studied Markov model of evolution on a smooth fitness landscape; this new process allows alleles to be exchanged with those in the surrounding medium. Our results, both numerical and analytic, indicate that for a wide range of intermediate population sizes, recombination dramatically speeds up the evolutionary advance

    Evolution of virulence: triggering host inflammation allows invading pathogens to exclude competitors.

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    Virulence is generally considered to benefit parasites by enhancing resource-transfer from host to pathogen. Here, we offer an alternative framework where virulent immune-provoking behaviours and enhanced immune resistance are joint tactics of invading pathogens to eliminate resident competitors (transferring resources from resident to invading pathogen). The pathogen wins by creating a novel immunological challenge to which it is already adapted. We analyse a general ecological model of 'proactive invasion' where invaders not adapted to a local environment can succeed by changing it to one where they are better adapted than residents. However, the two-trait nature of the 'proactive' strategy (provocation of, and adaptation to environmental change) presents an evolutionary conundrum, as neither trait alone is favoured in a homogenous host population. We show that this conundrum can be resolved by allowing for host heterogeneity. We relate our model to emerging empirical findings on immunological mediation of parasite competition

    A variable time step self-consistent mean field DSMC model for three-dimensional environments

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    A self-consistent mean field direct simulation Monte Carlo (SCMFD) algorithm was recently proposed for simulating collision environments for a range of one-dimensional model systems. This work extends the one-dimensional SCMFD approach to three dimensions and introduces a variable time step (3D-vt-SCMFD), enabling the modeling of a considerably wider range of different collision environments. We demonstrate the performance of the augmented method by modeling a varied set of test systems: ideal gas mixtures, Poiseuille flow of argon, and expansion of gas into high vacuum. For the gas mixtures, the 3D-vt-SCMFD method reproduces the properties (mean free path, mean free time, collision frequency, and temperature) in excellent agreement with theoretical predictions. From the Poiseuille flow simulations, we extract flow profiles that agree with the solution to the Navier–Stokes equations in the high-density limit and resemble free molecular flow at low densities, as expected. The measured viscosity from 3D-vt-SCMF is ∼15% lower than the theoretical prediction from Chapman–Enskog theory. The expansion of gas into vacuum is examined in the effusive regime and at the hydrodynamic limit. In both cases, 3D-vt-SCMDF simulations produce gas beam density, velocity, and temperature profiles in excellent agreement with analytical models. In summary, our tests show that 3D-vt-SCMFD is robust and computationally efficient, while also illustrating the diversity of systems the SCMFD model can be successfully applied to

    Topological interactions in systems of mutually interlinked polymer rings

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    The topological interaction arising in interlinked polymeric rings such as DNA catenanes is considered. More specifically, the free energy for a pair of linked random walk rings is derived where the distance RR between two segments each of which is part of a different ring is kept constant. The topology conservation is imposed by the Gauss invariant. A previous approach (M.Otto, T.A. Vilgis, Phys.Rev.Lett. {\bf 80}, 881 (1998)) to the problem is refined in several ways. It is confirmed, that asymptotically, i.e. for large RRGR\gg R_G where RGR_G is average size of single random walk ring, the effective topological interaction (free energy) scales R4\propto R^4.Comment: 16 pages, 3 figur

    Grouping based feature attribution in metacontrast masking

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    The visibility of a target can be strongly suppressed by metacontrast masking. Still, some features of the target can be perceived within the mask. Usually, these rare cases of feature mis-localizations are assumed to reflect errors of the visual system. To the contrary, I will show that feature "mis-localizations" in metacontrast masking follow rules of motion grouping and, hence, should be viewed as part of a systematic feature attribution process

    AnEnPi: Identification and annotation of analogous enzymes

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    Enzymes are responsible for the catalysis of the biochemical reactions in metabolic pathways. Analogous enzymes are able to catalyze the same reactions, but they present no significant sequence similarity at the primary level, and possibly different tertiary structures as well. They are thought to have arisen as the result of independent evolutionary events. A detailed study of analogous enzymes may reveal new catalytic mechanisms, add information about the origin and evolution of biochemical pathways and disclose potential targets for drug development. Results: In this work, we have constructed and implemented a new approach, AnEnPi (the Analogous Enzyme Pipeline), using a combination of bioinformatics tools like BLAST, HMMer, and in-house scripts, to assist in the identification, annotation, comparison and study of analogous and homologous enzymes. The algorithm for the detection of analogy is based i) on the construction of groups of homologous enzymes and ii) on the identification of cases where a given enzymatic activity is performed by two or more proteins without significant similarity between their primary structures. We applied this approach to a dataset obtained from KEGG Comprising all annotated enzymes, which resulted in the identification of 986 EC classes where putative analogy was detected (40.5% of all EC classes). AnEnPi is of considerable value in the construction of initial datasets that can be further curated, particularly in gene and genome annotation, in studies involving molecular evolution and metabolism and in the identification of new potential drug targets. Conclusion: AnEnPi is an efficient tool for detection and annotation of analogous enzymes and other enzymes in whole genomes
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