French Africa

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Nested sampling for Bayesian model comparison in the context of Salmonella disease dynamics.

Understanding the mechanisms underlying the observed dynamics of complex biological systems requires the statistical assessment and comparison of multiple alternative models. Although this has traditionally been done using maximum likelihood-based methods such as Akaike's Information Criterion (AIC), Bayesian methods have gained in popularity because they provide more informative output in the form of posterior probability distributions. However, comparison between multiple models in a Bayesian framework is made difficult by the computational cost of numerical integration over large parameter spaces. A new, efficient method for the computation of posterior probabilities has recently been proposed and applied to complex problems from the physical sciences. Here we demonstrate how nested sampling can be used for inference and model comparison in biological sciences. We present a reanalysis of data from experimental infection of mice with Salmonella enterica showing the distribution of bacteria in liver cells. In addition to confirming the main finding of the original analysis, which relied on AIC, our approach provides: (a) integration across the parameter space, (b) estimation of the posterior parameter distributions (with visualisations of parameter correlations), and (c) estimation of the posterior predictive distributions for goodness-of-fit assessments of the models. The goodness-of-fit results suggest that alternative mechanistic models and a relaxation of the quasi-stationary assumption should be considered.RD was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (grant number BB/I002189/1). TJM was funded by the Biotechnology and Biological Sciences Research Council (BBSRC) (grant number BB/I012192/1). OR was funded by the Royal Society. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

I-127 and Pb-207 Solid-state NMR spectroscopy and nuclear spin relaxation in lead iodide: A preliminary study

Lead iodide is a layered structure that experiences polytypism. The 2H polytype contains high rates of iodine vacancies but retains its stoichiometry. This characteristic feature makes it potentially useful for many practical applications. The hopping of iodine ions among vacancies is the dominant motion in the 2H polytype. We present a preliminary 127I and 207Pb solid-state nuclear magnetic resonance (NMR) spectroscopy and spin–lattice relaxation study of the 2H polytype below 400 K. We present reasonable models for the solid-state NMR results in terms of the effects of iodine hopping and lattice vibrations

Within-host spatiotemporal dynamics of systemic Salmonella infection during and after antimicrobial treatment

$\textbf{Objectives:}$ We determined the interactions between efficacy of antibiotic treatment, pathogen growth rates and between-organ spread during systemic $\textit{Salmonella}$ infections. $\textbf{Methods:}$ We infected mice with isogenic molecularly tagged subpopulations of either a fast-growing WT or a slow-growing $\Delta$$\textit{aroC Salmonella}$ strain. We monitored viable bacterial numbers and fluctuations in the proportions of each bacterial subpopulation in spleen, liver, blood and mesenteric lymph nodes (MLNs) before, during and after the cessation of treatment with ampicillin and ciprofloxacin. $\textbf{Results:}$ Both antimicrobials induced a reduction in viable bacterial numbers in the spleen, liver and blood. This reduction was biphasic in infections with fast-growing bacteria, with a rapid initial reduction followed by a phase of lower effect. Conversely, a slow and gradual reduction of the bacterial load was seen in infections with the slow-growing strain, indicating a positive correlation between bacterial net growth rates and the efficacy of ampicillin and ciprofloxacin. The viable numbers of either bacterial strain remained constant in MLNs throughout the treatment with a relapse of the infection with WT bacteria occurring after cessation of the treatment. The frequency of each tagged bacterial subpopulation was similar in the spleen and liver, but different from that of the MLNs before, during and after treatment. $\textbf{Conclusions:}$ In $\textit{Salmonella}$ infections, bacterial growth rates correlate with treatment efficacy. MLNs are a site with a bacterial population structure different to those of the spleen and liver and where the total viable bacterial load remains largely unaffected by antimicrobials, but can resume growth after cessation of treatment.This work was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) grant number BB/M000982/1 (http://www.bbsrc.ac.uk/research/grants/grants/AwardDetails.aspx?FundingReference=BB/M000982/1). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Inferring within-host bottleneck size: A Bayesian approach.

Recent technical developments in microbiology have led to new discoveries on the within-host dynamics of bacterial infections in laboratory animals. In particular, they have highlighted the importance of stochastic bottlenecks at the onset of invasive disease. A number of approaches exist for bottleneck-size estimation with respect to within-host bacterial infections; however, some are more appropriate than others under certain circumstances. A Bayesian comparison of several approaches is made in terms of the availability of isogenic multitype bacteria (e.g., WITS), knowledge of post-bottleneck dynamics, and the suitability of dilution with monotype bacteria. A sampling approach to bottleneck-size estimation is also introduced. The results are summarised by a guiding flowchart, which we hope will promote the use of quantitative models in microbiology to refine the analysis of animal experiment data

Framework Mobility in the Metal−Organic Framework Crystal IRMOF-3: Evidence for Aromatic Ring and Amine

a b s t r a c t The framework motions in IRMOF-3 (Zn 4 O(BDC-NH 2 ) 3 ), where BDC-NH 2 represents 2-amino-1,4-ben zenedicarboxylate, have been investigated with 1 H NMR relaxation measurements. Isotopic enrichment of the 2-amino group with 15 N was critical in elucidating the lattice dynamics and enhancing spectral resolution. These results indicate a low energy process associated with rotation of the amino group, with an activation energy of 1.8 ± 0.6 kcal/mol, and full 180°rotation of the phenylene group in the BDC-NH 2 moiety with an activation energy of 5.0 ± 0.2 kcal/mol. A relatively low pre-exponential factor for amine rotation (1.3 Â 10 7 s À1 ) is tentatively associated with the need to break a hydrogen bond as the rate-limiting step. Both amine rotation and the aromatic ring flip occur at frequencies that provide an effective relaxation mechanism for the 99.6% natural abundance quadrupola

Incorporating prior knowledge improves detection of differences in bacterial growth rate

BACKGROUND: Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. RESULTS: We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. CONCLUSIONS: We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate

Interpol: An R package for preprocessing of protein sequences

<p>Abstract</p> <p>Background</p> <p>Most machine learning techniques currently applied in the literature need a fixed dimensionality of input data. However, this requirement is frequently violated by real input data, such as DNA and protein sequences, that often differ in length due to insertions and deletions. It is also notable that performance in classification and regression is often improved by numerical encoding of amino acids, compared to the commonly used sparse encoding.</p> <p>Results</p> <p>The software "Interpol" encodes amino acid sequences as numerical descriptor vectors using a database of currently 532 descriptors (mainly from AAindex), and normalizes sequences to uniform length with one of five linear or non-linear interpolation algorithms. Interpol is distributed with open source as platform independent R-package. It is typically used for preprocessing of amino acid sequences for classification or regression.</p> <p>Conclusions</p> <p>The functionality of Interpol widens the spectrum of machine learning methods that can be applied to biological sequences, and it will in many cases improve their performance in classification and regression.</p

Improved Bevirimat resistance prediction by combination of structural and sequence-based classifiers

<p>Abstract</p> <p>Background</p> <p>Maturation inhibitors such as Bevirimat are a new class of antiretroviral drugs that hamper the cleavage of HIV-1 proteins into their functional active forms. They bind to these preproteins and inhibit their cleavage by the HIV-1 protease, resulting in non-functional virus particles. Nevertheless, there exist mutations in this region leading to resistance against Bevirimat. Highly specific and accurate tools to predict resistance to maturation inhibitors can help to identify patients, who might benefit from the usage of these new drugs.</p> <p>Results</p> <p>We tested several methods to improve Bevirimat resistance prediction in HIV-1. It turned out that combining structural and sequence-based information in classifier ensembles led to accurate and reliable predictions. Moreover, we were able to identify the most crucial regions for Bevirimat resistance computationally, which are in line with experimental results from other studies.</p> <p>Conclusions</p> <p>Our analysis demonstrated the use of machine learning techniques to predict HIV-1 resistance against maturation inhibitors such as Bevirimat. New maturation inhibitors are already under development and might enlarge the arsenal of antiretroviral drugs in the future. Thus, accurate prediction tools are very useful to enable a personalized therapy.</p