Baselines are often chosen by visual inspection of their effect on selected spectra. A more objective procedure for choosing baseline correction algorithms and their parameter values for use in statistical analysis is presented. When the goal of the baseline correction is spectra with a pleasing appearance, visual inspection can be a satisfactory approach. If the spectra are to be used in a statistical analysis, objectivity and reproducibility are essential for good prediction. Variations in baselines from dataset to dataset means we have no guarantee that the best-performing algorithm from one analysis will be the best when applied to a new dataset. This paper focuses on choosing baseline correction algorithms and optimizing their parameter values based on the performance of the quality measure from the given analysis. Results presented in this paper illustrate the potential benefits of the optimization and points out some of the possible pitfalls of baseline correction.
BackgroundThe size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology.ResultsWe estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection probabilities. Estimated pan-genome sizes range from small (around 2600 gene families) in Buchnera aphidicola to large (around 43000 gene families) in Escherichia coli. Results for Echerichia coli show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely occurring genes in the population.ConclusionAnalyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present. A bottleneck in the estimation procedure is the annotation of rarely occurring genes.
China is the world's largest producer and consumer of fertilizer N, and decades of overuse has caused nitrate leaching and possibly soil acidification. We hypothesized that this would enhance the soils' propensity to emit N2O from denitrification by reducing the expression of the enzyme N2O reductase. We investigated this by standardized oxic/anoxic incubations of soils from five long-term fertilization experiments in different regions of China. After adjusting the nitrate concentration to 2 mM, we measured oxic respiration (R), potential denitrification (D), substrate-induced denitrification, and the denitrification product stoichiometry (NO, N2O, N2). Soils with a history of high fertilizer N levels had high N2O/(N2O+N2) ratios, but only in those field experiments where soil pH had been lowered by N fertilization. By comparing all soils, we found a strong negative correlation between pH and the N2O/(N2O+N2) product ratio (r2 = 0.759, P < 0.001). In contrast, the potential denitrification (D) was found to be a linear function of oxic respiration (R), and the ratio D/R was largely unaffected by soil pH. The immediate effect of liming acidified soils was lowered N2O/(N2O+N2) ratios. The results provide evidence that soil pH has a marginal direct effect on potential denitrification, but that it is the master variable controlling the percentage of denitrified N emitted as N2O. It has been known for long that low pH may result in high N2O/(N2O+N2) product ratios of denitrification, but our documentation of a pervasive pH-control of this ratio across soil types and management practices is new. The results are in good agreement with new understanding of how pH may interfere with the expression of N2O reductase. We argue that the management of soil pH should be high on the agenda for mitigating N2O emissions in the future, particularly for countries where ongoing intensification of plant production is likely to acidify the soils.
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