Interpreting the flock algorithm: a reply to Anderson & Barry (2015)

Anderson & Barry (Molecular Ecology Resources, 2015, 10, 1020–1030) compared a reprogrammed version of flock (Duchesne & Turgeon , Molecular Ecology Resources, 2009, 9, 1333–1344), flockture , to a particular model of structure (Pritchard , Genetics, 2000, 155, 945–959) that they propose is equivalent to flock , a non‐MCMC, non‐Bayesian algorithm. They conclude that structure performs better than flockture at clustering individuals from simulated populations with very low level of differentiation (F_ST c. 0.008) based on 15 microsatellites or 96 SNPs. We rather consider that both algorithms failed, with proportions of correct allocations lower than 50%. The authors also noted the slightly better performance of flockture with SNPs at intermediate F_ST values (c. 0.02–0.04) but did not comment. Finally, we disagree with the way the processing time of each program was compared. When compared on the basis of a run leading to a clustering solution, the main output of any clustering algorithm, flock , is, as users can readily experience, much faster. In all, we feel that flock performs at least as well as structure as a clustering algorithm. Moreover, flock has two major assets: high speed and clear, well validated, rules to estimate K, the number of populations. It thus provides a valuable addition to the set of tools at the disposal of the many researchers dealing with real empirical data sets.     

Effect of halide ions on the fluorescence properties of 3-aminoquinoline in aqueous medium

Fluorescence (FL) quenching of 3-aminoquinoline (3AQ) by halide ions $\left({\mathrm{Cl}}^{-},{\mathrm{Br}}^{-}\text{and}{\mathrm{I}}^{-}\right)$ has been explored in an aqueous acidic medium using the steady-state and time-domain FL measurement techniques. The halide ions showed no significant change in the absorption spectra of 3AQ in an aqueous acidic medium. The FL intensity was strongly quenched by ${\mathrm{I}}^{-}$ ions and the order of FL quenching by halide ions was ${\mathrm{I}}^{-}>{\mathrm{Br}}^{-}>{\mathrm{Cl}}^{-}$. The decrease in FL lifetime along with the reduction in FL intensity of 3AQ suggested the dynamic nature of quenching. The obtained ${\mathrm{K}}_{\mathrm{SV}}$ values were 328 ${\mathrm{M}}^{-1}$ for ${\mathrm{I}}^{-}$ ions and 119 ${\mathrm{M}}^{-1}$ for ${\mathrm{Br}}^{-}$ ions and the ${\mathrm{k}}_{\mathrm{q}}$ values were ~ $1.66\times {10}^{10}{\mathrm{M}}^{-1}{\mathrm{s}}^{-1}$ and $6.02\times {10}^9{\mathrm{M}}^{-1}{\mathrm{s}}^{-1}$, respectively. The observations suggested that the likely governing mechanism for FL quenching may be an electron transfer process and the involvement of the heavy atom effects.     

speed‐ne: Software to simulate and estimate genetic effective population size (Ne) from linkage disequilibrium observed in single samples

The genetic effective population size, N_e, can be estimated from the average gametic disequilibrium () between pairs of loci, but such estimates require evaluation of assumptions and currently have few methods to estimate confidence intervals. speed‐ne is a suite of matlab computer code functions to estimate from with a graphical user interface and a rich set of outputs that aid in understanding data patterns and comparing multiple estimators. speed‐ne includes functions to either generate or input simulated genotype data to facilitate comparative studies of estimators under various population genetic scenarios. speed‐ne was validated with data simulated under both time‐forward and time‐backward coalescent models of genetic drift. Three classes of estimators were compared with simulated data to examine several general questions: what are the impacts of microsatellite null alleles on , how should missing data be treated, and does disequilibrium contributed by reduced recombination among some loci in a sample impact . Estimators differed greatly in precision in the scenarios examined, and a widely employed estimator exhibited the largest variances among replicate data sets. speed‐ne implements several jackknife approaches to estimate confidence intervals, and simulated data showed that jackknifing over loci and jackknifing over individuals provided ~95% confidence interval coverage for some estimators and should be useful for empirical studies. speed‐ne provides an open‐source extensible tool for estimation of from empirical genotype data and to conduct simulations of both microsatellite and single nucleotide polymorphism (SNP) data types to develop expectations and to compare estimators.     

Spatial-Temporal Variation and Primary Ecological Drivers of Anopheles sinensis Human Biting Rates in Malaria Epidemic-Prone Regions of China

Background

Robust malaria vector surveillance is essential for optimally selecting and targeting vector control measures. Sixty-two vector surveillance sites were established between 2005 and 2008 by the national malaria surveillance program in China to measure Anopheles sinensis human biting rates. Using these data to determine the primary ecological drivers of malaria vector human biting rates in malaria epidemic-prone regions of China will allow better targeting of vector control resources in space and time as the country aims to eliminate malaria.

Methods

We analyzed data from 62 malaria surveillance sentinel sites from 2005 to 2008. Linear mixed effects models were used to identify the primary ecological drivers for Anopheles sinensis human biting rates as well as to explore the spatial-temporal variation of relevant factors at surveillance sites throughout China.

Results

Minimum semimonthly temperature (β = 2.99; 95% confidence interval (CI) 2.07- 3.92), enhanced vegetation index (β =1.07; 95% CI 0.11–2.03), and paddy index (the percentage of rice paddy field in the total cultivated land area of each site) (β = 0.86; 95% CI 0.17–1.56) were associated with greater An. Sinensis human biting rates, while increasing distance to the nearest river was associated with lower An. Sinensis human biting rates (β = −1.47; 95% CI −2.88, −0.06). The temporal variation (σt02=1.35) in biting rates was much larger than the spatial variation (σs02=0.83), with 19.3% of temporal variation attributable to differences in minimum temperature and enhanced vegetation index and 16.9% of spatial variance due to distance to the nearest river and the paddy index.

Discussion

Substantial spatial-temporal variation in An. Sinensis human biting rates exists in malaria epidemic-prone regions of China, with minimum temperature and enhanced vegetation index accounting for the greatest proportion of temporal variation and distance to nearest river and paddy index accounting for the greatest proportion of spatial variation amongst observed ecological drivers.

Conclusions

Targeted vector control measures based on these findings can support the ongoing malaria elimination efforts in China more effectively.     

A robust removing unwanted variation–testing procedure via γ‐divergence

Identification of differentially expressed genes (DE genes) is commonly conducted in modern biomedical research. However, unwanted variation inevitably arises during the data collection process, which can make the detection results heavily biased. Various methods have been suggested for removing the unwanted variation while keeping the biological variation to ensure a reliable analysis result. Removing unwanted variation (RUV) has recently been proposed for this purpose, which works by virtue of negative control genes. On the other hand, outliers frequently appear in modern high‐throughput genetic data, which can heavily affect the performances of RUV and its downstream analysis. In this work, we propose a robust RUV‐testing procedure (a robust RUV procedure to remove unwanted variance, followed by a robust testing procedure to identify DE genes) via $\gamma$‐divergence. The advantages of our method are twofold: (a) it does not involve any modeling for the outlier distribution, which makes it applicable to various situations; (b) it is easy to implement in the sense that its robustness is controlled by a single tuning parameter $\gamma$ of $\gamma$‐divergence, and a data‐driven criterion is developed to select $\gamma$. When applied to real data sets, our method can successfully remove unwanted variation, and was able to identify more DE genes than conventional methods.     

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ($k_{\mathrm{cat}}$) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 $k_{\mathrm{cat}}$c), highly sensitive (1759 $k_{\mathrm{cat}}$), and nonsensitive (2502 $k_{\mathrm{cat}}$) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.