Common Mechanistic Action of Bacteriocins from Lactic Acid Bacteria

The influence of four bacteriocins from lactic acid bacteria on the proton motive force (PMF) of sensitive organisms was investigated. Pediocin PA-1 (20 μg/ml) and leuconocin S (48.5 μg/ml) mediated total or major PMF dissipation of energized Listeria monocytogenes Scott A cells in a concentration-dependent manner, as has been shown for nisin. Lactacin F (13.5 μg/ml) caused 87% PMF depletion of energized Lactobacillus delbrueckii ATCC 4797 cells, also in a concentration-dependent fashion. The energy requirements for the activity of these four bacteriocins were determined by using the ionophores nigericin and valinomycin to carry out partial and specific deenergization of the target organisms. Pediocin PA-1, leuconocin S, and lactacin F acted in an energy-independent manner, whereas the activity of nisin was confirmed to be energy dependent. These results together with published reports on other bacteriocins suggest that the bacteriocins of lactic acid bacteria share a common mechanism, the depletion of PMF.     

There Is No Need To Be Normal: Generalized Linear Models of Natural Variation

Both ecological field studies and attempts to extrapolate from laboratory experiments to natural populations generally encounter the high degree of natural variability and chaotic behavior that typify natural ecosystems. Regardless of this variability and non-normal distribution, most statistical models of natural systems use normal error which assumes independence between the variance and mean. However, environmental data are often random or clustered and are better described by probability distributions which have more realistic variance to mean relationships. Until recently statistical software packages modeled only with normal error and researchers had to assume approximate normality on the original or transformed scale of measurement and had to live with the consequences of often incorrectly assuming independence between the variance and mean. Recent developments in statistical software allow researchers to use generalized linear models (GLMs) and analysis can now proceed with probability distributions from the exponential family which more realistically describe natural conditions: binomial (even distribution with variance less than mean), Poisson (random distribution with variance equal mean), negative binomial (clustered distribution with variance greater than mean). GLMs fit parameters on the original scale of measurement and eliminate the need for obfuscating transformations, reduce bias for proportions with unequal sample size, and provide realistic estimates of variance which can increase power of tests. Because GLMs permit modeling according to the non-normal behavior of natural systems and obviate the need for normality assumptions, they will likely become a widely used tool for analyzing toxicity data. To demonstrate the broad-scale utility of GLMs, we present several examples where the use of GLMs improved the statistical power of field and laboratory studies to document the rapid ecological recovery of Prince William Sound following the Exxon Valdez oil spill.     

Optimising and Communicating Options for the Control of Invasive Plant Disease When There Is Epidemiological Uncertainty

Although local eradication is routinely attempted following introduction of disease into a new region, failure is commonplace. Epidemiological principles governing the design of successful control are not well-understood. We analyse factors underlying the effectiveness of reactive eradication of localised outbreaks of invading plant disease, using citrus canker in Florida as a case study, although our results are largely generic, and apply to other plant pathogens (as we show via our second case study, citrus greening). We demonstrate how to optimise control via removal of hosts surrounding detected infection (i.e. localised culling) using a spatially-explicit, stochastic epidemiological model. We show how to define optimal culling strategies that take account of stochasticity in disease spread, and how the effectiveness of disease control depends on epidemiological parameters determining pathogen infectivity, symptom emergence and spread, the initial level of infection, and the logistics and implementation of detection and control. We also consider how optimal culling strategies are conditioned on the levels of risk acceptance/aversion of decision makers, and show how to extend the analyses to account for potential larger-scale impacts of a small-scale outbreak. Control of local outbreaks by culling can be very effective, particularly when started quickly, but the optimum strategy and its performance are strongly dependent on epidemiological parameters (particularly those controlling dispersal and the extent of any cryptic infection, i.e. infectious hosts prior to symptoms), the logistics of detection and control, and the level of local and global risk that is deemed to be acceptable. A version of the model we developed to illustrate our methodology and results to an audience of stakeholders, including policy makers, regulators and growers, is available online as an interactive, user-friendly interface at http://www.webidemics.com/. This version of our model allows the complex epidemiological principles that underlie our results to be communicated to a non-specialist audience.     

There Is No Association between MicroRNA Gene Polymorphisms and Risk of Triple Negative Breast Cancer in a Chinese Han Population

Triple-negative breast cancer (TNBC) is defined by the lack of the expression of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). It is characterized by aggressive behavior, poor prognosis and lack of targeted therapies. MicroRNA (miRNA) as a novel modulator of gene expression has played an important regulatory role in the malignancy. Dysregulation and/or mutation of the miRNAs may also contribute to the TNBC susceptibility since it is associated with the expression of ER, PR and HER2. Single nucleotide polymorphisms (SNPs) in miRNAs may be extremely relevant for TNBC. We tried to validate the hypothesis that genetic variations in miRNA are associated with TNBC development, and identify candidate biomarkers for TNBC susceptibility and clinical treatment. We screened the genetic variants in all miRNA genes listed in the public database miRBase and NCBI. A total of 23 common SNPs in 22 miRNAs, which tagged the known common variants in the Chinese Han people with a minor allele frequency greater than 0.05, were genotyped. This case-control study involved 191 patients with TNBC and 192 healthy female controls. Frequencies of SNPs were compared between cases and controls to identify the SNPs associated with TNBC susceptibility. No significant association was found between TNBC risk and the SNPs in the miRNA genes in the Chinese Han people (P>0.05), but this warrants further studies.     

Is There a Political Ecology of the Sierra Leonean Landscape?

Corruption and State Politics in Sierra Leone. William Reno. Cambridge: Cambridge University Press, 1995. 229 pp.
Fighting for the Rain Forest: War, Youth and Resources in Sierra Leone. Paul Richards. Portsmouth, NH: Heinemann, 1996. 182 pp.
Myth and Reality in the Rain Forest: How Conservation Strategies Are Failing in West Africa. John F. Oates. Berkeley: University of California Press, 1999. 310 pp.     

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.     

Estimation of Global Network Statistics from Incomplete Data

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar''s hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week.     

Estimation of Growth Norms from Incomplete Longitudinal Data

Longitudinal studies are rarely complete due to attrition, mistimed visits and observations missing at random. When the data are missing at random it is possible to estimate the primary location parameters of interest by constructing a modification of Zellner's (1962) seemingly unrelated regression estimator. Such a procedure is developed in this paper and is applied to a longitudinal study of coronary risk factors in children. The method consists of two stages in which the covariance matrix is estimated at the first stage. Using the estimated covariance matrix a generalized least squares estimator of the regression parameter vector is then determined at the second stage. Limitations of the procedure are also discussed.     

On the Estimation of Heritability from Unbalanced Data

The distribution and moments, of ANOVA estimator of heritability are given under unbalanced random model. These expressions are used to investigate the effect of unbalancedness on the bias and variance/MSE of the estimator and also the validity of certain approximations for its variance, numerically. The computed results reveal that the unbalancedness increases both the bias and variance/MSE of the estimator and the Smith-approximation for the variance of the estimator provides better accuracy.     

Estimation of a Secondary Parameter in a Group Sequential Clinical Trial

In this article, we investigate estimation of a secondary parameter in group sequential tests. We study the model in which the secondary parameter is the mean of the normal distribution in a subgroup of the subjects. The bias of the naive secondary parameter estimator is studied. It is shown that the sampling proportions of the subgroup have a crucial effect on the bias: As the sampling proportion of the subgroup at or just before the stopping time increases, the bias of the naive subgroup parameter estimator increases as well. An unbiased estimator for the subgroup parameter and an unbiased estimator for its variance are derived. Using simulations, we compare the mean squared error of the unbiased estimator to that of the naive estimator, and we show that the differences are negligible. As an example, the methods of estimation are applied to an actual group sequential clinical trial, The Beta-Blocker Heart Attack Trial.     

Individual Differences and Decision Making: When the Lure Effect of Gain Is a Matter of Size

The Iowa Gambling Task (IGT) is widely used in investigations of decision making. A growing number of studies have linked performance on this task to personality differences, with the aim of explaining the large degree of variability in healthy individuals'' performance of the task. However, this line of research has yielded inconsistent results. In the present study, we tested whether increasing the conflict between short-term and long-term gains in the IGT can clarify personality-related modulations of decision making. We assessed performance on the original IGT as a function of the personality traits typically involved in risky decision making (i.e., impulsivity, sensation seeking, sensitivity to reward and punishment). The impact of these same personality traits was also evaluated on a modified version of the task in which the difference in immediate reward magnitude between disadvantageous and advantageous decks was increased, while keeping the net gain fixed. The results showed that only in this latter IGT variant were highly impulsive individuals and high sensation seekers lured into making disadvantageous choices. The opposite seems to be the case for participants who were highly sensitive to punishment, although further data are needed to corroborate this finding. The present preliminary results suggest that the IGT variant used in this study could be more effective than the original task at identifying personality effects in decision making. Implications for dispositional and situational effects on decision making are discussed.     

Optimal Experimental Design for Parameter Estimation of a Cell Signaling Model

Differential equation models that describe the dynamic changes of biochemical signaling states are important tools to understand cellular behavior. An essential task in building such representations is to infer the affinities, rate constants, and other parameters of a model from actual measurement data. However, intuitive measurement protocols often fail to generate data that restrict the range of possible parameter values. Here we utilized a numerical method to iteratively design optimal live-cell fluorescence microscopy experiments in order to reveal pharmacological and kinetic parameters of a phosphatidylinositol 3,4,5-trisphosphate (PIP₃) second messenger signaling process that is deregulated in many tumors. The experimental approach included the activation of endogenous phosphoinositide 3-kinase (PI3K) by chemically induced recruitment of a regulatory peptide, reversible inhibition of PI3K using a kinase inhibitor, and monitoring of the PI3K-mediated production of PIP₃ lipids using the pleckstrin homology (PH) domain of Akt. We found that an intuitively planned and established experimental protocol did not yield data from which relevant parameters could be inferred. Starting from a set of poorly defined model parameters derived from the intuitively planned experiment, we calculated concentration-time profiles for both the inducing and the inhibitory compound that would minimize the predicted uncertainty of parameter estimates. Two cycles of optimization and experimentation were sufficient to narrowly confine the model parameters, with the mean variance of estimates dropping more than sixty-fold. Thus, optimal experimental design proved to be a powerful strategy to minimize the number of experiments needed to infer biological parameters from a cell signaling assay.     

Common TLR1 Genetic Variation Is Not Associated with Death from Melioidosis,a Common Cause of Sepsis in Rural Thailand

Melioidosis, infection caused by the Gram-negative bacterium Burkholderia pseudomallei, is a common cause of sepsis in northeast Thailand. In white North Americans, common functional genetic variation in TLR1 is associated with organ failure and death from sepsis. We hypothesized that TLR1 variants would be associated with outcomes in Thais with melioidosis. We collated the global frequencies of three TLR1 variants that are common in white North American populations: rs5743551 (-7202A/G), rs4833095 (742A/G), and rs5743618 (1804G/T). We noted a reversal of the minor allele from white North American subjects to Asian populations that was particularly pronounced for rs5743618. In the Utah residents of European ancestry, the frequency of the rs5743618 T allele was 17% whereas in Vietnamese subjects the frequency was >99%. We conducted a genetic association study in 427 patients with melioidosis to determine the association of TLR1 variation with organ failure or death. We genotyped rs5743551 and rs4833095. The variants were in high linkage disequilibrium but neither variant was associated with organ failure or in-hospital death. In 300 healthy Thai individuals we further tested the association of TLR1 variation with ex vivo blood responses to Pam3CSK4, a TLR1 agonist. Neither variant was robustly associated with blood cytokine responses induced by Pam3CSK4. We identified additional common variation in TLR1 by searching public databases and the published literature and screened three additional TLR1 variants for associations with Pam3CSK4-induced responses but found none. We conclude that the genetic architecture of TLR1 variation differs substantially in southeast Asians compared to other populations and common variation in TLR1 in Thais is not associated with outcome from melioidosis or with altered blood responses to Pam3CSK4. Our findings highlight the need for additional studies of TLR1 and other innate immune genetic modulators of the inflammatory host response and determinants of sepsis in southeast Asian populations.