Phenotypic biomonitoring using multivariate flow cytometric analysis of multi-stained microorganisms

A new method for monitoring phenotypic profiles of pure cultures and complex microbial communities was evaluated. The approach was to stain microorganisms with a battery of fluorescent dyes prior to flow cytometry analysis (FCM) and to analyse the data using multivariate methods, including principal component analysis and partial least squares. The FCM method was quantitatively evaluated using different mixtures of pure cultures as well as microbial communities. The results showed that the method could quantitatively and reproducibly resolve both populations and communities of microorganisms with 5% abundance in a diverse microbial background. The feasibility of monitoring complex microbial communities over time during the biodegradation of naphthalene using the FCM method was demonstrated. The biodegradation of naphthalene occurred to differing extents in microcosms representing three different types of aromatic-contaminated groundwater and a sample of bio-basin water. The FCM method distinguished each of these four microbial communities. The phenotypic profiles were compared with genotypic profiles generated by random-amplified polymorphic DNA analysis. The genotypic profiles of the microbial communities described only the microbial composition, and not their functional change, whereas the phenotypic profiles seemed to contain information on both the composition and the functional change of the microorganisms. Furthermore, event analysis of the FCM data showed that microbial communities with initially differing compositions could converge towards a similar composition if they had a capacity for high levels of degradation, whereas microbial communities with similar initial compositions could diverge if they differed in biodegrading ability.     

A linear mixed-effects model for multivariate censored data

We apply a linear mixed-effects model to multivariate failure time data. Computation of the regression parameters involves the Buckley-James method in an iterated Monte Carlo expectation-maximization algorithm, wherein the Monte Carlo E-step is implemented using the Metropolis-Hastings algorithm. From simulation studies, this approach compares favorably with the marginal independence approach, especially when there is a strong within-cluster correlation.     

Sampling effort, regression method, and the shape and slope of size–abundance relations

1. Despite a substantial body of work there remains much disagreement about the form of the relationship between organism abundance and body size. In an attempt at resolving these disagreements the shape and slope of samples from simulated and real abundance–mass distributions were assessed by ordinary least squares regression (OLS) and the reduced major axis method (RMA).
2. It is suggested that the data gathered by ecologists to assess these relationships are usually truncated in respect of density. Under these conditions RMA gives slope estimates which are consistently closer to the true slopes than OLS regression.
3. The triangular relationships reported by some workers are found over smaller mass and abundance ranges than linear relations. Scatter in slope estimates is much greater and positive slopes more common at small sample sizes and sample ranges. These results support the notion that inadequate and truncated sampling is responsible for much of the disagreement reported in the literature.
4. The results strongly support the notion that density declines with increasing body mass in a broad, linear band with a slope around −1. However there is some evidence to suggest that this overall relation results from a series of component relations with slopes which differ from the overall slope.     

A new method for the improvement of data production in phytochemical analysis

Introduction – The use of the average analytical signal for the construction of curves by the least squares method (LSM) over the standard addition method (SAM) is widespread. It would be advantageous, however, to find a way to avoid intermediary averages, which are known to be the cause of significant increases in standard deviations (SD). Objective – To develop a protocol that uses all gathered data to create curves by LSM over SAM. To use Excel^® for the estimation of y = mx + b and R² rather than using LSM equations for the SD of m, x and b. Methodology – The level of lead (II) in the bark (cork) of Quercus suber Linnaeus was determined using differential pulse anodic stripping voltammetry (DPASV). Three current samples were taken for each of the four standard additions. These signals were combined for adjustment by LSM. The results were compared with those obtained after averaging the current for each addition, and the expression of uncertainty in the measurements determined. Results – The new method shows an expanded uncertainty of ± 0.3321 μg/g (nearly 1.42%). The difference between the results obtained by the new and the old method is 0.01 μg/g (23.41 and 23.40 μg/g). The limit of detection changed approximately from 4.8 to 4 μg/g and the relative SD approximately from 9 to 6%. Conclusion – The absence of intermediary averages in curves improved the determination of lead (II) in cork by DPASV. Estimation of SD only with LSM equations produced results that were significantly worse. The changes are large enough to transform an apparently internally non‐validated procedure (repeatability for precision) into an internally validated procedure. Copyright © 2009 John Wiley & Sons, Ltd.     

Why do Anatolian ground squirrels exhibit a Bergmannian size pattern? A phylogenetic comparative analysis of geographic variation in body size

A phylogenetic comparative analysis of geographic variation in body size of an obligately hibernating marmotine species (Anatolian ground squirrels, Spermophilus xanthoprymnus) is presented in relation to environmental variables that pertain to four principal hypotheses (heat conservation, heat dissipation, primary productivity, and seasonality hypotheses). Adult Anatolian ground squirrels (78 males and 90 females) were collected from ten geographic localities in Anatolia for use in morphometric analyses. First, the study tested whether significant variation in body size occurs over the geographic range of S. xanthoprymnus. Then, to understand the possible cause(s) of the observed pattern of geographic variation in body size of Anatolian ground squirrels, four hypotheses were tested, separately and simultaneously, using a phylogenetic comparative method. Overall, food availability (primary productivity hypothesis) and, especially in males, over‐winter fasting endurance (seasonality hypothesis) are likely the primary underlying mechanisms generating the observed pattern of increasing body size towards colder, more seasonal environments, with higher summer precipitation and productivity (or a Bergmannian size pattern). © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100 , 695–710.     

基于最小二乘支持向量机的农田水汽通量建模

支持向量机是在统计学习理论基础上发展起来的一种新型的机器学习方法,已在模式识别、非线性建模等领域中得到了应用．本文将最小二乘支持向量机方法应用于农田水汽通量的建模中,并同前馈反向传播神经网络的建模性能进行了比较．结果表明,最小二乘支持向量机方法具有可调参数少、学习速度较快等优点,具有更好的推广能力,以更高的精度建立农田水汽通量模型．模型的敏感性分析进一步显示,用最小二乘支持向量机方法建立的农田水汽通量模型是合理可行的．     

Quantitative structure-activity relationship study of bitter di- and tri-peptides including relationship with angiotensin I-converting enzyme inhibitory activity.

Bitterness represents a major challenge in industrial application of food protein hydrolysates or bioactive peptides and is a major factor that controls the flavor of formulated therapeutic products. The aim of this work was to apply quantitative structure-activity relationship modeling as a tool to determine the type and position of amino acids that contribute to bitterness of di- and tri-peptides. Datasets of bitter di- and tri-peptides were constructed using values from available literature, followed by modeling using partial least square (PLS) regression based on the three z-scores of 20 coded amino acids. Prediction models were validated using cross-validation and permutation tests. Results showed that a single-component model could explain 52 and 50% of the Y variance (bitterness threshold) of bitter di- and tri-peptides, respectively. Using PLS regression coefficients, it was determined that hydrophobic amino acids at the carboxyl-terminus and bulky amino acid residues adjacent to the carboxyl terminal are the major determinants of the intensity of bitterness of di- and tri-peptides. However, there was no significant (p > 0.05) correlation between bitterness of di- and tri-peptides and their angiotensin I-converting enzyme-inhibitory properties.     

A novel bacterial community index to assess stream ecological health

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