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.     

Improved Logrank-Type Tests for Survival Data Using Adaptive Weights

Summary .  For testing for treatment effects with time-to-event data, the logrank test is the most popular choice and has some optimality properties under proportional hazards alternatives. It may also be combined with other tests when a range of nonproportional alternatives are entertained. We introduce some versatile tests that use adaptively weighted logrank statistics. The adaptive weights utilize the hazard ratio obtained by fitting the model of Yang and Prentice (2005,  Biometrika   92 , 1–17). Extensive numerical studies have been performed under proportional and nonproportional alternatives, with a wide range of hazard ratios patterns. These studies show that these new tests typically improve the tests they are designed to modify. In particular, the adaptively weighted logrank test maintains optimality at the proportional alternatives, while improving the power over a wide range of nonproportional alternatives. The new tests are illustrated in several real data examples.     

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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