Metabolomics method to comprehensively analyze amino acids in different domains

Amino acids play essential roles in both metabolism and the proteome. Many studies have profiled free amino acids (FAAs) or proteins; however, few have connected the measurement of FAA with individual amino acids in the proteome. In this study, we developed a metabolomics method to comprehensively analyze amino acids in different domains, using two examples of different sample types and disease models. We first examined the responses of FAAs and insoluble-proteome amino acids (IPAAs) to the Myc oncogene in Tet21N human neuroblastoma cells. The metabolic and proteomic amino acid profiles were quite different, even under the same Myc condition, and their combination provided a better understanding of the biological status. In addition, amino acids were measured in 3 domains (FAAs, free and soluble-proteome amino acids (FSPAAs), and IPAAs) to study changes in serum amino acid profiles related to colon cancer. A penalized logistic regression model based on the amino acids from the three domains had better sensitivity and specificity than that from each individual domain. To the best of our knowledge, this is the first study to perform a combined analysis of amino acids in different domains, and indicates the useful biological information available from a metabolomics analysis of the protein pellet. This study lays the foundation for further quantitative tracking of the distribution of amino acids in different domains, with opportunities for better diagnosis and mechanistic studies of various diseases.

Theoretical approaches to liana management: a search for a less harmful method

Lianas can change forest dynamics, slowing down forest regeneration after a perturbation. In these cases, it may be necessary to manage these woody climbers. Our aim was to simulate two management strategies: (1) focusing on abundant liana species and (2) focusing on the largest lianas, and contrast them with the random removal of lianas. We applied mathematical simulations for liana removal in three different vegetation types in southeastern Brazil: a Rainforest, a Seasonal Tropical Forest, and a Woodland Savanna. Using these samples, we performed simulations based on two liana removal procedures and compared them with random removal. We also used regression analysis with quasi-Poisson distribution to test whether larger lianas were aggressive, i.e., if they climbed into many trees. The procedure of cutting larger lianas was as effective as cutting them randomly and proved not to be a good method for liana management. Moreover, most of the lianas climbed into one or two trees, i.e., were not aggressive. Cutting the most abundant lianas proved to be a more effective method than cutting lianas randomly. This method could maintain liana richness and presumably should accelerate forest regeneration.

Improved Regression Estimation of a Multivariate Relationship with Population Data on the Bivariate Relationship

Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We show here that such population- level data can be used to reduce variance and bias about estimates of those regression coefficients from sample survey data. The method of constrained MLE is used to achieve these improvements. Its statistical properties are first described. The method constrains the weighted sum of all the covariate-specific associations (partial effects) of the regressors on the dependent variable to equal the overall association of one or more regressors, where the latter is known exactly from the population data. We refer to those regressors whose bivariate or limited multivariate relationships with the dependent variable are constrained by population data as being ‘‘directly constrained.’’ Our study investigates the improvements in the estimation of directly constrained variables as well as the improvements in the estimation of other regressor variables that may be correlated with the directly constrained variables, and thus ‘‘indirectly constrained’’ by the population data. The example application is to the marital fertility of black versus white women. The difference between white and black women’s rates of marital fertility, available from population-level data, gives the overall association of race with fertility. We show that the constrained MLE technique both provides a far more powerful statistical test of the partial effect of being black and purges the test of a bias that would otherwise distort the estimated magnitude of this effect. We find only trivial reductions, however, in the standard errors of the parameters for indirectly constrained regressors.

Development and validation of a simple and sensitive high performance liquid chromatographic method for the simultaneous determination of anastrozole, bicalutamide, tamoxifen, and their synthetic impurities

A simple and sensitive analytical method for simultaneous determination of anastrozole, bicalutamide, and tamoxifen as well as their synthetic impurities, anastrozole pentamethyl, bicalutamide 3-fluoro-isomer, and tamoxifen e-isomer, was developed and validated by using high performance liquid chromatography (HPLC). The separation was achieved on a Symmetry (R) C-8 column (100 x 4.6 mm i.d., 3.5 mu m) at room temperature (+/- 24 degrees C), with a mobile phase consisting of acetonitrile/water containing 0.18% N,N dimethyloctylamine and pH adjusted to 3.0 with orthophosphoric acid (46.5/53.5, v/v) at a flow rate of 1.0 mL min(-1) within 20 min. The detection was made at a wavelength of 270 nm by using ultraviolet (UV) detector. No interference peaks from excipients and relative retention time indicated the specificity of the method. The calibration curve showed correlation coefficients (r) > 0.99 calculated by linear regression and analysis of variance (ANOVA). The limit of detection (LOD) and limit of quantitation (LOQ), respectively, were 2.2 and 6.7 mu g mL(-1) for anastrozole, 2.61 and 8.72 mu g mL(-1) for bicalutamide, 2.0 and 6.7 mu g mL(-1) for tamoxifen, 0.06 and 0.22 mu g mL(-1) for anastrozole pentamethyl, 0.02 and 0.07 mu g mL(-1) for bicalutamide 3-fluoro-isomer, and 0.002 and 0.007 mu g mL(-1) for tamoxifen e-isomer. Intraday and interday relative standard deviations (RSDs) were <2.0% (drugs) and <10% (degradation products) as well as the comparison between two different analysts, which were calculated by f test. (C) 2012 Elsevier B.V. All rights reserved.

Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES

We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model (beta-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z is an element of [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high-and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high-and low-substructure level clusters) are different (they present an offset, i. e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.

The Poisson-exponential regression model under different latent activation schemes

In this paper, a new family of survival distributions is presented. It is derived by considering that the latent number of failure causes follows a Poisson distribution and the time for these causes to be activated follows an exponential distribution. Three different activation schemes are also considered. Moreover, we propose the inclusion of covariates in the model formulation in order to study their effect on the expected value of the number of causes and on the failure rate function. Inferential procedure based on the maximum likelihood method is discussed and evaluated via simulation. The developed methodology is illustrated on a real data set on ovarian cancer.

Response plateau models fitting via isotonic regression

Within the nutritional context, the supplementation of microminerals in bird food is often made in quantities exceeding those required in the attempt to ensure the proper performance of the animals. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.

A New Method to Predict the Epidemiology of Fungal Keratitis by Monitoring the Sales Distribution of Antifungal Eye Drops in Brazil

Purpose: Fungi are a major cause of keratitis, although few medications are licensed for their treatment. The aim of this study is to observe the variation in commercialisation of antifungal eye drops, and to predict the seasonal distribution of fungal keratitis in Brazil. Methods: Data from a retrospective study of antifungal eye drops sales from the only pharmaceutical ophthalmologic laboratory, authorized to dispense them in Brazil (Opthalmos) were gathered. These data were correlated with geographic and seasonal distribution of fungal keratitis in Brazil between July 2002 and June 2008. Results: A total of 26,087 antifungal eye drop units were sold, with a mean of 2.3 per patient. There was significant variation in antifungal sales during the year (p < 0.01). A linear regression model displayed a significant association between reduced relative humidity and antifungal drug sales (R-2 = 0.17, p < 0.01). Conclusions: Antifungal eye drops sales suggest that there is a seasonal distribution of fungal keratitis. A possible interpretation is that the third quarter of the year (a period when the climate is drier), when agricultural activity is more intense in Brazil, suggests a correlation with a higher incidence of fungal keratitis. A similar model could be applied to other diseases, that are managed with unique, or few, and monitorable medications to predict epidemiological aspects.

Competing regression models for longitudinal data

The choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. In particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.

Maximal exercise test is a useful method for physical capacity and oxygen consumption determination in streptozotocin-diabetic rats

Abstract Background The aim of the present study was to investigate the relationship between speed during maximum exercise test (ET) and oxygen consumption (VO2) in control and STZ-diabetic rats, in order to provide a useful method to determine exercise capacity and prescription in researches involving STZ-diabetic rats. Methods Male Wistar rats were divided into two groups: control (CG, n = 10) and diabetic (DG, n = 8). The animals were submitted to ET on treadmill with simultaneous gas analysis through open respirometry system. ET and VO2 were assessed 60 days after diabetes induction (STZ, 50 mg/Kg). Results VO2 maximum was reduced in STZ-diabetic rats (72.5 ± 1 mL/Kg/min-1) compared to CG rats (81.1 ± 1 mL/Kg/min-1). There were positive correlations between ET speed and VO2 (r = 0.87 for CG and r = 0.8 for DG), as well as between ET speed and VO2 reserve (r = 0.77 for CG and r = 0.7 for DG). Positive correlations were also obtained between measured VO2 and VO2 predicted values (r = 0.81 for CG and r = 0.75 for DG) by linear regression equations to CG (VO2 = 1.54 * ET speed + 52.34) and DG (VO2 = 1.16 * ET speed + 51.99). Moreover, we observed that 60% of ET speed corresponded to 72 and 75% of VO2 reserve for CG and DG, respectively. The maximum ET speed was also correlated with VO2 maximum for both groups (CG: r = 0.7 and DG: r = 0.7). Conclusion These results suggest that: a) VO2 and VO2 reserve can be estimated using linear regression equations obtained from correlations with ET speed for each studied group; b) exercise training can be prescribed based on ET in control and diabetic-STZ rats; c) physical capacity can be determined by ET. Therefore, ET, which involves a relatively simple methodology and low cost, can be used as an indicator of cardio-respiratory capacity in future studies that investigate the physiological effect of acute or chronic exercise in control and STZ-diabetic male rats.

Estimation of transpiration of the 'Valencia' orange young plant using thermal dissipation probe method

Most studies on measures of transpiration of plants, especially woody fruit, relies on methods of heat supply in the trunk. This study aimed to calibrate the Thermal Dissipation Probe Method (TDP) to estimate the transpiration, study the effects of natural thermal gradients and determine the relation between outside diameter and area of xylem in 'Valencia' orange young plants. TDP were installed in 40 orange plants of 15 months old, planted in boxes of 500 L, in a greenhouse. It was tested the correction of the natural thermal differences (DTN) for the estimation based on two unheated probes. The area of the conductive section was related to the outside diameter of the stem by means of polynomial regression. The equation for estimation of sap flow was calibrated having as standard lysimeter measures of a representative plant. The angular coefficient of the equation for estimating sap flow was adjusted by minimizing the absolute deviation between the sap flow and daily transpiration measured by lysimeter. Based on these results, it was concluded that the method of TDP, adjusting the original calibration and correction of the DTN, was effective in transpiration assessment.

Development of a field method to assess root parasites of woody plants - ecological analyses of the grapevine-phylloxera interaction

The development and the growth of plants is strongly affected by the interactions between roots, rootrnassociated organisms and rhizosphere communities. Methods to assess such interactions are hardly torndevelop particularly in perennial and woody plants, due to their complex root system structure and theirrntemporal change in physiology patterns. In this respect, grape root systems are not investigated veryrnwell. The aim of the present work was the development of a method to assess and predict interactionsrnat the root system of rootstocks (Vitis berlandieri x Vitis riparia) in field. To achieve this aim, grapernphylloxera (Daktulosphaira vitifoliae Fitch, Hemiptera, Aphidoidea) was used as a graperoot parasitizingrnmodel.rnTo develop the methodical approach, a longt-term trial (2006-2009) was arranged on a commercial usedrnvineyard in Geisenheim/Rheingau. All 2 to 8 weeks the top most 20 cm of soil under the foliage wallrnwere investigated and root material was extracted (n=8-10). To include temporal, spatial and cultivarrnspecific root system dynamics, the extracted root material was analyzed digitally on the morphologicalrnproperties. The grape phylloxera population was quantified and characterized visually on base of theirrnlarvalstages (oviparous, non oviparous and winged preliminary stages). Infection patches (nodosities)rnwere characterized visually as well, partly supported by digital root color analyses. Due to the knownrneffects of fungal endophytes on the vitality of grape phylloxera infested grapevines, fungal endophytesrnwere isolated from nodosity and root tissue and characterized (morphotypes) afterwards. Further abioticrnand biotic soil conditions of the vineyards were assessed. The temporal, spatial and cultivar specificrnsensitivity of single parameters were analyzed by omnibus tests (ANOVAs) and adjacent post-hoc tests.rnThe relations between different parameters were analyzed by multiple regression models.rnQuantitative parameters to assess the degeneration of nodosity, the development nodosity attachedrnroots and to differentiate between nodosities and other root swellings in field were developed. Significantrndifferences were shown between root dynamic including parameters and root dynamic ignoringrnparameters. Regarding the description of grape phylloxera population and root system dynamic, thernmethod showed a high temporal, spatial and cultivar specific sensitivity. Further, specific differencesrncould be shown in the frequency of endophyte morphotypes between root and nodosity tissue as wellrnas between cultivars. Degeneration of nodosities as well as nodosity occupation rates could be relatedrnto the calculated abundances of grape phylloxera population. Further ecological questions consideringrngrape root development (e.g. relation between moisture and root development) and grape phylloxerarnpopulation development (e.g. relation between temperature and population structure) could be answeredrnfor field conditions.rnGenerally, the presented work provides an approach to evaluate vitality of grape root systems. Thisrnapproach can be useful, considering the development of control strategies against soilborne pests inrnviticulture (e.g. grape phylloxera, Sorospheara viticola, Roesleria subterranea (Weinm.) Redhaed) as well as considering the evaluation of integrated management systems in viticulture.

A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

Correlation properties of spontaneous motor activity in healthy infants: a new computer-assisted method to evaluate neurological maturation

Qualitative assessment of spontaneous motor activity in early infancy is widely used in clinical practice. It enables the description of maturational changes of motor behavior in both healthy infants and infants who are at risk for later neurological impairment. These assessments are, however, time-consuming and are dependent upon professional experience. Therefore, a simple physiological method that describes the complex behavior of spontaneous movements (SMs) in infants would be helpful. In this methodological study, we aimed to determine whether time series of motor acceleration measurements at 40-44 weeks and 50-55 weeks gestational age in healthy infants exhibit fractal-like properties and if this self-affinity of the acceleration signal is sensitive to maturation. Healthy motor state was ensured by General Movement assessment. We assessed statistical persistence in the acceleration time series by calculating the scaling exponent α via detrended fluctuation analysis of the time series. In hand trajectories of SMs in infants we found a mean α value of 1.198 (95 % CI 1.167-1.230) at 40-44 weeks. Alpha changed significantly (p = 0.001) at 50-55 weeks to a mean of 1.102 (1.055-1.149). Complementary multilevel regression analysis confirmed a decreasing trend of α with increasing age. Statistical persistence of fluctuation in hand trajectories of SMs is sensitive to neurological maturation and can be characterized by a simple parameter α in an automated and observer-independent fashion. Future studies including children at risk for neurological impairment should evaluate whether this method could be used as an early clinical screening tool for later neurological compromise.