Petroleum contamination impact on macrobenthic communities under the influence of an oil refinery: Integrating chemical and biological multivariate data

Petroleum contamination impact on macrobenthic communities in the northeast portion of Todos os Santos Bay was assessed combining in multivariate analyses, chemical parameters such as aliphatic and polycyclic aromatic hydrocarbon indices and concentration ratios with benthic ecological parameters. Sediment samples were taken in August 2000 with a 0.05 m(2) van Veen grab at 28 sampling locations. The predominance of n-alkanes with more than 24 carbons, together with CPI values close to one, and the fact that most of the stations showed UCM/resolved aliphatic hydrocarbons ratios (UCM:R) higher than two, indicated a high degree of anthropogenic contribution, the presence of terrestrial plant detritus, petroleum products and evidence of chronic oil pollution. The indices used to determine the origin of PAH indicated the occurrence of a petrogenic contribution. A pyrolytic contribution constituted mainly by fossil fuel combustion derived PAH was also observed. The results of the stepwise multiple regression analysis performed with chemical data and benthic ecological descriptors demonstrated that not only total PAH concentrations but also specific concentration ratios or indices such as >= C24:< C24, An/178 and Fl/Fl + Py, are determining the structure of benthic communities within the study area. According to the BIO-ENV results petroleum related variables seemed to have a main influence on macrofauna community structure. The PCA ordination performed with the chemical data resulted in the formation of three groups of stations. The decrease in macrofauna density, number of species and diversity from groups III to I seemed to be related to the occurrence of high aliphatic hydrocarbon and PAH concentrations associated with fine sediments. Our results showed that macrobenthic communities in the northeast portion of Todos os Santos Bay are subjected to the impact of chronic oil pollution as was reflected by the reduction in the number of species and diversity. These results emphasise the importance to combine in multivariate approaches not only total hydrocarbon concentrations but also indices, isomer pair ratios and specific compound concentrations with biological data to improve the assessment of anthropogenic impact on marine ecosystems. (c) 2008 Elsevier Ltd. All rights reserved.

Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission (P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

Covariates of high-risk human papillomavirus (HPV) infections are distinct for incident CIN1, CIN2 and CIN3 as disclosed by competing-risks regression models

Background: In addition to the oncogenic human papillomavirus (HPV), several cofactors are needed in cervical carcinogenesis, but whether the HPV covariates associated with incident i) CIN1 are different from those of incident ii) CIN2 and iii) CIN3 needs further assessment. Objectives: To gain further insights into the true biological differences between CIN1, CIN2 and CIN3, we assessed HPV covariates associated with incident CIN1, CIN2, and CIN3. Study Design and Methods: HPV covariates associated with progression to CIN1, CIN2 and CIN3 were analysed in the combined cohort of the NIS (n = 3,187) and LAMS study (n = 12,114), using competing-risks regression models (in panel data) for baseline HR-HPV-positive women (n = 1,105), who represent a sub-cohort of all 1,865 women prospectively followed-up in these two studies. Results: Altogether, 90 (4.8%), 39 (2.1%) and 14 (1.4%) cases progressed to CIN1, CIN2, and CIN3, respectively. Among these baseline HR-HPV-positive women, the risk profiles of incident GIN I, CIN2 and CIN3 were unique in that completely different HPV covariates were associated with progression to CIN1, CIN2 and CIN3, irrespective which categories (non-progression, CIN1, CIN2, CIN3 or all) were used as competing-risks events in univariate and multivariate models. Conclusions: These data confirm our previous analysis based on multinomial regression models implicating that distinct covariates of HR-HPV are associated with progression to CIN1, CIN2 and CIN3. This emphasises true biological differences between the three grades of GIN, which revisits the concept of combining CIN2 with CIN3 or with CIN1 in histological classification or used as a common end-point, e.g., in HPV vaccine trials.

Random regression analyses using B-spline functions to model growth of Nellore cattle

The objective of this study was to estimate (co)variance components using random regression on B-spline functions to weight records obtained from birth to adulthood. A total of 82 064 weight records of 8145 females obtained from the data bank of the Nellore Breeding Program (PMGRN/Nellore Brazil) which started in 1987, were used. The models included direct additive and maternal genetic effects and animal and maternal permanent environmental effects as random. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of age (cubic regression) were considered as random covariate. The random effects were modeled using B-spline functions considering linear, quadratic and cubic polynomials for each individual segment. Residual variances were grouped in five age classes. Direct additive genetic and animal permanent environmental effects were modeled using up to seven knots (six segments). A single segment with two knots at the end points of the curve was used for the estimation of maternal genetic and maternal permanent environmental effects. A total of 15 models were studied, with the number of parameters ranging from 17 to 81. The models that used B-splines were compared with multi-trait analyses with nine weight traits and to a random regression model that used orthogonal Legendre polynomials. A model fitting quadratic B-splines, with four knots or three segments for direct additive genetic effect and animal permanent environmental effect and two knots for maternal additive genetic effect and maternal permanent environmental effect, was the most appropriate and parsimonious model to describe the covariance structure of the data. Selection for higher weight, such as at young ages, should be performed taking into account an increase in mature cow weight. Particularly, this is important in most of Nellore beef cattle production systems, where the cow herd is maintained on range conditions. There is limited modification of the growth curve of Nellore cattle with respect to the aim of selecting them for rapid growth at young ages while maintaining constant adult weight.

Multivariate spectroscopic methods for the analysis of solutions

In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. These spectroscopic methods consisting of spectroscopic analysis, a high level of automation and chemometric data evaluation can lead to analytical methods with a high analytical capacity, and for these methods, the term high-capacity analysis (HCA) is suggested. It is further shown how chemometric evaluation of the multivariate data in chromatographic analyses decreases the need for baseline separation. The thesis is based on six papers and the chemometric tools used are experimental design, principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), partial least squares regression (PLS) and parallel factor analysis (PARAFAC). The analytical techniques utilised are scanning ultraviolet-visible (UV-Vis) spectroscopy, diode array detection (DAD) used in non-column chromatographic diode array UV spectroscopy, high-performance liquid chromatography with diode array detection (HPLC-DAD) and fluorescence spectroscopy. The methods proposed are exemplified in the analysis of pharmaceutical solutions and serum proteins. In Paper I a method is proposed for the determination of the content and identity of the active compound in pharmaceutical solutions by means of UV-Vis spectroscopy, orthogonal signal correction and multivariate calibration with PLS and SIMCA classification. Paper II proposes a new method for the rapid determination of pharmaceutical solutions by the use of non-column chromatographic diode array UV spectroscopy, i.e. a conventional HPLC-DAD system without any chromatographic column connected. In Paper III an investigation is made of the ability of a control sample, of known content and identity to diagnose and correct errors in multivariate predictions something that together with use of multivariate residuals can make it possible to use the same calibration model over time. In Paper IV a method is proposed for simultaneous determination of serum proteins with fluorescence spectroscopy and multivariate calibration. Paper V proposes a method for the determination of chromatographic peak purity by means of PCA of HPLC-DAD data. In Paper VI PARAFAC is applied for the decomposition of DAD data of some partially separated peaks into the pure chromatographic, spectral and concentration profiles.

Integrative Musterbildung kranialer Strukturen

Form und Gestalt kraniofazialer Strukturen sind primär beeinflusst durch die inhärente Integration der unterschiedlichsten Funktionssysteme und externer selektiver Einflüsse. Die Variabilität der Schädel-Morphe ist ein Indikator für solche Einflussfaktoren und damit ein idealer Gegenstand für vergleichende Analysen morphogenetischer Formbildung. Zur Ermittlung morphologisch-adaptiver Trends und Muster wurden sowohl Hypothesen zur morphologischen Differenziertheit als auch zu Korrelationen zwischen modularen Schädelkompartimenten (fazial, neurokranial, basikranial) untersucht. Zusätzlich wurden aus Schichtröntgenaufnahmen (CT) virtuelle Modelle rekonstruiert, welche die Interpretation der statistischen Befunde unterstützen sollten. Zur Berechnung der Gestaltunterschiede wurden mittels eines mechanischen Gelenkarm-Messgerätes (MicroScribe-G2) max. 85 ektokraniale Messpunkte (Landmarks) bzw. dreidimensionale Koordinaten an ca. 520 Schädeln von fünf rezenten Gattungen der Überfamilie Hominoidea (Hylobates, Pongo, Gorilla, Pan und Homo) akquiriert. Aus dem Datensatz wurden geometrische Störfaktoren (Größe, Translation, Rotation) mathematisch eliminiert und die verbleibenden Residuale bzw. ‚Gestalt-Variablen‘ diversen multivariat-statistischen Verfahren unterzogen (Faktoren, Cluster-, Regressions- und Korrelationsanalysen sowie statistische Tests). Die angewandten Methoden erhalten die geometrische Information der Untersuchungsobjekte über alle Analyseschritte hinweg und werden unter der Bezeichnung „Geometric Morphometrics (GMM)“ als aktueller Ansatz der Morphometrie zusammengefasst. Für die unterschiedlichen Fragestellungen wurden spezifische Datensätze generiert. Es konnten diverse morphologische Trends und adaptive Muster mit Hilfe der Synthese statistischer Methoden und computer-basierter Rekonstruktionen aus den generierten Datensätzen ermittelt werden. Außerdem war es möglich, präzise zu rekonstruieren, welche kranialen Strukturen innerhalb der Stichprobe miteinander wechselwirken, einzigartige Variabilitäten repräsentieren oder eher homogen gestaltet sind. Die vorliegenden Befunde lassen erkennen, dass Fazial- und Neurokranium am stärksten miteinander korrelieren, während das Basikranium geringe Abhängigkeiten in Bezug auf Gesichts- oder Hirnschädelveränderungen zeigte. Das Basikranium erweist sich zudem bei den nicht-menschlichen Hominoidea und über alle Analysen hinweg als konservative und evolutiv-persistente Struktur mit dem geringsten Veränderungs-Potential. Juvenile Individuen zeigen eine hohe Affinität zueinander und zu Formen mit einem kleinem Gesichts- und großem Hirnschädel. Während das Kranium des rezenten Menschen primär von Enkephalisation und fazialer Retraktion (Orthognathisierung) dominiert ist und somit eine einzigartige Gestalt aufweist, zeigt sich der Kauapparat als maßgeblich formbildendes Kompartiment bei den nicht-menschlichen Formen. Die Verbindung von GMM mit den interaktiven Möglichkeiten computergenerierter Modelle erwies sich als valides Werkzeug zur Erfassung der aufgeworfenen Fragestellungen. Die Interpretation der Befunde ist durch massive Interkorrelationen der untersuchten Strukturen und der statistisch-mathematischen Prozeduren als hoch komplex zu kennzeichnen. Die Studie präsentiert einen innovativen Ansatz der modernen Morphometrie, welcher für zukünftige Untersuchungen im Bereich der kraniofazialen Gestaltanalyse ausgebaut werden könnte. Dabei verspricht die Verknüpfung mit ‚klassischen’ und modernen Zugängen (z. B. Molekularbiologie) gesteigerte Erkenntnismöglichkeiten für künftige morphometrische Fragestellungen.

Valutazione della qualità ed autenticità di prodotti oleari e lattiero caseari mediante metodi fisici di analisi accoppiati a tecniche statistiche multivariate

Questa tesi descrive alcuni studi di messa a punto di metodi di analisi fisici accoppiati con tecniche statistiche multivariate per valutare la qualità e l’autenticità di oli vegetali e prodotti caseari. L’applicazione di strumenti fisici permette di abbattere i costi ed i tempi necessari per le analisi classiche ed allo stesso tempo può fornire un insieme diverso di informazioni che possono riguardare tanto la qualità come l’autenticità di prodotti. Per il buon funzionamento di tali metodi è necessaria la costruzione di modelli statistici robusti che utilizzino set di dati correttamente raccolti e rappresentativi del campo di applicazione. In questo lavoro di tesi sono stati analizzati oli vegetali e alcune tipologie di formaggi (in particolare pecorini per due lavori di ricerca e Parmigiano-Reggiano per un altro). Sono stati utilizzati diversi strumenti di analisi (metodi fisici), in particolare la spettroscopia, l’analisi termica differenziale, il naso elettronico, oltre a metodiche separative tradizionali. I dati ottenuti dalle analisi sono stati trattati mediante diverse tecniche statistiche, soprattutto: minimi quadrati parziali; regressione lineare multipla ed analisi discriminante lineare.

Urinary metabolomics in Fxr-null mice reveals activated adaptive metabolic pathways upon bile acid challenge

Farnesoid X receptor (FXR) is a nuclear receptor that regulates genes involved in synthesis, metabolism, and transport of bile acids and thus plays a major role in maintaining bile acid homeostasis. In this study, metabolomic responses were investigated in urine of wild-type and Fxr-null mice fed cholic acid, an FXR ligand, using ultra-performance liquid chromatography (UPLC) coupled with electrospray time-of-flight mass spectrometry (TOFMS). Multivariate data analysis between wild-type and Fxr-null mice on a cholic acid diet revealed that the most increased ions were metabolites of p-cresol (4-methylphenol), corticosterone, and cholic acid in Fxr-null mice. The structural identities of the above metabolites were confirmed by chemical synthesis and by comparing retention time (RT) and/or tandem mass fragmentation patterns of the urinary metabolites with the authentic standards. Tauro-3alpha,6,7alpha,12alpha-tetrol (3alpha,6,7alpha,12alpha-tetrahydroxy-5beta-cholestan-26-oyltaurine), one of the most increased metabolites in Fxr-null mice on a CA diet, is a marker for efficient hydroxylation of toxic bile acids possibly through induction of Cyp3a11. A cholestatic model induced by lithocholic acid revealed that enhanced expression of Cyp3a11 is the major defense mechanism to detoxify cholestatic bile acids in Fxr-null mice. These results will be useful for identification of biomarkers for cholestasis and for determination of adaptive molecular mechanisms in cholestasis.

Choosing Smoothness Parameters for Smoothing Splines by Minimizing and Estimate of Risk

Smoothing splines are a popular approach for non-parametric regression problems. We use periodic smoothing splines to fit a periodic signal plus noise model to data for which we assume there are underlying circadian patterns. In the smoothing spline methodology, choosing an appropriate smoothness parameter is an important step in practice. In this paper, we draw a connection between smoothing splines and REACT estimators that provides motivation for the creation of criteria for choosing the smoothness parameter. The new criteria are compared to three existing methods, namely cross-validation, generalized cross-validation, and generalization of maximum likelihood criteria, by a Monte Carlo simulation and by an application to the study of circadian patterns. For most of the situations presented in the simulations, including the practical example, the new criteria out-perform the three existing criteria.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Graft coronary vasculopathy in cardiac transplantation--evaluation of risk factors by multivariate analysis

The development of coronary vasculopathy is the main determinant of long-term survival in cardiac transplantation. The identification of risk factors, therefore, seems necessary in order to identify possible treatment strategies. Ninety-five out of 397 patients, undergoing orthotopic cardiac transplantation from 10/1985 to 10/1992 were evaluated retrospectively on the basis of perioperative and postoperative variables including age, sex, diagnosis, previous operations, renal function, cholesterol levels, dosage of immunosuppressive drugs (cyclosporin A, azathioprine, steroids), incidence of rejection, treatment with calcium channel blockers at 3, 6, 12, and 18 months postoperatively. Coronary vasculopathy was assessed by annual angiography at 1 and 2 years postoperatively. After univariate analysis, data were evaluated by stepwise multiple logistic regression analysis. Coronary vasculopathy was assessed in 15 patients at 1 (16%), and in 23 patients (24%) at 2, years. On multivariate analysis, previous operations and the incidence of rejections were identified as significant risk factors (P < 0.05), whereas the underlying diagnosis had borderline significance (P = 0.058) for the development of graft coronary vasculopathy. In contrast, all other variables were not significant in our subset of patients investigated. We therefore conclude that the development of coronary vasculopathy in cardiac transplant patients mainly depends on the rejection process itself, aside from patient-dependent factors. Therapeutic measures, such as the administration of calcium channel blockers and regulation of lipid disorders, may therefore only reduce the progress of native atherosclerotic disease in the posttransplant setting.

Selection on floral traits through male fertility in a natural plant population

Most studies on selection in plants estimate female fitness components and neglect male mating success, although the latter might also be fundamental to understand adaptive evolution. Information from molecular genetic markers can be used to assess determinants of male mating success through parentage analyses. We estimated paternal selection gradients on floral traits in a large natural population of the herb Mimulus guttatus using a paternity probability model and maximum likelihood methods. This analysis revealed more significant selection gradients than a previous analysis based on regression of estimated male fertilities on floral traits. There were differences between results of univariate and multivariate analyses most likely due to the underlying covariance structure of the traits. Multivariate analysis, which corrects for the covariance structure of the traits, indicated that male mating success declined with distance from and depended on the direction to the mother plants. Moreover, there was directional selection for plants with fewer open flowers which have smaller corollas, a smaller anther-stigma separation, more red dots on the corolla and a larger fluctuating asymmetry therein. For most of these traits, however, there was also stabilizing selection indicating that there are intermediate optima for these traits. The large number of significant selection gradients in this study shows that even in relatively large natural populations where not all males can be sampled, it is possible to detect significant paternal selection gradients, and that such studies can give us valuable information required to better understand adaptive plant evolution.

Cichlid species-area relationships are shaped by adaptive radiations that scale with area

A positive relationship between species richness and island size is thought to emerge from an equilibrium between immigration and extinction rates, but the influence of species diversification on the form of this relationship is poorly understood. Here, we show that within-lake adaptive radiation strongly modifies the species-area relationship for African cichlid fishes. The total number of species derived from in situ speciation increases with lake size, resulting in faunas orders of magnitude higher in species richness than faunas assembled by immigration alone. Multivariate models provide evidence for added influence of lake depth on the species-area relationship. Diversity of clades representing within-lake radiations show responses to lake area, depth and energy consistent with limitation by these factors, suggesting that ecological factors influence the species richness of radiating clades within these ecosystems. Together, these processes produce lake fish faunas with highly variable composition, but with diversities that are well predicted by environmental variables.