Correlation of wear in vivo and six laboratory wear methods.

OBJECTIVE: We examined the correlation between clinical wear rates of restorative materials and enamel (TRAC Research Foundation, Provo, USA) and the results of six laboratory test methods (ACTA, Alabama (generalized, localized), Ivoclar (vertical, volumetric), Munich, OHSU (abrasion, attrition), Zurich). METHODS: Individual clinical wear data were available from clinical trials that were conducted by TRAC Research Foundation (formerly CRA) together with general practitioners. For each of the n=28 materials (21 composite resins for intra-coronal restorations [20 direct and 1 indirect], 5 resin materials for crowns, 1 amalgam, enamel) a minimum of 30 restorations had been placed in posterior teeth, mainly molars. The recall intervals were up to 5 years with the majority of materials (n=27) being monitored, however, only for up to 2 years. For the laboratory data, the databases MEDLINE and IADR abstracts were searched for wear data on materials which were also clinically tested by TRAC Research Foundation. Only those data for which the same test parameters (e.g. number of cycles, loading force, type of antagonist) had been published were included in the study. A different quantity of data was available for each laboratory method: Ivoclar (n=22), Zurich (n=20), Alabama (n=17), OHSU and ACTA (n=12), Munich (n=7). The clinical results were summed up in an index and a linear mixed model was fitted to the log wear measurements including the following factors: material, time (0.5, 1, 2 and 3 years), tooth (premolar/molar) and gender (male/female) as fixed effects, and patient as random effect. Relative ranks were created for each material and method; the same was performed with the clinical results. RESULTS: The mean age of the subjects was 40 (±12) years. The materials had been mostly applied in molars (81%) and 95% of the intracoronal restorations were Class II restorations. The mean number of individual wear data per material was 25 (range 14-42). The mean coefficient of variation of clinical wear data was 53%. The only significant correlation was reached by OHSU (abrasion) with a Spearman r of 0.86 (p=0.001). Zurich, ACTA, Alabama generalized wear and Ivoclar (volume) had correlation coefficients between 0.3 and 0.4. For Zurich, Alabama generalized wear and Munich, the correlation coefficient improved if only composites for direct use were taken into consideration. The combination of different laboratory methods did not significantly improve the correlation. SIGNIFICANCE: The clinical wear of composite resins is mainly dependent on differences between patients and less on the differences between materials. Laboratory methods to test conventional resins for wear are therefore less important, especially since most of them do not reflect the clinical wear.

Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies

Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particulary in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process. Genet Epidemiol 25:106-114, 2003. (C) 2003 Wiley-Liss, Inc.

Correlation-type structure activity relationships for the kinetics of gas-phase RO2 self-reactions and reaction with HO2

Structure activity relationships (SARs) are presented for the gas-phase reactions of RO2 with HO2, and the self- and cross-reactions of RO2. For RO2+HO2 the SAR is based upon a correlation between the logarithm of the measured rate coefficient and a calculated ionisation potential for the molecule R-CH=CH2, R being the same group in both the radical and molecular analogue. The correlation observed is strong and only for one RO2 species does the measured rate coefficient deviate by more than a factor of two from the linear least-squares regression line. For the self- and cross-reactions of RO2 radicals, the SAR is based upon a correlation between the logarithm of the measured rate coefficient and the calculated electrostatic potential (ESP) at the equivalent carbon atom in the RH molecule to which oxygen is attached in RO2, again R being the same group in the molecule and the radical. For cases where R is a simple alkyl-group, a strong linear correlation observed. For RO2 radicals which contain lone pair-bearing substituents and for which the calculated ESP<-0.05 self-reaction rate coefficients appear to be insensitive to the value of the ESP. For RO2 of this type with ESP>-0.05 a linear relationship between log k and the ESP is again observed. Using the relationships, 84 out of the 85 rate coefficients used to develop the SARs are predicted to within a factor of three of their measured values. A relationship is also presented that allows the prediction of the Arrhenius parameters for the self-reactions of simple alkyl RO2 radicals. On the basis of the correlations, predictions of room-temperature rate coefficients are made for a number of atmospherically important peroxyl-peroxyl radical reactions. (C) 2003 Elsevier Ltd. All rights reserved.

Correlation between Clinical/Radiographic Features and Inflammatory Cytokine Networks Produced by Macrophages Stimulated with Endodontic Content

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Stratigraphic correlation of ODP Site 199-1218 and 199-1219

During Ocean Drilling Program Leg 199 a high-resolution (~1-2 cm/k.y.) biogenic sediment record from the late Paleocene to the early Miocene was recovered, containing an uninterrupted set of geomagnetic chrons as well as a detailed record of calcareous and siliceous biostratigraphic datum events. Shipboard lithologic proxy measurements and shore-based determinations of CaCO3 revealed regular cycles that can be attributed to climatic forcing. Discovering drill sites with well defined magneto- and biostratigraphic records that also show clear lithologic cycles is rare and valuable and creates the opportunity to develop a detailed stratigraphic intersite correlation, providing the basis to study paleoceanographic processes and mass accumulation rates at high resolution. Here we present extensive postcruise work that extends the shipboard composite depth stratigraphy by providing a high-resolution revised meters composite depth (rmcd) scale to compensate for depth distortion within individual cores. The depth-aligned data were then used to generate stacked records of lithologic proxy measurements. Making use of the increased signal-to-noise ratio in the stacked records, we then proceeded to generate a detailed site-to-site correlation between Sites 1218 and 1219 in order to decrease the depth uncertainty for magneto- and biostratigraphic datums. Stacked lithologic proxy records in combination with discrete measurements of CaCO3 were then exploited to calculate high-resolution carbonate concentration curves by regression of the multisensor track data with discrete measurements. By matching correlative features between the cores and wireline logging data, we also rescaled our core rmcd back to in situ depths. Our study identifies lithology-dependent core expansion due to unloading as the mechanism of varying stratigraphic thicknesses between cores.

A nova família de distribuições odd log-logística: teoria e aplicações

Neste trabalho, foi proposta uma nova família de distribuições, a qual permite modelar dados de sobrevivência quando a função de risco tem formas unimodal e U (banheira). Ainda, foram consideradas as modificações das distribuições Weibull, Fréchet, half-normal generalizada, log-logística e lognormal. Tomando dados não-censurados e censurados, considerou-se os estimadores de máxima verossimilhança para o modelo proposto, a fim de verificar a flexibilidade da nova família. Além disso, um modelo de regressão locação-escala foi utilizado para verificar a influência de covariáveis nos tempos de sobrevida. Adicionalmente, conduziu-se uma análise de resíduos baseada nos resíduos deviance modificada. Estudos de simulação, utilizando-se de diferentes atribuições dos parâmetros, porcentagens de censura e tamanhos amostrais, foram conduzidos com o objetivo de verificar a distribuição empírica dos resíduos tipo martingale e deviance modificada. Para detectar observações influentes, foram utilizadas medidas de influência local, que são medidas de diagnóstico baseadas em pequenas perturbações nos dados ou no modelo proposto. Podem ocorrer situações em que a suposição de independência entre os tempos de falha e censura não seja válida. Assim, outro objetivo desse trabalho é considerar o mecanismo de censura informativa, baseado na verossimilhança marginal, considerando a distribuição log-odd log-logística Weibull na modelagem. Por fim, as metodologias descritas são aplicadas a conjuntos de dados reais.

Statnote 35: are the data log-normal?

In many of the Statnotes described in this series, the statistical tests assume the data are a random sample from a normal distribution These Statnotes include most of the familiar statistical tests such as the ‘t’ test, analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’). Nevertheless, many variables exhibit a more or less ‘skewed’ distribution. A skewed distribution is asymmetrical and the mean is displaced either to the left (positive skew) or to the right (negative skew). If the mean of the distribution is low, the degree of variation large, and when values can only be positive, a positively skewed distribution is usually the result. Many distributions have potentially a low mean and high variance including that of the abundance of bacterial species on plants, the latent period of an infectious disease, and the sensitivity of certain fungi to fungicides. These positively skewed distributions are often fitted successfully by a variant of the normal distribution called the log-normal distribution. This statnote describes fitting the log-normal distribution with reference to two scenarios: (1) the frequency distribution of bacterial numbers isolated from cloths in a domestic environment and (2), the sizes of lichenised ‘areolae’ growing on the hypothalus of Rhizocarpon geographicum (L.) DC.

Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

Seasonality Shows Evidence for Polygenic Architecture and Genetic Correlation With Schizophrenia and Bipolar Disorder

OBJECTIVE: To test common genetic variants for association with seasonality (seasonal changes in mood and behavior) and to investigate whether there are shared genetic risk factors between psychiatric disorders and seasonality. METHOD: Genome-wide association studies (GWASs) were conducted in Australian (between 1988 and 1990 and between 2010 and 2013) and Amish (between May 2010 and December 2011) samples in whom the Seasonal Pattern Assessment Questionnaire (SPAQ) had been administered, and the results were meta-analyzed in a total sample of 4,156 individuals. Genetic risk scores based on results from prior large GWAS studies of bipolar disorder, major depressive disorder (MDD), and schizophrenia were calculated to test for overlap in risk between psychiatric disorders and seasonality. RESULTS: The most significant association was with rs11825064 (P = 1.7 × 10⁻⁶, β = 0.64, standard error = 0.13), an intergenic single nucleotide polymorphism (SNP) found on chromosome 11. The evidence for overlap in risk factors was strongest for schizophrenia and seasonality, with the schizophrenia genetic profile scores explaining 3% of the variance in log-transformed global seasonality scores. Bipolar disorder genetic profile scores were also associated with seasonality, although at much weaker levels (minimum P value = 3.4 × 10⁻³), and no evidence for overlap in risk was detected between MDD and seasonality. CONCLUSIONS: Common SNPs of large effect most likely do not exist for seasonality in the populations examined. As expected, there were overlapping genetic risk factors for bipolar disorder (but not MDD) with seasonality. Unexpectedly, the risk for schizophrenia and seasonality had the largest overlap, an unprecedented finding that requires replication in other populations and has potential clinical implications considering overlapping cognitive deficits in seasonal affective disorders and schizophrenia.