Influence of Tapered and External Hexagon Connections on Bone Stresses Around Tilted Dental Implants: Three-Dimensional Finite Element Method With Statistical Analysis

Background: The purpose of this study is to analyze the tension distribution on bone tissue around implants with different angulations (0 degrees, 17 degrees, and 30 degrees) and connections (external hexagon and tapered) through the use of three-dimensional finite element and statistical analyses.Methods: Twelve different configurations of three-dimensional finite element models, including three inclinations of the implants (0 degrees, 17 degrees, and 30 degrees), two connections (an external hexagon and a tapered), and two load applications (axial and oblique), were simulated. The maximum principal stress values for cortical bone were measured at the mesial, distal, buccal, and lingual regions around the implant for each analyzed situation, totaling 48 groups. Loads of 200 and 100 N were applied at the occlusal surface in the axial and oblique directions, respectively. Maximum principal stress values were measured at the bone crest and statistically analyzed using analysis of variance. Stress patterns in the bone tissue around the implant were analyzed qualitatively.Results: The results demonstrated that under the oblique loading process, the external hexagon connection showed significantly higher stress concentrations in the bone tissue (P < 0.05) compared with the tapered connection. Moreover, the buccal and mesial regions of the cortical bone concentrated significantly higher stress (P < 0.005) to the external hexagon implant type. Under the oblique loading direction, the increased external hexagon implant angulation induced a significantly higher stress concentration (P = 0.045).Conclusions: The study results show that: 1) the oblique load was more damaging to bone tissue, mainly when associated with external hexagon implants; and 2) there was a higher stress concentration on the buccal region in comparison to all other regions under oblique load.

Influence of soil texture in the recovery of Toxocara canis eggs by a flotation method

In epidemiological surveys, the evaluation of soil contamination by Toxocara canis eggs requires a quick and easy method for the isolation of parasite eggs from soil samples. The efficiency of flotation methods is influenced by sample size, soil texture, degree of soil contamination, pretreatment, flotation solutions and time of flotation. This investigation was designed to evaluate the influence of soil texture in the recovery of T. canis eggs with the centrifugal flotation technique of Dada (Dada, B.J.O., 1979. A new technique for the recovery of Toxocara eggs from soil. J. Helminthol., 53: 141-144). Four types of soil (clay silt, sandy, silty clay and sand) were artificially contaminated with T. canis eggs (200 eggs per gram). Zinc sulphate (specific gravity 1.20) and sodium dichromate (specific gravity 1.35) were used as flotation solutions. Twenty replicated examinations were performed for each type of soil and flotation solution. There was a statistically significant difference in the results depending on soil type. The highest recovery percentages were observed in soils rich in sand (62.5% for sand and 38.0% for sandy soil). Differences were also observed with different flotation solutions. Sodium dichromate solution was more efficient for recovering T. canis eggs, regardless of the soil texture. © 1994.

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.

Thermo and mechanical cycling and veneering method do not influence Y-TZP core/veneer interface bond strength

Objectives: The purpose of this study was to evaluate the influence of thermal and mechanical cycling and veneering technique on the shear bond strength of Y-TZP (yttrium oxide partially stabilized tetragonal zirconia polycrystal) core–veneer interfaces. Materials and methods: Cylindrical Y-TZP specimens were veneered either by layering (n = 20) or by pressing technique (n = 20). A metal ceramic group (CoCr) was used as control (n = 20). Ten specimens for each group were thermal and mechanical cycled and then all samples were subjected to shear bond strength in a universal testing machine with a 0.5 mm/min crosshead speed. Mean shear bond strength (MPa) was analysed with a 2-way analysis of variance and Tukey’s test ( p < 0.05). Failure mode was determined using stereomicroscopy and scanning electron microscopy (SEM). Results: Thermal and mechanical cycling had no influence on the shear bond strength for all groups. The CoCr group presented the highest bond strength value ( p < 0.05) (34.72 7.05 MPa). There was no significant difference between Y-TZP veneered by layering (22.46 2.08 MPa) or pressing (23.58 2.1 MPa) technique. Failure modes were predominantly adhesive for CoCr group, and cohesive within veneer for Y-TZP groups. Conclusions: Thermal and mechanical cycling, as well as the veneering technique does not affect Y-TZP core–veneer bond strength. Clinical significance: Different methods of veneering Y-TZP restorations would not influence the clinical performance of the core/veneer interfaces.

Variance Reduction in Analytical Chemistry : New Numerical Methods in Chemometrics and Molecular Simulation

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods. Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules. Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments. Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV. Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed. Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

A multi-level decomposition of variance in somatic symptom reporting in families with adolescent children

OBJECTIVES: This paper examines four different levels of possible variation in symptom reporting: occasion, day, person and family. DESIGN: In order to rule out effects of retrospection, concurrent symptom reporting was assessed prospectively using a computer-assisted self-report method. METHODS: A decomposition of variance in symptom reporting was conducted using diary data from families with adolescent children. We used palmtop computers to assess concurrent somatic complaints from parents and children six times a day for seven consecutive days. In two separate studies, 314 and 254 participants from 96 and 77 families, respectively, participated. A generalized multilevel linear models approach was used to analyze the data. Symptom reports were modelled using a logistic response function, and random effects were allowed at the family, person and day level, with extra-binomial variation allowed for on the occasion level. RESULTS: Substantial variability was observed at the person, day and occasion level but not at the family level. CONCLUSIONS: To explain symptom reporting in normally healthy individuals, situational as well as person characteristics should be taken into account. Family characteristics, however, would not help to clarify symptom reporting in all family members.

Precipitation over the past four centuries in the Dieshan Mountains as inferred from tree rings: An introduction to an HHT-based method

To improve our understanding of the Asian monsoon system, we developed a hydroclimate reconstruction in a marginal monsoon shoulder region for the period prior to the industrial era. Here, we present the first moisture sensitive tree-ring chronology, spanning 501 years for the Dieshan Mountain area, a boundary region of the Asian summer monsoon in the northeastern Tibetan Plateau. This reconstruction was derived from 101 cores of 68 old-growth Chinese pine (Pinus tabulaeformis) trees. We introduce a Hilbert–Huang Transform (HHT) based standardization method to develop the tree-ring chronology, which has the advantages of excluding non-climatic disturbances in individual tree-ring series. Based on the reliable portion of the chronology, we reconstructed the annual (prior July to current June) precipitation history since 1637 for the Dieshan Mountain area and were able to explain 41.3% of the variance. The extremely dry years in this reconstruction were also found in historical documents and are also associated with El Niño episodes. Dry periods were reconstructed for 1718–1725, 1766–1770 and 1920–1933, whereas 1782–1788 and 1979–1985 were wet periods. The spatial signatures of these events were supported by data from other marginal regions of the Asian summer monsoon. Over the past four centuries, out-of-phase relationships between hydroclimate variations in the Dieshan Mountain area and far western Mongolia were observed during the 1718–1725 and 1766–1770 dry periods and the 1979–1985 wet period.

A nonparametric method of comparing the partial areas under two correlated receiver operating characteristic curves

A non-parametric method was developed and tested to compare the partial areas under two correlated Receiver Operating Characteristic curves. Based on the theory of generalized U-statistics the mathematical formulas have been derived for computing ROC area, and the variance and covariance between the portions of two ROC curves. A practical SAS application also has been developed to facilitate the calculations. The accuracy of the non-parametric method was evaluated by comparing it to other methods. By applying our method to the data from a published ROC analysis of CT image, our results are very close to theirs. A hypothetical example was used to demonstrate the effects of two crossed ROC curves. The two ROC areas are the same. However each portion of the area between two ROC curves were found to be significantly different by the partial ROC curve analysis. For computation of ROC curves with large scales, such as a logistic regression model, we applied our method to the breast cancer study with Medicare claims data. It yielded the same ROC area computation as the SAS Logistic procedure. Our method also provides an alternative to the global summary of ROC area comparison by directly comparing the true-positive rates for two regression models and by determining the range of false-positive values where the models differ. ^

A novel method for evaluating an interaction effect of correlated continuous covariates in a linear model

Interaction effect is an important scientific interest for many areas of research. Common approach for investigating the interaction effect of two continuous covariates on a response variable is through a cross-product term in multiple linear regression. In epidemiological studies, the two-way analysis of variance (ANOVA) type of method has also been utilized to examine the interaction effect by replacing the continuous covariates with their discretized levels. However, the implications of model assumptions of either approach have not been examined and the statistical validation has only focused on the general method, not specifically for the interaction effect.^ In this dissertation, we investigated the validity of both approaches based on the mathematical assumptions for non-skewed data. We showed that linear regression may not be an appropriate model when the interaction effect exists because it implies a highly skewed distribution for the response variable. We also showed that the normality and constant variance assumptions required by ANOVA are not satisfied in the model where the continuous covariates are replaced with their discretized levels. Therefore, naïve application of ANOVA method may lead to an incorrect conclusion. ^ Given the problems identified above, we proposed a novel method modifying from the traditional ANOVA approach to rigorously evaluate the interaction effect. The analytical expression of the interaction effect was derived based on the conditional distribution of the response variable given the discretized continuous covariates. A testing procedure that combines the p-values from each level of the discretized covariates was developed to test the overall significance of the interaction effect. According to the simulation study, the proposed method is more powerful then the least squares regression and the ANOVA method in detecting the interaction effect when data comes from a trivariate normal distribution. The proposed method was applied to a dataset from the National Institute of Neurological Disorders and Stroke (NINDS) tissue plasminogen activator (t-PA) stroke trial, and baseline age-by-weight interaction effect was found significant in predicting the change from baseline in NIHSS at Month-3 among patients received t-PA therapy.^

A hybrid method in combining treatment effects from matched and unmatched studies

In the biomedical studies, the general data structures have been the matched (paired) and unmatched designs. Recently, many researchers are interested in Meta-Analysis to obtain a better understanding from several clinical data of a medical treatment. The hybrid design, which is combined two data structures, may create the fundamental question for statistical methods and the challenges for statistical inferences. The applied methods are depending on the underlying distribution. If the outcomes are normally distributed, we would use the classic paired and two independent sample T-tests on the matched and unmatched cases. If not, we can apply Wilcoxon signed rank and rank sum test on each case. ^ To assess an overall treatment effect on a hybrid design, we can apply the inverse variance weight method used in Meta-Analysis. On the nonparametric case, we can use a test statistic which is combined on two Wilcoxon test statistics. However, these two test statistics are not in same scale. We propose the Hybrid Test Statistic based on the Hodges-Lehmann estimates of the treatment effects, which are medians in the same scale.^ To compare the proposed method, we use the classic meta-analysis T-test statistic on the combined the estimates of the treatment effects from two T-test statistics. Theoretically, the efficiency of two unbiased estimators of a parameter is the ratio of their variances. With the concept of Asymptotic Relative Efficiency (ARE) developed by Pitman, we show ARE of the hybrid test statistic relative to classic meta-analysis T-test statistic using the Hodges-Lemann estimators associated with two test statistics.^ From several simulation studies, we calculate the empirical type I error rate and power of the test statistics. The proposed statistic would provide effective tool to evaluate and understand the treatment effect in various public health studies as well as clinical trials.^

Estimates of hospital and physician costs of epilepsy in the United States for 1995 comparing the provider -based survey method and a patient -based medical charts and billing method

This investigation compares two different methodologies for calculating the national cost of epilepsy: provider-based survey method (PBSM) and the patient-based medical charts and billing method (PBMC&BM). The PBSM uses the National Hospital Discharge Survey (NHDS), the National Hospital Ambulatory Medical Care Survey (NHAMCS) and the National Ambulatory Medical Care Survey (NAMCS) as the sources of utilization. The PBMC&BM uses patient data, charts and billings, to determine utilization rates for specific components of hospital, physician and drug prescriptions. ^ The 1995 hospital and physician cost of epilepsy is estimated to be $722 million using the PBSM and $1,058 million using the PBMC&BM. The difference of $336 million results from $136 million difference in utilization and $200 million difference in unit cost. ^ Utilization. The utilization difference of $136 million is composed of an inpatient variation of $129 million, $100 million hospital and $29 million physician, and an ambulatory variation of $7 million. The $100 million hospital variance is attributed to inclusion of febrile seizures in the PBSM, $−79 million, and the exclusion of admissions attributed to epilepsy, $179 million. The former suggests that the diagnostic codes used in the NHDS may not properly match the current definition of epilepsy as used in the PBMC&BM. The latter suggests NHDS errors in the attribution of an admission to the principal diagnosis. ^ The $29 million variance in inpatient physician utilization is the result of different per-day-of-care physician visit rates, 1.3 for the PBMC&BM versus 1.0 for the PBSM. The absence of visit frequency measures in the NHDS affects the internal validity of the PBSM estimate and requires the investigator to make conservative assumptions. ^ The remaining ambulatory resource utilization variance is $7 million. Of this amount, $22 million is the result of an underestimate of ancillaries in the NHAMCS and NAMCS extrapolations using the patient visit weight. ^ Unit cost. The resource cost variation is $200 million, inpatient is $22 million and ambulatory is $178 million. The inpatient variation of $22 million is composed of $19 million in hospital per day rates, due to a higher cost per day in the PBMC&BM, and $3 million in physician visit rates, due to a higher cost per visit in the PBMC&BM. ^ The ambulatory cost variance is $178 million, composed of higher per-physician-visit costs of $97 million and higher per-ancillary costs of $81 million. Both are attributed to the PBMC&BM's precise identification of resource utilization that permits accurate valuation. ^ Conclusion. Both methods have specific limitations. The PBSM strengths are its sample designs that lead to nationally representative estimates and permit statistical point and confidence interval estimation for the nation for certain variables under investigation. However, the findings of this investigation suggest the internal validity of the estimates derived is questionable and important additional information required to precisely estimate the cost of an illness is absent. ^ The PBMC&BM is a superior method in identifying resources utilized in the physician encounter with the patient permitting more accurate valuation. However, the PBMC&BM does not have the statistical reliability of the PBSM; it relies on synthesized national prevalence estimates to extrapolate a national cost estimate. While precision is important, the ability to generalize to the nation may be limited due to the small number of patients that are followed. ^

Q method factor analysis of stakeholder social perspectives of the effectiveness of Environmental Impact Assessment public participation in the Western Cape, South Africa

Public participation is an integral part of Environmental Impact Assessment (EIA), and as such, has been incorporated into regulatory norms. Assessment of the effectiveness of public participation has remained elusive however. This is partly due to the difficulty in identifying appropriate effectiveness criteria. This research uses Q methodology to discover and analyze stakeholder's social perspectives of the effectiveness of EIAs in the Western Cape, South Africa. It considers two case studies (Main Road and Saldanha Bay EIAs) for contextual participant perspectives of the effectiveness based on their experience. It further considers the more general opinion of provincial consent regulator staff at the Department of Environmental Affairs and the Department of Planning (DEA&DP). Two main themes of investigation are drawn from the South African National Environmental Management Act imperative for effectiveness: firstly, the participation procedure, and secondly, the stakeholder capabilities necessary for effective participation. Four theoretical frameworks drawn from planning, politics and EIA theory are adapted to public participation and used to triangulate the analysis and discussion of the revealed social perspectives. They consider citizen power in deliberation, Habermas' preconditions for the Ideal Speech Situation (ISS), a Foucauldian perspective of knowledge, power and politics, and a Capabilities Approach to public participation effectiveness. The empirical evidence from this research shows that the capacity and contextual constraints faced by participants demand the legislative imperatives for effective participation set out in the NEMA. The implementation of effective public participation has been shown to be a complex, dynamic and sometimes nebulous practice. The functional level of participant understanding of the process was found to be significantly wide-ranging with consequences of unequal and dissatisfied stakeholder engagements. Furthermore, the considerable variance of stakeholder capabilities in the South African social context, resulted in inequalities in deliberation. The social perspectives revealed significant differences in participant experience in terms of citizen power in deliberation. The ISS preconditions are highly contested in both the Saldanha EIA case study and the DEA&DP social perspectives. Only one Main Road EIA case study social perspective considered Foucault's notion of governmentality as a reality in EIA public participation. The freedom of control of ones environment, based on a Capabilities approach, is a highly contested notion. Although agreed with in principle, all of the social perspectives indicate that contextual and capacity realities constrain its realisation. This research has shown that Q method can be applied to EIA public participation in South Africa and, with the appropriate research or monitoring applications it could serve as a useful feedback tool to inform best practice public participation.

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re-assign non identified species in each site to any of the other species found in the remaining sites. After each re-assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re-assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus-resolution, family-resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.

Variance and covariance of actual relationship between relatives at one locus.

The relationship between pairs of individuals is an important topic in many areas of population and quantitative genetics. It is usually measured as the proportion of thegenome identical by descent shared by the pair and it can be inferred from pedigree information. But there is a variance in actual relationships as a consequence of Mendelian sampling, whose general formula has not been developed. The goal of this work is to develop this general formula for the one-locus situation,. We provide simple expressions for the variances and covariances of all actual relationships in an arbitrary complex pedigree. The proposed method relies on the use of the nine identity coefficients and the generalized relationship coefficients; formulas have been checked by computer simulation. Finally two examples for a short pedigree of dogs and a long pedigree of sheep are given.