Effect of dentin pretreatment on the resulting abrasive dentin wear

Background This study aims to investigate the influence of different dentin pretreatment procedures on the resulting abrasive dentin wear. Methods Two groups (A, B) of 60 dentin samples each were prepared. Group A was brushed with an abrasive slurry (RDA = 85) and group B with a different abrasive slurry (RDA = 174). Four subgroups in each group (n = 15) were created (A1–A4) and (B1–B4). The subgroups were pretreated as follows: A1 + B1 with 1200-grit grinding paper, A2 + B2 with 1200- and 2000-grit papers, A3 + B3 with 1200-, 2000-, and 4000-grit papers, A4 + B4 with 1200-, 2000-, 4000-grit papers and with 1000 brushing strokes with a slurry of Elmex toothpaste. All samples were brushed for 25 min at 120 strokes/min. Abrasive dentin wear was measured for each sample profilometrically and the subgroups were compared with each other within the same group. Repeated measures one-way ANOVA was conducted to compare the subgroups and pairwise contrasts were estimated for multiple testing according to Tukey (α = 0.05). Results The resulting abrasive dentin wear in group A ranged between 15.3 ± 3.4 µm and 17.3 ± 5.5 µm and between 20.3 ± 6.8 µm and 22.5 ± 2.6 µm in group B. No statistically significant difference was noticed between any subgroups within the same group (p ˃ 0.05). Conclusions Different dentin pretreatment procedures do not affect the resulting abrasive dentin wear independent of the RDA value of the employed abrasive slurry.

Fracture Load of CAD/CAM Feldspathic Crowns Influenced by Abutment Material

In vitro studies investigating the mechanical properties of dental reconstructions use various materials to replicate prepared teeth. However, no uniform recommendation exists as to which material is most suitable for standardized testing. The purpose of this study was to identify a material that resembles human dentin in fracture load tests. Sixteen human teeth were scanned with an intraoral scanner to obtain copies of the original crown morphology and were then prepared for crowns. Replica dies of the prepared teeth including the root morphology were fabricated with a Computer-aided design and computer-aided manufacturing (CAD/CAM) system and divided into four groups: (A) reinforced composite (RC); (B) human dentin (HD); (C) polymethyl methacrylate (PM); and (D) hybrid ceramic (HC). Sixty-four feldspar ceramic crowns were designed with the biocopy mode, fabricated with a CAD/CAM system, luted on the dies, and then with the roots embedded in polymethyl methacrylate. Care was taken to position all specimens of the same morphology identically. Thermo-mechanical load cycling was performed in a chewing simulator followed by fractural loading of the crowns. A mixed effect linear model was fitted to the data, and pairwise contrasts were estimated on the marginal means and corrected for multiple testing according to Tukey (α = 0.05). The means for fracture load (N) were 2435 N (95% CI (2162, 2709)) for hybrid ceramic, 1838 N (95% CI (1565, 2112)) for reinforced composite, 1670 N (95% CI (1396, 1943)) for human tooth and 1142 N (95% CI (868, 1415)) for polymethyl methacrylate abutment materials. Post-hoc pairwise contrasts revealed a statistically significant (p < 0.05) difference among all groups except for reinforced composite and human dentin (p = 0.76). The results indicate that the mechanical properties of abutment dies play a significant role for a possible substitution of natural teeth in in vitro studies.

Effectiveness of S-PRG Filler-Containing Toothpaste in Inhibiting Demineralization of Human Tooth Surface

Objectives: Using an established pH-cycling caries model, the authors evaluated the effectiveness of toothpastes containing Surface Pre-reacted Glass-ionomer filler (S-PRG) in preventing tooth surface demineralization. Materials and Methods: 210 tooth blocks were randomly assigned to seven experimental groups (30 blocks/group): no treatment (A), and toothpaste containing either NaF (B), 0 wt% S-PRG (C), 1 wt% S-PRG (D), 5 wt% S-PRG (E), 20 wt% S-PRG (F) or 30 wt% S-PRG (G). Groups were subjected to 14-day demineralization for development of early caries lesions using a pH-cycling caries model. Demineralization was assessed using Quantitative Light-induced Fluorescence (QLF) and Transverse Microradiography (TMR). All pairwise contrasts (between treatments) were tested using Analysis of Variance (ANOVA), and then Tukey’s HSD for multiple comparisons. All p-values are considered significant if <0.05. Results: With QLF, there was a significant (ANOVA; p<0.001) difference in mean percent fluorescence loss (∆F) observed among the groups. Relative to control, all S-PRG-containing toothpastes significantly (Tukey’s; p<0.0001) inhibited demineralization at varying percentages (48.6%, 61.3%, 67.4% and 69.8% reduction with S-PRG 1%, 5%, 20% and 30% respectively). Demineralization reduction was not significant with either NaF (15.6% reduction) or 0% S-PRG (-2.5% reduction i.e. 2.5% more demineralization than the Control) when compared to control group. Mineral loss assessed using TMR followed a similar trend as fluorescence loss. Conclusion: Toothpaste containing S-PRG filler can serve as an effective caries control tool. S-PRG filler-containing dentifrice to be more effective in preventing tooth demineralization than 1100 ppm fluoride provided as sodium fluoride.

Design-Based Estimators for Average Treatment Effects for Multi-Armed RCTs

Design-based methods have recently been developed as a way to analyze randomized controlled trial (RCT) data for designs with a single treatment and control group. This article builds on this framework to develop design-based estimators for evaluations with multiple research groups. Results are provided for a wide range of designs used in education research, including clustered and blocked designs. Because analysis in the multi-armed setting involves pairwise contrasts across the research groups, the key methodological question addressed is: How do the estimators for the two-group design need to be adjusted for multi-armed trials? The critical insight is that in multi-armed trials where the goal is to identify the most effective treatments, the samples for each pairwise contrast are representative of the full set of randomized units, not just of themselves. The implication is that variance terms need to be adjusted slightly under the finite-population framework that can reduce precision, and blocks need to be weighted to reflect the full randomized sample in the block or biases can result. An empirical example using data from a multi-armed education RCT demonstrates the issues.

Quantifying how constraints limit the diversity of viable routes to adaptation

Convergent adaptation can occur at the genome scale when independently evolving lineages use the same genes to respond to similar selection pressures. These patterns provide insights into the factors that facilitate or constrain the diversity of genetic responses that contribute to adaptive evolution. A first step in studying such factors is to quantify the observed amount of repeatability relative to expectations under a null hypothesis. Here, we formulate a novel metric to quantify the constraints driving the observed amount of repeated adaptation in pairwise contrasts based on the hypergeometric distribution, and then generalize this for simultaneous analysis of multiple lineages. This metric is explicitly based on the probability of observing a given amount of repeatability by chance under an arbitrary null hypothesis, and is readily compared among different species and types of trait. We also formulate a metric to quantify the effective proportion of genes in the genome that have the potential to contribute to adaptation. As an example of how these metrics can be used to draw inferences, we assess the amount of repeatability observed in existing datasets on adaptation to antibiotics in yeast and climate in conifers. This approach provides a method to test a wide range of hypotheses about how different kinds of factors can facilitate or constrain the diversity of genetic responses observed during adaptive evolution.

Evaluation of Annual Bluegrass Control Programs for Bermudagrass Turf Overseeded with Perennial Ryegrass

Few herbicides are available that will selectively control annual bluegrass in a polyculture of bermudagrass overseeded with perennial ryegrass. Research was conducted to evaluate multifaceted annual bluegrass control programs in overseeded bermudagrass. Treatments included various combinations of four herbicides: foramsulfuron applied 2 wk prior to overseeding (WPO); ethofumesate and bispyribac-sodium (bispyribac) applied 12 or 12 followed by (fb) 15 wk after overseeding (WAO); and prodiamine applied 15 WAO to provide continued PRE annual bluegrass control. Foramsulfuron at 0.03 kg ha−1applied 2 WPO controlled annual bluegrass 63% 28 WAO. Foramsulfuron fb ethofumesate or bispyribac applied 12 or 12 fb 15 WAO improved control to 91% or greater. Ethofumesate or bispyribac applied with prodiamine at 1.1 kg ha−1at 15 WAO did not improve annual bluegrass control compared with ethofumesate or bispyribac treatments alone according to pairwise contrasts. Only bispyribac-containing treatments induced unacceptable perennial ryegrass injury. Bispyribac applied with prodiamine reduced perennial ryegrass cover greater than bispyribac alone according to pairwise contrast. These data indicate that ethofumesate or bispyribac applied sequentially 12 and 15 WPO can effectively control annual bluegrass in bermudagrass turf overseeded with perennial ryegrass.

A Statistical Comparison of the Blossom Blight Forecasts of MARYBLYT and Cougarblight with Receiver Operating Characteristic Curve Analysis

Blossom blight forecasting is an important aspect of fire blight, caused by Erwinia amylovora, management for both apple and pear. A comparison of the forecast accuracy of two common fire blight forecasters, MARYBLYT and Cougarblight, was performed with receiver operating characteristic (ROC) curve analysis and 243 data sets. The rain threshold of Cougarblight was analyzed as a separate model termed Cougarblight and rain. Data were used as a whole and then grouped into geographic regions and cultivar susceptibilities. Frequency distributions of cases and controls, orchards or regions (depending on the data set), with and without observed disease, respectively, in all data sets overlapped. MARYBLYT, Cougarblight, and Cougarblight and rain all predicted blossom blight infection better than chance (P = 0.05). It was found that the blossom blight forecasters performed equivalently in the geographic regions of the east and west coasts of North America and moderately susceptible cultivars based on the 95% confidence intervals and pairwise contrasts of the area under the ROC curve. Significant differences (P < 0.05) between the forecasts of Cougarblight and MARYBLYT were found with pairwise contrasts in the England and very susceptible cultivar data sets. Youden's index was used to determine the optimal cutpoint of both forecasters. The greatest sensitivity and specificity for MARYBLYT coincided with the use of the highest risk threshold for predictions of infection; with Cougarblight, there was no clear single risk threshold across all data sets.

Improved Repeated Measures Stepwise Multiple Comparison Procedures

Approximate degrees of freedom omnibus and pairwise test statistics of Johansen (1980) and Keselman, Keselman, and Shaffer (1991) , respectively, were used with numerous stepwise multiple comparison procedures (MCPs) to perform pairwise contrasts on repeated measures means. The MCPs were compared for their overall familywise rates of Type I error and for their sensitivity to detect true pairwise differences among means when multisample sphericity and multivariate normality assumptions were not satisfied. Results indicated that multiple range procedures which were modified according to the method described by Duncan (1957) were always robust with respect to Type I errors and were at least as powerful as the unmodified range procedures, and could result in increases in power as large as 22%. Overall, the Welsch (1977a) step-up, Peritz-Duncan ( Peritz, 1970 ), and Ryan-Welsch-Duncan ( Ryan, 1960 ; Welsch, 1977a ) multiple range procedures were found to be most powerful.

Determining the Most Significant Parametric Function for a Given Linear Hypothesis

After a hypothesis about some linear statistical model has been tested and rejected (e.g., in an ANOVA), many researchers employ the Scheffe procedure to locate the source(s) of the rejection. This procedure guarantees that there is at least one linear combination of the model parameters (consistent with the hypothesis) that is significantly different from its hypothesized value. This most significant parametric function is not always easy to find, however, because it may not manifest itself in simple functions (such as pairwise contrasts between groups) or in “obvious” functions (such as those suggested by the graph of an interaction). A general solution to this problem is presented along with a practical example of its application.