Pairwise Statistical Significance Research Articles

The effects of mouth rinses on the color stability of resin-based restorative materials.

The aim of this study was to assess the color stability of three direct resin-based restorative materials: IPS Empress Direct (Ivoclar Vivadent, Schaan, Liechtenstein), Nano-filled composite (Filtek Z 350 XT, 3 M ESPE, St. Paul, MN, USA), and Nano-hybrid composite (Tetric Evo Ceram, Ivoclar Vivadent, Schaan, Liechtenstein) upon immersion into the following three mouth rinses: Antiseptol (Kahira Pharmaceuticals and Chemical Industries Co. Cairo-Egypt), Flucal (Alexandria Co. Pharmaceuticals Alexandria, Egypt), and Listerine (Johnson & Johnson, UK). Ninety disc-shaped, 12 mm × 1 mm specimens were divided into three groups according to the type of direct resin-based restorative material used. The specimens were randomly subdivided into three different subgroups (N = 10) in terms of immersion medium. Color change was evaluated prior to and after immersion into the mouth rinses for 24 hours by spectrophotometry (Shimadzu, UV-3101 PC Shimadzu Corporation. Kyoto, Japan). Data were statistically analyzed using two-way analysis of variance (ANOVA) to assess the color stability of the restorative materials. The post hoc Scheffe's test was applied to clarify pair-wise statistical significance. Results with p-values < 0.05 were considered statistically significant. IPS Empress Direct (ΔE = 1.48) exhibited more favorable stability than the other tested composite resins, Filtek Z 350 X (ΔE = 3.05) and Tetric Evo Ceram (ΔE = 10.35). The immersion media elicited a significant effect on the color stability of the tested, resin-based restorative materials, where Flucal elicited the most significant color change, followed by Listerine and Antiseptol, which elicited the least significant color change. Within the limitation of this laboratory study, the following conclusions could be drawn: (1) The composite structure, namely the resin formulation, which includes the filler size and type of photo-initiator, has a direct impact on its susceptibility to stain by external agents; (2) Mouth rinses can be considered stainable solutions; (3) The chemical formulation of individual mouth rinses can significantly control their ability to stain. Patient use of mouth rinses should be subject to dental supervision to control their adverse effects on the aesthetic quality of the restoration. Knowing the composition of the restorative material is important, as is its polymerization cycle and the promotion of adequate surface texture in order to select the appropriate material for each clinical application, and to use it in an effective way to promote its best properties.

Network meta-analysis of randomized clinical trials: reporting the proper summaries.

In the absence of sufficient data directly comparing multiple treatments, indirect comparisons using network meta-analyses (NMAs) can provide useful information. Under current contrast-based (CB) methods for binary outcomes, the patient-centered measures including the treatment-specific event rates and risk differences (RDs) are not provided, which may create some unnecessary obstacles for patients to comprehensively trade-off efficacy and safety measures. We aim to develop NMA to accurately estimate the treatment-specific event rates. A Bayesian hierarchical model is developed to illustrate how treatment-specific event rates, RDs, and risk ratios (RRs) can be estimated. We first compare our approach to alternative methods using two hypothetical NMAs assuming a fixed RR or RD, and then use two published NMAs to illustrate the improved reporting. In the hypothetical NMAs, our approach outperforms current CB NMA methods in terms of bias. In the two published NMAs, noticeable differences are observed in the magnitude of relative treatment effects and several pairwise statistical significance tests from previous report. First, to facilitate the estimation, each study is assumed to hypothetically compare all treatments, with unstudied arms being missing at random. It is plausible that investigators may have selected treatment arms on purpose based on the results of previous trials, which may lead to 'nonignorable missingness' and potentially bias our estimates. Second, we have not considered methods to identify and account for potential inconsistency between direct and indirect comparisons. The proposed NMA method can accurately estimate treatment-specific event rates, RDs, and RRs and is recommended.

Structured association analysis leads to insight into Saccharomyces cerevisiaegene regulation by finding multiple contributing eQTL hotspots associated with functional gene modules

BackgroundAssociation analysis using genome-wide expression quantitative trait locus (eQTL) data investigates the effect that genetic variation has on cellular pathways and leads to the discovery of candidate regulators. Traditional analysis of eQTL data via pairwise statistical significance tests or linear regression does not leverage the availability of the structural information of the transcriptome, such as presence of gene networks that reveal correlation and potentially regulatory relationships among the study genes. We employ a new eQTL mapping algorithm, GFlasso, which we have previously developed for sparse structured regression, to reanalyze a genome-wide yeast dataset. GFlasso fully takes into account the dependencies among expression traits to suppress false positives and to enhance the signal/noise ratio. Thus, GFlasso leverages the gene-interaction network to discover the pleiotropic effects of genetic loci that perturb the expression level of multiple (rather than individual) genes, which enables us to gain more power in detecting previously neglected signals that are marginally weak but pleiotropically significant.ResultsWhile eQTL hotspots in yeast have been reported previously as genomic regions controlling multiple genes, our analysis reveals additional novel eQTL hotspots and, more interestingly, uncovers groups of multiple contributing eQTL hotspots that affect the expression level of functional gene modules. To our knowledge, our study is the first to report this type of gene regulation stemming from multiple eQTL hotspots. Additionally, we report the results from in-depth bioinformatics analysis for three groups of these eQTL hotspots: ribosome biogenesis, telomere silencing, and retrotransposon biology. We suggest candidate regulators for the functional gene modules that map to each group of hotspots. Not only do we find that many of these candidate regulators contain mutations in the promoter and coding regions of the genes, in the case of the Ribi group, we provide experimental evidence suggesting that the identified candidates do regulate the target genes predicted by GFlasso.ConclusionsThus, this structured association analysis of a yeast eQTL dataset via GFlasso, coupled with extensive bioinformatics analysis, discovers a novel regulation pattern between multiple eQTL hotspots and functional gene modules. Furthermore, this analysis demonstrates the potential of GFlasso as a powerful computational tool for eQTL studies that exploit the rich structural information among expression traits due to correlation, regulation, or other forms of biological dependencies.

FPGA architecture for pairwise statistical significance estimation

Sequence comparison is one of the most fundamental computational problems in bioinformatics. Pairwise sequence alignment methods align two sequences using a substitution matrix consisting of pairwise scores of aligning different residues with each other like BLOSUM62, and give an alignment score for the given sequence-pair. This work addresses the problem of accurately estimating statistical significance of pairwise alignment for the purpose of identifying related sequences, by making the sequence comparison process more sequence-specific. Specifically, we develop algorithms for sequence-specific strategies for hardware acceleration of pairwise sequence alignment in conjunction with statistical significance estimation. Using pairwise statistical significance has been shown to give better retrieval accuracy compared to database statistical significance reported by popular database search programmes like BLAST and PSI-BLAST. We provide a 'flexible array' hardware architecture which provides a scalable systolic array suitable for both long and short sequences. The results with Xtremedata XD1000 FPGA platform show a speed-up by up to a factor of more than 200.

Accelerating pairwise statistical significance estimation for local alignment by harvesting GPU's power

BackgroundPairwise statistical significance has been recognized to be able to accurately identify related sequences, which is a very important cornerstone procedure in numerous bioinformatics applications. However, it is both computationally and data intensive, which poses a big challenge in terms of performance and scalability.ResultsWe present a GPU implementation to accelerate pairwise statistical significance estimation of local sequence alignment using standard substitution matrices. By carefully studying the algorithm's data access characteristics, we developed a tile-based scheme that can produce a contiguous data access in the GPU global memory and sustain a large number of threads to achieve a high GPU occupancy. We further extend the parallelization technique to estimate pairwise statistical significance using position-specific substitution matrices, which has earlier demonstrated significantly better sequence comparison accuracy than using standard substitution matrices. The implementation is also extended to take advantage of dual-GPUs. We observe end-to-end speedups of nearly 250 (370) × using single-GPU Tesla C2050 GPU (dual-Tesla C2050) over the CPU implementation using Intel© Core™i7 CPU 920 processor.ConclusionsHarvesting the high performance of modern GPUs is a promising approach to accelerate pairwise statistical significance estimation for local sequence alignment.

Par-PSSE: Software for Pairwise Statistical Significance Estimation in Parallel for Local Sequence Alignment

Pairwise statistical significance (PSS) has been recognized as a very useful method for homology detection. It can help in estimating whether the output of sequence alignment is evolutionarily link or just arisen by accident. However, pairwise statistical significance estimation (PSSE) poses a big challenge in terms of performance and scalability since it is both computationally intensive and data intensive to construct the empirical score distribution during the estimation. This paper presents a software library for estimating pairwise statistical significance in parallel, named Par-PSSE, implemented in C++ using OpenMP, MPI paradigms and their hybrids. Further, we apply the parallelization technique to estimate non-conservative PSS using standard, sequence-specific, and position-specific substitution matrices. These extensions have been found superior compared to the standard pairwise statistical significance in term of retrieval accuracy. Through distributing the compute-intensive kernels of the pairwise statistical significance estimation across multiple computational units, we achieve a speedup of up to 621.73× over the corresponding sequential implementation when using1024 cores.

Parallel pairwise statistical significance estimation of local sequence alignment using Message Passing Interface library

SUMMARYHomology detection is a fundamental step in sequence analysis. In the recent years, pairwise statistical significance has emerged as a promising alternative to database statistical significance for homology detection. Although more accurate, currently it is much time consuming because it involves generating tens of hundreds of alignment scores to construct the empirical score distribution. This paper presents a parallel algorithm for pairwise statistical significance estimation, called MPIPairwiseStatSig, implemented in C using MPI library. We further apply the parallelization technique to estimate non‐conservative pairwise statistical significance using standard, sequence‐specific, and position‐specific substitution matrices, which has earlier demonstrated superior sequence comparison accuracy than original pairwise statistical significance. Distributing the most compute‐intensive portions of the pairwise statistical significance estimation procedure across multiple processors has been shown to result in near‐linear speed‐ups for the application. The MPIPairwiseStatSig program for pairwise statistical significance estimation is available for free academic use at www.cs.iastate.edu~ankitag/MPIPairwiseStatSig.html. Copyright © 2011 John Wiley &amp; Sons, Ltd.

Pairwise Statistical Significance of Local Sequence Alignment Using Sequence-Specific and Position-Specific Substitution Matrices

Pairwise sequence alignment is a central problem in bioinformatics, which forms the basis of various other applications. Two related sequences are expected to have a high alignment score, but relatedness is usually judged by statistical significance rather than by alignment score. Recently, it was shown that pairwise statistical significance gives promising results as an alternative to database statistical significance for getting individual significance estimates of pairwise alignment scores. The improvement was mainly attributed to making the statistical significance estimation process more sequence-specific and database-independent. In this paper, we use sequence-specific and position-specific substitution matrices to derive the estimates of pairwise statistical significance, which is expected to use more sequence-specific information in estimating pairwise statistical significance. Experiments on a benchmark database with sequence-specific substitution matrices at different levels of sequence-specific contribution were conducted, and results confirm that using sequence-specific substitution matrices for estimating pairwise statistical significance is significantly better than using a standard matrix like BLOSUM62, and than database statistical significance estimates reported by popular database search programs like BLAST, PSI-BLAST (without pretrained PSSMs), and SSEARCH on a benchmark database, but with pretrained PSSMs, PSI-BLAST results are significantly better. Further, using position-specific substitution matrices for estimating pairwise statistical significance gives significantly better results even than PSI-BLAST using pretrained PSSMs.

Efficacy, pharmacodynamics, and safety of VX‐702, a novel p38 MAPK inhibitor, in rheumatoid arthritis: Results of two randomized, double‐blind, placebo‐controlled clinical studies

To assess the efficacy and safety of VX-702, a p38 MAPK inhibitor, in patients with active, moderate-to-severe rheumatoid arthritis (RA). Two 12-week, double-blind, placebo-controlled studies of VX-702 were conducted in patients with active, moderate-to-severe RA. In the VeRA study, 313 patients received placebo or 2 daily doses of VX-702. In Study 304, 117 patients received placebo, daily VX-702, or twice weekly VX-702 in addition to concomitant methotrexate (MTX). Study end points included the proportion of patients meeting the American College of Rheumatology 20% improvement criteria (an ACR20 response), ACR50 and ACR70 responses, changes in the serum levels of biomarkers of inflammation, and safety assessments. The numerically superior ACR20 response rates among patients receiving VX-702 compared with those receiving placebo in both studies did not reach pairwise statistical significance at the highest doses in either study. At week 12 in the VeRA study, ACR20 response rates were 40%, 36%, and 28% among patients receiving 10 mg of VX-702, 5 mg of VX-702, and placebo, respectively. In Study 304, the response rates were 40%, 44%, and 22% for patients receiving 10 mg VX-702 daily plus MTX, 10 mg VX-702 twice weekly plus MTX, and placebo, respectively. Reductions in the levels of C-reactive protein, soluble tumor necrosis factor receptor p55, and serum amyloid A were observed as early as week 1 in both studies, but these levels rapidly returned to baseline values by week 4. The overall frequency of adverse events was similar between the VX-702 and placebo groups. In the VeRA study, serious infections were more frequent in the VX-702 groups compared with the placebo group (2.4% versus 0%) but not in Study 304 (2.6% versus 4.9%). The modest clinical efficacy plus the transient suppression of biomarkers of inflammation observed in this study suggest that p38 MAPK inhibition may not provide meaningful, sustained suppression of the chronic inflammation seen in RA.

Pairwise statistical significance of local sequence alignment using multiple parameter sets and empirical justification of parameter set change penalty

BackgroundAccurate estimation of statistical significance of a pairwise alignment is an important problem in sequence comparison. Recently, a comparative study of pairwise statistical significance with database statistical significance was conducted. In this paper, we extend the earlier work on pairwise statistical significance by incorporating with it the use of multiple parameter sets.ResultsResults for a knowledge discovery application of homology detection reveal that using multiple parameter sets for pairwise statistical significance estimates gives better coverage than using a single parameter set, at least at some error levels. Further, the results of pairwise statistical significance using multiple parameter sets are shown to be significantly better than database statistical significance estimates reported by BLAST and PSI-BLAST, and comparable and at times significantly better than SSEARCH. Using non-zero parameter set change penalty values give better performance than zero penalty.ConclusionThe fact that the homology detection performance does not degrade when using multiple parameter sets is a strong evidence for the validity of the assumption that the alignment score distribution follows an extreme value distribution even when using multiple parameter sets. Parameter set change penalty is a useful parameter for alignment using multiple parameter sets. Pairwise statistical significance using multiple parameter sets can be effectively used to determine the relatedness of a (or a few) pair(s) of sequences without performing a time-consuming database search.

PSIBLAST_PairwiseStatSig: reordering PSI-BLAST hits using pairwise statistical significance

We present an add-on to BLAST and PSI-BLAST programs to reorder their hits using pairwise statistical significance. Using position-specific substitution matrices to estimate pairwise statistical significance has been recently shown to give promising results in terms of retrieval accuracy, which motivates its use to refine PSI-BLAST results, since PSI-BLAST also constructs a position-specific substitution matrix for the query sequence during the search. The obvious advantage of the approach is more accurate estimates of statistical significance because of pairwise statistical significance, along with the advantage of BLAST/PSI-BLAST in terms of speed.

Pairwise statistical significance and empirical determination of effective gap opening penalties for protein local sequence alignment

We evaluate various methods to estimate pairwise statistical significance of a pairwise local sequence alignment in terms of statistical significance accuracy and compare it with popular database search programs in terms of retrieval accuracy on a benchmark database. Results indicate that using pairwise statistical significance using standard substitution matrices is significantly better than database statistical significance reported by BLAST and PSI-BLAST, and that it is comparable and at times significantly better than SSEARCH. An application of pairwise statistical significance to empirically determine effective gap opening penalties for protein local sequence alignment using the widely used BLOSUM matrices is also presented.