A stitch in time: Efficient computation of genomic DNA melting bubbles

Background: It is of biological interest to make genome-wide predictions of the locations of DNA melting bubbles using statistical mechanics models. Computationally, this poses the challenge that a generic search through all combinations of bubble starts and ends is quadratic. Results: An efficient algorithm is described, which shows that the time complexity of the task is O(NlogN) rather than quadratic. The algorithm exploits that bubble lengths may be limited, but without a prior assumption of a maximal bubble length. No approximations, such as windowing, have been introduced to reduce the time complexity. More than just finding the bubbles, the algorithm produces a stitch profile, which is a probabilistic graphical model of bubbles and helical regions. The algorithm applies a probability peak finding method based on a hierarchical analysis of the energy barriers in the Poland-Scheraga model. Conclusions: Exact and fast computation of genomic stitch profiles is thus feasible. Sequences of several megabases have been computed, only limited by computer memory. Possible applications are the genome-wide comparisons of bubbles with promotors, TSS, viral integration sites, and other melting-related regions.Comment: 16 pages, 10 figure

Clustering and meso-level variables in cross-sectional surveys: an example of food aid during the Bosnian crisis

<p>Abstract</p> <p>Background</p> <p>Focus groups, rapid assessment procedures, key informant interviews and institutional reviews of local health services provide valuable insights on health service resources and performance. A long-standing challenge of health planning is to combine this sort of qualitative evidence in a unified analysis with quantitative evidence from household surveys. A particular challenge in this regard is to take account of the neighbourhood or clustering effects, recognising that these can be informative or incidental.</p> <p>Methods</p> <p>An example of food aid and food sufficiency from the Bosnian emergency (1995-96) illustrates two Lamothe cluster-adjustments of the Mantel Haenszel (MH) procedure, one assuming a fixed odds ratio and the other allowing for informative clustering by not assuming a fixed odds ratio. We compared these with conventional generalised estimating equations and a generalised linear mixed (GLMM) model, using a Laplace adjustment.</p> <p>Results</p> <p>The MH adjustment assuming incidental clustering generated a final model very similar to GEE. The adjustment that does not assume a fixed odds ratio produced a final multivariate model and effect sizes very similar to GLMM.</p> <p>Discussion</p> <p>In medium or large data sets with stratified last stage random sampling, the cluster adjusted MH is substantially more conservative than the naïve MH computation. In the example of food aid in the Bosnian crisis, the cluster adjusted MH that does not assume a fixed odds ratio produced similar results to the GLMM, which identified informative clustering.</p

Bayesian tolerance intervals for the balanced two-factor nested random effects model

Tolerance intervals, Random effects, Bayesian procedure, Monte Carlo simulation, Predictive density, 62F15, 62F25, 62P30, 65C05,

A retrospective study on ultra‐wide diameter dental implants for immediate molar replacement

Background Although immediate implant placement for single rooted teeth is well documented, the prognosis of ultra-wide implants in molar sockets lacks data. Purpose To evaluate the outcome of ultra-wide implants, immediately placed in molar sockets. Materials and Methods Patients treated with immediate ultra-wide diameter implants that have been in function for at least 40 months, were invited for a clinical evaluation. A radiograph was taken to measure marginal bone loss. Probing depths as well as plaque and bleeding scores were recorded at the implant and contralateral tooth. Crown and soft tissue dimensions were measured, and patients filled out an OHIP-14 questionnaire. Results Eighty-five out of 230 patients (37%) were evaluated. Twenty-eight implants received a bone graft to fill the residual space. Average bone loss was 0.19 mm after a mean follow-up of 67 months, with no significant changes over time (P = 0.170). There was no significant difference in bone loss between the maxilla and mandible (P = 0.797), male or female (P = 0.128), smoking and nonsmoking (P = 0.219), grafted and nongrafted sites (P = 0.098), or the different bio-types (P = 0.404). The distal papillae were significantly higher if a contact point was present (P = 0.002). Plaque was more frequent at the contralateral tooth (P < 0.001), but more bleeding on probing was observed around the implants (P = 0.021). Overall, 63.5% of the patients experienced no problems at all. Conclusion Ultra-wide diameter implants for immediate molar replacement demonstrate little bone loss and stable soft tissue conditions over a 4 to 7-year period

A Two One-Sided Parametric Tolerance Interval Test for Control of Delivered Dose Uniformity. Part 1—Characterization of FDA Proposed Test

The FDA proposed a parametric tolerance interval (PTI) test at the October 2005 Advisory Committee meeting as a replacement of the attribute (counting) test for delivered dose uniformity (DDU), published in the 1998 draft guidance for metered dose inhalers (MDIs) and dry powder inhalers (DPIs) and the 2002 final guidance for inhalation sprays and intranasal products. This article (first in a series of three) focuses on the test named by the FDA “87.5% coverage.” Unlike a typical two-sided PTI test, which controls the proportion of the DDU distribution within a target interval (coverage), this test is comprised of two one-sided tests (TOST) designed to control the maximum amount of DDU values in either tail of the distribution above and below the target interval. Through simulations, this article characterizes the properties and performance of the proposed PTI-TOST under different scenarios. The results show that coverages of 99% or greater are needed for a batch to have acceptance probability 98% or greater with the test named by the FDA “87.5% coverage” (95% confidence level), while batches with 87.5% coverage have less than 1% probability of being accepted. The results also illustrate that with this PTI-TOST, the coverage requirement for a given acceptance probability increases as the batch mean deviates from target. The accompanying articles study the effects of changing test parameters and the test robustness to deviations from normality