Search PubMed⌕ Search

PubMed · 12097448

Helical axis errors affect computer-generated occlusal contacts.

Abstract

A helical axis describing mandibular motion can be calculated from two distinct positions of the mandible; however, as these positions come closer together, calculation errors increase. This study investigated the effects of errors in the calculated helical axis on simulated mandibular motion by the measurement of changes in occlusal contacts. A standard helical axis was calculated from a simulated lateral movement. A series of digital interocclusal records from centric to a 5 degree mandibular rotation about the standard helical axis was created. Digital dental cast models were aligned to the interocclusal records. Helical axis parameters and occlusal contacts calculated with the use of the aligned digital models were compared with those of the standard. Helical axes calculated from mandibular positions separated by 1.5 degrees to 5.0 degrees yielded equivalent occlusal contacts. Qualitatively, contacts for helical axes calculated from jaw rotations of 0.7 degrees or larger were nearly identical to those of the standard.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R DeLong, C-C Ko, I Olson, J S Hodges, W H Douglas. 2002. Helical axis errors affect computer-generated occlusal contacts.. https://doi.org/10.1177/154405910208100510

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Generating correlated data for omics simulation.

Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each sample and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to computational and statistical hurdles of doing so. To alleviate this, we describe three approaches for generating omics-scale data with correlated measures which mimic real datasets. These approaches are all based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. This decomposition allows for extremely efficient simulation, overcoming a hurdle for adoption of past methods. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary marginal distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.

Computer Simulation↗

Addressing current challenges in cancer immunotherapy with mathematical and computational modelling.

The goal of cancer immunotherapy is to boost a patient's immune response to a tumour. Yet, the design of an effective immunotherapy is complicated by various factors, including a potentially immunosuppressive tumour microenvironment, immune-modulating effects of conventional treatments and therapy-related toxicities. These complexities can be incorporated into mathematical and computational models of cancer immunotherapy that can then be used to aid in rational therapy design. In this review, we survey modelling approaches under the umbrella of the major challenges facing immunotherapy development, which encompass tumour classification, optimal treatment scheduling and combination therapy design. Although overlapping, each challenge has presented unique opportunities for modellers to make contributions using analytical and numerical analysis of model outcomes, as well as optimization algorithms. We discuss several examples of models that have grown in complexity as more biological information has become available, showcasing how model development is a dynamic process interlinked with the rapid advances in tumour-immune biology. We conclude the review with recommendations for modellers both with respect to methodology and biological direction that might help keep modellers at the forefront of cancer immunotherapy development.

Computer Simulation↗

Scale-down of continuous filtration for rapid bioprocess design: Recovery and dewatering of protein precipitate suspensions.

The early specification of bioprocesses often has to be achieved with small (tens of millilitres) quantities of process material. If extensive process discovery is to be avoided at pilot or industrial scale, it is necessary that scale-down methods be created that not only examine the conditions of process stages but also allows production of realistic output streams (i.e., streams truly representative of the large scale). These output streams can then be used in the development of subsequent purification operations. The traditional approach to predicting filtration operations is via a bench-scale pressure filter using constant pressure tests to examine the effect of pressure on the filtrate flux rate and filter cake dewatering. Interpretation of the results into cake resistance at unit applied pressure (alpha) and compressibility (n) is used to predict the pressure profile required to maintain the filtrate flux rate at a constant predetermined value. This article reports on the operation of a continuous mode laboratory filter in such a way as to prepare filter cakes and filtrate similar to what may be achieved at the industrial scale. Analysis of the filtration rate profile indicated the filter cake to have changing properties (compressibility) with time. Using the insight gained from the new scale-down methodology gave predictions of the flux profile in a pilot-scale candle filter superior to those obtained from the traditional batch filter used for laboratory development.

Computer Simulation↗