Improving the accuracy of death certification

BACKGROUND: Population-based mortality statistics are derived from the information recorded on death certificates. This information is used for many important purposes, such as the development of public health programs and the allocation of health care resources. Although most physicians are confronted with the task of completing death certificates, many do not receive adequate training in this skill. Resulting inaccuracies in information undermine the quality of the data derived from death certificates. METHODS: An educational intervention was designed and implemented to improve internal medicine residents'' accuracy in death certificate completion. A total of 229 death certificates (146 completed before and 83 completed after the intervention) were audited for major and minor errors, and the rates of errors before and after the intervention were compared. RESULTS: Major errors were identified on 32.9% of the death certificates completed before the intervention, a rate comparable to previously reported rates for internal medicine services in teaching hospitals. Following the intervention the major error rate decreased to 15.7% (p = 0.01). The reduction in the major error rate was accounted for by significant reductions in the rate of listing of mechanism of death without a legitimate underlying cause of death (15.8% v. 4.8%) (p = 0.01) and the rate of improper sequencing of death certificate information (15.8% v. 6.0%) (p = 0.03). INTERPRETATION: Errors are common in the completion of death certificates in the inpatient teaching hospital setting. The accuracy of death certification can be improved with the implementation of a simple educational intervention.     

Improving the accuracy of secondary structure predictions

The present paper studies the way of introducing in secondary structure predictions the different kinds of interactions which infer on the protein folding. An estimation of the expected improvement is given in the different cases.     

Improving the accuracy of a solid spherical source radius and depth estimation using the diffusion equation in fluorescence reflectance mode

Background  

Non-invasive planar fluorescence reflectance imaging (FRI) is used for accessing physiological and molecular processes in biological tissue. This method is efficiently used to detect superficial fluorescent inclusions. FRI is based on recording the spatial radiance distribution (SRD) at the surface of a sample. SRD provides information for measuring structural parameters of a fluorescent source (such as radius and depth). The aim of this article is to estimate the depth and radius of the source distribution from SRD, measured at the sample surface. For this reason, a theoretical expression for the SRD at the surface of a turbid sample arising from a spherical light source embedded in the sample, was derived using a steady-state solution of the diffusion equation with an appropriate boundary condition.     

Ultrasonographic estimation of fetal weight--residents accuracy

The aim of this retrospective study was to evaluate the accuracy of gynecology and obstetrics residents when performing ultrasonographic estimation of fetal weight. The total of 400 ultrasonographic estimations of fetal weight and corresponding neonatal weight were collected and divided into 3 groups according to physicians' experience (junior and senior residents, staff physicians). The accuracy of fetal weight estimation correlated positively with the level of physicians'experience. The proportional difference between ultrasound estimation and actual birth weight varied from 8.45% to 6.88% (junior residents 8.45%, senior residents 6.95%, staff physicians 6.88%). The proportion of ultrasonograhic estimates that fell within 10% of birth weight varied from 59.09% to 79.21% (junior residents 59.09%, senior residents 78.44%, staff physicians 79.21%). Senior residents reach a highly acceptable accuracy in ultrasonographic estimation of fetal weight which is comparable to staff physicians.     

Improving false discovery rate estimation

MOTIVATION: Recent attempts to account for multiple testing in the analysis of microarray data have focused on controlling the false discovery rate (FDR). However, rigorous control of the FDR at a preselected level is often impractical. Consequently, it has been suggested to use the q-value as an estimate of the proportion of false discoveries among a set of significant findings. However, such an interpretation of the q-value may be unwarranted considering that the q-value is based on an unstable estimator of the positive FDR (pFDR). Another method proposes estimating the FDR by modeling p-values as arising from a beta-uniform mixture (BUM) distribution. Unfortunately, the BUM approach is reliable only in settings where the assumed model accurately represents the actual distribution of p-values. METHODS: A method called the spacings LOESS histogram (SPLOSH) is proposed for estimating the conditional FDR (cFDR), the expected proportion of false positives conditioned on having k 'significant' findings. SPLOSH is designed to be more stable than the q-value and applicable in a wider variety of settings than BUM. RESULTS: In a simulation study and data analysis example, SPLOSH exhibits the desired characteristics relative to the q-value and BUM. AVAILABILITY: The Web site www.stjuderesearch.org/statistics/splosh.html has links to freely available S-plus code to implement the proposed procedure.     

Improving cancer classification accuracy using gene pairs

Recent studies suggest that the deregulation of pathways, rather than individual genes, may be critical in triggering carcinogenesis. The pathway deregulation is often caused by the simultaneous deregulation of more than one gene in the pathway. This suggests that robust gene pair combinations may exploit the underlying bio-molecular reactions that are relevant to the pathway deregulation and thus they could provide better biomarkers for cancer, as compared to individual genes. In order to validate this hypothesis, in this paper, we used gene pair combinations, called doublets, as input to the cancer classification algorithms, instead of the original expression values, and we showed that the classification accuracy was consistently improved across different datasets and classification algorithms. We validated the proposed approach using nine cancer datasets and five classification algorithms including Prediction Analysis for Microarrays (PAM), C4.5 Decision Trees (DT), Naive Bayesian (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN).     

Improving the accuracy of predicting secondary structure for aligned RNA sequences

Considerable attention has been focused on predicting the secondary structure for aligned RNA sequences since it is useful not only for improving the limiting accuracy of conventional secondary structure prediction but also for finding non-coding RNAs in genomic sequences. Although there exist many algorithms of predicting secondary structure for aligned RNA sequences, further improvement of the accuracy is still awaited. In this article, toward improving the accuracy, a theoretical classification of state-of-the-art algorithms of predicting secondary structure for aligned RNA sequences is presented. The classification is based on the viewpoint of maximum expected accuracy (MEA), which has been successfully applied in various problems in bioinformatics. The classification reveals several disadvantages of the current algorithms but we propose an improvement of a previously introduced algorithm (CentroidAlifold). Finally, computational experiments strongly support the theoretical classification and indicate that the improved CentroidAlifold substantially outperforms other algorithms.     

Improving the accuracy of transmembrane protein topology prediction using evolutionary information

MOTIVATION: Many important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion are mediated by membrane proteins. Unfortunately, as these proteins are not water soluble, it is extremely hard to experimentally determine their structure. Therefore, improved methods for predicting the structure of these proteins are vital in biological research. In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm. RESULTS: A new method (MEMSAT3) for predicting transmembrane protein topology from sequence profiles is described and benchmarked with full cross-validation on a standard data set of 184 transmembrane proteins. The method is found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set. This compares with accuracies of 62-72% for other popular methods on the same benchmark. By using a second neural network specifically to discriminate transmembrane from globular proteins, a very low overall false positive rate (0.5%) can also be achieved in detecting transmembrane proteins. AVAILABILITY: An implementation of the described method is available both as a web server (http://www.psipred.net) and as downloadable source code from http://bioinf.cs.ucl.ac.uk/memsat. Both the server and source code files are free to non-commercial users. Benchmark and training data are also available from http://bioinf.cs.ucl.ac.uk/memsat.     

Improving the accuracy of protein secondary structure prediction using structural alignment

Background  

The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high.     

Multiple sequence alignment accuracy and evolutionary distance estimation

Background  

Sequence alignment is a common tool in bioinformatics and comparative genomics. It is generally assumed that multiple sequence alignment yields better results than pair wise sequence alignment, but this assumption has rarely been tested, and never with the control provided by simulation analysis. This study used sequence simulation to examine the gain in accuracy of adding a third sequence to a pair wise alignment, particularly concentrating on how the phylogenetic position of the additional sequence relative to the first pair changes the accuracy of the initial pair's alignment as well as their estimated evolutionary distance.     

Improving the positional accuracy of the goniometer on the Philips CM series TEM

We have developed a method to improve the accuracy for absolute relocation of a target specimen using the goniometer on a Philips transmission electron microscope. We have achieved this by characterizing the performance of the Philips compustage, modeling its behavior, and using this model to calculate the goniometer movements required for accurate target relocation. This resulted in a 10-fold improvement in the positioning accuracy of the goniometer.     

Effect of random sample size on the accuracy of nucleotide diversity estimation

Mathematical simulation has been used to analyze how the sample size affects the accuracy of the estimation of molecular variation in a population. The sample size was varied from 1/200 to 1/4 of the total size of the simulated population. The possible effect of the length of the nucleotide sequences compared has also been estimated; it was varied from 500 to 15 000 bp. A tendency towards underestimation of the mean nucleotide diversity (??) by about 25% of the expected value has been found. The sample size and/or the length of the nucleotide sequence used have been shown to affect more the scatter of the ?? values than the accuracy of its measurement (the proportion of correct estimates of ?? is about 14%). The assumption is made that the sample size affects the probability of accepting a false null hypothesis in analysis of the demographic history of a species.     

Improving the local solution accuracy of large-scale digital image-based finite element analyses

Digital image-based finite element modeling (DIBFEM) has become a widely utilized approach for efficiently meshing complex biological structures such as trabecular bone. While DIBFEM can provide accurate predictions of apparent mechanical properties, its application to simulate local phenomena such as tissue failure or adaptation has been limited by high local solution errors at digital model boundaries. Furthermore, refinement of digital meshes does not necessarily reduce local maximum errors. The purpose of this study was to evaluate the potential to reduce local mean and maximum solution errors in digital meshes using a post-processing filtration method. The effectiveness of a three-dimensional, boundary-specific filtering algorithm was found to be mesh size dependent. Mean absolute and maximum errors were reduced for meshes with more than five elements through the diameter of a cantilever beam considered representative of a single trabecula. Furthermore, mesh refinement consistently decreased errors for filtered solutions but not necessarily for non-filtered solutions. Models with more than five elements through the beam diameter yielded absolute mean errors of less than 15% for both Von Mises stress and maximum principal strain. When applied to a high-resolution model of trabecular bone microstructure, boundary filtering produced a more continuous solution distribution and reduced the predicted maximum stress by 30%. Boundary-specific filtering provides a simple means of improving local solution accuracy while retaining the model generation and numerical storage efficiency of the DIBFEM technique.     

The accuracy of thin-shell theory in estimation of aneurysm rupture

If the mechanical stress in the aneurysm wall exceeds the strength of the wall tissue, aneurysm rupture occurs. Therefore, the mechanical stress distribution should be calculated to evaluate the need for surgical intervention to avoid aneurysm rupture. To find the stress distribution in the wall, several methods such as thick-wall and thin-shell theories can be used. The thick-wall theory, using full equilibrium equations, gives the most accurate results. However, because the thick-wall theory is more complicated and takes longer CPU time, usually simpler theories like the thin-shell theory are preferred. In this study, the results of the thick-wall theory are compared with those of the thin-shell theory to evaluate the accuracy of the thin-shell theory in calculating stress distribution. The results show that for many cases the thin-shell theory is not sufficiently accurate, and, thus, the use of the thin-shell theory may be misleading in evaluating the need for surgical intervention.