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PubMed2006

Using ensemble classifier to identify membrane protein types.

Predicting membrane protein type is both an important and challenging topic in current molecular and cellular biology. This is because knowledge of membrane protein type often provides useful clues for determining, or sheds light upon, the function of an uncharacterized membrane protein. With the explosion of newly-found protein sequences in the post-genomic era, it is in a great demand to develop a computational method for fast and reliably identifying the types of membrane proteins according to their primary sequences. In this paper, a novel classifier, the so-called "ensemble classifier", was introduced. It is formed by fusing a set of nearest neighbor (NN) classifiers, each of which is defined in a different pseudo amino acid composition space. The type for a query protein is determined by the outcome of voting among these constituent individual classifiers. It was demonstrated through the self-consistency test, jackknife test, and independent dataset test that the ensemble classifier outperformed other existing classifiers widely used in biological literatures. It is anticipated that the idea of ensemble classifier can also be used to improve the prediction quality in classifying other attributes of proteins according to their sequences.

Algorithms↗
PubMed2003

The BrainIT group: concept and core dataset definition.

INTRODUCTION: An open collaborative international network has been established which aims to improve inter-centre standards for collection of high-resolution, neurointensive care data on patients with traumatic brain injury. The group is also working towards the creation of an open access, detailed and validated database that will be useful for post-hoc hypothesis testing. In Part A, the underlying concept, the group coordination structure, membership guidelines and database access and publication criteria are described. Secondly, in part B, we describe a set of meetings funded by the EEC that allowed us to define a "Core Dataset" and we present the results of a feasibility exercise for collection of this core dataset. METHODS: Four group meetings funded by the EEC have enabled definition of a "Core Dataset" to be collected from all centres regardless of specific project aim. A paper based pilot collection of data was conducted to determine the feasibility for collection of the core dataset. Specially designed forms to collect the core dataset demographic and clinical information as well as sample the time-series data elements were distributed by both email and standard mail to 22 BrainIT centres. A deadline of two months was set to receive completed forms back from centres. A pilot data collection of minute by minute physiological monitoring data was also performed. FINDINGS: A core-dataset was defined and can be downloaded from the BrainIT web-site (go to "Core dataset" link at: www.brainit.org). Eighteen centres (82%) returned completed forms by the set deadline. Overall the feasibility for collection of the core data elements was high with only 10 of the 64 questions (16%) showing missing data. Of those 10 fields with missing data, the average number of centres not responding was 12% and the median 6%. An SQL database to hold the data has been designed and is being tested. Software tools for collection of the core dataset have been developed. Ethics approval has been granted for collection of multi-centre data as part of a pilot data collection study. INTERPRETATION: The BrainIT network provides a more standardised and higher resolution data collection mechanism for research groups, organisations and the device industry to conduct multi-centre trials of new health care technology in patients with traumatic brain injury.

Brain Injuries↗
PubMed1999

Ethylene oxide cancer risk assessment based on epidemiological data: application of revised regulatory guidelines.

Ethylene oxide (EO) research has significantly increased since the 1980s, when regulatory risk assessments were last completed on the basis of the animal cancer chronic bioassays. In tandem with the new scientific understanding, there have been evolutionary changes in regulatory risk assessment guidelines, that encourage flexibility and greater use of scientific information. The results of an updated meta-analysis of the findings from 10 unique EO study cohorts from five countries, including nearly 33,000 workers, and over 800 cancers are presented, indicating that EO does not cause increased risk of cancers overall or of brain, stomach or pancreatic cancers. The findings for leukemia and non-Hodgkin's lymphoma (NHL) are inconclusive. Two studies with the requisite attributes of size, individual exposure estimates and follow up are the basis for dose-response modeling and added lifetime risk predictions under environmental and occupational exposure scenarios and a variety of plausible alternative assumptions. A point of departure analysis, with various margins of exposure, is also illustrated using human data. The two datasets produce remarkably similar leukemia added risk predictions, orders of magnitude lower than prior animal-based predictions under conservative, default assumptions, with risks on the order of 1 x 10(-6) or lower for exposures in the low ppb range. Inconsistent results for "lymphoid" tumors, a non-standard grouping using histologic information from death certificates, are discussed. This assessment demonstrates the applicability of the current risk assessment paradigm to epidemiological data.

Animals↗
PubMed2005

Mean particle diameters. From statistical definition to physical understanding.

Mean particle diameters may be used to describe and to model physical, chemical, or physiological properties of products or materials containing dispersed phases. There are different notation systems for these mean diameters, which may cause much confusion. This equally applies to their nomenclature. This article introduces the Moment-Ratio definition system and evaluates briefly the ISO definition system. The ISO system appears to have serious drawbacks. Mean particle diameters can be estimated from histograms of size distributions by Summation (M-R system) and by Integration (ISO system) over the histogram intervals. Summation tends to be more accurate than Integration and is less sensitive to low values of the lower limit of size distributions. The Summation method equations are straightforward and generally applicable. The mathematical formulas of the Integration method are difficult to apply in daily practice, and their complexity may easily hide the physical background of a mean particle diameter. A coherent nomenclature system for denoting mean particle diameters is recommended. This nomenclature system does not contain any ambiguities and clearly conveys the physical meanings of mean particle diameters. This article deals also with an empirical method to select the proper type of mean diameter to describe a physical, chemical, or physiological property of a product or material containing dispersed phases. After calculation of the mean diameters from experimental data, the relationships between the product property and these mean diameters are investigated statistically. The selection method has been illustrated by two examples. The dataset of each example consists of a set of particle size distributions and the corresponding physical product properties that are influenced by the particle sizes. Hypotheses are formulated to explain the types of selected mean diameters. Sharing results from all over the world of applications of the developed selection method will lead to a buildup of knowledge of physical meanings and application areas of the types of mean particle diameters, which will support decision making in product development.

Algorithms↗
PubMed2003

Why does grassland productivity increase with species richness? Disentangling species richness and composition with tests for overyielding and superyielding in biodiversity experiments.

The causal relationship between the biodiversity of natural and modified environments and their net primary production has been a topic of significant scientific controversy and scrutiny. Early theoretical and empirical results indicated that production was sometimes significantly correlated with species richness when species richness was directly manipulated in experimental systems. Possible mechanisms for this phenomenon include statistical sampling effects, complementary resource use and mutualistic interactions. However, the interpretation of experimental results has sometimes confounded species richness with species composition, and disentangling the effects of species diversity from species identity has proved a formidable challenge. Here, I present a statistical method that is based on simple probability models and does not rely on the species composition of individual plots to distinguish among three phenomena that occur in biodiversity-production experiments: underyielding, overyielding and (a new concept) superyielding. In some cases, distinguishing these phenomena will provide evidence for underlying mechanisms. As a proof-of-concept, I first applied this technique to a simulated dataset, indicating the strengths of the method with both clear and ambiguous cases. I then analysed data from the BIODEPTH experimental biodiversity manipulations. No evidence of either overyielding or superyielding was detected in the BIODEPTH experiment.

Biomass↗
PubMed2006

Analysis and validation of automated skull stripping tools: a validation study based on 296 MR images from the Honolulu Asia aging study.

As population-based epidemiologic studies may acquire images from thousands of subjects, automated image post-processing is needed. However, error in these methods may be biased and related to subject characteristics relevant to the research question. Here, we compare two automated methods of brain extraction against manually segmented images and evaluate whether method accuracy is associated with subject demographic and health characteristics. MRI data (n = 296) are from the Honolulu Asia Aging Study, a population-based study of elderly Japanese-American men. The intracranial space was manually outlined on the axial proton density sequence by a single operator. The brain was extracted automatically using BET (Brain Extraction Tool) and BSE (Brain Surface Extractor) on axial proton density images. Total intracranial volume was calculated for the manually segmented images (ticvM), the BET segmented images (ticvBET) and the BSE segmented images (ticvBSE). Mean ticvBSE was closer to that of ticvM, but ticvBET was more highly correlated with ticvM than ticvBSE. BSE had significant over (positive error) and underestimated (negative error) ticv, but net error was relatively low. BET had large positive and very low negative error. Method accuracy, measured in percent positive and negative error, varied slightly with age, head circumference, presence of the apolipoprotein eepsilon4 polymorphism, subcortical and cortical infracts and enlarged ventricles. This epidemiologic approach to the assessment of potential bias in image post-processing tasks shows both skull-stripping programs performed well in this large image dataset when compared to manually segmented images. Although method accuracy was statistically associated with some subject characteristics, the extent of the misclassification (in terms of percent of brain volume) was small.

Aged↗
PubMedDate not supplied

The International Bladder Cancer Bank: proposal for a new study concept.

At present, results of marker studies are often inconsistent and sometimes contradictory. Recognized problems include multiple different methods of performing the assays, different subsets of patients and different endpoints, leading to incompatible datasets. Although there has been discussion of establishing general methodological principles and guidelines (analogous to those for clinical trials) for design, conduct, analysis, and reporting of marker studies, these have not been widely implemented. There are no well-recognized prototypes or examples that the urologic researcher can use to model future marker studies. We will discuss our plans to establish a multi-institutional bladder cancer data base and virtual tumor bank as a resource for participating institutions to evaluate the biological and prognostic significance of potential markers for bladder cancer. Samples will be identified and stored at each participating institution and will be available for analysis. A standard, minimal set of patient and pathologic information will be collected. The use of common software, as part of this proposal will facilitate the data transfer of updated patient information to a central database. All contributing centers will have access to summarized information, also to simplify the process of finding collaborating partners. Prospectively collected, consistent datasets with available long-term follow-up, should provide information sooner than with a conventional prospective study. Furthermore, the quality of these data and samples may be superior to that of retrospectively collected data and samples. The proposed International Bladder Cancer Bank of specimens and data will be an effective tool during all phases of marker development.

Biomarkers, Tumor↗
PubMed2004

Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry.

MOTIVATION: The correlation among fragment ions in a tandem mass spectrum is crucial in reducing stochastic mismatches for peptide identification by database searching. Until now, an efficient scoring algorithm that considers the correlative information in a tunable and comprehensive manner has been lacking. RESULTS: This paper provides a promising approach to utilizing the correlative information for improving the peptide identification accuracy. The kernel trick, rooted in the statistical learning theory, is exploited to address this issue with low computational effort. The common scoring method, the tandem mass spectral dot product (SDP), is extended to the kernel SDP (KSDP). Experiments on a dataset reported previously demonstrate the effectiveness of the KSDP. The implementation on consecutive fragments shows a decrease of 10% in the error rate compared with the SDP. Our software tool, pFind, using a simple scoring function based on the KSDP, outperforms two SDP-based software tools, SEQUEST and Sonar MS/MS, in terms of identification accuracy. SUPPLEMENTARY INFORMATION: http://www.jdl.ac.cn/user/yfu/pfind/index.html

Algorithms↗
PubMed1998

Dynamic imaging and tracer kinetic modeling for emission tomography using rotating detectors.

When performing dynamic studies using emission tomography the tracer distribution changes during acquisition of a single set of projections. This is particularly true for some positron emission tomography (PET) systems which, like single photon emission computed tomography (SPECT), acquire data over a limited angle at any time, with full projections obtained by rotation of the detectors. In this paper, an approach is proposed for processing data from these systems, applicable to either PET or SPECT. A method of interpolation, based on overlapped parabolas, is used to obtain an estimate of the total counts in each pixel of the projections for each required frame-interval, which is the total time to acquire a single complete set of projections necessary for reconstruction. The resultant projections are reconstructed using traditional filtered backprojection (FBP) and tracer kinetic parameters are estimated using a method which relies on counts integrated over the frame-interval rather than instantaneous values. Simulated data were used to illustrate the technique's capabilities with noise levels typical of those encountered in either PET or SPECT. Dynamic datasets were constructed, based on kinetic parameters for fluoro-deoxy-glucose (FDG) and use of either a full ring detector or rotating detector acquisition. For the rotating detector, use of the interpolation scheme provided reconstructed dynamic images with reduced artefacts compared to unprocessed data or use of linear interpolation. Estimates for the metabolic rate of glucose had similar bias to those obtained from a full ring detector.

Algorithms↗
PubMed1994

The influence of radiotherapy treatment time on the control of laryngeal cancer: a direct analysis of data from two British Institute of Radiology trials to calculate the lag period and the time factor.

This study analyses node-negative laryngeal tumour control data from two clinical trials conducted by the British Institute of Radiology in order to determine the time factors and the presence or absence of a lag period before the time factor takes effect. A direct maximum likelihood approach is used to fit a double-logarithmic model including a repopulation term which commences after an initial lag period, Tk. The analysis yields a time factor of 0.8 Gy per day (95% confidence interval 0.5-1.1 Gy per day) as the extra dose required to counteract the reduction in tumour control probability (TCP) with extension of the treatment time. The latter reduction amounted to between 5 and 12% TCP per week, depending on the stage and time period. With this dataset, where few patients were treated for short times, no statistically significant lag phase can be demonstrated. However, the best estimate of Tk is 21 days (95% confidence interval 0-27 days), which is consistent with estimates from other studies on other datasets. If a lag phase exists, this study would indicate that the duration is less than 27 days. Other studies have used retrospective data and are subject to a number of potential biases. The present study, using data from multicentre prospective randomized clinical trials, is free from some of these sources of bias. The fact that very similar estimates of the radiobiological parameters are obtained lends credence to these other studies and suggests that the potential biases may be small in practice.

Clinical Trials as Topic↗
PubMed2006

Are there differential symptom profiles that improve in response to different pharmacological treatments of premenstrual syndrome/premenstrual dysphoric disorder?

Current evidence suggests that the accepted treatments for premenstrual syndrome (PMS)/premenstrual dysphoric disorder (PMDD) have similar overall efficacy. While these treatments are more effective than placebo, response rates associated with them are far from satisfactory (<60%), such that, irrespective of treatment modality, there remain a significant number of women who are unresponsive to current conventional pharmacological therapy. The available data on response rates of specific types of premenstrual symptoms to, or symptom profiles that are most amenable to, each treatment modality are limited and not well defined because most studies were not designed to assess specific symptom profiles. Those studies that have attempted to evaluate which symptom profiles respond to specific therapies have revealed variations within the individual modalities, as well as between the different modalities. It appears that suppression of ovulation ameliorates a broad range of behavioural as well as physical premenstrual symptoms. SSRIs are most effective for irritability and anxiety symptoms, with lesser efficacy for 'atypical' premenstrual symptoms. GABAergic compounds are most efficacious for anxiety and anxious/depressive symptoms, while dopamine agonists, particularly bromocriptine, are perhaps most efficacious for mastalgia. Overall treatment response rates may improve if treatments are targeted at well-defined subgroups of patients. Re-analysis of available datasets from randomised clinical trials may shed more light on the notion that targeting women with specific premenstrual symptom profiles for specific treatment modalities would improve response rates beyond the current ceiling of approximately 60%. Such information would also improve understanding of the putative pathophysiological mechanisms underlying PMS and PMDD, and may point to a more specific diagnosis of these conditions.

Central Nervous System↗
PubMedDate not supplied

Raymond Pearl's legacy: the proper measure of man.

During much of his academic career, Raymond Pearl advocated the employment of quantitative and statistical methods in biology and applied them in his own voluminous research output, much of it in various aspects of human biology. If he were to return today, he would be pleased to note the fruits of his advocacy-the widespread quantification of biological and anthropological research. He would undoubtedly be impressed with the tremendous progress in statistical analysis in the 63 years since he ceased his work and with that amazing tool, the personal computer. But he would also note the dangers inherent in applying these powerful methods without sufficient regard for the experimental or observational designs they require and for their basic assumptions. Among the errors Pearl would note are: 1) the use of canned computer programs in ignorance of or inattention to their implied assumptions; 2) testing many contrasts in one dataset without making allowances for the lack of independence of these tests; 3) ignoring the effects of lack of independence (autocorrelation) when working with spatially, temporally, or phylogenetically related data; 4) presenting distorted ordination plots; and 5) using an unrealistic null distribution in computation or simulation, as when employing sample sizes too small to test the hypotheses the investigator is interested in. All of these errors are common not only in publications on physical anthropology and human biology, but also in the wider biological literature.

Anthropometry↗
PubMedDate not supplied

Towards a consensus on datasets and evaluation metrics for developing B-cell epitope prediction tools.

A B-cell epitope is the three-dimensional structure within an antigen that can be bound to the variable region of an antibody. The prediction of B-cell epitopes is highly desirable for various immunological applications, but has presented a set of unique challenges to the bioinformatics and immunology communities. Improving the accuracy of B-cell epitope prediction methods depends on a community consensus on the data and metrics utilized to develop and evaluate such tools. A workshop, sponsored by the National Institute of Allergy and Infectious Disease (NIAID), was recently held in Washington, DC to discuss the current state of the B-cell epitope prediction field. Many of the currently available tools were surveyed and a set of recommendations was devised to facilitate improvements in the currently existing tools and to expedite future tool development. An underlying theme of the recommendations put forth by the panel is increased collaboration among research groups. By developing common datasets, standardized data formats, and the means with which to consolidate information, we hope to greatly enhance the development of B-cell epitope prediction tools.

Animals↗
PubMed2005

Correlation between omega and psi dihedral angles in protein structures.

The planarity of the peptide group is one of the fundamental features of protein structure that is described in every chemistry and biochemistry textbook. By surveying a dataset of 163 atomic resolution protein structures we here identify the stereochemical conditions that favor significant deformations of peptide bond planarity. In particular, we demonstrate that the values of the omega dihedral angle are strictly correlated to the values of the adjacent psi angle. This trend is also observed in highly strained states such as those occurring in vicinal disulfide bridges. These findings provide direct evidence for the mutual influence of the geometrical parameters that describe the protein structure.

Databases, Protein↗
PubMed2002

Functional MRI experiments: acquisition, analysis and interpretation of data.

Functional MRI is widely used to address basic and clinical neuroscience questions. In the key domains of fMRI experiments, i.e. acquisition, processing and analysis, and interpretation of data, developments are ongoing. The main issues are sensitivity for changes in fMRI signal that are associated with brain function, and the design of tasks with which brain functions are invoked. In this paper we address these issues, in terms of strengths, weaknesses and future developments. Acquisition of data is commonly achieved with techniques that measure blood oxygen level-dependent (BOLD) signal changes. Although the mechanisms that affect BOLD signal are complex and not well understood, fMRI yields results that agree with known functional topography. Sensitivity for task-related brain activity is expected to benefit from technological advances in acquisition, i.e. SENSE or parallel imaging, and higher field scanners (3 T). Data analysis is geared towards modelling sources of signal variation, i.e. reducing noise in the data time-series, and the cerebrovascular response to task-related changes in neuronal activity. Analytical algorithms such as connectivity and component analysis contribute to the extraction of meaningful information from fMRI datasets. The choice of tasks, and consequently of the statistical evaluation procedures, is best guided by the specific questions that are formulated a priori. The future is expected to bring more sophisticated questions, and tasks that allow for accurate modelling of involved brain functions. An example of a hypothesis-driven experiment is presented, where we investigated whether practise of a working memory task caused a shift in the neuronal representation of working memory or not.

Brain↗
PubMed2002

Virtual image grafting: image based generation and visualization of virtual skin defects.

We have developed methods for rapidly generating 3d dermatologic datasets for use in education, training simulations and procedure planning. By compositing local surface features of cutaneous wounds onto patient images and 3d models, one can flexibly generate large patient variability from a small initial 3d library. The wound database is generated from clinically captured photographic images. The wound image is extracted from the image of the surrounding tissue. The 3d anatomy database is generated from MRI scans and from multiple photographic views. Extracted wound images can be moved rotated and scaled and blended in the final composite.

Databases as Topic↗
PubMed2005

Barriers to healthy eating amongst men: a qualitative analysis.

Currently, little is known about the meanings men attach to food or to the links between food and health. The burgeoning literature on men's health highlights forms of masculinity (e.g. risk-taking, invulnerability) as a factor (negatively) influencing men's health practices. The aim of this study was to provide an analysis of men's accounts of food and health using concepts pertaining to masculinity. We report on a qualitative analysis of a dataset comprising 24 interviews with UK men from a range of age and social class groups. Our findings suggest two principal barriers to healthy eating in men: cynicism about government health messages and a rejection of healthy food on grounds of poor taste and inability to satisfy. These findings are discussed in relation to masculine ideals such as rationality, autonomy and strength. The implications of our analysis for future research and men's health promotion policy are discussed.

Adult↗
PubMed2005

Ensemble dependence model for classification and prediction of cancer and normal gene expression data.

MOTIVATION: DNA microarray technologies make it possible to simultaneously monitor thousands of genes' expression levels. A topic of great interest is to study the different expression profiles between microarray samples from cancer patients and normal subjects, by classifying them at gene expression levels. Currently, various clustering methods have been proposed in the literature to classify cancer and normal samples based on microarray data, and they are predominantly data-driven approaches. In this paper, we propose an alternative approach, a model-driven approach, which can reveal the relationship between the global gene expression profile and the subject's health status, and thus is promising in predicting the early development of cancer. RESULTS: In this work, we propose an ensemble dependence model, aimed at exploring the group dependence relationship of gene clusters. Under the framework of hypothesis-testing, we employ genes' dependence relationship as a feature to model and classify cancer and normal samples. The proposed classification scheme is applied to several real cancer datasets, including cDNA, Affymetrix microarray and proteomic data. It is noted that the proposed method yields very promising performance. We further investigate the eigenvalue pattern of the proposed method, and we discover different patterns between cancer and normal samples. Moreover, the transition between cancer and normal patterns suggests that the eigenvalue pattern of the proposed models may have potential to predict the early stage of cancer development. In addition, we examine the effects of possible model mismatch on the proposed scheme.

Algorithms↗