Public Domain Data Research Articles

Extent of genome-wide linkage disequilibrium in Australian Holstein-Friesian cattle based on a high-density SNP panel

BackgroundThe extent of linkage disequilibrium (LD) within a population determines the number of markers that will be required for successful association mapping and marker-assisted selection. Most studies on LD in cattle reported to date are based on microsatellite markers or small numbers of single nucleotide polymorphisms (SNPs) covering one or only a few chromosomes. This is the first comprehensive study on the extent of LD in cattle by analyzing data on 1,546 Holstein-Friesian bulls genotyped for 15,036 SNP markers covering all regions of all autosomes. Furthermore, most studies in cattle have used relatively small sample sizes and, consequently, may have had biased estimates of measures commonly used to describe LD. We examine minimum sample sizes required to estimate LD without bias and loss in accuracy. Finally, relatively little information is available on comparative LD structures including other mammalian species such as human and mouse, and we compare LD structure in cattle with public-domain data from both human and mouse.ResultsWe computed three LD estimates, D', Dvol and r2, for 1,566,890 syntenic SNP pairs and a sample of 365,400 non-syntenic pairs. Mean D' is 0.189 among syntenic SNPs, and 0.105 among non-syntenic SNPs; mean r2 is 0.024 among syntenic SNPs and 0.0032 among non-syntenic SNPs. All three measures of LD for syntenic pairs decline with distance; the decline is much steeper for r2 than for D' and Dvol. The value of D' and Dvol are quite similar. Significant LD in cattle extends to 40 kb (when estimated as r2) and 8.2 Mb (when estimated as D'). The mean values for LD at large physical distances are close to those for non-syntenic SNPs. Minor allelic frequency threshold affects the distribution and extent of LD. For unbiased and accurate estimates of LD across marker intervals spanning < 1 kb to > 50 Mb, minimum sample sizes of 400 (for D') and 75 (for r2) are required. The bias due to small samples sizes increases with inter-marker interval. LD in cattle is much less extensive than in a mouse population created from crossing inbred lines, and more extensive than in humans.ConclusionFor association mapping in Holstein-Friesian cattle, for a given design, at least one SNP is required for each 40 kb, giving a total requirement of at least 75,000 SNPs for a low power whole-genome scan (median r2 > 0.19) and up to 300,000 markers at 10 kb intervals for a high power genome scan (median r2 > 0.62). For estimation of LD by D' and Dvol with sufficient precision, a sample size of at least 400 is required, whereas for r2 a minimum sample of 75 is adequate.

An Iterative Method for Blade Profile Loss Model Adaptation Using Streamline Curvature

The various incidence, deviation, and loss models used in through-flow analysis methods, such as streamline curvature, are nothing more than statistical curve fits. A closer look at public domain data reveals that these statistical correlations and curve fits are usually based on experimental cascade data that actually display a fairly large scatter, resulting in a relatively high degree of uncertainty. This usually leads to substantial differences between the calculated and actual performances of a given gas turbine engine component. Typically, matching calculated results from a through-flow analysis against experimental data requires the combination of various correlations available in the public domain, through a very tedious, complex, and time consuming “trial and error” process. This particular study supports the view that it might actually be much more time effective to “adopt” a given loss model against experimental data through an iterative, physics-based approach, rather than try to identify the best combination of available correlations. For example, the well-established “Swan’s model” for calculating the blade profile loss factor in subsonic and transonic axial flow compressors depends strongly on approximate correlations for calculating the blade wake momentum thickness, and therefore represents such a case. This study demonstrates this by looking into an iterative approach to blade profile loss model adaptation that can provide a relatively simple and quick, but also physics-based way of “calibrating” profile loss models against available experimental data for subsonic applications. This paper presents in detail all the analysis necessary to support the above concept and discusses Swan’s model in particular as an example. Finally, the paper discusses the performance comparison of a two-dimensional, streamline curvature compressor model against experimental data before and after the adaptation of that particular loss model. This analysis proves the potential of the simulation strategy presented in this paper to “adopt” a given loss model against experimental data through an iterative, physics-based approach.

Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods

We present a methodology for Bayesian analysis of software quality. We cast our research in the broader context of constructing a causal framework that can include process, product, and other diverse sources of information regarding fault introduction during the software development process. In this paper, we discuss the aspect of relating internal product metrics to external quality metrics. Specifically, we build a Bayesian network (BN) model to relate object-oriented software metrics to software fault content and fault proneness. Assuming that the relationship can be described as a generalized linear model, we derive parametric functional forms for the target node conditional distributions in the BN. These functional forms are shown to be able to represent linear, Poisson, and binomial logistic regression. The models are empirically evaluated using a public domain data set from a software subsystem. The results show that our approach produces statistically significant estimations and that our overall modeling method performs no worse than existing techniques.

Spectral analysis of two-signed microarray expression data

We give a simple and informative derivation of a spectral algorithm for clustering and reordering complementary DNA microarray expression data. Here, expression levels of a set of genes are recorded simultaneously across a number of samples, with a positive weight reflecting up-regulation and a negative weight reflecting down-regulation. We give theoretical support for the algorithm based on a biologically justified hypothesis about the structure of the data, and illustrate its use on public domain data in the context of unsupervised tumour classification. The algorithm is derived by considering a discrete optimization problem and then relaxing to the continuous realm. We prove that in the case where the data have an inherent 'checkerboard' sign pattern, the algorithm will automatically reveal that pattern. Further, our derivation shows that the algorithm may be regarded as imposing a random graph model on the expression levels and then clustering from a maximum likelihood perspective. This indicates that the output will be tolerant to perturbations and will reveal 'near-checkerboard' patterns when these are present in the data. It is interesting to note that the checkerboard structure is revealed by the first (dominant) singular vectors--previous work on spectral methods has focussed on the case of nonnegative edge weights, where only the second and higher singular vectors are relevant. We illustrate the algorithm on real and synthetic data, and then use it in a tumour classification context on three different cancer data sets. Our results show that respecting the two-signed nature of the data (thereby distinguishing between up-regulation and down-regulation) reveals structures that cannot be gleaned from the absolute value data (where up- and down-regulation are both regarded as 'changes').

Seasonal Variation in Leisure-time Physical Activity Among Canadians

Cardiovascular disease (CVD) mortality is higher in winter than summer, particularly in cold climates. Physical activity reduces CVD risk but climate impacts participation in physical activity. Canada has substantial climatic variation but its relation with physical activity is understudied. In this investigation, we evaluated the relation between seasonality and physical activity among Canadians. We used public domain data from the Canadian Community Health Survey, Cycle 2.2 (CCHS 2.2), a representative, cross-sectional sample of free-living Canadians in 2004. Leisure-time physical activity was measured using a modified version of the Physical Activity Monitor that was validated. Season was determined by the time of the interview, i.e., Winter: January 1 to March 31, Spring: April 1 to June 30, Summer: July 1 to September 30, and Fall: October 1 to December 31. In all multivariate models, we adjusted for age, sex, education, and income adequacy. There were 20,197 persons aged 19 years and older in this analysis. In the winter, 64% of Canadians were inactive as compared with 49% in the summer. Total average daily energy expenditure was 31.0% higher in summer than winter after multivariate adjustment. Leisure-time physical activity was 86% more likely in the summer than winter (multivariate OR = 1.86, 95% CI 1.40, 2.45). The relation between seasonality and physical activity was weakest in Newfoundland and Labrador and stronger in Saskatchewan and British Columbia (p-value for interaction = 0.02). Seasonality impacts physical activity patterns in Canada and varies across the provinces. This needs to be considered in physical activity programming.

Seizure Incidence in Psychopharmacological Clinical Trials: An Analysis of Food and Drug Administration (FDA) Summary Basis of Approval Reports

Clinical trial data provide an approach to the investigation of the effects of psychopharmacological agents, and psychiatric disorders themselves, on seizure threshold. We accessed public domain data from Food and Drug Administration (FDA) Phase II and III clinical trials as Summary Basis of Approval (SBA) reports that noted seizure incidence in trials of psychotropic drugs approved in the United States between 1985 and 2004, involving a total of 75,873 patients. We compared seizure incidence among active drug and placebo groups in psychopharmacological clinical trials and the published rates of unprovoked seizures in the general population. Increased seizure incidence was observed with antipsychotics that was accounted for by clozapine and olanzapine, and with drugs indicated for the treatment of OCD that was accounted for by clomipramine. Alprazolam, bupropion immediate release (IR) form, and quetiapine were also associated with higher seizure incidence. The incidence of seizures was significantly lower among patients assigned to antidepressants compared to placebo (standardized incidence ratio = .48; 95% CI, .36- .61). In patients assigned to placebo, seizure incidence was greater than the published incidence of unprovoked seizures in community nonpatient samples. Proconvulsant effects are associated with a subgroup of psychotropic drugs. Second-generation antidepressants other than bupropion have an apparent anticonvulsant effect. Depression, psychotic disorders, and OCD are associated with reduced seizure threshold.

Environmental sustainability and human well-being indicators from the ecosystem perspective in the Middle Paraíba Region, Rio de Janeiro State, Brazil

Population urbanization is a global trend, and socioeconomic activities in urban areas cause changes that affect the environment and human well-being beyond the specific territories, thus connecting urban to non-urban areas and creating city-regions. This article's objective, from an ecosystem perspective, is to gather a set of information on municipalities (counties) belonging to the Middle Paraíba River Valley Region in the State of Rio de Janeiro, Brazil, in order to identify conditions and trends in environmental sustainability and well-being. The conceptual framework adopted here was that of the Millennium Ecosystem Assessment, identifying direct and indirect driving forces that affect human well-being, with health as a component of the latter. We used a set of available public-domain data and information sources on the municipalities, grouped by socioeconomic and environmental conditions and the soundness of institutional structural well-being for sustainability. The indicators used here point to a process of degradation in the ecosystem services that has still not been prevented by the prevailing institutional structure, thus increasing constraints on well-being and the spread of socio-environmental impacts in the middle and long terms.

An Intelligent Data Mining Model Approach for Adverse Effects of Hormone Replacement Therapy

A number of controversial studies have been reported on the potential risk of breast cancer caused by hormone replacement therapy (HRT). Some studies showed a positive relationship between HRT and breast cancer onset, but other studies have not confirmed these results. To clarify the contradictory outcomes in the relationships between HRT and the onset of breast cancer, we have designed an intelligent data mining model (IDM), which is able to find proper prognostic factors for cancer onset and provides alternate measures in interpretation of outcome of clinical data through hierarchies of attributes. Based on the selection criteria, we selected 22 sets of random and case-control studies of the last 15 years, which identified any involvements of HRT with breast cancer. We analyzed the relationship between HRT and breast cancer using an IDM model consisting of data mining algorithms and public domain data mining tools. Prognostic factors which underline the major etiological dispositions of breast cancer were identified. The variables which are closely associated with cancer onset to some degree are age 60-69, age at menopause 40-49, parity 0, age 40-49, and types of menopause oophorectomy. An implementation of IDM model on overall pooled data indicated that there is no significant relationship between breast cancer onset and HRT. It is suggested that HRT patients with specific physiological and pathological conditions related with the higher ranks of prognostic factors may have a greater chance to get breast cancer. The results of this study may guide biomedical research directed at establishing the causal relationships between various medications and their complications, allowing an accurate assessment of efficacy and side effects of new therapeutic treatment in clinical trials without reliance on a large control population.

Commentary: N Eberstadt's ‘The health crisis in the USSR’ and sustainable mortality reversal in the post-Soviet space during communism and after

In 1965 life expectancy in the USSR began to decrease. Initially this was regarded as a minor and transient fluctuation of little significance. However by the mid-1970s both scholars and the Soviet authorities alike realized that the unfavourable trend was real and could last longer. In 1974 the Soviet government started to restrict the mortality data that was released such that by 1976 publication of these data in the public domain ceased entirely. This was justified as being politically desirable given the ongoing ideological struggle with imperialism. These developments came under intense scrutiny by Nick Eberstadt in his article Health Crisis in the USSR published in The New York Review in February 1981. The article was based on a few earlier studies evaluating mortality levels and trends in the Soviet Union over the 1950-70s from fragmentary statistical data. The most important of these-Christopher Davis and Murray Feshbachs book Rising Infant Mortality in the USSR in the 1970s was published in September 1980. This provided convincing evidence that the increase in the Soviet infant mortality between 1970 and 1977 was real and that official infant mortality rates were underestimated by about 15%. In addition it documented a significant increase in mortality of adults and a decline in life expectancy. Eberstadts article was written as a review of Davis and Feshbachs book although it goes far beyond the issues raised by Davis and Feshbach. Using numerous sources on a wide variety of features of Soviet society including its history economy and finances ideology health system alcohol consumption and other things he provided an extraordinarily accurate account of the Soviet-Russian health crisis and outlined its driving forces. The main points of the article can be summarized as follows. (excerpt)

Graph Signatures for Visual Analytics

We present a visual analytics technique to explore graphs using the concept of a data signature. A data signature, in our context, is a multidimensional vector that captures the local topology information surrounding each graph node. Signature vectors extracted from a graph are projected onto a low-dimensional scatterplot through the use of scaling. The resultant scatterplot, which reflects the similarities of the vectors, allows analysts to examine the graph structures and their corresponding real-life interpretations through repeated use of brushing and linking between the two visualizations. The interpretation of the graph structures is based on the outcomes of multiple participatory analysis sessions with intelligence analysts conducted by the authors at the Pacific Northwest National Laboratory. The paper first uses three public domain data sets with either well-known or obvious features to explain the rationale of our design and illustrate its results. More advanced examples are then used in a customized usability study to evaluate the effectiveness and efficiency of our approach. The study results reveal not only the limitations and weaknesses of the traditional approach based solely on graph visualization, but also the advantages and strengths of our signature-guided approach presented in the paper.

Loss of lag-response curvilinearity of indices of heart rate variability in congestive heart failure

BackgroundHeart rate variability (HRV) is known to be impaired in patients with congestive heart failure (CHF). Time-domain analysis of ECG signals traditionally relies heavily on linear indices of an essentially non-linear phenomenon. Poincaré plots are commonly used to study non-linear behavior of physiologic signals. Lagged Poincaré plots incorporate autocovariance information and analysis of Poincaré plots for various lags can provide interesting insights into the autonomic control of the heart.MethodsUsing Poincaré plot analysis, we assessed whether the relation of the lag between heart beats and HRV is altered in CHF. We studied the influence of lag on estimates of Poincaré plot indices for various lengths of beat sequence in a public domain data set (PhysioNet) of 29 subjects with CHF and 54 subjects with normal sinus rhythm.ResultsA curvilinear association was observed between lag and Poincaré plot indices (SD1, SD2, SDLD and SD1/SD2 ratio) in normal subjects even for a small sequence of 50 beats (p value for quadratic term 3 × 10-5, 0.002, 3.5 × 10-5 and 0.0003, respectively). This curvilinearity was lost in patients with CHF even after exploring sequences up to 50,000 beats (p values for quadratic term > 0.5).ConclusionSince lagged Poincaré plots incorporate autocovariance information, these analyses provide insights into the autonomic control of heart rate that is influenced by the non-linearity of the signal. The differences in lag-response in CHF patients and normal subjects exist even in the face of the treatment received by the CHF patients.

A flexible method for software effort estimation by analogy

Effort estimation by analogy uses information from former similar projects to predict the effort for a new project. Existing analogy-based methods are limited by their inability to handle non-quantitative data and missing values. The accuracy of predictions needs improvement as well. In this paper, we propose a new flexible method called AQUA that is able to overcome the limitations of former methods. AQUA combines ideas from two known analogy-based estimation techniques: case-based reasoning and collaborative filtering. The method is applicable to predict effort related to any object at the requirement, feature, or project levels. Which are the main contributions of AQUA when compared to other methods? First, AQUA supports non-quantitative data by defining similarity measures for different data types. Second, it is able to tolerate missing values. Third, the results from an explorative study in this paper shows that the prediction accuracy is sensitive to both the number N of analogies (similar objects) taken for adaptation and the threshold T for the degree of similarity, which is true especially for larger data sets. A fixed and small number of analogies, as assumed in existing analogy-based methods, may not produce the best accuracy of prediction. Fourth, a flexible mechanism based on learning of existing data is proposed for determining the appropriate values of N and T likely to offer the best accuracy of prediction. New criteria to measure the quality of prediction are proposed. AQUA was validated against two internal and one public domain data sets with non-quantitative attributes and missing values. The obtained results are encouraging. In addition, acomparative analysis with existing analogy-based estimation methods was conducted using three publicly available data sets that were used by these methods. Intwo of the three cases, AQUA outperformed all other methods.

Statistical properties of vortical structures with spanwise vorticity in zero pressure gradient turbulent boundary layers

Turbulent boundary layers at large Reynolds numbers (up to Reθ=17 500) are obtained in a very large recirculating water tunnel and are analyzed through PIV measurements in streamwise–wall–normal planes. Vortical structures in the outer region are tracked using a wavelet based technique. The adopted method, which can be applied also to time series obtained from pointwise measurements, provides the separation of coherent structures from the background flow and the measure of their length scale and of other relevant quantities. The range of Reθ under test is extended by analyzing also a PIV database obtained in a different laboratory flow (public domain data provided by Adrian and co-workers) at lower Reθ (1015 and 7700). In this way, statistical properties of the spanwise coherent structures are parametrically studied in terms of Reθ. The analysis of conditional vortices demonstrates the capability of the identification method to correctly track the spanwise vortical structures, to determine their length scales and, at low Reθ, to evidence the formation of vortex packets. The probability distribution functions of relevant quantities (such as the length scale, the local energy, and the vorticity of the educed vortices) are shown to collapse well when normalized with respect to low order statistical quantities (mean and variance). Analytical approximations useful for extrapolating the observed statistical properties at a large Reynolds number are also presented and discussed.

Towards development of a high quality public domain global roads database

There is clear demand for a global spatial public domain roads data set with improved geographic and temporal coverage, consistent coding of road types, and clear documentation of sources. The currently best available global public domain product covers only one-quarter to one-third of the existing road networks, and this varies considerably by region. Applications for such a data set span multiple sectors and would be particularly valuable for the international economic development, disaster relief, and biodiversity conservation communities, not to mention national and regional agencies and organizations around the world. The building blocks for such a global product are available for many countries and regions, yet thus far there has been neither strategy nor leadership for developing it. This paper evaluates the best available public domain and commercial data sets, assesses the gaps in global coverage, and proposes a number of strategies for filling them. It also identifies stakeholder organizations with an interest in such a data set that might either provide leadership or funding for its development. It closes with a proposed set of actions to begin the process.

Ring Systems in Mutagenicity Databases

The distribution of ring systems in public mutagenicity databases is analyzed. An automated enumeration of substructures permits determination of the occurrence of different scaffolds in data sets. The counts are used to perform population analysis via proportions and odds ratios of mutagenic compounds. Pairwise calculations of odds ratios between scaffolds allow comparison of ring systems for isostere replacement studies. These findings are presented in tables that readily show which scaffold is likely to occur in mutagenic compounds. Also, rings identified in public domain mutagenicity data sets are compared to rings in drugs data sets; unfortunately, public mutagenicity data sets do not reflect the types of scaffolds in drugs and those typically used in medicinal chemistry. The findings bring into question the utility of predictive models that were derived from public domain data sets. The automated ring identification and statistical approaches used here can be applied to other pharmacological properties to yield information about chemical scaffolds.

KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites

KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at .

Incorporating hidden Markov models for identifying protein kinase‐specific phosphorylation sites

Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity.

Herbal Use among US Elderly: 2002 National Health Interview Survey

Use of herbal products among the elderly is an important concern for healthcare professionals. The presence of polypharmacy and multiple comorbidities places the elderly at high risk for herb-drug and herb-disease interactions. Limited data exist regarding herbal use among the US elderly population. To evaluate the incidence of and attitudes toward herbal use in a nationally representative sample of US elderly patients >/=65 years of age. We performed a descriptive analysis of public domain data collected in the 2002 National Health Interview Survey. Statistical analyses were conducted through use of SUDAAN software with Taylor series linearization for variance estimation. Analysis of weighted data revealed that 12.9% +/- 0.5% (mean +/- SE) of US elderly people had used an herbal supplement within the past 12 months. Use was greatest among individuals 65-69 years of age, females, Hispanic and non-Hispanic ethnic minorities, and respondents with a greater income, higher education level, or more positive self-reported health status. Among elderly people purchasing over-the-counter and prescription drugs, herbal use was 13.9% +/- 0.6% and 12.8% +/- 0.6%, respectively. Glucosamine, echinacea, and garlic supplements represented the most common herbals used. Benefit from combined herbal and conventional therapy was the most common reason cited for use; however, 50.9% +/- 2.2% of users did not discuss herbal therapy with a medical professional. Several theoretical herb-disease interactions were identified. The use of herbal products among the US elderly has risen over the past 5 years, whereas discussion of such use with medical professionals remains suboptimal.

Social and Physical Environments and Physical Activity

0518 Most empirical studies in community-based physical activity focus on an individual level paradigm that often overlooks how ecological variables (e.g., social, physical, and built environment, public policy) influence such behavior. Setting-specific variables, that may be objectively derived or obtained from survey data, have the potential to complement variation in physical activity unexplained by individual level variables. Thus, there is a need to consider a multilevel approach that includes larger social unit level variables in explaining relationships to physical activity in community settings. PURPOSE: A multilevel conceptual paradigm is proposed that includes utilization and operationalization of existing social-unit systems that hold important implications for promoting physical activity in a new exercise research agenda. METHOD: Relevant research in aging and physical activity is reviewed and substantive, operational, and statistical issues in large-scale studies involving multilevel influences on the physical activity of individuals and populations will be discussed. RESULTS: Multi-level approaches require greater understanding of hierarchical data structures that can utilize the collective efficacy of self-report data, geographic information systems (GIS), Census, public domain data (e.g., crime, traffic density) and other objective measures to explain level specific influences on physical activity behavior in field settings. CONCLUSIONS: Multilevel data structures offer new challenges and opportunities to evaluate exogenous factors contained in the social, physical, and built environment that influence physical activity.

Incyte To Eliminate Genomics Division

Acknowledging that it can no longer make money by providing human gene sequencing information, Incyte Corp. says it will close its Palo Alto, Calif., research facilities, effective April 2. The move will eliminate 257 jobs—nearly 60% of the company's employees. As the volume of public domain data has increased, our ability to secure high-value subscriptions for our proprietary genomics data product lines has decreased, explains John Keller, chief business officer. Incyte says it will continue to collect milestone and royalty payments associated with subscriptions. Incyte is another casualty of a failed strategy to sell proprietary gene sequence data to drug company researchers. The genomics data business began to wilt in 2000 when the government-funded Human Genome Project accelerated its free Web publication of raw sequencing data. Like competitors Celera Genomics and Human Genome Sciences, Incyte is shifting gears toward drug discovery and development. A.G. Edwards stock analyst Craig West says Incyte...