Relationship between judges' scores and dive attributes from a video recording of a diving competition.

Sports such as diving, gymnastics, and ice skating rely on expert judges to score performance accurately. Human error and bias can affect the scores, sometimes leading to controversy, especially at high levels. Instant replay or recorded video can be used to assess judges' scores, or sometimes update judges' scores, during a competition. For diving in particular, judges are trained to look for certain characteristics of a dive, such as angle of entry, height of splash, and distance of the dive from the end of the board, to score each dive on a scale of 0 to 10, where a 0 is a failed dive and a 10 is a perfect dive. In an effort to obtain objective comparisons for judges' scores, a diving meet was filmed and the video footage used to measure certain characteristics of each dive for each participant. The variables measured from the video were height of the dive at its apex, angle of entry into the water, and distance of the dive from the end of the board. The measured items were then used as explanatory variables in a regression model where the judge's scores were the response. The measurements from the video are gathered to provide a gold standard that is specific to the athletic performances at the meet being judged, and supplement judges' scores with synergistic quantitative and visual information. In this article we show, via a series of regression analyses, that certain aspects of an athlete's performance measured from video after a meet provide similar information to the judges' scores. The model was shown to fit the data well enough to warrant use of characteristics from video footage to supplement judges' scores in future meets. In addition, we calibrated the results from the model against those of meets where the same divers competed to show that the measurement data ranks divers in approximately the same order as they were ranked in other meets, showing meet to meet consistency in measured data and judges' scores. Eventually, our findings could lead to use of video footage to supplement judges' scores in real time.

Use of DFT Distance Metrics for Classification of SARS-CoV-2 Genomes.

In this work, we investigate using Fourier coefficients (FCs) for capturing useful information about viral sequences in a computationally efficient and compact manner. Specifically, we extract geographic submission location from SARS-CoV-2 sequence headers submitted to the GISAID Initiative, calculate corresponding FCs, and use the FCs to classify these sequences according to geographic location. We show that the FCs serve as useful numerical summaries for sequences that allow manipulation, identification, and differentiation via classical mathematical and statistical methods that are not readily applicable for character strings. Further, we argue that subsets of the FCs may be usable for the same purposes, which results in a reduction in storage requirements. We conclude by offering extensions of the research and potential future directions for subsequent analyses, such as the use of other series transforms for discreetly indexed signals such as genomes.

Case for omitting tied observations in the two-sample t-test and the Wilcoxon-Mann-Whitney Test.

When the distributional assumptions for a t-test are not met, the default position of many analysts is to resort to a rank-based test, such as the Wilcoxon-Mann-Whitney Test to compare the difference in means between two samples. The Wilcoxon-Mann-Whitney Test presents no danger of tied observations when the observations in the data are continuous. However, in practice, observations are discretized due various logical reasons, or the data are ordinal in nature. When ranks are tied, most textbooks recommend using mid-ranks to replace the tied ranks, a practice that affects the distribution of the Wilcoxon-Mann-Whitney Test under the null hypothesis. Other methods for breaking ties have also been proposed. In this study, we examine four tie-breaking methods-average-scores, mid-ranks, jittering, and omission-for their effects on Type I and Type II error of the Wilcoxon-Mann-Whitney Test and the two-sample t-test for various combinations of sample sizes, underlying population distributions, and percentages of tied observations. We use the results to determine the maximum percentage of ties for which the power and size are seriously affected, and for which method of tie-breaking results in the best Type I and Type II error properties. Not surprisingly, the underlying population distribution of the data has less of an effect on the Wilcoxon-Mann-Whitney Test than on the t-test. Surprisingly, we find that the jittering and omission methods tend to hold Type I error at the nominal level, even for small sample sizes, with no substantial sacrifice in terms of Type II error. Furthermore, the t-test and the Wilcoxon-Mann-Whitney Test are equally effected by ties in terms of Type I and Type II error; therefore, we recommend omitting tied observations when they occur for both the two-sample t-test and the Wilcoxon-Mann-Whitney due to the bias in Type I error that is created when tied observations are left in the data, in the case of the t-test, or adjusted using mid-ranks or average-scores, in the case of the Wilcoxon-Mann-Whitney.

The Ontology for Biomedical Investigations.

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl.

Mapping cell populations in flow cytometry data for cross-sample comparison using the Friedman-Rafsky test statistic as a distance measure.

Flow cytometry (FCM) is a fluorescence-based single-cell experimental technology that is routinely applied in biomedical research for identifying cellular biomarkers of normal physiological responses and abnormal disease states. While many computational methods have been developed that focus on identifying cell populations in individual FCM samples, very few have addressed how the identified cell populations can be matched across samples for comparative analysis. This article presents FlowMap-FR, a novel method for cell population mapping across FCM samples. FlowMap-FR is based on the Friedman-Rafsky nonparametric test statistic (FR statistic), which quantifies the equivalence of multivariate distributions. As applied to FCM data by FlowMap-FR, the FR statistic objectively quantifies the similarity between cell populations based on the shapes, sizes, and positions of fluorescence data distributions in the multidimensional feature space. To test and evaluate the performance of FlowMap-FR, we simulated the kinds of biological and technical sample variations that are commonly observed in FCM data. The results show that FlowMap-FR is able to effectively identify equivalent cell populations between samples under scenarios of proportion differences and modest position shifts. As a statistical test, FlowMap-FR can be used to determine whether the expression of a cellular marker is statistically different between two cell populations, suggesting candidates for new cellular phenotypes by providing an objective statistical measure. In addition, FlowMap-FR can indicate situations in which inappropriate splitting or merging of cell populations has occurred during gating procedures. We compared the FR statistic with the symmetric version of Kullback-Leibler divergence measure used in a previous population matching method with both simulated and real data. The FR statistic outperforms the symmetric version of KL-distance in distinguishing equivalent from nonequivalent cell populations. FlowMap-FR was also employed as a distance metric to match cell populations delineated by manual gating across 30 FCM samples from a benchmark FlowCAP data set. An F-measure of 0.88 was obtained, indicating high precision and recall of the FR-based population matching results. FlowMap-FR has been implemented as a standalone R/Bioconductor package so that it can be easily incorporated into current FCM data analytical workflows.

Metagenomic Classification Using an Abstraction Augmented Markov Model.

The abstraction augmented Markov model (AAMM) is an extension of a Markov model that can be used for the analysis of genetic sequences. It is developed using the frequencies of all possible consecutive words with same length (p-mers). This article will review the theory behind AAMM and apply the theory behind AAMM in metagenomic classification.

Influenza virus sequence feature variant type analysis: evidence of a role for NS1 in influenza virus host range restriction.

Genetic drift of influenza virus genomic sequences occurs through the combined effects of sequence alterations introduced by a low-fidelity polymerase and the varying selective pressures experienced as the virus migrates through different host environments. While traditional phylogenetic analysis is useful in tracking the evolutionary heritage of these viruses, the specific genetic determinants that dictate important phenotypic characteristics are often difficult to discern within the complex genetic background arising through evolution. Here we describe a novel influenza virus sequence feature variant type (Flu-SFVT) approach, made available through the public Influenza Research Database resource (www.fludb.org), in which variant types (VTs) identified in defined influenza virus protein sequence features (SFs) are used for genotype-phenotype association studies. Since SFs have been defined for all influenza virus proteins based on known structural, functional, and immune epitope recognition properties, the Flu-SFVT approach allows the rapid identification of the molecular genetic determinants of important influenza virus characteristics and their connection to underlying biological functions. We demonstrate the use of the SFVT approach to obtain statistical evidence for effects of NS1 protein sequence variations in dictating influenza virus host range restriction.

A gene selection method for GeneChip array data with small sample sizes.

BACKGROUND: In microarray experiments with small sample sizes, it is a challenge to estimate p-values accurately and decide cutoff p-values for gene selection appropriately. Although permutation-based methods have proved to have greater sensitivity and specificity than the regular t-test, their p-values are highly discrete due to the limited number of permutations available in very small sample sizes. Furthermore, estimated permutation-based p-values for true nulls are highly correlated and not uniformly distributed between zero and one, making it difficult to use current false discovery rate (FDR)-controlling methods. RESULTS: We propose a model-based information sharing method (MBIS) that, after an appropriate data transformation, utilizes information shared among genes. We use a normal distribution to model the mean differences of true nulls across two experimental conditions. The parameters of the model are then estimated using all data in hand. Based on this model, p-values, which are uniformly distributed from true nulls, are calculated. Then, since FDR-controlling methods are generally not well suited to microarray data with very small sample sizes, we select genes for a given cutoff p-value and then estimate the false discovery rate. CONCLUSION: Simulation studies and analysis using real microarray data show that the proposed method, MBIS, is more powerful and reliable than current methods. It has wide application to a variety of situations.

Analyzing taxonomic classification using extensible Markov models.

MOTIVATION: As next generation sequencing is rapidly adding new genomes, their correct placement in the taxonomy needs verification. However, the current methods for confirming classification of a taxon or suggesting revision for a potential misplacement relies on computationally intense multi-sequence alignment followed by an iterative adjustment of the distance matrix. Due to intra-heterogeneity issues with the 16S rRNA marker, no classifier is available for sub-genus level, which could readily suggest a classification for a novel 16S rRNA sequence. Metagenomics further complicates the issue by generating fragmented 16S rRNA sequences. This article proposes a novel alignment-free method for representing the microbial profiles using extensible Markov models (EMMs) with an extended Karlin-Altschul statistical framework similar to the classic alignment paradigm. We propose a log odds (LODs) score classifier based on Gumbel difference distribution that confirms correct classifications with statistical significance qualifications and suggests revisions where necessary. RESULTS: We tested our method by generating a sub-genus level classifier with which we re-evaluated classifications of 676 microbial organisms using the NCBI FTP database for the 16S rRNA. The results confirm current classification for all genera while ascertaining significance at 95%. Furthermore, this novel classifier isolates heterogeneity issues to a mere 12 strains while confirming classifications with significance qualification for the remaining 98%. The models require less memory than that needed by multi-sequence alignments and have better time complexity than the current methods. The classifier operates at sub-genus level, and thus outperforms the naive Bayes classifier of the RNA Database Project where much of the taxonomic analysis is available online. Finally, using information redundancy in model building, we show that the method applies to metagenomic fragment classification of 19 Escherichia coli strains. AVAILABILITY AND IMPLEMENTATION: Source code and binaries freely available for download at http://lyle.smu.edu/IDA/EMMSA/, implemented in JAVA and supported on MS Windows.

Identifying Differentially Expressed Genes based on probe level data for GeneChip arrays.

We propose a new method based on probe-level data (PLIDEG) to filter differentially expressed genes from non-differentially expressed genes. We compare this new method with others based on expression values by using two spikein data sets. With the extra information provided by probe level data, PLIDEG not only controls type I error, but also increases the power of detecting DEGs, simultaneously. Therefore, PLIDEG can efficiently separate DEGs and non-DEGs without requiring the estimation of the number of non-DEGs. Based on theoretical analysis and results from application to real microarray data, we confirm these good features of this new method.

A Distribution-Free Convolution Model for background correction of oligonucleotide microarray data.

INTRODUCTION: Affymetrix GeneChip high-density oligonucleotide arrays are widely used in biological and medical research because of production reproducibility, which facilitates the comparison of results between experiment runs. In order to obtain high-level classification and cluster analysis that can be trusted, it is important to perform various pre-processing steps on the probe-level data to control for variability in sample processing and array hybridization. Many proposed preprocessing methods are parametric, in that they assume that the background noise generated by microarray data is a random sample from a statistical distribution, typically a normal distribution. The quality of the final results depends on the validity of such assumptions. RESULTS: We propose a Distribution Free Convolution Model (DFCM) to circumvent observed deficiencies in meeting and validating distribution assumptions of parametric methods. Knowledge of array structure and the biological function of the probes indicate that the intensities of mismatched (MM) probes that correspond to the smallest perfect match (PM) intensities can be used to estimate the background noise. Specifically, we obtain the smallest q2 percent of the MM intensities that are associated with the lowest q1 percent PM intensities, and use these intensities to estimate background. CONCLUSION: Using the Affymetrix Latin Square spike-in experiments, we show that the background noise generated by microarray experiments typically is not well modeled by a single overall normal distribution. We further show that the signal is not exponentially distributed, as is also commonly assumed. Therefore, DFCM has better sensitivity and specificity, as measured by ROC curves and area under the curve (AUC) than MAS 5.0, RMA, RMA with no background correction (RMA-noBG), GCRMA, PLIER, and dChip (MBEI) for preprocessing of Affymetrix microarray data. These results hold for two spike-in data sets and one real data set that were analyzed. Comparisons with other methods on two spike-in data sets and one real data set show that our nonparametric methods are a superior alternative for background correction of Affymetrix data.

The Werner syndrome helicase is a cofactor for HIV-1 long terminal repeat transactivation and retroviral replication.

The Werner syndrome helicase (WRN) participates in DNA replication, double strand break repair, telomere maintenance, and p53 activation. Mutations of wrn cause Werner syndrome (WS), an autosomal recessive premature aging disorder associated with cancer predisposition, atherosclerosis, and other aging related symptoms. Here, we report that WRN is a novel cofactor for HIV-1 replication. Immortalized human WRN(-/-) WS fibroblasts, lacking a functional wrn gene, are impaired for basal and Tat-activated HIV-1 transcription. Overexpression of wild-type WRN transactivates the HIV-1 long terminal repeat (LTR) in the absence of Tat, and WRN cooperates with Tat to promote high-level LTR transactivation. Ectopic WRN induces HIV-1 p24(Gag) production and retroviral replication in HIV-1-infected H9(HIV-1IIIB) lymphocytes. A dominant-negative helicase-minus mutant, WRN(K577M), inhibits LTR transactivation and HIV-1 replication. Inhibition of endogenous WRN, through co-expression of WRN(K577M), diminishes recruitment of p300/CREB-binding protein-associated factor (PCAF) and positive transcription elongation factor b (P-TEFb) to Tat/transactivation response-RNA complexes, and immortalized WRN(-/-) WS fibroblasts exhibit comparable defects in recruitment of PCAF and P-TEFb to the HIV-1 LTR. Our results demonstrate that WRN is a novel cellular cofactor for HIV-1 replication and suggest that the WRN helicase participates in the recruitment of PCAF/P-TEFb-containing transcription complexes. WRN may be a plausible target for antiretroviral therapy.

A distribution free summarization method for Affymetrix GeneChip arrays.

MOTIVATION: Affymetrix GeneChip arrays require summarization in order to combine the probe-level intensities into one value representing the expression level of a gene. However, probe intensity measurements are expected to be affected by different levels of non-specific- and cross-hybridization to non-specific transcripts. Here, we present a new summarization technique, the Distribution Free Weighted method (DFW), which uses information about the variability in probe behavior to estimate the extent of non-specific and cross-hybridization for each probe. The contribution of the probe is weighted accordingly during summarization, without making any distributional assumptions for the probe-level data. RESULTS: We compare DFW with several popular summarization methods on spike-in datasets, via both our own calculations and the 'Affycomp II' competition. The results show that DFW outperforms other methods when sensitivity and specificity are considered simultaneously. With the Affycomp spike-in datasets, the area under the receiver operating characteristic curve for DFW is nearly 1.0 (a perfect value), indicating that DFW can identify all differentially expressed genes with a few false positives. The approach used is also computationally faster than most other methods in current use. AVAILABILITY: The R code for DFW is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gabapentin in patients with the pruritus of cholestasis: a double-blind, randomized, placebo-controlled trial.

Pruritus is defined as the second order of nociception, the first being pain; thus, there is a rationale to study gabapentin, a drug that increases the threshold to experience nociception. The aim of this double-blind, randomized, placebo-controlled trial was to study the effect of gabapentin on the perception of pruritus and its behavioral manifestation, scratching, in cholestasis. The participants were 16 women with chronic liver disease and chronic pruritus. Hourly scratching activity (HSA) was continuously recorded for up to 48 hours at baseline and on treatment for at least 4 weeks in an inpatient setting. The perception of pruritus was assessed by interviews and by a visual analog score (VAS) of pruritus recorded every hour while patients were awake. Patients were randomized to the study drug (gabapentin or placebo) at a starting dose of 300 mg orally per day in divided doses to a maximum of 2,400 mg or until relief from pruritus. Gabapentin was associated with an increase in mean HSA, in contrast to the placebo, which was associated with a decrease. The mean VAS decreased significantly among those taking the placebo and in some patients on gabapentin. In conclusion, gabapentin did not provide a significant therapeutic advantage over the placebo; in fact, it was associated with an increase in the perception of pruritus and in HSA in some patients.

Parameter estimation for the exponential-normal convolution model for background correction of affymetrix GeneChip data.

There are many methods of correcting microarray data for non-biological sources of error. Authors routinely supply software or code so that interested analysts can implement their methods. Even with a thorough reading of associated references, it is not always clear how requisite parts of the method are calculated in the software packages. However, it is important to have an understanding of such details, as this understanding is necessary for proper use of the output, or for implementing extensions to the model. In this paper, the calculation of parameter estimates used in Robust Multichip Average (RMA), a popular preprocessing algorithm for Affymetrix GeneChip brand microarrays, is elucidated. The background correction method for RMA assumes that the perfect match (PM) intensities observed result from a convolution of the true signal, assumed to be exponentially distributed, and a background noise component, assumed to have a normal distribution. A conditional expectation is calculated to estimate signal. Estimates of the mean and variance of the normal distribution and the rate parameter of the exponential distribution are needed to calculate this expectation. Simulation studies show that the current estimates are flawed; therefore, new ones are suggested. We examine the performance of preprocessing under the exponential-normal convolution model using several different methods to estimate the parameters.

A human T-cell lymphotropic virus type 1 enhancer of Myc transforming potential stabilizes Myc-TIP60 transcriptional interactions.

The human T-cell lymphotropic virus type 1 (HTLV-1) infects and transforms CD4+ lymphocytes and causes adult T-cell leukemia/lymphoma (ATLL), an aggressive lymphoproliferative disease that is often fatal. Here, we demonstrate that the HTLV-1 pX splice-variant p30II markedly enhances the transforming potential of Myc and transcriptionally activates the human cyclin D2 promoter, dependent upon its conserved Myc-responsive E-box enhancer elements, which are associated with increased S-phase entry and multinucleation. Enhancement of c-Myc transforming activity by HTLV-1 p30II is dependent upon the transcriptional coactivators, transforming transcriptional activator protein/p434 and TIP60, and it requires TIP60 histone acetyltransferase (HAT) activity and correlates with the stabilization of HTLV-1 p30II/Myc-TIP60 chromatin-remodeling complexes. The p30II oncoprotein colocalizes and coimmunoprecipitates with Myc-TIP60 complexes in cultured HTLV-1-infected ATLL patient lymphocytes. Amino acid residues 99 to 154 within HTLV-1 p30II interact with the TIP60 HAT, and p30II transcriptionally activates numerous cellular genes in a TIP60-dependent or TIP60-independent manner, as determined by microarray gene expression analyses. Importantly, these results suggest that p30II functions as a novel retroviral modulator of Myc-TIP60-transforming interactions that may contribute to adult T-cell leukemogenesis.

A pilot study of mind-body changes in adults with asthma who practice mental imagery.

CONTEXT: Despite the growing number of studies of imagery and the use of complementary and alternative modalities as treatments for asthma, research on mental imagery in adults with asthma is practically, nonexistent. The purpose of this feasibility study was to lay groundwork for a larger follow-up clinical trial. OBJECTIVE: To determine whether pulmonary function, asthma symptoms, quality of life, depression, anxiety, and power differ over time in adults with asthma who do and do not practice mental imagery (MI). (Power is the ability to make aware choices with the intention of freely involving oneself in creating desired change.) DESIGN: Randomized controlled study using univariate repeated measures analysis of variance (ANOVA) and replacement through block design. SETTING: Lenox Hill Hospital, an affiliate of New York University Medical School, New York, NY. SUBJECTS: Sixty-eight adults with symptomatic asthma, after 4 weeks of baseline data collection and analysis, met requirements for this randomized controlled study. Thirty-three completed pulmonary function as well as self-report tests at 4 time points over 17 weeks. The 16 experimental participants also completed the 4-session imagery protocol. INTERVENTION: Individual imagery instruction (week 1) and follow-up (weeks 4, 9, 15). Participants were given 7 imagery exercises to select from and practice 3 times a day for a total of 15 minutes. MAIN OUTCOME MEASURES: 1) Spirometry (FEV1); 2) medication use; 3) Asthma Quality of Life Questionnaire; 4) Beck Depression Inventory; 5) Spielberger Anxiety Scales (A-State and A-Trait); 6) Barrett Power as Knowing Participation in Change Tool, Version II; 7) Epstein Balloon Test of Ability to Image. RESULTS: There was little evidence of statistical change in this feasibility study; yet, valuable lessons were learned. Paired t-tests indicated there was a significant difference in the total power scores in the imagery group, and in the expected direction (two-tailed, t-statistic = -2.3, P = 0.035) and the choices sub-scale (two-tailed, tstatistic = -2.93, P = 0.01) of the power instrument from weeks one to 16 of the study. Eight of 17 (47%) participants in the MI group reduced or discontinued their medications. Three of 16 (19%) participants in the control group reduced their medications; none discontinued. Chi-square indicated differences between groups (X2 = 4.66, P = 0.05). Persons who reduced or discontinued their medications showed neither an increase in pulmonary function prior to medication discontinuation, nor a fall in these parameters following discontinuation. CONCLUSIONS: Findings related to major outcome measures must be viewed with caution due to the small sample size resulting from attrition related to labor intensiveness and, therefore, low statistical power. However, the study did provide significant data to plan a larger scale study of the use of mental imagery with adult asthmatics. The study also demonstrated that imagery is inexpensive, safe and, with training, can be used as an adjunct therapy by patients themselves. Its efficacy needs additional exploration. Further research for adults with asthma who practice imagery is important, as current treatments are not entirely efficacious. Lessons learned in this study may facilitate improvement in research designs.