Phase II cancer clinical trials for biomarker-guided treatments.

Science.gov (United States)

Jung, Sin-Ho

2018-01-01

The design and analysis of cancer clinical trials with biomarker depend on various factors, such as the phase of trials, the type of biomarker, whether the used biomarker is validated or not, and the study objectives. In this article, we demonstrate the design and analysis of two Phase II cancer clinical trials, one with a predictive biomarker and the other with an imaging prognostic biomarker. Statistical testing methods and their sample size calculation methods are presented for each trial. We assume that the primary endpoint of these trials is a time to event variable, but this concept can be used for any type of endpoint.

A Pilot Proteomic Analysis of Salivary Biomarkers in Autism Spectrum Disorder.

Science.gov (United States)

Ngounou Wetie, Armand G; Wormwood, Kelly L; Russell, Stefanie; Ryan, Jeanne P; Darie, Costel C; Woods, Alisa G

2015-06-01

Autism spectrum disorder (ASD) prevalence is increasing, with current estimates at 1/68-1/50 individuals diagnosed with an ASD. Diagnosis is based on behavioral assessments. Early diagnosis and intervention is known to greatly improve functional outcomes in people with ASD. Diagnosis, treatment monitoring and prognosis of ASD symptoms could be facilitated with biomarkers to complement behavioral assessments. Mass spectrometry (MS) based proteomics may help reveal biomarkers for ASD. In this pilot study, we have analyzed the salivary proteome in individuals with ASD compared to neurotypical control subjects, using MS-based proteomics. Our goal is to optimize methods for salivary proteomic biomarker discovery and to identify initial putative biomarkers in people with ASDs. The salivary proteome is virtually unstudied in ASD, and saliva could provide an easily accessible biomaterial for analysis. Using nano liquid chromatography-tandem mass spectrometry, we found statistically significant differences in several salivary proteins, including elevated prolactin-inducible protein, lactotransferrin, Ig kappa chain C region, Ig gamma-1 chain C region, Ig lambda-2 chain C regions, neutrophil elastase, polymeric immunoglobulin receptor and deleted in malignant brain tumors 1. Our results indicate that this is an effective method for identification of salivary protein biomarkers, support the concept that immune system and gastrointestinal disturbances may be present in individuals with ASDs and point toward the need for larger studies in behaviorally-characterized individuals. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Imaging mass spectrometry statistical analysis.

Science.gov (United States)

Jones, Emrys A; Deininger, Sören-Oliver; Hogendoorn, Pancras C W; Deelder, André M; McDonnell, Liam A

2012-08-30

Imaging mass spectrometry is increasingly used to identify new candidate biomarkers. This clinical application of imaging mass spectrometry is highly multidisciplinary: expertise in mass spectrometry is necessary to acquire high quality data, histology is required to accurately label the origin of each pixel's mass spectrum, disease biology is necessary to understand the potential meaning of the imaging mass spectrometry results, and statistics to assess the confidence of any findings. Imaging mass spectrometry data analysis is further complicated because of the unique nature of the data (within the mass spectrometry field); several of the assumptions implicit in the analysis of LC-MS/profiling datasets are not applicable to imaging. The very large size of imaging datasets and the reporting of many data analysis routines, combined with inadequate training and accessible reviews, have exacerbated this problem. In this paper we provide an accessible review of the nature of imaging data and the different strategies by which the data may be analyzed. Particular attention is paid to the assumptions of the data analysis routines to ensure that the reader is apprised of their correct usage in imaging mass spectrometry research. Copyright © 2012 Elsevier B.V. All rights reserved.

2015 ICSA/Graybill Applied Statistics Symposium

CERN Document Server

Wang, Bushi; Hu, Xiaowen; Chen, Kun; Liu, Ray

2016-01-01

The papers in this volume represent a broad, applied swath of advanced contributions to the 2015 ICSA/Graybill Applied Statistics Symposium of the International Chinese Statistical Association, held at Colorado State University in Fort Collins. The contributions cover topics that range from statistical applications in business and finance to applications in clinical trials and biomarker analysis. Each papers was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. Focuses on statistical applications from clinical trials, biomarker analysis, and personalized medicine to applications in finance and business analytics A unique selection of papers from broad and multi-disciplinary critical hot topics - from academic, government, and industry perspectives - to appeal to a wide variety of applied research interests All papers feature origina...

Evaluating biomarkers for prognostic enrichment of clinical trials.

Science.gov (United States)

Kerr, Kathleen F; Roth, Jeremy; Zhu, Kehao; Thiessen-Philbrook, Heather; Meisner, Allison; Wilson, Francis Perry; Coca, Steven; Parikh, Chirag R

2017-12-01

A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment. We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency. We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform. In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Association between biomarkers and clinical characteristics in chronic subdural hematoma patients assessed with lasso regression.

Directory of Open Access Journals (Sweden)

Are Hugo Pripp

Full Text Available Chronic subdural hematoma (CSDH is characterized by an "old" encapsulated collection of blood and blood breakdown products between the brain and its outermost covering (the dura. Recognized risk factors for development of CSDH are head injury, old age and using anticoagulation medication, but its underlying pathophysiological processes are still unclear. It is assumed that a complex local process of interrelated mechanisms including inflammation, neomembrane formation, angiogenesis and fibrinolysis could be related to its development and propagation. However, the association between the biomarkers of inflammation and angiogenesis, and the clinical and radiological characteristics of CSDH patients, need further investigation. The high number of biomarkers compared to the number of observations, the correlation between biomarkers, missing data and skewed distributions may limit the usefulness of classical statistical methods. We therefore explored lasso regression to assess the association between 30 biomarkers of inflammation and angiogenesis at the site of lesions, and selected clinical and radiological characteristics in a cohort of 93 patients. Lasso regression performs both variable selection and regularization to improve the predictive accuracy and interpretability of the statistical model. The results from the lasso regression showed analysis exhibited lack of robust statistical association between the biomarkers in hematoma fluid with age, gender, brain infarct, neurological deficiencies and volume of hematoma. However, there were associations between several of the biomarkers with postoperative recurrence requiring reoperation. The statistical analysis with lasso regression supported previous findings that the immunological characteristics of CSDH are local. The relationship between biomarkers, the radiological appearance of lesions and recurrence requiring reoperation have been inclusive using classical statistical methods on these data

A simple method to combine multiple molecular biomarkers for dichotomous diagnostic classification

Directory of Open Access Journals (Sweden)

Amin Manik A

2006-10-01

Full Text Available Abstract Background In spite of the recognized diagnostic potential of biomarkers, the quest for squelching noise and wringing in information from a given set of biomarkers continues. Here, we suggest a statistical algorithm that – assuming each molecular biomarker to be a diagnostic test – enriches the diagnostic performance of an optimized set of independent biomarkers employing established statistical techniques. We validated the proposed algorithm using several simulation datasets in addition to four publicly available real datasets that compared i subjects having cancer with those without; ii subjects with two different cancers; iii subjects with two different types of one cancer; and iv subjects with same cancer resulting in differential time to metastasis. Results Our algorithm comprises of three steps: estimating the area under the receiver operating characteristic curve for each biomarker, identifying a subset of biomarkers using linear regression and combining the chosen biomarkers using linear discriminant function analysis. Combining these established statistical methods that are available in most statistical packages, we observed that the diagnostic accuracy of our approach was 100%, 99.94%, 96.67% and 93.92% for the real datasets used in the study. These estimates were comparable to or better than the ones previously reported using alternative methods. In a synthetic dataset, we also observed that all the biomarkers chosen by our algorithm were indeed truly differentially expressed. Conclusion The proposed algorithm can be used for accurate diagnosis in the setting of dichotomous classification of disease states.

Biomarkers identified by urinary metabonomics for noninvasive diagnosis of nutritional rickets.

Science.gov (United States)

Wang, Maoqing; Yang, Xue; Ren, Lihong; Li, Songtao; He, Xuan; Wu, Xiaoyan; Liu, Tingting; Lin, Liqun; Li, Ying; Sun, Changhao

2014-09-05

Nutritional rickets is a worldwide public health problem; however, the current diagnostic methods retain shortcomings for accurate diagnosis of nutritional rickets. To identify urinary biomarkers associated with nutritional rickets and establish a noninvasive diagnosis method, urinary metabonomics analysis by ultra-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis were employed to investigate the metabolic alterations associated with nutritional rickets in 200 children with or without nutritional rickets. The pathophysiological changes and pathogenesis of nutritional rickets were illustrated by the identified biomarkers. By urinary metabolic profiling, 31 biomarkers of nutritional rickets were identified and five candidate biomarkers for clinical diagnosis were screened and identified by quantitative analysis and receiver operating curve analysis. Urinary levels of five candidate biomarkers were measured using mass spectrometry or commercial kits. In the validation step, the combination of phosphate and sebacic acid was able to give a noninvasive and accurate diagnostic with high sensitivity (94.0%) and specificity (71.2%). Furthermore, on the basis of the pathway analysis of biomarkers, our urinary metabonomics analysis gives new insight into the pathogenesis and pathophysiology of nutritional rickets.

Mesothelin as a biomarker for ovarian carcinoma: a meta-analysis

Directory of Open Access Journals (Sweden)

KRISTIAN MADEIRA

2016-06-01

Full Text Available The objective of this work was to estimate the accuracy of mesothelin as a biomarker for ovarian cancer. A quantitative systematic review was performed. A comprehensive search of the Medline, LILACS, SCOPUS, Embase, Cochrane Central Register of Controlled Trials, Biomed Central, and ISI Web of Science databases was conducted from January 1990 to June 2015. For inclusion in this systematic review, the papers must have measured mesothelin levels in at least two histological diagnoses; ovarian cancer (borderline or ovarian tumor vs. benign or normal ovarian tissue. For each study, 2 x 2 contingency tables were constructed. We calculated the sensitivity, specificity and diagnostic odds ratio. The verification bias was performed according to QUADAS-2. Statistical analysis was performed with the software Stata 11, Meta-DiSc(r and RevMan 5.2. Twelve studies were analyzed, which included 1,561 women. The pooled sensitivity was 0.62 (CI 95% 0.58 - 0.66 and specificity was 0.94 (CI 95% 0.92 - 0.95. The DOR was 38.92 (CI 95% 17.82 - 84.99. Our systematic review shows that mesothelin cannot serve alone as a biomarker for the detection of ovarian cancer.

Biomarkers of tolerance: searching for the hidden phenotype.

Science.gov (United States)

Perucha, Esperanza; Rebollo-Mesa, Irene; Sagoo, Pervinder; Hernandez-Fuentes, Maria P

2011-08-01

Induction of transplantation tolerance remains the ideal long-term clinical and logistic solution to the current challenges facing the management of renal allograft recipients. In this review, we describe the recent studies and advances made in identifying biomarkers of renal transplant tolerance, from study inceptions, to the lessons learned and their implications for current and future studies with the same goal. With the age of biomarker discovery entering a new dimension of high-throughput technologies, here we also review the current approaches, developments, and pitfalls faced in the subsequent statistical analysis required to identify valid biomarker candidates.

Study design and statistical analysis of data in human population studies with the micronucleus assay.

Science.gov (United States)

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

The most common study design performed in population studies based on the micronucleus (MN) assay, is the cross-sectional study, which is largely performed to evaluate the DNA damaging effects of exposure to genotoxic agents in the workplace, in the environment, as well as from diet or lifestyle factors. Sample size is still a critical issue in the design of MN studies since most recent studies considering gene-environment interaction, often require a sample size of several hundred subjects, which is in many cases difficult to achieve. The control of confounding is another major threat to the validity of causal inference. The most popular confounders considered in population studies using MN are age, gender and smoking habit. Extensive attention is given to the assessment of effect modification, given the increasing inclusion of biomarkers of genetic susceptibility in the study design. Selected issues concerning the statistical treatment of data have been addressed in this mini-review, starting from data description, which is a critical step of statistical analysis, since it allows to detect possible errors in the dataset to be analysed and to check the validity of assumptions required for more complex analyses. Basic issues dealing with statistical analysis of biomarkers are extensively evaluated, including methods to explore the dose-response relationship among two continuous variables and inferential analysis. A critical approach to the use of parametric and non-parametric methods is presented, before addressing the issue of most suitable multivariate models to fit MN data. In the last decade, the quality of statistical analysis of MN data has certainly evolved, although even nowadays only a small number of studies apply the Poisson model, which is the most suitable method for the analysis of MN data.

Biomarker Identification and Pathway Analysis by Serum Metabolomics of Lung Cancer

Directory of Open Access Journals (Sweden)

Yingrong Chen

2015-01-01

Full Text Available Lung cancer is one of the most common causes of cancer death, for which no validated tumor biomarker is sufficiently accurate to be useful for diagnosis. Additionally, the metabolic alterations associated with the disease are unclear. In this study, we investigated the construction, interaction, and pathways of potential lung cancer biomarkers using metabolomics pathway analysis based on the Kyoto Encyclopedia of Genes and Genomes database and the Human Metabolome Database to identify the top altered pathways for analysis and visualization. We constructed a diagnostic model using potential serum biomarkers from patients with lung cancer. We assessed their specificity and sensitivity according to the area under the curve of the receiver operator characteristic (ROC curves, which could be used to distinguish patients with lung cancer from normal subjects. The pathway analysis indicated that sphingolipid metabolism was the top altered pathway in lung cancer. ROC curve analysis indicated that glycerophospho-N-arachidonoyl ethanolamine (GpAEA and sphingosine were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. Compared with the traditional lung cancer diagnostic biomarkers carcinoembryonic antigen and cytokeratin 19 fragment, GpAEA and sphingosine were as good or more appropriate for detecting lung cancer. We report our identification of potential metabolic diagnostic and prognostic biomarkers of lung cancer and clarify the metabolic alterations in lung cancer.

Polyamine Metabolites Profiling for Characterization of Lung and Liver Cancer Using an LC-Tandem MS Method with Multiple Statistical Data Mining Strategies: Discovering Potential Cancer Biomarkers in Human Plasma and Urine

Directory of Open Access Journals (Sweden)

Huarong Xu

2016-08-01

Full Text Available Polyamines, one of the most important kind of biomarkers in cancer research, were investigated in order to characterize different cancer types. An integrative approach which combined ultra-high performance liquid chromatography—tandem mass spectrometry detection and multiple statistical data processing strategies including outlier elimination, binary logistic regression analysis and cluster analysis had been developed to discover the characteristic biomarkers of lung and liver cancer. The concentrations of 14 polyamine metabolites in biosamples from lung (n = 50 and liver cancer patients (n = 50 were detected by a validated UHPLC-MS/MS method. Then the concentrations were converted into independent variables to characterize patients of lung and liver cancer by binary logic regression analysis. Significant independent variables were regarded as the potential biomarkers. Cluster analysis was engaged for further verifying. As a result, two values was discovered to identify lung and liver cancer, which were the product of the plasma concentration of putrescine and spermidine; and the ratio of the urine concentration of S-adenosyl-l-methionine and N-acetylspermidine. Results indicated that the established advanced method could be successfully applied to characterize lung and liver cancer, and may also enable a new way of discovering cancer biomarkers and characterizing other types of cancer.

Statistical methods for the analysis of high-throughput metabolomics data

Directory of Open Access Journals (Sweden)

Fabian J. Theis

2013-01-01

Full Text Available Metabolomics is a relatively new high-throughput technology that aims at measuring all endogenous metabolites within a biological sample in an unbiased fashion. The resulting metabolic profiles may be regarded as functional signatures of the physiological state, and have been shown to comprise effects of genetic regulation as well as environmental factors. This potential to connect genotypic to phenotypic information promises new insights and biomarkers for different research fields, including biomedical and pharmaceutical research. In the statistical analysis of metabolomics data, many techniques from other omics fields can be reused. However recently, a number of tools specific for metabolomics data have been developed as well. The focus of this mini review will be on recent advancements in the analysis of metabolomics data especially by utilizing Gaussian graphical models and independent component analysis.

atBioNet– an integrated network analysis tool for genomics and biomarker discovery

Directory of Open Access Journals (Sweden)

Ding Yijun

2012-07-01

Full Text Available Abstract Background Large amounts of mammalian protein-protein interaction (PPI data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. Results atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks. The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. Conclusion atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http

atBioNet--an integrated network analysis tool for genomics and biomarker discovery.

Science.gov (United States)

Ding, Yijun; Chen, Minjun; Liu, Zhichao; Ding, Don; Ye, Yanbin; Zhang, Min; Kelly, Reagan; Guo, Li; Su, Zhenqiang; Harris, Stephen C; Qian, Feng; Ge, Weigong; Fang, Hong; Xu, Xiaowei; Tong, Weida

2012-07-20

Large amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity. atBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis. atBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

Biomarkers of Pediatric Brain Tumors

Directory of Open Access Journals (Sweden)

Mark D Russell

2013-03-01

Full Text Available Background and Need for Novel Biomarkers: Brain tumors are the leading cause of death by solid tumors in children. Although improvements have been made in their radiological detection and treatment, our capacity to promptly diagnose pediatric brain tumors in their early stages remains limited. This contrasts several other cancers where serum biomarkers such as CA 19-9 and CA 125 facilitate early diagnosis and treatment. Aim: The aim of this article is to review the latest literature and highlight biomarkers which may be of clinical use in the common types of primary pediatric brain tumor. Methods: A PubMed search was performed to identify studies reporting biomarkers in the bodily fluids of pediatric patients with brain tumors. Details regarding the sample type (serum, cerebrospinal fluid or urine, biomarkers analyzed, methodology, tumor type and statistical significance were recorded. Results: A total of 12 manuscripts reporting 19 biomarkers in 367 patients vs. 397 controls were identified in the literature. Of the 19 biomarkers identified, 12 were isolated from cerebrospinal fluid, 2 from serum, 3 from urine, and 2 from multiple bodily fluids. All but one study reported statistically significant differences in biomarker expression between patient and control groups.Conclusions: This review identifies a panel of novel biomarkers for pediatric brain tumors. It provides a platform for the further studies necessary to validate these biomarkers and, in addition, highlights several techniques through which new biomarkers can be discovered.

Integrative analysis to select cancer candidate biomarkers to targeted validation

Science.gov (United States)

Heberle, Henry; Domingues, Romênia R.; Granato, Daniela C.; Yokoo, Sami; Canevarolo, Rafael R.; Winck, Flavia V.; Ribeiro, Ana Carolina P.; Brandão, Thaís Bianca; Filgueiras, Paulo R.; Cruz, Karen S. P.; Barbuto, José Alexandre; Poppi, Ronei J.; Minghim, Rosane; Telles, Guilherme P.; Fonseca, Felipe Paiva; Fox, Jay W.; Santos-Silva, Alan R.; Coletta, Ricardo D.; Sherman, Nicholas E.; Paes Leme, Adriana F.

2015-01-01

Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS. PMID:26540631

Topic model-based mass spectrometric data analysis in cancer biomarker discovery studies.

Science.gov (United States)

Wang, Minkun; Tsai, Tsung-Heng; Di Poto, Cristina; Ferrarini, Alessia; Yu, Guoqiang; Ressom, Habtom W

2016-08-18

that incorporation of scan-level features have the potential to lead to more accurate purification results by alleviating the loss in information as a result of integrating peaks. We believe cancer biomarker discovery studies that use mass spectrometric analysis of human biospecimens can greatly benefit from topic model-based purification of the data prior to statistical and pathway analyses.

Cloud-based solution to identify statistically significant MS peaks differentiating sample categories.

Science.gov (United States)

Ji, Jun; Ling, Jeffrey; Jiang, Helen; Wen, Qiaojun; Whitin, John C; Tian, Lu; Cohen, Harvey J; Ling, Xuefeng B

2013-03-23

Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). "Turnkey" solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. Here we present an efficient and effective solution, which provides experimental biologists easy access to "cloud" computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/. Presented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS.

Meta-analysis of the technical performance of an imaging procedure: guidelines and statistical methodology.

Science.gov (United States)

Huang, Erich P; Wang, Xiao-Feng; Choudhury, Kingshuk Roy; McShane, Lisa M; Gönen, Mithat; Ye, Jingjing; Buckler, Andrew J; Kinahan, Paul E; Reeves, Anthony P; Jackson, Edward F; Guimaraes, Alexander R; Zahlmann, Gudrun

2015-02-01

Medical imaging serves many roles in patient care and the drug approval process, including assessing treatment response and guiding treatment decisions. These roles often involve a quantitative imaging biomarker, an objectively measured characteristic of the underlying anatomic structure or biochemical process derived from medical images. Before a quantitative imaging biomarker is accepted for use in such roles, the imaging procedure to acquire it must undergo evaluation of its technical performance, which entails assessment of performance metrics such as repeatability and reproducibility of the quantitative imaging biomarker. Ideally, this evaluation will involve quantitative summaries of results from multiple studies to overcome limitations due to the typically small sample sizes of technical performance studies and/or to include a broader range of clinical settings and patient populations. This paper is a review of meta-analysis procedures for such an evaluation, including identification of suitable studies, statistical methodology to evaluate and summarize the performance metrics, and complete and transparent reporting of the results. This review addresses challenges typical of meta-analyses of technical performance, particularly small study sizes, which often causes violations of assumptions underlying standard meta-analysis techniques. Alternative approaches to address these difficulties are also presented; simulation studies indicate that they outperform standard techniques when some studies are small. The meta-analysis procedures presented are also applied to actual [18F]-fluorodeoxyglucose positron emission tomography (FDG-PET) test-retest repeatability data for illustrative purposes. © The Author(s) 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Comprehensive Analysis of MILE Gene Expression Data Set Advances Discovery of Leukaemia Type and Subtype Biomarkers.

Science.gov (United States)

Labaj, Wojciech; Papiez, Anna; Polanski, Andrzej; Polanska, Joanna

2017-03-01

Large collections of data in studies on cancer such as leukaemia provoke the necessity of applying tailored analysis algorithms to ensure supreme information extraction. In this work, a custom-fit pipeline is demonstrated for thorough investigation of the voluminous MILE gene expression data set. Three analyses are accomplished, each for gaining a deeper understanding of the processes underlying leukaemia types and subtypes. First, the main disease groups are tested for differential expression against the healthy control as in a standard case-control study. Here, the basic knowledge on molecular mechanisms is confirmed quantitatively and by literature references. Second, pairwise comparison testing is performed for juxtaposing the main leukaemia types among each other. In this case by means of the Dice coefficient similarity measure the general relations are pointed out. Moreover, lists of candidate main leukaemia group biomarkers are proposed. Finally, with this approach being successful, the third analysis provides insight into all of the studied subtypes, followed by the emergence of four leukaemia subtype biomarkers. In addition, the class enhanced DEG signature obtained on the basis of novel pipeline processing leads to significantly better classification power of multi-class data classifiers. The developed methodology consisting of batch effect adjustment, adaptive noise and feature filtration coupled with adequate statistical testing and biomarker definition proves to be an effective approach towards knowledge discovery in high-throughput molecular biology experiments.

Addressing the Challenge of Defining Valid Proteomic Biomarkers and Classifiers

LENUS (Irish Health Repository)

Dakna, Mohammed

2010-12-10

Abstract Background The purpose of this manuscript is to provide, based on an extensive analysis of a proteomic data set, suggestions for proper statistical analysis for the discovery of sets of clinically relevant biomarkers. As tractable example we define the measurable proteomic differences between apparently healthy adult males and females. We choose urine as body-fluid of interest and CE-MS, a thoroughly validated platform technology, allowing for routine analysis of a large number of samples. The second urine of the morning was collected from apparently healthy male and female volunteers (aged 21-40) in the course of the routine medical check-up before recruitment at the Hannover Medical School. Results We found that the Wilcoxon-test is best suited for the definition of potential biomarkers. Adjustment for multiple testing is necessary. Sample size estimation can be performed based on a small number of observations via resampling from pilot data. Machine learning algorithms appear ideally suited to generate classifiers. Assessment of any results in an independent test-set is essential. Conclusions Valid proteomic biomarkers for diagnosis and prognosis only can be defined by applying proper statistical data mining procedures. In particular, a justification of the sample size should be part of the study design.

Adaptive regression modeling of biomarkers of potential harm in a population of U.S. adult cigarette smokers and nonsmokers

Directory of Open Access Journals (Sweden)

Mendes Paul E

2010-03-01

Full Text Available Abstract Background This article describes the data mining analysis of a clinical exposure study of 3585 adult smokers and 1077 nonsmokers. The analysis focused on developing models for four biomarkers of potential harm (BOPH: white blood cell count (WBC, 24 h urine 8-epi-prostaglandin F2α (EPI8, 24 h urine 11-dehydro-thromboxane B2 (DEH11, and high-density lipoprotein cholesterol (HDL. Methods Random Forest was used for initial variable selection and Multivariate Adaptive Regression Spline was used for developing the final statistical models Results The analysis resulted in the generation of models that predict each of the BOPH as function of selected variables from the smokers and nonsmokers. The statistically significant variables in the models were: platelet count, hemoglobin, C-reactive protein, triglycerides, race and biomarkers of exposure to cigarette smoke for WBC (R-squared = 0.29; creatinine clearance, liver enzymes, weight, vitamin use and biomarkers of exposure for EPI8 (R-squared = 0.41; creatinine clearance, urine creatinine excretion, liver enzymes, use of Non-steroidal antiinflammatory drugs, vitamins and biomarkers of exposure for DEH11 (R-squared = 0.29; and triglycerides, weight, age, sex, alcohol consumption and biomarkers of exposure for HDL (R-squared = 0.39. Conclusions Levels of WBC, EPI8, DEH11 and HDL were statistically associated with biomarkers of exposure to cigarette smoking and demographics and life style factors. All of the predictors togather explain 29%-41% of the variability in the BOPH.

Statistical data analysis using SAS intermediate statistical methods

CERN Document Server

Marasinghe, Mervyn G

2018-01-01

The aim of this textbook (previously titled SAS for Data Analytics) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data. The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude. Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAS code and data sets, and advanced material suitab...

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers.

Science.gov (United States)

Irigoyen, Antonio; Jimenez-Luna, Cristina; Benavides, Manuel; Caba, Octavio; Gallego, Javier; Ortuño, Francisco Manuel; Guillen-Ponce, Carmen; Rojas, Ignacio; Aranda, Enrique; Torres, Carolina; Prados, Jose

2018-01-01

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

Searching for New Biomarkers and the Use of Multivariate Analysis in Gastric Cancer Diagnostics.

Science.gov (United States)

Kucera, Radek; Smid, David; Topolcan, Ondrej; Karlikova, Marie; Fiala, Ondrej; Slouka, David; Skalicky, Tomas; Treska, Vladislav; Kulda, Vlastimil; Simanek, Vaclav; Safanda, Martin; Pesta, Martin

2016-04-01

The first aim of this study was to search for new biomarkers to be used in gastric cancer diagnostics. The second aim was to verify the findings presented in literature on a sample of the local population and investigate the risk of gastric cancer in that population using a multivariant statistical analysis. We assessed a group of 36 patients with gastric cancer and 69 healthy individuals. We determined carcinoembryonic antigen, cancer antigen 19-9, cancer antigen 72-4, matrix metalloproteinases (-1, -2, -7, -8 and -9), osteoprotegerin, osteopontin, prothrombin induced by vitamin K absence-II, pepsinogen I, pepsinogen II, gastrin and Helicobacter pylori for each sample. The multivariate stepwise logistic regression identified the following biomarkers as the best gastric cancer predictors: CEA, CA72-4, pepsinogen I, Helicobacter pylori presence and MMP7. CEA and CA72-4 remain the best markers for gastric cancer diagnostics. We suggest a mathematical model for the assessment of risk of gastric cancer. Copyright© 2016 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

The use of mass spectrometry for analysing metabolite biomarkers in epidemiology: methodological and statistical considerations for application to large numbers of biological samples.

Science.gov (United States)

Lind, Mads V; Savolainen, Otto I; Ross, Alastair B

2016-08-01

Data quality is critical for epidemiology, and as scientific understanding expands, the range of data available for epidemiological studies and the types of tools used for measurement have also expanded. It is essential for the epidemiologist to have a grasp of the issues involved with different measurement tools. One tool that is increasingly being used for measuring biomarkers in epidemiological cohorts is mass spectrometry (MS), because of the high specificity and sensitivity of MS-based methods and the expanding range of biomarkers that can be measured. Further, the ability of MS to quantify many biomarkers simultaneously is advantageously compared to single biomarker methods. However, as with all methods used to measure biomarkers, there are a number of pitfalls to consider which may have an impact on results when used in epidemiology. In this review we discuss the use of MS for biomarker analyses, focusing on metabolites and their application and potential issues related to large-scale epidemiology studies, the use of MS "omics" approaches for biomarker discovery and how MS-based results can be used for increasing biological knowledge gained from epidemiological studies. Better understanding of the possibilities and possible problems related to MS-based measurements will help the epidemiologist in their discussions with analytical chemists and lead to the use of the most appropriate statistical tools for these data.

MortalityPredictors.org: a manually-curated database of published biomarkers of human all-cause mortality.

Science.gov (United States)

Peto, Maximus V; De la Guardia, Carlos; Winslow, Ksenia; Ho, Andrew; Fortney, Kristen; Morgen, Eric

2017-08-31

Biomarkers of all-cause mortality are of tremendous clinical and research interest. Because of the long potential duration of prospective human lifespan studies, such biomarkers can play a key role in quantifying human aging and quickly evaluating any potential therapies. Decades of research into mortality biomarkers have resulted in numerous associations documented across hundreds of publications. Here, we present MortalityPredictors.org , a manually-curated, publicly accessible database, housing published, statistically-significant relationships between biomarkers and all-cause mortality in population-based or generally healthy samples. To gather the information for this database, we searched PubMed for appropriate research papers and then manually curated relevant data from each paper. We manually curated 1,576 biomarker associations, involving 471 distinct biomarkers. Biomarkers ranged in type from hematologic (red blood cell distribution width) to molecular (DNA methylation changes) to physical (grip strength). Via the web interface, the resulting data can be easily browsed, searched, and downloaded for further analysis. MortalityPredictors.org provides comprehensive results on published biomarkers of human all-cause mortality that can be used to compare biomarkers, facilitate meta-analysis, assist with the experimental design of aging studies, and serve as a central resource for analysis. We hope that it will facilitate future research into human mortality and aging.

Bioinformatics tools for the analysis of NMR metabolomics studies focused on the identification of clinically relevant biomarkers.

Science.gov (United States)

Puchades-Carrasco, Leonor; Palomino-Schätzlein, Martina; Pérez-Rambla, Clara; Pineda-Lucena, Antonio

2016-05-01

Metabolomics, a systems biology approach focused on the global study of the metabolome, offers a tremendous potential in the analysis of clinical samples. Among other applications, metabolomics enables mapping of biochemical alterations involved in the pathogenesis of diseases, and offers the opportunity to noninvasively identify diagnostic, prognostic and predictive biomarkers that could translate into early therapeutic interventions. Particularly, metabolomics by Nuclear Magnetic Resonance (NMR) has the ability to simultaneously detect and structurally characterize an abundance of metabolic components, even when their identities are unknown. Analysis of the data generated using this experimental approach requires the application of statistical and bioinformatics tools for the correct interpretation of the results. This review focuses on the different steps involved in the metabolomics characterization of biofluids for clinical applications, ranging from the design of the study to the biological interpretation of the results. Particular emphasis is devoted to the specific procedures required for the processing and interpretation of NMR data with a focus on the identification of clinically relevant biomarkers. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

The Knowledge-Integrated Network Biomarkers Discovery for Major Adverse Cardiac Events

Science.gov (United States)

Jin, Guangxu; Zhou, Xiaobo; Wang, Honghui; Zhao, Hong; Cui, Kemi; Zhang, Xiang-Sun; Chen, Luonan; Hazen, Stanley L.; Li, King; Wong, Stephen T. C.

2010-01-01

The mass spectrometry (MS) technology in clinical proteomics is very promising for discovery of new biomarkers for diseases management. To overcome the obstacles of data noises in MS analysis, we proposed a new approach of knowledge-integrated biomarker discovery using data from Major Adverse Cardiac Events (MACE) patients. We first built up a cardiovascular-related network based on protein information coming from protein annotations in Uniprot, protein–protein interaction (PPI), and signal transduction database. Distinct from the previous machine learning methods in MS data processing, we then used statistical methods to discover biomarkers in cardiovascular-related network. Through the tradeoff between known protein information and data noises in mass spectrometry data, we finally could firmly identify those high-confident biomarkers. Most importantly, aided by protein–protein interaction network, that is, cardiovascular-related network, we proposed a new type of biomarkers, that is, network biomarkers, composed of a set of proteins and the interactions among them. The candidate network biomarkers can classify the two groups of patients more accurately than current single ones without consideration of biological molecular interaction. PMID:18665624

MULTIPLEX ANALYSIS OF BIOMARKERS OF NEOANGIOGENESIS AND INFLAMMATION IN HEART TRANSPLANT RECIPIENTS

Directory of Open Access Journals (Sweden)

O. P. Shevchenko

2015-01-01

Full Text Available Aim of study: multiplex analysis of the levels of biomarkers of neoangiogenesis and inflammation in cardiac transplant recipients. Materials and methods. 59 pts. with heart failure III–IV according to NYHA FC, waiting for a heart transplant, aged 22 to 73 years, 48 males and 11 females. 41 recipient (30 men and 11 women had dilated cardiomyopathy, 18 – coronary heart disease (CHD. The concentration of VEGF-A, VEGF-D, PlGF, PDGF-BB, FGF, sCD40L, MCP-1 was measured using xMAP technology, the sets of reagents Simplex ProcartaPlexTM (Affymetrix, USA. Results. There are four levels of seven biomarkers of neoangiogenesis and inflammation method for multiplex analysis in patients with heart failure. A year after transplantation, the mean levels of biomarkers VEGF-A (p = 0.001, PDGF-BB (p = 0.018, MCP-1 (p = 0.003 was significantly decreased, and the others had a tendency to decrease relative to the level before transplantation. It was shown individual differences of levels of VEGF-A, VEGF-D and PlGF before and after transplantation. There were found different dynamics of the concentrations of biomarkers and growth factors before and after heart transplantation in patients with cardiovascular complications and without them. Conclusion. Multiplex analysis allows to measure the concentration range of analyte biomarkers of neoangiogenesis, inflammation in one sample of blood serum of patients with severe heart failure and after transplantation. There are marked individual differences in the concentration of biomarkers in different clinical situations that may have clinical significance in the conduct and supervision of recipients after transplantation.

Tumor antigens as proteogenomic biomarkers in invasive ductal carcinomas

DEFF Research Database (Denmark)

Olsen, Lars Rønn; Campos, Benito; Winther, Ole

2014-01-01

directly linked to the hallmarks of cancer. The results found by proteogenomic analysis of the 32 tumor antigens studied here, capture largely the same pathway irregularities as those elucidated from large-scale screening of genomics analyses, where several thousands of genes are often found......Background: The majority of genetic biomarkers for human cancers are defined by statistical screening of high-throughput genomics data. While a large number of genetic biomarkers have been proposed for diagnostic and prognostic applications, only a small number have been applied in the clinic....... Similarly, the use of proteomics methods for the discovery of cancer biomarkers is increasing. The emerging field of proteogenomics seeks to enrich the value of genomics and proteomics approaches by studying the intersection of genomics and proteomics data. This task is challenging due to the complex nature...

The Biomarker-Surrogacy Evaluation Schema: a review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints.

Science.gov (United States)

Lassere, Marissa N

2008-06-01

There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Section 2 is a systematic, historical review of the biomarker-surrogate endpoint literature with special reference to the nomenclature, the systems of classification and statistical methods developed for their evaluation. In Section 3 an explicit, criterion-based, quantitative, multidimensional hierarchical levels of evidence schema - Biomarker-Surrogacy Evaluation Schema - is proposed to evaluate and co-ordinate the multiple dimensions (biological, epidemiological, statistical, clinical trial and risk-benefit evidence) of the biomarker clinical endpoint relationships. The schema systematically evaluates and ranks the surrogacy status of biomarkers and surrogate endpoints using defined levels of evidence. The schema incorporates the three independent domains: Study Design, Target Outcome and Statistical Evaluation. Each domain has items ranked from zero to five. An additional category called Penalties incorporates additional considerations of biological plausibility, risk-benefit and generalizability. The total score (0-15) determines the level of evidence, with Level 1 the strongest and Level 5 the weakest. The term ;surrogate' is restricted to markers attaining Levels 1 or 2 only. Surrogacy status of markers can then be directly compared within and across different areas of medicine to guide individual, trial-based or drug-development decisions. This schema would facilitate communication between clinical, researcher, regulatory, industry and consumer participants necessary for evaluation of the biomarker-surrogate-clinical endpoint relationship in their different settings.

Metabolome analysis for discovering biomarkers of gastroenterological cancer.

Science.gov (United States)

Suzuki, Makoto; Nishiumi, Shin; Matsubara, Atsuki; Azuma, Takeshi; Yoshida, Masaru

2014-09-01

Improvements in analytical technologies have made it possible to rapidly determine the concentrations of thousands of metabolites in any biological sample, which has resulted in metabolome analysis being applied to various types of research, such as clinical, cell biology, and plant/food science studies. The metabolome represents all of the end products and by-products of the numerous complex metabolic pathways operating in a biological system. Thus, metabolome analysis allows one to survey the global changes in an organism's metabolic profile and gain a holistic understanding of the changes that occur in organisms during various biological processes, e.g., during disease development. In clinical metabolomic studies, there is a strong possibility that differences in the metabolic profiles of human specimens reflect disease-specific states. Recently, metabolome analysis of biofluids, e.g., blood, urine, or saliva, has been increasingly used for biomarker discovery and disease diagnosis. Mass spectrometry-based techniques have been extensively used for metabolome analysis because they exhibit high selectivity and sensitivity during the identification and quantification of metabolites. Here, we describe metabolome analysis using liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry. Furthermore, the findings of studies that attempted to discover biomarkers of gastroenterological cancer are also outlined. Finally, we discuss metabolome analysis-based disease diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

On the assessment of the added value of new predictive biomarkers.

Science.gov (United States)

Chen, Weijie; Samuelson, Frank W; Gallas, Brandon D; Kang, Le; Sahiner, Berkman; Petrick, Nicholas

2013-07-29

The surge in biomarker development calls for research on statistical evaluation methodology to rigorously assess emerging biomarkers and classification models. Recently, several authors reported the puzzling observation that, in assessing the added value of new biomarkers to existing ones in a logistic regression model, statistical significance of new predictor variables does not necessarily translate into a statistically significant increase in the area under the ROC curve (AUC). Vickers et al. concluded that this inconsistency is because AUC "has vastly inferior statistical properties," i.e., it is extremely conservative. This statement is based on simulations that misuse the DeLong et al. method. Our purpose is to provide a fair comparison of the likelihood ratio (LR) test and the Wald test versus diagnostic accuracy (AUC) tests. We present a test to compare ideal AUCs of nested linear discriminant functions via an F test. We compare it with the LR test and the Wald test for the logistic regression model. The null hypotheses of these three tests are equivalent; however, the F test is an exact test whereas the LR test and the Wald test are asymptotic tests. Our simulation shows that the F test has the nominal type I error even with a small sample size. Our results also indicate that the LR test and the Wald test have inflated type I errors when the sample size is small, while the type I error converges to the nominal value asymptotically with increasing sample size as expected. We further show that the DeLong et al. method tests a different hypothesis and has the nominal type I error when it is used within its designed scope. Finally, we summarize the pros and cons of all four methods we consider in this paper. We show that there is nothing inherently less powerful or disagreeable about ROC analysis for showing the usefulness of new biomarkers or characterizing the performance of classification models. Each statistical method for assessing biomarkers and

Biomarkers that Discriminate Multiple Myeloma Patients with or without Skeletal Involvement Detected Using SELDI-TOF Mass Spectrometry and Statistical and Machine Learning Tools

Directory of Open Access Journals (Sweden)

Sudeepa Bhattacharyya

2006-01-01

Full Text Available Multiple Myeloma (MM is a severely debilitating neoplastic disease of B cell origin, with the primary source of morbidity and mortality associated with unrestrained bone destruction. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS was used to screen for potential biomarkers indicative of skeletal involvement in patients with MM. Serum samples from 48 MM patients, 24 with more than three bone lesions and 24 with no evidence of bone lesions were fractionated and analyzed in duplicate using copper ion loaded immobilized metal affinity SELDI chip arrays. The spectra obtained were compiled, normalized, and mass peaks with mass-to-charge ratios (m/z between 2000 and 20,000 Da identified. Peak information from all fractions was combined together and analyzed using univariate statistics, as well as a linear, partial least squares discriminant analysis (PLS-DA, and a non-linear, random forest (RF, classification algorithm. The PLS-DA model resulted in prediction accuracy between 96–100%, while the RF model was able to achieve a specificity and sensitivity of 87.5% each. Both models as well as multiple comparison adjusted univariate analysis identified a set of four peaks that were the most discriminating between the two groups of patients and hold promise as potential biomarkers for future diagnostic and/or therapeutic purposes.

NMR-based metabonomics and correlation analysis reveal potential biomarkers associated with chronic atrophic gastritis.

Science.gov (United States)

Cui, Jiajia; Liu, Yuetao; Hu, Yinghuan; Tong, Jiayu; Li, Aiping; Qu, Tingli; Qin, Xuemei; Du, Guanhua

2017-01-05

Chronic atrophic gastritis (CAG) is one of the most important pre-cancerous states with a high prevalence. Exploring of the underlying mechanism and potential biomarkers is of significant importance for CAG. In the present work, 1 H NMR-based metabonomics with correlative analysis was performed to analyze the metabolic features of CAG. 19 plasma metabolites and 18 urine metabolites were enrolled to construct the circulatory and excretory metabolome of CAG, which was in response to alterations of energy metabolism, inflammation, immune dysfunction, as well as oxidative stress. 7 plasma biomarkers and 7 urine biomarkers were screened to elucidate the pathogenesis of CAG based on the further correlation analysis with biochemical indexes. Finally, 3 plasma biomarkers (arginine, succinate and 3-hydroxybutyrate) and 2 urine biomarkers (α-ketoglutarate and valine) highlighted the potential to indicate risks of CAG in virtue of correlation with pepsin activity and ROC analysis. Here, our results paved a way for elucidating the underlying mechanisms in the development of CAG, and provided new avenues for the diagnosis of CAG and presented potential drug targets for treatment of CAG. Copyright © 2016 Elsevier B.V. All rights reserved.

MALDI-TOF mass spectrometry analysis of small molecular weight compounds (under 10 KDa) as biomarkers of rat hearts undergoing arecoline challenge.

Science.gov (United States)

Chen, Tung-Sheng; Chang, Mu-Hsin; Kuo, Wei-Wen; Lin, Yueh-Min; Yeh, Yu-Lan; Day, Cecilia Hsuan; Lin, Chien-Chung; Tsai, Fuu-Jen; Tsai, Chang-Hai; Huang, Chih-Yang

2013-04-01

Statistical and clinical reports indicate that betel nut chewing is strongly associated with progression of oral cancer because some ingredients in betel nuts are potential cancer promoters, especially arecoline. Early diagnosis for cancer biomarkers is the best strategy for prevention of cancer progression. Several methods are suggested for investigating cancer biomarkers. Among these methods, gel-based proteomics approach is the most powerful and recommended tool for investigating biomarkers due to its high-throughput. However, this proteomics approach is not suitable for screening biomarkers with molecular weight under 10 KDa because of the characteristics of gel electrophoresis. This study investigated biomarkers with molecular weight under 10 KDa in rats with arecoline challenge. The centrifuging vials with membrane (10 KDa molecular weight cut-off) played a crucial role in this study. After centrifuging, the filtrate (containing compounds with molecular weight under 10 KDa) was collected and spotted on a sample plate for MALDI-TOF mass spectrometry analysis. Compared to control, three extra peaks (m/z values were 1553.1611, 1668.2097 and 1740.1832, respectively) were found in sera and two extra peaks were found in heart tissue samples (408.9719 and 524.9961, respectively). These small compounds should play important roles and may be potential biomarker candidates in rats with arecoline. This study successfully reports a mass-based method for investigating biomarker candidates with small molecular weight in different types of sample (including serum and tissue). In addition, this reported method is more time-efficient (1 working day) than gel-based proteomics approach (5~7 working days).

Systematic Evaluation of the Prognostic Impact and Intratumour Heterogeneity of Clear Cell Renal Cell Carcinoma Biomarkers

DEFF Research Database (Denmark)

Gulati, Sakshi; Martinez, Pierre; Joshi, Tejal

2014-01-01

and statistical analysisBiomarker association with CSS was analysed by univariate and multivariate analyses. Results and limitationsA total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7...... expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations...... that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers. ConclusionsThe ccB signature was the only independent prognostic biomarker. Enrichment...

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression

OpenAIRE

Dipnall, Joanna F.; Pasco, Julie A.; Berk, Michael; Williams, Lana J.; Dodd, Seetal; Jacka, Felice N.; Meyer, Denny

2016-01-01

Background Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study. Methods The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted reg...

Blood-based biomarkers of aggressive prostate cancer.

Directory of Open Access Journals (Sweden)

Men Long Liong

Full Text Available PURPOSE: Prostate cancer is a bimodal disease with aggressive and indolent forms. Current prostate-specific-antigen testing and digital rectal examination screening provide ambiguous results leading to both under-and over-treatment. Accurate, consistent diagnosis is crucial to risk-stratify patients and facilitate clinical decision making as to treatment versus active surveillance. Diagnosis is currently achieved by needle biopsy, a painful procedure. Thus, there is a clinical need for a minimally-invasive test to determine prostate cancer aggressiveness. A blood sample to predict Gleason score, which is known to reflect aggressiveness of the cancer, could serve as such a test. MATERIALS AND METHODS: Blood mRNA was isolated from North American and Malaysian prostate cancer patients/controls. Microarray analysis was conducted utilizing the Affymetrix U133 plus 2·0 platform. Expression profiles from 255 patients/controls generated 85 candidate biomarkers. Following quantitative real-time PCR (qRT-PCR analysis, ten disease-associated biomarkers remained for paired statistical analysis and normalization. RESULTS: Microarray analysis was conducted to identify 85 genes differentially expressed between aggressive prostate cancer (Gleason score ≥8 and controls. Expression of these genes was qRT-PCR verified. Statistical analysis yielded a final seven-gene panel evaluated as six gene-ratio duplexes. This molecular signature predicted as aggressive (ie, Gleason score ≥8 55% of G6 samples, 49% of G7(3+4, 79% of G7(4+3 and 83% of G8-10, while rejecting 98% of controls. CONCLUSION: In this study, we have developed a novel, blood-based biomarker panel which can be used as the basis of a simple blood test to identify men with aggressive prostate cancer and thereby reduce the overdiagnosis and overtreatment that currently results from diagnosis using PSA alone. We discuss possible clinical uses of the panel to identify men more likely to benefit from

Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

Science.gov (United States)

Conley, Samantha

2017-12-01

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

Osteopontin (OPN) as a CSF and blood biomarker for multiple sclerosis: A systematic review and meta-analysis.

Science.gov (United States)

Agah, Elmira; Zardoui, Arshia; Saghazadeh, Amene; Ahmadi, Mona; Tafakhori, Abbas; Rezaei, Nima

2018-01-01

Identifying a reliable biomarker may accelerate diagnosis of multiple sclerosis (MS) and lead to early management of the disease. Accumulating evidence suggest that cerebrospinal fluid (CSF) and peripheral blood concentration of osteopontin (OPN) may have diagnostic and prognostic value in MS. We conducted a systematic review and meta-analysis of studies that measured peripheral blood and CSF levels of OPN in MS patients and controls to evaluate the diagnostic potential of this biomarker better. We searched PubMed, Web of Science and Scopus databases to find articles that measured OPN concentration in peripheral blood and CSF samples from MS patients up to October 19, 2016. Q statistic tests and the I2 index were applied for heterogeneity assessment. If the I2 index was less than 40%, the fixed-effects model was used for meta-analysis. Random-effects meta-analysis was chosen if the I2 value was greater than 40%. After removal of duplicates, 918 articles were identified, and 27 of them fulfilled the inclusion criteria. We included 22 eligible studies in the final meta-analysis. MS patients, in general, had considerably higher levels of OPN in their CSF and blood when compared to all types of controls (pCSF of MS subgroups (pCSF concentrations of OPN between MS patient subtypes. CIS patients had significantly lower levels of OPN both in their peripheral blood and CSF compared to patients with progressive subtypes of MS (pCSF concentration of OPN was significantly higher among RRMS patients compared to the CIS patients and SPMS patients (PCSF compared to patients with stable disease (P = 0.007). The result of this study confirms that increased levels of OPN exist in CSF and peripheral blood of MS patients and strengthens the evidence regarding the clinical utility of OPN as a promising and validated biomarker for MS.

Automated microfluidic devices integrating solid-phase extraction, fluorescent labeling, and microchip electrophoresis for preterm birth biomarker analysis.

Science.gov (United States)

Sahore, Vishal; Sonker, Mukul; Nielsen, Anna V; Knob, Radim; Kumar, Suresh; Woolley, Adam T

2018-01-01

We have developed multichannel integrated microfluidic devices for automated preconcentration, labeling, purification, and separation of preterm birth (PTB) biomarkers. We fabricated multilayer poly(dimethylsiloxane)-cyclic olefin copolymer (PDMS-COC) devices that perform solid-phase extraction (SPE) and microchip electrophoresis (μCE) for automated PTB biomarker analysis. The PDMS control layer had a peristaltic pump and pneumatic valves for flow control, while the PDMS fluidic layer had five input reservoirs connected to microchannels and a μCE system. The COC layers had a reversed-phase octyl methacrylate porous polymer monolith for SPE and fluorescent labeling of PTB biomarkers. We determined μCE conditions for two PTB biomarkers, ferritin (Fer) and corticotropin-releasing factor (CRF). We used these integrated microfluidic devices to preconcentrate and purify off-chip-labeled Fer and CRF in an automated fashion. Finally, we performed a fully automated on-chip analysis of unlabeled PTB biomarkers, involving SPE, labeling, and μCE separation with 1 h total analysis time. These integrated systems have strong potential to be combined with upstream immunoaffinity extraction, offering a compact sample-to-answer biomarker analysis platform. Graphical abstract Pressure-actuated integrated microfluidic devices have been developed for automated solid-phase extraction, fluorescent labeling, and microchip electrophoresis of preterm birth biomarkers.

Beginning statistics with data analysis

CERN Document Server

Mosteller, Frederick; Rourke, Robert EK

2013-01-01

This introduction to the world of statistics covers exploratory data analysis, methods for collecting data, formal statistical inference, and techniques of regression and analysis of variance. 1983 edition.

Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment

Science.gov (United States)

Welzenbach, Julia; Neuhoff, Christiane; Looft, Christian; Schellander, Karl; Tholen, Ernst; Große-Brinkhaus, Christine

2016-01-01

The aim of this study was to elucidate the underlying biochemical processes to identify potential key molecules of meat quality traits drip loss, pH of meat 1 h post-mortem (pH1), pH in meat 24 h post-mortem (pH24) and meat color. An untargeted metabolomics approach detected the profiles of 393 annotated and 1,600 unknown metabolites in 97 Duroc × Pietrain pigs. Despite obvious differences regarding the statistical approaches, the four applied methods, namely correlation analysis, principal component analysis, weighted network analysis (WNA) and random forest regression (RFR), revealed mainly concordant results. Our findings lead to the conclusion that meat quality traits pH1, pH24 and color are strongly influenced by processes of post-mortem energy metabolism like glycolysis and pentose phosphate pathway, whereas drip loss is significantly associated with metabolites of lipid metabolism. In case of drip loss, RFR was the most suitable method to identify reliable biomarkers and to predict the phenotype based on metabolites. On the other hand, WNA provides the best parameters to investigate the metabolite interactions and to clarify the complex molecular background of meat quality traits. In summary, it was possible to attain findings on the interaction of meat quality traits and their underlying biochemical processes. The detected key metabolites might be better indicators of meat quality especially of drip loss than the measured phenotype itself and potentially might be used as bio indicators. PMID:26919205

The extended statistical analysis of toxicity tests using standardised effect sizes (SESs): a comparison of nine published papers.

Science.gov (United States)

Festing, Michael F W

2014-01-01

The safety of chemicals, drugs, novel foods and genetically modified crops is often tested using repeat-dose sub-acute toxicity tests in rats or mice. It is important to avoid misinterpretations of the results as these tests are used to help determine safe exposure levels in humans. Treated and control groups are compared for a range of haematological, biochemical and other biomarkers which may indicate tissue damage or other adverse effects. However, the statistical analysis and presentation of such data poses problems due to the large number of statistical tests which are involved. Often, it is not clear whether a "statistically significant" effect is real or a false positive (type I error) due to sampling variation. The author's conclusions appear to be reached somewhat subjectively by the pattern of statistical significances, discounting those which they judge to be type I errors and ignoring any biomarker where the p-value is greater than p = 0.05. However, by using standardised effect sizes (SESs) a range of graphical methods and an over-all assessment of the mean absolute response can be made. The approach is an extension, not a replacement of existing methods. It is intended to assist toxicologists and regulators in the interpretation of the results. Here, the SES analysis has been applied to data from nine published sub-acute toxicity tests in order to compare the findings with those of the author's. Line plots, box plots and bar plots show the pattern of response. Dose-response relationships are easily seen. A "bootstrap" test compares the mean absolute differences across dose groups. In four out of seven papers where the no observed adverse effect level (NOAEL) was estimated by the authors, it was set too high according to the bootstrap test, suggesting that possible toxicity is under-estimated.

The extended statistical analysis of toxicity tests using standardised effect sizes (SESs: a comparison of nine published papers.

Directory of Open Access Journals (Sweden)

Michael F W Festing

Full Text Available The safety of chemicals, drugs, novel foods and genetically modified crops is often tested using repeat-dose sub-acute toxicity tests in rats or mice. It is important to avoid misinterpretations of the results as these tests are used to help determine safe exposure levels in humans. Treated and control groups are compared for a range of haematological, biochemical and other biomarkers which may indicate tissue damage or other adverse effects. However, the statistical analysis and presentation of such data poses problems due to the large number of statistical tests which are involved. Often, it is not clear whether a "statistically significant" effect is real or a false positive (type I error due to sampling variation. The author's conclusions appear to be reached somewhat subjectively by the pattern of statistical significances, discounting those which they judge to be type I errors and ignoring any biomarker where the p-value is greater than p = 0.05. However, by using standardised effect sizes (SESs a range of graphical methods and an over-all assessment of the mean absolute response can be made. The approach is an extension, not a replacement of existing methods. It is intended to assist toxicologists and regulators in the interpretation of the results. Here, the SES analysis has been applied to data from nine published sub-acute toxicity tests in order to compare the findings with those of the author's. Line plots, box plots and bar plots show the pattern of response. Dose-response relationships are easily seen. A "bootstrap" test compares the mean absolute differences across dose groups. In four out of seven papers where the no observed adverse effect level (NOAEL was estimated by the authors, it was set too high according to the bootstrap test, suggesting that possible toxicity is under-estimated.

Research design and statistical analysis

CERN Document Server

Myers, Jerome L; Lorch Jr, Robert F

2013-01-01

Research Design and Statistical Analysis provides comprehensive coverage of the design principles and statistical concepts necessary to make sense of real data.  The book's goal is to provide a strong conceptual foundation to enable readers to generalize concepts to new research situations.  Emphasis is placed on the underlying logic and assumptions of the analysis and what it tells the researcher, the limitations of the analysis, and the consequences of violating assumptions.  Sampling, design efficiency, and statistical models are emphasized throughout. As per APA recommendations

Latent class models for joint analysis of disease prevalence and high-dimensional semicontinuous biomarker data.

Science.gov (United States)

Zhang, Bo; Chen, Zhen; Albert, Paul S

2012-01-01

High-dimensional biomarker data are often collected in epidemiological studies when assessing the association between biomarkers and human disease is of interest. We develop a latent class modeling approach for joint analysis of high-dimensional semicontinuous biomarker data and a binary disease outcome. To model the relationship between complex biomarker expression patterns and disease risk, we use latent risk classes to link the 2 modeling components. We characterize complex biomarker-specific differences through biomarker-specific random effects, so that different biomarkers can have different baseline (low-risk) values as well as different between-class differences. The proposed approach also accommodates data features that are common in environmental toxicology and other biomarker exposure data, including a large number of biomarkers, numerous zero values, and complex mean-variance relationship in the biomarkers levels. A Monte Carlo EM (MCEM) algorithm is proposed for parameter estimation. Both the MCEM algorithm and model selection procedures are shown to work well in simulations and applications. In applying the proposed approach to an epidemiological study that examined the relationship between environmental polychlorinated biphenyl (PCB) exposure and the risk of endometriosis, we identified a highly significant overall effect of PCB concentrations on the risk of endometriosis.

Urine Metabonomics Reveals Early Biomarkers in Diabetic Cognitive Dysfunction.

Science.gov (United States)

Song, Lili; Zhuang, Pengwei; Lin, Mengya; Kang, Mingqin; Liu, Hongyue; Zhang, Yuping; Yang, Zhen; Chen, Yunlong; Zhang, Yanjun

2017-09-01

Recently, increasing attention has been paid to diabetic encephalopathy, which is a frequent diabetic complication and affects nearly 30% of diabetics. Because cognitive dysfunction from diabetic encephalopathy might develop into irreversible dementia, early diagnosis and detection of this disease is of great significance for its prevention and treatment. This study is to investigate the early specific metabolites biomarkers in urine prior to the onset of diabetic cognitive dysfunction (DCD) by using metabolomics technology. An ultra-high performance liquid-chromatography-quadrupole time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) platform was used to analyze the urine samples from diabetic mice that were associated with mild cognitive impairment (MCI) and nonassociated with MCI in the stage of diabetes (prior to the onset of DCD). We then screened and validated the early biomarkers using OPLS-DA model and support vector machine (SVM) method. Following multivariate statistical and integration analysis, we found that seven metabolites could be accepted as early biomarkers of DCD, and the SVM results showed that the prediction accuracy is as high as 91.66%. The identities of four biomarkers were determined by mass spectrometry. The identified biomarkers were largely involved in nicotinate and nicotinamide metabolism, glutathione metabolism, tryptophan metabolism, and sphingolipid metabolism. The present study first revealed reliable biomarkers for early diagnosis of DCD. It provides new insight and strategy for the early diagnosis and treatment of DCD.

Statistical data analysis handbook

National Research Council Canada - National Science Library

Wall, Francis J

1986-01-01

It must be emphasized that this is not a text book on statistics. Instead it is a working tool that presents data analysis in clear, concise terms which can be readily understood even by those without formal training in statistics...

Population-Sequencing as a Biomarker of Burkholderia mallei and Burkholderia pseudomallei Evolution through Microbial Forensic Analysis

Directory of Open Access Journals (Sweden)

John P. Jakupciak

2013-01-01

Full Text Available Large-scale genomics projects are identifying biomarkers to detect human disease. B. pseudomallei and B. mallei are two closely related select agents that cause melioidosis and glanders. Accurate characterization of metagenomic samples is dependent on accurate measurements of genetic variation between isolates with resolution down to strain level. Often single biomarker sensitivity is augmented by use of multiple or panels of biomarkers. In parallel with single biomarker validation, advances in DNA sequencing enable analysis of entire genomes in a single run: population-sequencing. Potentially, direct sequencing could be used to analyze an entire genome to serve as the biomarker for genome identification. However, genome variation and population diversity complicate use of direct sequencing, as well as differences caused by sample preparation protocols including sequencing artifacts and mistakes. As part of a Department of Homeland Security program in bacterial forensics, we examined how to implement whole genome sequencing (WGS analysis as a judicially defensible forensic method for attributing microbial sample relatedness; and also to determine the strengths and limitations of whole genome sequence analysis in a forensics context. Herein, we demonstrate use of sequencing to provide genetic characterization of populations: direct sequencing of populations.

Preservation of terrestrial plant biomarkers from Nachukui Formation sediments and their viability for stable isotope analysis

Science.gov (United States)

Kahle, E.; Uno, K. T.; Polissar, P. J.; Lepre, C. J.; deMenocal, P. B.

2013-12-01

Plio-Pleistocene sedimentary records from the Turkana Basin in eastern Africa provide a unique opportunity to compare a high-resolution record of climate and terrestrial vegetation with important changes in the record of human evolution. Molecular biomarkers from terrestrial vegetation can yield stable isotope ratios of hydrogen and carbon that reflect ancient climate and vegetation. However, the preservation of long-chain plant wax biomarkers in these paleosol, fluvial, and lacustrine sediments is not known, and this preservation must be studied to establish their utility for molecular stable isotope studies. We investigated leaf wax biomarkers in Nachukui Formation sediments deposited between 2.3 and 1.7 Ma to assess biomarker preservation. We analyzed n alkane and n alkanoic acid concentrations and, where suitable, molecular carbon and hydrogen isotope ratios. Molecular abundance distributions show a great deal of variance in biomarker preservation and plant-type source as indicated by the carbon preference index and average chain length. This variation suggests that some samples are suitable for isotopic analysis, while other samples lack primary terrestrial plant biomarker signatures. The biomarker signal in many samples contains significant additional material from unidentified sources. For example, the n-alkane distributions contain an unresolved complex mixture underlying the short and mid-chain n-alkanes. Samples from lacustrine intervals include long-chain diacids, hydroxy acids and (ω-1) ketoacids that suggest degradation of the original acids. Degradation of poorly preserved samples and the addition of non-terrestrial plant biomarkers may originate from a number of processes including forest fire or microbial alteration. Isotopic analysis of well-preserved terrestrial plant biomarkers will be presented along with examples where the original biomarker distribution has been altered.

Urinary and Blood MicroRNA-126 and -770 are Potential Noninvasive Biomarker Candidates for Diabetic Nephropathy: a Meta-Analysis.

Science.gov (United States)

Park, Sungjin; Moon, SeongRyeol; Lee, Kiyoung; Park, Ie Byung; Lee, Dae Ho; Nam, Seungyoon

2018-01-01

Diabetic nephropathy (DN), a major diabetic microvascular complication, has a long and growing list of biomarkers, including microRNA biomarkers, which have not been consistent across preclinical and clinical studies. This meta-analysis aims to identify significant blood- and urine-incident microRNAs as diagnostic/prognostic biomarker candidates for DN. PubMed, Web of Science, and Cochrane Library were searched from their earliest records through 12th Dec 2016. Relevant publications for the meta-analysis included (1) human participants; (2) microRNAs in blood and urine; (3) DN studies; and (4) English language. Four reviewers, including two physicians, independently and blindly extracted published data regarding microRNA profiles in blood and/or urine from subjects with diabetic nephropathy. A random-effect model was used to pool the data. Statistical associations between diabetic nephropathy and urinary or blood microRNA expression levels were assessed. Fourteen out of 327 studies (n=2,747 patients) were selected. Blood or urinary microRNA expression data of diabetic nephropathy were pooled for this analysis. The hsa-miR-126 family was significantly (OR: 0.57; 95% CI: 0.44-0.74; p-value diabetic kidney disease, while its urinary level was upregulated (OR: 2931.12; 95% CI: 9.96-862623.21; p-value = 0.0059). The hsa-miR-770 family microRNA were significantly (OR: 10.24; 95% CI: 2.37-44.25; p-value = 0.0018) upregulated in both blood and urine from patients with diabetic nephropathy. Our meta-analysis suggests that hsa-miR-126 and hsa-miR-770 family microRNA may have important diagnostic and pathogenetic implications for DN, which warrants further systematic clinical studies. © 2018 The Author(s). Published by S. Karger AG, Basel.

Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

Directory of Open Access Journals (Sweden)

Federica Villanova

Full Text Available Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid flow cytometry platform (CFP and a unique lyoplate-based flow cytometry platform (LFP in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10 and activation markers (Foxp3 and CD25. Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

Integration of lyoplate based flow cytometry and computational analysis for standardized immunological biomarker discovery.

Science.gov (United States)

Villanova, Federica; Di Meglio, Paola; Inokuma, Margaret; Aghaeepour, Nima; Perucha, Esperanza; Mollon, Jennifer; Nomura, Laurel; Hernandez-Fuentes, Maria; Cope, Andrew; Prevost, A Toby; Heck, Susanne; Maino, Vernon; Lord, Graham; Brinkman, Ryan R; Nestle, Frank O

2013-01-01

Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases.

Core cerebrospinal fluid biomarker profile in cerebral amyloid angiopathy: A meta-analysis.

Science.gov (United States)

Charidimou, Andreas; Friedrich, Jan O; Greenberg, Steven M; Viswanathan, Anand

2018-02-27

To perform a meta-analysis of 4 core CSF biomarkers (β-amyloid [Aβ]42, Aβ40, total tau [t-tau], and phosphorylated tau [p-tau]) to assess which of these are most altered in sporadic cerebral amyloid angiopathy (CAA). We systematically searched PubMed for eligible studies reporting data on CSF biomarkers reflecting amyloid precursor protein metabolism (Aβ42, Aβ40), neurodegeneration (t-tau), and tangle pathology (p-tau) in symptomatic sporadic CAA cohorts vs controls and patients with Alzheimer disease (AD). Biomarker performance was assessed in random-effects meta-analysis based on ratio of mean (RoM) biomarker concentrations: (1) in patients with CAA vs healthy controls and (2) in patients with CAA vs patients with AD. RoM >1 indicates higher biomarker concentration in patients with CAA vs comparison population and RoM <1 indicates higher concentration in comparison groups. Three studies met inclusion criteria. These comprised 5 CAA patient cohorts (n = 59 patients) vs healthy controls (n = 94 cases) and AD cohorts (n = 158). Three core biomarkers differentiated CAA from controls: CSF Aβ42 (RoM 0.49, 95% confidence interval [CI] 0.38-0.64, p < 0.003), Aβ40 (RoM 0.70, 95% CI 0.63-0.78, p < 0.0001), and t-tau (RoM 1.54, 95% CI 1.15-2.07, p = 0.004); p-tau was marginal (RoM 1.24, 95% CI 0.99-1.54, p = 0.062). Differentiation between CAA and AD was strong for CSF Aβ40 (RoM 0.76, 95% CI 0.69-0.83, p < 0.0001), but not Aβ42 (RoM 1.00; 95% CI 0.81-1.23, p = 0.970). For t-tau and p-tau, average CSF ratios in patients with CAA vs patients with AD were 0.63 (95% CI 0.54-0.74, p < 0.0001) and 0.60 (95% CI 0.50-0.71, p < 0.0001), respectively. Specific CSF patterns of Aβ42, Aβ40, t-tau, and p-tau might serve as molecular biomarkers of CAA, but analyses in larger CAA cohorts are needed. © 2018 American Academy of Neurology.

Serum prognostic biomarkers in head and neck cancer patients.

Science.gov (United States)

Lin, Ho-Sheng; Siddiq, Fauzia; Talwar, Harvinder S; Chen, Wei; Voichita, Calin; Draghici, Sorin; Jeyapalan, Gerald; Chatterjee, Madhumita; Fribley, Andrew; Yoo, George H; Sethi, Seema; Kim, Harold; Sukari, Ammar; Folbe, Adam J; Tainsky, Michael A

2014-08-01

A reliable estimate of survival is important as it may impact treatment choice. The objective of this study is to identify serum autoantibody biomarkers that can be used to improve prognostication for patients affected with head and neck squamous cell carcinoma (HNSCC). Prospective cohort study. A panel of 130 serum biomarkers, previously selected for cancer detection using microarray-based serological profiling and specialized bioinformatics, were evaluated for their potential as prognostic biomarkers in a cohort of 119 HNSCC patients followed for up to 12.7 years. A biomarker was considered positive if its reactivity to the particular patient's serum was greater than one standard deviation above the mean reactivity to sera from the other 118 patients, using a leave-one-out cross-validation model. Survival curves were estimated according to the Kaplan-Meier method, and statistically significant differences in survival were examined using the log rank test. Independent prognostic biomarkers were identified following analysis using multivariate Cox proportional hazards models. Poor overall survival was associated with African Americans (hazard ratio [HR] for death = 2.61; 95% confidence interval [CI]: 1.58-4.33; P = .000), advanced stage (HR = 2.79; 95% CI: 1.40-5.57; P = .004), and recurrent disease (HR = 6.66; 95% CI: 2.54-17.44; P = .000). On multivariable Cox analysis adjusted for covariates (race and stage), six of the 130 markers evaluated were found to be independent prognosticators of overall survival. The results shown here are promising and demonstrate the potential use of serum biomarkers for prognostication in HNSCC patients. Further clinical trials to include larger samples of patients across multiple centers may be warranted. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta.

Science.gov (United States)

Bruse, Jan L; McLeod, Kristin; Biglino, Giovanni; Ntsinjana, Hopewell N; Capelli, Claudio; Hsia, Tain-Yen; Sermesant, Maxime; Pennec, Xavier; Taylor, Andrew M; Schievano, Silvia

2016-05-31

Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient's anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. The suggested method has the potential to discover

Statistical Power in Meta-Analysis

Science.gov (United States)

Liu, Jin

2015-01-01

Statistical power is important in a meta-analysis study, although few studies have examined the performance of simulated power in meta-analysis. The purpose of this study is to inform researchers about statistical power estimation on two sample mean difference test under different situations: (1) the discrepancy between the analytical power and…

A statistical method to base nutrient recommendations on meta-analysis of intake and health-related status biomarkers.

Directory of Open Access Journals (Sweden)

Hilko van der Voet

Full Text Available Nutrient recommendations in use today are often derived from relatively old data of few studies with few individuals. However, for many nutrients, including vitamin B-12, extensive data have now become available from both observational studies and randomized controlled trials, addressing the relation between intake and health-related status biomarkers. The purpose of this article is to provide new methodology for dietary planning based on dose-response data and meta-analysis. The methodology builds on existing work, and is consistent with current methodology and measurement error models for dietary assessment. The detailed purposes of this paper are twofold. Firstly, to define a Population Nutrient Level (PNL for dietary planning in groups. Secondly, to show how data from different sources can be combined in an extended meta-analysis of intake-status datasets for estimating PNL as well as other nutrient intake values, such as the Average Nutrient Requirement (ANR and the Individual Nutrient Level (INL. For this, a computational method is presented for comparing a bivariate lognormal distribution to a health criterion value. Procedures to meta-analyse available data in different ways are described. Example calculations on vitamin B-12 requirements were made for four models, assuming different ways of estimating the dose-response relation, and different values of the health criterion. Resulting estimates of ANRs and less so for INLs were found to be sensitive to model assumptions, whereas estimates of PNLs were much less sensitive to these assumptions as they were closer to the average nutrient intake in the available data.

Acoustic and temporal analysis of speech: A potential biomarker for schizophrenia.

LENUS (Irish Health Repository)

Rapcan, Viliam

2010-11-01

Currently, there are no established objective biomarkers for the diagnosis or monitoring of schizophrenia. It has been previously reported that there are notable qualitative differences in the speech of schizophrenics. The objective of this study was to determine whether a quantitative acoustic and temporal analysis of speech may be a potential biomarker for schizophrenia. In this study, 39 schizophrenic patients and 18 controls were digitally recorded reading aloud an emotionally neutral text passage from a children\\'s story. Temporal, energy and vocal pitch features were automatically extracted from the recordings. A classifier based on linear discriminant analysis was employed to differentiate between controls and schizophrenic subjects. Processing the recordings with the algorithm developed demonstrated that it is possible to differentiate schizophrenic patients and controls with a classification accuracy of 79.4% (specificity=83.6%, sensitivity=75.2%) based on speech pause related parameters extracted from recordings carried out in standard office (non-studio) environments. Acoustic and temporal analysis of speech may represent a potential tool for the objective analysis in schizophrenia.

Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making.

Science.gov (United States)

Prescott, Jeffrey William

2013-02-01

The importance of medical imaging for clinical decision making has been steadily increasing over the last four decades. Recently, there has also been an emphasis on medical imaging for preclinical decision making, i.e., for use in pharamaceutical and medical device development. There is also a drive towards quantification of imaging findings by using quantitative imaging biomarkers, which can improve sensitivity, specificity, accuracy and reproducibility of imaged characteristics used for diagnostic and therapeutic decisions. An important component of the discovery, characterization, validation and application of quantitative imaging biomarkers is the extraction of information and meaning from images through image processing and subsequent analysis. However, many advanced image processing and analysis methods are not applied directly to questions of clinical interest, i.e., for diagnostic and therapeutic decision making, which is a consideration that should be closely linked to the development of such algorithms. This article is meant to address these concerns. First, quantitative imaging biomarkers are introduced by providing definitions and concepts. Then, potential applications of advanced image processing and analysis to areas of quantitative imaging biomarker research are described; specifically, research into osteoarthritis (OA), Alzheimer's disease (AD) and cancer is presented. Then, challenges in quantitative imaging biomarker research are discussed. Finally, a conceptual framework for integrating clinical and preclinical considerations into the development of quantitative imaging biomarkers and their computer-assisted methods of extraction is presented.

Rweb:Web-based Statistical Analysis

Directory of Open Access Journals (Sweden)

Jeff Banfield

1999-03-01

Full Text Available Rweb is a freely accessible statistical analysis environment that is delivered through the World Wide Web (WWW. It is based on R, a well known statistical analysis package. The only requirement to run the basic Rweb interface is a WWW browser that supports forms. If you want graphical output you must, of course, have a browser that supports graphics. The interface provides access to WWW accessible data sets, so you may run Rweb on your own data. Rweb can provide a four window statistical computing environment (code input, text output, graphical output, and error information through browsers that support Javascript. There is also a set of point and click modules under development for use in introductory statistics courses.

Accounting for control mislabeling in case-control biomarker studies.

Science.gov (United States)

Rantalainen, Mattias; Holmes, Chris C

2011-12-02

In biomarker discovery studies, uncertainty associated with case and control labels is often overlooked. By omitting to take into account label uncertainty, model parameters and the predictive risk can become biased, sometimes severely. The most common situation is when the control set contains an unknown number of undiagnosed, or future, cases. This has a marked impact in situations where the model needs to be well-calibrated, e.g., when the prediction performance of a biomarker panel is evaluated. Failing to account for class label uncertainty may lead to underestimation of classification performance and bias in parameter estimates. This can further impact on meta-analysis for combining evidence from multiple studies. Using a simulation study, we outline how conventional statistical models can be modified to address class label uncertainty leading to well-calibrated prediction performance estimates and reduced bias in meta-analysis. We focus on the problem of mislabeled control subjects in case-control studies, i.e., when some of the control subjects are undiagnosed cases, although the procedures we report are generic. The uncertainty in control status is a particular situation common in biomarker discovery studies in the context of genomic and molecular epidemiology, where control subjects are commonly sampled from the general population with an established expected disease incidence rate.

Regularized Statistical Analysis of Anatomy

DEFF Research Database (Denmark)

Sjöstrand, Karl

2007-01-01

This thesis presents the application and development of regularized methods for the statistical analysis of anatomical structures. Focus is on structure-function relationships in the human brain, such as the connection between early onset of Alzheimer’s disease and shape changes of the corpus...... and mind. Statistics represents a quintessential part of such investigations as they are preluded by a clinical hypothesis that must be verified based on observed data. The massive amounts of image data produced in each examination pose an important and interesting statistical challenge...... efficient algorithms which make the analysis of large data sets feasible, and gives examples of applications....

Breath Analysis Using Laser Spectroscopic Techniques: Breath Biomarkers, Spectral Fingerprints, and Detection Limits

Directory of Open Access Journals (Sweden)

Peeyush Sahay

2009-10-01

Full Text Available Breath analysis, a promising new field of medicine and medical instrumentation, potentially offers noninvasive, real-time, and point-of-care (POC disease diagnostics and metabolic status monitoring. Numerous breath biomarkers have been detected and quantified so far by using the GC-MS technique. Recent advances in laser spectroscopic techniques and laser sources have driven breath analysis to new heights, moving from laboratory research to commercial reality. Laser spectroscopic detection techniques not only have high-sensitivity and high-selectivity, as equivalently offered by the MS-based techniques, but also have the advantageous features of near real-time response, low instrument costs, and POC function. Of the approximately 35 established breath biomarkers, such as acetone, ammonia, carbon dioxide, ethane, methane, and nitric oxide, 14 species in exhaled human breath have been analyzed by high-sensitivity laser spectroscopic techniques, namely, tunable diode laser absorption spectroscopy (TDLAS, cavity ringdown spectroscopy (CRDS, integrated cavity output spectroscopy (ICOS, cavity enhanced absorption spectroscopy (CEAS, cavity leak-out spectroscopy (CALOS, photoacoustic spectroscopy (PAS, quartz-enhanced photoacoustic spectroscopy (QEPAS, and optical frequency comb cavity-enhanced absorption spectroscopy (OFC-CEAS. Spectral fingerprints of the measured biomarkers span from the UV to the mid-IR spectral regions and the detection limits achieved by the laser techniques range from parts per million to parts per billion levels. Sensors using the laser spectroscopic techniques for a few breath biomarkers, e.g., carbon dioxide, nitric oxide, etc. are commercially available. This review presents an update on the latest developments in laser-based breath analysis.

Proteomics analysis after traumatic brain injury in rats: the search for potential biomarkers

Directory of Open Access Journals (Sweden)

Jun Ding

2015-04-01

Full Text Available Many studies of protein expression after traumatic brain injury (TBI have identified biomarkers for diagnosing or determining the prognosis of TBI. In this study, we searched for additional protein markers of TBI using a fluid perfusion impact device to model TBI in S-D rats. Two-dimensional gel electrophoresis and mass spectrometry were used to identify differentially expressed proteins. After proteomic analysis, we detected 405 and 371 protein spots within a pH range of 3-10 from sham-treated and contused brain cortex, respectively. Eighty protein spots were differentially expressed in the two groups and 20 of these proteins were identified. This study validated the established biomarkers of TBI and identified potential biomarkers that could be examined in future work.

Flow Injection/Sequential Injection Analysis Systems: Potential Use as Tools for Rapid Liver Diseases Biomarker Study

Directory of Open Access Journals (Sweden)

Supaporn Kradtap Hartwell

2012-01-01

Full Text Available Flow injection/sequential injection analysis (FIA/SIA systems are suitable for carrying out automatic wet chemical/biochemical reactions with reduced volume and time consumption. Various parts of the system such as pump, valve, and reactor may be built or adapted from available materials. Therefore the systems can be at lower cost as compared to other instrumentation-based analysis systems. Their applications for determination of biomarkers for liver diseases have been demonstrated in various formats of operation but only a few and limited types of biomarkers have been used as model analytes. This paper summarizes these applications for different types of reactions as a guide for using flow-based systems in more biomarker and/or multibiomarker studies.

Addressing small sample size bias in multiple-biomarker trials: Inclusion of biomarker-negative patients and Firth correction.

Science.gov (United States)

Habermehl, Christina; Benner, Axel; Kopp-Schneider, Annette

2018-03-01

In recent years, numerous approaches for biomarker-based clinical trials have been developed. One of these developments are multiple-biomarker trials, which aim to investigate multiple biomarkers simultaneously in independent subtrials. For low-prevalence biomarkers, small sample sizes within the subtrials have to be expected, as well as many biomarker-negative patients at the screening stage. The small sample sizes may make it unfeasible to analyze the subtrials individually. This imposes the need to develop new approaches for the analysis of such trials. With an expected large group of biomarker-negative patients, it seems reasonable to explore options to benefit from including them in such trials. We consider advantages and disadvantages of the inclusion of biomarker-negative patients in a multiple-biomarker trial with a survival endpoint. We discuss design options that include biomarker-negative patients in the study and address the issue of small sample size bias in such trials. We carry out a simulation study for a design where biomarker-negative patients are kept in the study and are treated with standard of care. We compare three different analysis approaches based on the Cox model to examine if the inclusion of biomarker-negative patients can provide a benefit with respect to bias and variance of the treatment effect estimates. We apply the Firth correction to reduce the small sample size bias. The results of the simulation study suggest that for small sample situations, the Firth correction should be applied to adjust for the small sample size bias. Additional to the Firth penalty, the inclusion of biomarker-negative patients in the analysis can lead to further but small improvements in bias and standard deviation of the estimates. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A general framework for the regression analysis of pooled biomarker assessments.

Science.gov (United States)

Liu, Yan; McMahan, Christopher; Gallagher, Colin

2017-07-10

As a cost-efficient data collection mechanism, the process of assaying pooled biospecimens is becoming increasingly common in epidemiological research; for example, pooling has been proposed for the purpose of evaluating the diagnostic efficacy of biological markers (biomarkers). To this end, several authors have proposed techniques that allow for the analysis of continuous pooled biomarker assessments. Regretfully, most of these techniques proceed under restrictive assumptions, are unable to account for the effects of measurement error, and fail to control for confounding variables. These limitations are understandably attributable to the complex structure that is inherent to measurements taken on pooled specimens. Consequently, in order to provide practitioners with the tools necessary to accurately and efficiently analyze pooled biomarker assessments, herein, a general Monte Carlo maximum likelihood-based procedure is presented. The proposed approach allows for the regression analysis of pooled data under practically all parametric models and can be used to directly account for the effects of measurement error. Through simulation, it is shown that the proposed approach can accurately and efficiently estimate all unknown parameters and is more computational efficient than existing techniques. This new methodology is further illustrated using monocyte chemotactic protein-1 data collected by the Collaborative Perinatal Project in an effort to assess the relationship between this chemokine and the risk of miscarriage. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Biomarkers in differentiating clinical dengue cases: A prospective cohort study

Directory of Open Access Journals (Sweden)

Gary Kim Kuan Low

2015-12-01

Full Text Available Objective: To evaluate five biomarkers (neopterin, vascular endothelial growth factor-A, thrombomodulin, soluble vascular cell adhesion molecule 1 and pentraxin 3 in differentiating clinical dengue cases. Methods: A prospective cohort study was conducted whereby the blood samples were obtained at day of presentation and the final diagnosis were obtained at the end of patients’ follow-up. All patients included in the study were 15 years old or older, not pregnant, not infected by dengue previously and did not have cancer, autoimmune or haematological disorder. Median test was performed to compare the biomarker levels. A subgroup Mann-Whitney U test was analysed between severe dengue and non-severe dengue cases. Monte Carlo method was used to estimate the 2-tailed probability (P value for independent variables with unequal number of patients. Results: All biomarkers except thrombomodulin has P value < 0.001 in differentiating among the healthy subjects, non-dengue fever, dengue without warning signs and dengue with warning signs/severe dengue. Subgroup analysis for all the biomarkers between severe dengue and non-severe dengue cases was not statistically significant except vascular endothelial growth factor-A (P < 0.05. Conclusions: Certain biomarkers were able to differentiate the clinical dengue cases. This could be potentially useful in classifying and determining the severity of dengue infected patients in the hospital.

Statistical methods for astronomical data analysis

CERN Document Server

Chattopadhyay, Asis Kumar

2014-01-01

This book introduces “Astrostatistics” as a subject in its own right with rewarding examples, including work by the authors with galaxy and Gamma Ray Burst data to engage the reader. This includes a comprehensive blending of Astrophysics and Statistics. The first chapter’s coverage of preliminary concepts and terminologies for astronomical phenomenon will appeal to both Statistics and Astrophysics readers as helpful context. Statistics concepts covered in the book provide a methodological framework. A unique feature is the inclusion of different possible sources of astronomical data, as well as software packages for converting the raw data into appropriate forms for data analysis. Readers can then use the appropriate statistical packages for their particular data analysis needs. The ideas of statistical inference discussed in the book help readers determine how to apply statistical tests. The authors cover different applications of statistical techniques already developed or specifically introduced for ...

Quantitative proteomic analysis of microdissected oral epithelium for cancer biomarker discovery.

Science.gov (United States)

Xiao, Hua; Langerman, Alexander; Zhang, Yan; Khalid, Omar; Hu, Shen; Cao, Cheng-Xi; Lingen, Mark W; Wong, David T W

2015-11-01

Specific biomarkers are urgently needed for the detection and progression of oral cancer. The objective of this study was to discover cancer biomarkers from oral epithelium through utilizing high throughput quantitative proteomics approaches. Morphologically malignant, epithelial dysplasia, and adjacent normal epithelial tissues were laser capture microdissected (LCM) from 19 patients and used for proteomics analysis. Total proteins from each group were extracted, digested and then labelled with corresponding isobaric tags for relative and absolute quantitation (iTRAQ). Labelled peptides from each sample were combined and analyzed by liquid chromatography-mass spectrometry (LC-MS/MS) for protein identification and quantification. In total, 500 proteins were identified and 425 of them were quantified. When compared with adjacent normal oral epithelium, 17 and 15 proteins were consistently up-regulated or down-regulated in malignant and epithelial dysplasia, respectively. Half of these candidate biomarkers were discovered for oral cancer for the first time. Cornulin was initially confirmed in tissue protein extracts and was further validated in tissue microarray. Its presence in the saliva of oral cancer patients was also explored. Myoglobin and S100A8 were pre-validated by tissue microarray. These data demonstrated that the proteomic biomarkers discovered through this strategy are potential targets for oral cancer detection and salivary diagnostics. Copyright © 2015 Elsevier Ltd. All rights reserved.

Salivary pH: A diagnostic biomarker.

Science.gov (United States)

Baliga, Sharmila; Muglikar, Sangeeta; Kale, Rahul

2013-07-01

Saliva contains a variety of host defense factors. It influences calculus formation and periodontal disease. Different studies have been done to find exact correlation of salivary biomarkers with periodontal disease. With a multitude of biomarkers and complexities in their determination, the salivary pH may be tried to be used as a quick chairside test. The aim of this study was to analyze the pH of saliva and determine its relevance to the severity of periodontal disease. The study population consisted of 300 patients. They were divided into three groups of 100 patients each: Group A had clinically healthy gingiva, Group B who had generalized chronic gingivitis and Group C who had generalized chronic periodontitis. The randomized unstimulated saliva from each patient was collected and pH was tested. Data was analyzed statistically using analysis of variance technique. The salivary pH was more alkaline for patients with generalized chronic gingivitis as compared with the control group (P = 0.001) whereas patients with generalized chronic periodontitis had more acidic pH as compared with the control group (P = 0.001). These results indicate a significant change in the pH depending on the severity of the periodontal condition. The salivary pH shows significant changes and thus relevance to the severity of periodontal disease. Salivary pH may thus be used as a quick chairside diagnostic biomarker.

The added value of biomarker analysis to the genesis of Plaggic Anthrosols.

Science.gov (United States)

van Mourik, Jan; Jansen, Boris

2015-04-01

Coversands (chemical poor Late-glacial aeolian sand deposits) dominate the surface geology of an extensive area in northwestern Europe. Plaggic Anthrosols occur in cultural landscapes, developed on coversands. They are the characteristic soils that developed on ancient fertilized arable fields. Plaggic Anthrosols have a complex genesis. They are records of aspects environmental and agricultural history. In previous studies information of the soil records was unlocked by application of pollen analysis, 14C and OSL dating. In this study we applied biomarker analysis to unlock additional information about the applied organic sources in the production of plaggic manure. Radiocarbon dating suggested the start of sedentary agriculture (after a period, characterized by shifting cultivation and Celtic fields) between 3000 and 2000 BP. In previous studies is assumed that farmers applied organic sods, dug on forest soils and heath to produce organic stable manure to fertilize the fields. The mineral fraction of the sods was supposed to be responsible for the development of the plaggic horizon and the raise of the land surface. Optically stimulated Luminescence dating however suggested that plaggic deposition on the fields started relatively late, in the 18th century. The use of ectorganic matter from the forest soils must have been ended in the 10th-12th century, due to commercial forest clear cuttings as recorded in archived documents. These deforestations resulted in the first extension of sand drifting and famers had to protect the valuable heath against this ' environmental catastrophe' . The use of heath for sheep grazing and other purposes as honey production could continue till the 18th century, as recorded in archived documents. In the course of the 18th century, the population growth resulted in increasing demand for food. The deep stable economy was introduced and the booming demand for manure resulted in intensive sod digging on the heath. This caused heath

Diagnostic value of a pattern of exhaled breath condensate biomarkers in asthmatic children.

Science.gov (United States)

Maloča Vuljanko, I; Turkalj, M; Nogalo, B; Bulat Lokas, S; Plavec, D

Diagnosing asthma in children is a challenge and using a single biomarker from exhaled breath condensate (EBC) showed the lack of improvement in it. The aim of this study was to assess the diagnostic potential of a pattern of simple chemical biomarkers from EBC in diagnosing asthma in children in a real-life setting, its association with lung function and gastroesophageal reflux disease (GERD). In 75 consecutive children aged 5-7 years with asthma-like symptoms the following tests were performed: skin prick tests, spirometry, impulse oscillometry (IOS), exhaled NO (F E NO), 24-hour oesophageal pH monitoring and EBC collection with subsequent analysis of pH, carbon dioxide tension, oxygen tension, and concentrations of magnesium, calcium, iron and urates. No significant differences were found for individual EBC biomarkers between asthmatics and non-asthmatics (p>0.05 for all). A pattern of six EBC biomarkers showed a statistically significant (p=0.046) predictive model for asthma (AUC=0.698, PPV=84.2%, NPV=38.9%). None to moderate association (R 2 up to 0.43) between EBC biomarkers and lung function measures and F E NO was found, with IOS parameters showing the best association with EBC biomarkers. A significantly higher EBC Fe was found in children with asthma and GERD compared to asthmatics without GERD (p=0.049). An approach that involves a pattern of EBC biomarkers had a better diagnostic accuracy for asthma in children in real-life settings compared to a single one. Poor to moderate association of EBC biomarkers with lung function suggests a complementary value of EBC analysis for asthma diagnosis in children. Copyright © 2016 SEICAP. Published by Elsevier España, S.L.U. All rights reserved.

Rapid point-of-care breath test for biomarkers of breast cancer and abnormal mammograms.

Directory of Open Access Journals (Sweden)

Michael Phillips

Full Text Available BACKGROUND: Previous studies have reported volatile organic compounds (VOCs in breath as biomarkers of breast cancer and abnormal mammograms, apparently resulting from increased oxidative stress and cytochrome p450 induction. We evaluated a six-minute point-of-care breath test for VOC biomarkers in women screened for breast cancer at centers in the USA and the Netherlands. METHODS: 244 women had a screening mammogram (93/37 normal/abnormal or a breast biopsy (cancer/no cancer 35/79. A mobile point-of-care system collected and concentrated breath and air VOCs for analysis with gas chromatography and surface acoustic wave detection. Chromatograms were segmented into a time series of alveolar gradients (breath minus room air. Segmental alveolar gradients were ranked as candidate biomarkers by C-statistic value (area under curve [AUC] of receiver operating characteristic [ROC] curve. Multivariate predictive algorithms were constructed employing significant biomarkers identified with multiple Monte Carlo simulations and cross validated with a leave-one-out (LOO procedure. RESULTS: Performance of breath biomarker algorithms was determined in three groups: breast cancer on biopsy versus normal screening mammograms (81.8% sensitivity, 70.0% specificity, accuracy 79% (73% on LOO [C-statistic value], negative predictive value 99.9%; normal versus abnormal screening mammograms (86.5% sensitivity, 66.7% specificity, accuracy 83%, 62% on LOO; and cancer versus no cancer on breast biopsy (75.8% sensitivity, 74.0% specificity, accuracy 78%, 67% on LOO. CONCLUSIONS: A pilot study of a six-minute point-of-care breath test for volatile biomarkers accurately identified women with breast cancer and with abnormal mammograms. Breath testing could potentially reduce the number of needless mammograms without loss of diagnostic sensitivity.

Identification of candidate diagnostic serum biomarkers for Kawasaki disease using proteomic analysis

Science.gov (United States)

Kimura, Yayoi; Yanagimachi, Masakatsu; Ino, Yoko; Aketagawa, Mao; Matsuo, Michie; Okayama, Akiko; Shimizu, Hiroyuki; Oba, Kunihiro; Morioka, Ichiro; Imagawa, Tomoyuki; Kaneko, Tetsuji; Yokota, Shumpei; Hirano, Hisashi; Mori, Masaaki

2017-01-01

Kawasaki disease (KD) is a systemic vasculitis and childhood febrile disease that can lead to cardiovascular complications. The diagnosis of KD depends on its clinical features, and thus it is sometimes difficult to make a definitive diagnosis. In order to identify diagnostic serum biomarkers for KD, we explored serum KD-related proteins, which differentially expressed during the acute and recovery phases of two patients by mass spectrometry (MS). We identified a total of 1,879 proteins by MS-based proteomic analysis. The levels of three of these proteins, namely lipopolysaccharide-binding protein (LBP), leucine-rich alpha-2-glycoprotein (LRG1), and angiotensinogen (AGT), were higher in acute phase patients. In contrast, the level of retinol-binding protein 4 (RBP4) was decreased. To confirm the usefulness of these proteins as biomarkers, we analyzed a total of 270 samples, including those collected from 55 patients with acute phase KD, by using western blot analysis and microarray enzyme-linked immunosorbent assays (ELISAs). Over the course of this experiment, we determined that the expression level of these proteins changes specifically in the acute phase of KD, rather than the recovery phase of KD or other febrile illness. Thus, LRG1 could be used as biomarkers to facilitate KD diagnosis based on clinical features. PMID:28262744

Reinventing clinical trials: a review of innovative biomarker trial designs in cancer therapies.

Science.gov (United States)

Lin, Ja-An; He, Pei

2015-06-01

Recently, new clinical trial designs involving biomarkers have been studied and proposed in cancer clinical research, in the hope of incorporating the rapid growing basic research into clinical practices. Journal articles related to various biomarkers and their role in cancer clinical trial, articles and books about statistical issues in trial design, and regulatory website, documents, and guidance for submission of targeted cancer therapies. The drug development process involves four phases. The confirmatory Phase III is essential in regulatory approval of a special treatment. Regulatory agency has restrictions on confirmatory trials 'using adaptive designs'. No rule of thumb to pick the most appropriate design for biomarker-related trials. Statistical issues to solve in new designs. Regulatory acceptance of the 'newly proposed trial designs'. Biomarker-related trial designs that can resolve the statistical issues and satisfy the regulatory requirement. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: assessing arch morphology of repaired coarctation of the aorta

International Nuclear Information System (INIS)

Bruse, Jan L.; McLeod, Kristin; Biglino, Giovanni; Ntsinjana, Hopewell N.; Capelli, Claudio

2016-01-01

Medical image analysis in clinical practice is commonly carried out on 2D image data, without fully exploiting the detailed 3D anatomical information that is provided by modern non-invasive medical imaging techniques. In this paper, a statistical shape analysis method is presented, which enables the extraction of 3D anatomical shape features from cardiovascular magnetic resonance (CMR) image data, with no need for manual landmarking. The method was applied to repaired aortic coarctation arches that present complex shapes, with the aim of capturing shape features as biomarkers of potential functional relevance. The method is presented from the user-perspective and is evaluated by comparing results with traditional morphometric measurements. Steps required to set up the statistical shape modelling analyses, from pre-processing of the CMR images to parameter setting and strategies to account for size differences and outliers, are described in detail. The anatomical mean shape of 20 aortic arches post-aortic coarctation repair (CoA) was computed based on surface models reconstructed from CMR data. By analysing transformations that deform the mean shape towards each of the individual patient’s anatomy, shape patterns related to differences in body surface area (BSA) and ejection fraction (EF) were extracted. The resulting shape vectors, describing shape features in 3D, were compared with traditionally measured 2D and 3D morphometric parameters. The computed 3D mean shape was close to population mean values of geometric shape descriptors and visually integrated characteristic shape features associated with our population of CoA shapes. After removing size effects due to differences in body surface area (BSA) between patients, distinct 3D shape features of the aortic arch correlated significantly with EF (r = 0.521, p = .022) and were well in agreement with trends as shown by traditional shape descriptors. The suggested method has the potential to discover previously

Large-scale Metabolomic Analysis Reveals Potential Biomarkers for Early Stage Coronary Atherosclerosis.

Science.gov (United States)

Gao, Xueqin; Ke, Chaofu; Liu, Haixia; Liu, Wei; Li, Kang; Yu, Bo; Sun, Meng

2017-09-18

Coronary atherosclerosis (CAS) is the pathogenesis of coronary heart disease, which is a prevalent and chronic life-threatening disease. Initially, this disease is not always detected until a patient presents with seriously vascular occlusion. Therefore, new biomarkers for appropriate and timely diagnosis of early CAS is needed for screening to initiate therapy on time. In this study, we used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAS detection. Score plots from partial least-squares discriminant analysis clearly separated early-stage CAS patients from controls. Meanwhile, the levels of 24 metabolites increased greatly and those of 18 metabolites decreased markedly in early CAS patients compared with the controls, which suggested significant metabolic dysfunction in phospholipid, sphingolipid, and fatty acid metabolism in the patients. Furthermore, binary logistic regression showed that nine metabolites could be used as a combinatorial biomarker to distinguish early-stage CAS patients from controls. The panel of nine metabolites was then tested with an independent cohort of samples, which also yielded satisfactory diagnostic accuracy (AUC = 0.890). In conclusion, our findings provide insight into the pathological mechanism of early-stage CAS and also supply a combinatorial biomarker to aid clinical diagnosis of early-stage CAS.

Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers.

Science.gov (United States)

Kerr, Kathleen F; Meisner, Allison; Thiessen-Philbrook, Heather; Coca, Steven G; Parikh, Chirag R

2014-08-07

The field of nephrology is actively involved in developing biomarkers and improving models for predicting patients' risks of AKI and CKD and their outcomes. However, some important aspects of evaluating biomarkers and risk models are not widely appreciated, and statistical methods are still evolving. This review describes some of the most important statistical concepts for this area of research and identifies common pitfalls. Particular attention is paid to metrics proposed within the last 5 years for quantifying the incremental predictive value of a new biomarker. Copyright © 2014 by the American Society of Nephrology.

Recommendations and Standardization of Biomarker Quantification Using NMR-based Metabolomics with Particular Focus on Urinary Analysis

KAUST Repository

Emwas, Abdul-Hamid M.

2016-01-08

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to non-destructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Indeed, precise metabolite quantification is a necessary prerequisite to move any chemical biomarker or biomarker panel from the lab into the clinic. Among the many biofluids (urine, serum, plasma, cerebrospinal fluid and saliva) commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, easily obtained, needs little sample preparation and does not require any invasive medical procedures for collection. Furthermore, urine captures and concentrates many “unwanted” or “undesirable” compounds throughout the body, thereby providing a rich source of potentially useful disease biomarkers. However, the incredible variation in urine chemical concentrations due to effects such as gender, age, diet, life style, health conditions, and physical activity make the analysis of urine and the identification of useful urinary biomarkers by NMR quite challenging. In this review, we discuss a number of the most significant issues regarding NMR-based urinary metabolomics with a specific emphasis on metabolite quantification for disease biomarker applications. We also propose a number of data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, as well as recommendations regarding sample preparation and biomarker assessment.

Recommendations and Standardization of Biomarker Quantification Using NMR-based Metabolomics with Particular Focus on Urinary Analysis

KAUST Repository

Emwas, Abdul-Hamid M.; Roy, Raja; McKay, Ryan T.; Ryan, Danielle; Brennan, Lorraine; Tenori, Leonardo; Luchinat, Claudio; Gao, Xin; Zeri, Ana Carolina; Gowda, G. A. Nagana; Raftery, Daniel; Steinbeck, Christoph; Salek, Reza M; Wishart, David S.

2016-01-01

NMR-based metabolomics has shown considerable promise in disease diagnosis and biomarker discovery because it allows one to non-destructively identify and quantify large numbers of novel metabolite biomarkers in both biofluids and tissues. Indeed, precise metabolite quantification is a necessary prerequisite to move any chemical biomarker or biomarker panel from the lab into the clinic. Among the many biofluids (urine, serum, plasma, cerebrospinal fluid and saliva) commonly used for disease diagnosis and prognosis, urine has several advantages. It is abundant, sterile, easily obtained, needs little sample preparation and does not require any invasive medical procedures for collection. Furthermore, urine captures and concentrates many “unwanted” or “undesirable” compounds throughout the body, thereby providing a rich source of potentially useful disease biomarkers. However, the incredible variation in urine chemical concentrations due to effects such as gender, age, diet, life style, health conditions, and physical activity make the analysis of urine and the identification of useful urinary biomarkers by NMR quite challenging. In this review, we discuss a number of the most significant issues regarding NMR-based urinary metabolomics with a specific emphasis on metabolite quantification for disease biomarker applications. We also propose a number of data collection and instrumental recommendations regarding NMR pulse sequences, acceptable acquisition parameter ranges, relaxation effects on quantitation, proper handling of instrumental differences, as well as recommendations regarding sample preparation and biomarker assessment.

Noninvasive analysis of volatile biomarkers in human emanations for health and early disease diagnosis.

Science.gov (United States)

Kataoka, Hiroyuki; Saito, Keita; Kato, Hisato; Masuda, Kazufumi

2013-06-01

Early disease diagnosis is crucial for human healthcare and successful therapy. Since any changes in homeostatic balance can alter human emanations, the components of breath exhalations and skin emissions may be diagnostic biomarkers for various diseases and metabolic disorders. Since hundreds of endogenous and exogenous volatile organic compounds (VOCs) are released from the human body, analysis of these VOCs may be a noninvasive, painless, and easy diagnostic tool. Sampling and preconcentration by sorbent tubes/traps and solid-phase microextraction, in combination with GC or GC-MS, are usually used to analyze VOCs. In addition, GC-MS-olfactometry is useful for simultaneous analysis of odorants and odor quality. Direct MS techniques are also useful for the online real-time detection of VOCs. This review focuses on recent developments in sampling and analysis of volatile biomarkers in human odors and/or emanations, and discusses future use of VOC analysis.

A Statistical Toolkit for Data Analysis

International Nuclear Information System (INIS)

Donadio, S.; Guatelli, S.; Mascialino, B.; Pfeiffer, A.; Pia, M.G.; Ribon, A.; Viarengo, P.

2006-01-01

The present project aims to develop an open-source and object-oriented software Toolkit for statistical data analysis. Its statistical testing component contains a variety of Goodness-of-Fit tests, from Chi-squared to Kolmogorov-Smirnov, to less known, but generally much more powerful tests such as Anderson-Darling, Goodman, Fisz-Cramer-von Mises, Kuiper, Tiku. Thanks to the component-based design and the usage of the standard abstract interfaces for data analysis, this tool can be used by other data analysis systems or integrated in experimental software frameworks. This Toolkit has been released and is downloadable from the web. In this paper we describe the statistical details of the algorithms, the computational features of the Toolkit and describe the code validation

ANALYSIS OR THE POTENTIAL SPERM BIOMARKER, SP22, IN HUMAN SEMEN

Science.gov (United States)

ANALYSIS OF THE POTENTIAL SPERM BIOMARKER SP22 IN HUMAN SEMEN Rebecca A. Morris Ph.D.1, Gary R. Klinefelter Ph.D.1, Naomi L. Roberts 1, Juan D. Suarez 1, Lillian F. Strader 1, Susan C. Jeffay 1 and Sally D. Perreault Ph.D.1 1 U.S. EPA / ORD / National Health a...

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

Directory of Open Access Journals (Sweden)

Borlawsky Tara B

2010-10-01

Full Text Available Abstract Background Chronic lymphocytic leukemia (CLL is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. Results In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. Conclusions We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

Salivary pH: A diagnostic biomarker

Directory of Open Access Journals (Sweden)

Sharmila Baliga

2013-01-01

Full Text Available Objectives: Saliva contains a variety of host defense factors. It influences calculus formation and periodontal disease. Different studies have been done to find exact correlation of salivary biomarkers with periodontal disease. With a multitude of biomarkers and complexities in their determination, the salivary pH may be tried to be used as a quick chairside test. The aim of this study was to analyze the pH of saliva and determine its relevance to the severity of periodontal disease. Study Design: The study population consisted of 300 patients. They were divided into three groups of 100 patients each: Group A had clinically healthy gingiva, Group B who had generalized chronic gingivitis and Group C who had generalized chronic periodontitis. The randomized unstimulated saliva from each patient was collected and pH was tested. Data was analyzed statistically using analysis of variance technique. Results: The salivary pH was more alkaline for patients with generalized chronic gingivitis as compared with the control group (P = 0.001 whereas patients with generalized chronic periodontitis had more acidic pH as compared with the control group (P = 0.001. Conclusion: These results indicate a significant change in the pH depending on the severity of the periodontal condition. The salivary pH shows significant changes and thus relevance to the severity of periodontal disease. Salivary pH may thus be used as a quick chairside diagnostic biomarker.

A Systematic Review and Meta-Analysis of Circulating Biomarkers Associated with Failure of Arteriovenous Fistulae for Haemodialysis.

Directory of Open Access Journals (Sweden)

Susan K Morton

Full Text Available Arteriovenous fistula (AVF failure is a significant cause of morbidity and expense in patients on maintenance haemodialysis (HD. Circulating biomarkers could be valuable in detecting patients at risk of AVF failure and may identify targets to improve AVF outcome. Currently there is little consensus on the relationship between circulating biomarkers and AVF failure. The aim of this systematic review was to identify circulating biomarkers associated with AVF failure.Studies evaluating the association between circulating biomarkers and the presence or risk of AVF failure were systematically identified from the MEDLINE, EMBASE and Cochrane Library databases. No restrictions on the type of study were imposed. Concentrations of circulating biomarkers of routine HD patients with and without AVF failure were recorded and meta-analyses were performed on biomarkers that were assessed in three or more studies with a composite population of at least 100 participants. Biomarker concentrations were synthesized into inverse-variance random-effects models to calculate standardized mean differences (SMD and 95% confidence intervals (CI.Thirteen studies comprising a combined population of 1512 participants were included after screening 2835 unique abstracts. These studies collectively investigated 48 biomarkers, predominantly circulating molecules which were assessed as part of routine clinical care. Meta-analysis was performed on twelve eligible biomarkers. No significant association between any of the assessed biomarkers and AVF failure was observed.This paper is the first systematic review of biomarkers associated with AVF failure. Our results suggest that blood markers currently assessed do not identify an at-risk AVF. Further, rigorously designed studies assessing biological plausible biomarkers are needed to clarify whether assessment of circulating markers can be of any clinical value. PROSPERO registration number CRD42016033845.

Statistical considerations on safety analysis

International Nuclear Information System (INIS)

Pal, L.; Makai, M.

2004-01-01

The authors have investigated the statistical methods applied to safety analysis of nuclear reactors and arrived at alarming conclusions: a series of calculations with the generally appreciated safety code ATHLET were carried out to ascertain the stability of the results against input uncertainties in a simple experimental situation. Scrutinizing those calculations, we came to the conclusion that the ATHLET results may exhibit chaotic behavior. A further conclusion is that the technological limits are incorrectly set when the output variables are correlated. Another formerly unnoticed conclusion of the previous ATHLET calculations that certain innocent looking parameters (like wall roughness factor, the number of bubbles per unit volume, the number of droplets per unit volume) can influence considerably such output parameters as water levels. The authors are concerned with the statistical foundation of present day safety analysis practices and can only hope that their own misjudgment will be dispelled. Until then, the authors suggest applying correct statistical methods in safety analysis even if it makes the analysis more expensive. It would be desirable to continue exploring the role of internal parameters (wall roughness factor, steam-water surface in thermal hydraulics codes, homogenization methods in neutronics codes) in system safety codes and to study their effects on the analysis. In the validation and verification process of a code one carries out a series of computations. The input data are not precisely determined because measured data have an error, calculated data are often obtained from a more or less accurate model. Some users of large codes are content with comparing the nominal output obtained from the nominal input, whereas all the possible inputs should be taken into account when judging safety. At the same time, any statement concerning safety must be aleatory, and its merit can be judged only when the probability is known with which the

Statistical shape analysis with applications in R

CERN Document Server

Dryden, Ian L

2016-01-01

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features. Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded `Statistical Shape Analysis’ by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while reta...

Spatial analysis statistics, visualization, and computational methods

CERN Document Server

Oyana, Tonny J

2015-01-01

An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies. The book enables readers to: Identify types and characterize non-spatial and spatial data Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results Construct testable hypotheses that require inferential statistical analysis Process spatial data, extract explanatory variables, conduct statisti...

Application of descriptive statistics in analysis of experimental data

OpenAIRE

Mirilović Milorad; Pejin Ivana

2008-01-01

Statistics today represent a group of scientific methods for the quantitative and qualitative investigation of variations in mass appearances. In fact, statistics present a group of methods that are used for the accumulation, analysis, presentation and interpretation of data necessary for reaching certain conclusions. Statistical analysis is divided into descriptive statistical analysis and inferential statistics. The values which represent the results of an experiment, and which are the subj...

Statistical Analysis of Research Data | Center for Cancer Research

Science.gov (United States)

Recent advances in cancer biology have resulted in the need for increased statistical analysis of research data. The Statistical Analysis of Research Data (SARD) course will be held on April 5-6, 2018 from 9 a.m.-5 p.m. at the National Institutes of Health's Natcher Conference Center, Balcony C on the Bethesda Campus. SARD is designed to provide an overview on the general principles of statistical analysis of research data.  The first day will feature univariate data analysis, including descriptive statistics, probability distributions, one- and two-sample inferential statistics.

Statistical analysis with Excel for dummies

CERN Document Server

Schmuller, Joseph

2013-01-01

Take the mystery out of statistical terms and put Excel to work! If you need to create and interpret statistics in business or classroom settings, this easy-to-use guide is just what you need. It shows you how to use Excel's powerful tools for statistical analysis, even if you've never taken a course in statistics. Learn the meaning of terms like mean and median, margin of error, standard deviation, and permutations, and discover how to interpret the statistics of everyday life. You'll learn to use Excel formulas, charts, PivotTables, and other tools to make sense of everything fro

A computerized MRI biomarker quantification scheme for a canine model of Duchenne muscular dystrophy.

Science.gov (United States)

Wang, Jiahui; Fan, Zheng; Vandenborne, Krista; Walter, Glenn; Shiloh-Malawsky, Yael; An, Hongyu; Kornegay, Joe N; Styner, Martin A

2013-09-01

Golden retriever muscular dystrophy (GRMD) is a widely used canine model of Duchenne muscular dystrophy (DMD). Recent studies have shown that magnetic resonance imaging (MRI) can be used to non-invasively detect consistent changes in both DMD and GRMD. In this paper, we propose a semiautomated system to quantify MRI biomarkers of GRMD. Our system was applied to a database of 45 MRI scans from 8 normal and 10 GRMD dogs in a longitudinal natural history study. We first segmented six proximal pelvic limb muscles using a semiautomated full muscle segmentation method. We then performed preprocessing, including intensity inhomogeneity correction, spatial registration of different image sequences, intensity calibration of T2-weighted and T2-weighted fat-suppressed images, and calculation of MRI biomarker maps. Finally, for each of the segmented muscles, we automatically measured MRI biomarkers of muscle volume, intensity statistics over MRI biomarker maps, and statistical image texture features. The muscle volume and the mean intensities in T2 value, fat, and water maps showed group differences between normal and GRMD dogs. For the statistical texture biomarkers, both the histogram and run-length matrix features showed obvious group differences between normal and GRMD dogs. The full muscle segmentation showed significantly less error and variability in the proposed biomarkers when compared to the standard, limited muscle range segmentation. The experimental results demonstrated that this quantification tool could reliably quantify MRI biomarkers in GRMD dogs, suggesting that it would also be useful for quantifying disease progression and measuring therapeutic effect in DMD patients.

Genomic Biomarkers for Personalized Medicine: Development and Validation in Clinical Studies

Directory of Open Access Journals (Sweden)

Shigeyuki Matsui

2013-01-01

Full Text Available The establishment of high-throughput technologies has brought substantial advances to our understanding of the biology of many diseases at the molecular level and increasing expectations on the development of innovative molecularly targeted treatments and molecular biomarkers or diagnostic tests in the context of clinical studies. In this review article, we position the two critical statistical analyses of high-dimensional genomic data, gene screening and prediction, in the framework of development and validation of genomic biomarkers or signatures, through taking into consideration the possible different strategies for developing genomic signatures. A wide variety of biomarker-based clinical trial designs to assess clinical utility of a biomarker or a new treatment with a companion biomarker are also discussed.

Meeting Report--NASA Radiation Biomarker Workshop

Energy Technology Data Exchange (ETDEWEB)

Straume, Tore; Amundson, Sally A,; Blakely, William F.; Burns, Frederic J.; Chen, Allen; Dainiak, Nicholas; Franklin, Stephen; Leary, Julie A.; Loftus, David J.; Morgan, William F.; Pellmar, Terry C.; Stolc, Viktor; Turteltaub, Kenneth W.; Vaughan, Andrew T.; Vijayakumar, Srinivasan; Wyrobek, Andrew J.

2008-05-01

A summary is provided of presentations and discussions from the NASA Radiation Biomarker Workshop held September 27-28, 2007, at NASA Ames Research Center in Mountain View, California. Invited speakers were distinguished scientists representing key sectors of the radiation research community. Speakers addressed recent developments in the biomarker and biotechnology fields that may provide new opportunities for health-related assessment of radiation-exposed individuals, including for long-duration space travel. Topics discussed include the space radiation environment, biomarkers of radiation sensitivity and individual susceptibility, molecular signatures of low-dose responses, multivariate analysis of gene expression, biomarkers in biodefense, biomarkers in radiation oncology, biomarkers and triage following large-scale radiological incidents, integrated and multiple biomarker approaches, advances in whole-genome tiling arrays, advances in mass-spectrometry proteomics, radiation biodosimetry for estimation of cancer risk in a rat skin model, and confounding factors. Summary conclusions are provided at the end of the report.

Biomarkers in Alzheimer’s Disease Analysis by Mass Spectrometry-Based Proteomics

Directory of Open Access Journals (Sweden)

Yahui Liu

2014-05-01

Full Text Available Alzheimer’s disease (AD is a common chronic and destructive disease. The early diagnosis of AD is difficult, thus the need for clinically applicable biomarkers development is growing rapidly. There are many methods to biomarker discovery and identification. In this review, we aim to summarize Mass spectrometry (MS-based proteomics studies on AD and discuss thoroughly the methods to identify candidate biomarkers in cerebrospinal fluid (CSF and blood. This review will also discuss the potential research areas on biomarkers.

Statistical analysis of dynamic parameters of the core

International Nuclear Information System (INIS)

Ionov, V.S.

2007-01-01

The transients of various types were investigated for the cores of zero power critical facilities in RRC KI and NPP. Dynamic parameters of neutron transients were explored by tool statistical analysis. Its have sufficient duration, few channels for currents of chambers and reactivity and also some channels for technological parameters. On these values the inverse period. reactivity, lifetime of neutrons, reactivity coefficients and some effects of a reactivity are determinate, and on the values were restored values of measured dynamic parameters as result of the analysis. The mathematical means of statistical analysis were used: approximation(A), filtration (F), rejection (R), estimation of parameters of descriptive statistic (DSP), correlation performances (kk), regression analysis(KP), the prognosis (P), statistician criteria (SC). The calculation procedures were realized by computer language MATLAB. The reasons of methodical and statistical errors are submitted: inadequacy of model operation, precision neutron-physical parameters, features of registered processes, used mathematical model in reactivity meters, technique of processing for registered data etc. Examples of results of statistical analysis. Problems of validity of the methods used for definition and certification of values of statistical parameters and dynamic characteristics are considered (Authors)

Quantifying Treatment Benefit in Molecular Subgroups to Assess a Predictive Biomarker.

Science.gov (United States)

Iasonos, Alexia; Chapman, Paul B; Satagopan, Jaya M

2016-05-01

An increased interest has been expressed in finding predictive biomarkers that can guide treatment options for both mutation carriers and noncarriers. The statistical assessment of variation in treatment benefit (TB) according to the biomarker carrier status plays an important role in evaluating predictive biomarkers. For time-to-event endpoints, the hazard ratio (HR) for interaction between treatment and a biomarker from a proportional hazards regression model is commonly used as a measure of variation in TB. Although this can be easily obtained using available statistical software packages, the interpretation of HR is not straightforward. In this article, we propose different summary measures of variation in TB on the scale of survival probabilities for evaluating a predictive biomarker. The proposed summary measures can be easily interpreted as quantifying differential in TB in terms of relative risk or excess absolute risk due to treatment in carriers versus noncarriers. We illustrate the use and interpretation of the proposed measures with data from completed clinical trials. We encourage clinical practitioners to interpret variation in TB in terms of measures based on survival probabilities, particularly in terms of excess absolute risk, as opposed to HR. Clin Cancer Res; 22(9); 2114-20. ©2016 AACR. ©2016 American Association for Cancer Research.

CONFIDENCE LEVELS AND/VS. STATISTICAL HYPOTHESIS TESTING IN STATISTICAL ANALYSIS. CASE STUDY

Directory of Open Access Journals (Sweden)

ILEANA BRUDIU

2009-05-01

Full Text Available Estimated parameters with confidence intervals and testing statistical assumptions used in statistical analysis to obtain conclusions on research from a sample extracted from the population. Paper to the case study presented aims to highlight the importance of volume of sample taken in the study and how this reflects on the results obtained when using confidence intervals and testing for pregnant. If statistical testing hypotheses not only give an answer "yes" or "no" to some questions of statistical estimation using statistical confidence intervals provides more information than a test statistic, show high degree of uncertainty arising from small samples and findings build in the "marginally significant" or "almost significant (p very close to 0.05.

Collecting operational event data for statistical analysis

International Nuclear Information System (INIS)

Atwood, C.L.

1994-09-01

This report gives guidance for collecting operational data to be used for statistical analysis, especially analysis of event counts. It discusses how to define the purpose of the study, the unit (system, component, etc.) to be studied, events to be counted, and demand or exposure time. Examples are given of classification systems for events in the data sources. A checklist summarizes the essential steps in data collection for statistical analysis

Variable importance analysis based on rank aggregation with applications in metabolomics for biomarker discovery.

Science.gov (United States)

Yun, Yong-Huan; Deng, Bai-Chuan; Cao, Dong-Sheng; Wang, Wei-Ting; Liang, Yi-Zeng

2016-03-10

Biomarker discovery is one important goal in metabolomics, which is typically modeled as selecting the most discriminating metabolites for classification and often referred to as variable importance analysis or variable selection. Until now, a number of variable importance analysis methods to discover biomarkers in the metabolomics studies have been proposed. However, different methods are mostly likely to generate different variable ranking results due to their different principles. Each method generates a variable ranking list just as an expert presents an opinion. The problem of inconsistency between different variable ranking methods is often ignored. To address this problem, a simple and ideal solution is that every ranking should be taken into account. In this study, a strategy, called rank aggregation, was employed. It is an indispensable tool for merging individual ranking lists into a single "super"-list reflective of the overall preference or importance within the population. This "super"-list is regarded as the final ranking for biomarker discovery. Finally, it was used for biomarkers discovery and selecting the best variable subset with the highest predictive classification accuracy. Nine methods were used, including three univariate filtering and six multivariate methods. When applied to two metabolic datasets (Childhood overweight dataset and Tubulointerstitial lesions dataset), the results show that the performance of rank aggregation has improved greatly with higher prediction accuracy compared with using all variables. Moreover, it is also better than penalized method, least absolute shrinkage and selectionator operator (LASSO), with higher prediction accuracy or less number of selected variables which are more interpretable. Copyright © 2016 Elsevier B.V. All rights reserved.

Applying petroleum biomarkers as a tool for confirmation of petroleum hydrocarbons in high organic content soils

Energy Technology Data Exchange (ETDEWEB)

O' Sullivan, G.; Martin, E.J.; Waddell, J.; Sandau, C.D. [TRIUM Environmental Solutions, Cochrane, AB (Canada); Denham, G. [Nexen Inc., Calgary, AB (Canada); Samis, M.W. [Great Plains Environmental Management Ltd., Medecine Hat, AB (Canada)

2009-10-01

It is often difficult to separate naturally occurring phytogenic organic materials from petrogenic sources in routine gas chromatography flame ionization detection (GC-FID) analyses. Phytogenic compounds include tannins, waxes, terpenes, fats and oils. This study examined the use of petroleum biomarkers as a means of determining the nature, sources, type and geological conditions of the formation of petroleum hydrocarbons (PHCs). The analysis was conducted at a former well site consisting of low-lying peat marshlands that had the potential to interfere with the delineation of PHC impacts. Fourteen boreholes and 8 hand auger holes were placed at the site. Soil samples were analyzed for salinity, metals, and PHC constituents. Biomarker targets included acyclic isoprenoid compounds, polycyclic aromatic hydrocarbon (PAH) compounds, terpanes, hopanes, and triaromatic steranes. A grain-size analysis showed the presence of peat materials within the saturated zone. Results of the study demonstrated the presence of PHC constituents that exceeded applicable guidelines. The biomarker analysis was used to statistically determine site-specific background levels of hydrocarbons. Nearly 3000 tonnes of soil were excavated from the site. It was concluded that site-specific conditions should be taken into consideration when evaluating reclamation targets. 3 refs., 6 figs.

Integrative analysis of gene expression and DNA methylation using unsupervised feature extraction for detecting candidate cancer biomarkers.

Science.gov (United States)

Moon, Myungjin; Nakai, Kenta

2018-04-01

Currently, cancer biomarker discovery is one of the important research topics worldwide. In particular, detecting significant genes related to cancer is an important task for early diagnosis and treatment of cancer. Conventional studies mostly focus on genes that are differentially expressed in different states of cancer; however, noise in gene expression datasets and insufficient information in limited datasets impede precise analysis of novel candidate biomarkers. In this study, we propose an integrative analysis of gene expression and DNA methylation using normalization and unsupervised feature extractions to identify candidate biomarkers of cancer using renal cell carcinoma RNA-seq datasets. Gene expression and DNA methylation datasets are normalized by Box-Cox transformation and integrated into a one-dimensional dataset that retains the major characteristics of the original datasets by unsupervised feature extraction methods, and differentially expressed genes are selected from the integrated dataset. Use of the integrated dataset demonstrated improved performance as compared with conventional approaches that utilize gene expression or DNA methylation datasets alone. Validation based on the literature showed that a considerable number of top-ranked genes from the integrated dataset have known relationships with cancer, implying that novel candidate biomarkers can also be acquired from the proposed analysis method. Furthermore, we expect that the proposed method can be expanded for applications involving various types of multi-omics datasets.

Statistics and analysis of scientific data

CERN Document Server

Bonamente, Massimiliano

2013-01-01

Statistics and Analysis of Scientific Data covers the foundations of probability theory and statistics, and a number of numerical and analytical methods that are essential for the present-day analyst of scientific data. Topics covered include probability theory, distribution functions of statistics, fits to two-dimensional datasheets and parameter estimation, Monte Carlo methods and Markov chains. Equal attention is paid to the theory and its practical application, and results from classic experiments in various fields are used to illustrate the importance of statistics in the analysis of scientific data. The main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and proactive use of the material for practical applications. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is us...

Analysis of the immunological biomarker profile during acute Zika virus infection reveals the overexpression of CXCL10, a chemokine linked to neuronal damage

Directory of Open Access Journals (Sweden)

Felipe Gomes Naveca

2018-05-01

Full Text Available BACKGROUND Infection with Zika virus (ZIKV manifests in a broad spectrum of disease ranging from mild illness to severe neurological complications and little is known about Zika immunopathogenesis. OBJECTIVES To define the immunologic biomarkers that correlate with acute ZIKV infection. METHODS We characterized the levels of circulating cytokines, chemokines, and growth factors in 54 infected patients of both genders at five different time points after symptom onset using microbeads multiplex immunoassay; comparison to 100 age-matched controls was performed for statistical analysis and data mining. FINDINGS ZIKV-infected patients present a striking systemic inflammatory response with high levels of pro-inflammatory mediators. Despite the strong inflammatory pattern, IL-1Ra and IL-4 are also induced during the acute infection. Interestingly, the inflammatory cytokines IL-1β, IL-13, IL-17, TNF-α, and IFN-γ; chemokines CXCL8, CCL2, CCL5; and the growth factor G-CSF, displayed a bimodal distribution accompanying viremia. While this is the first manuscript to document bimodal distributions of viremia in ZIKV infection, this has been documented in other viral infections, with a primary viremia peak during mild systemic disease and a secondary peak associated with distribution of the virus to organs and tissues. MAIN CONCLUSIONS Biomarker network analysis demonstrated distinct dynamics in concurrence with the bimodal viremia profiles at different time points during ZIKV infection. Such a robust cytokine and chemokine response has been associated with blood-brain barrier permeability and neuroinvasiveness in other flaviviral infections. High-dimensional data analysis further identified CXCL10, a chemokine involved in foetal neuron apoptosis and Guillain-Barré syndrome, as the most promising biomarker of acute ZIKV infection for potential clinical application.

Method for statistical data analysis of multivariate observations

CERN Document Server

Gnanadesikan, R

1997-01-01

A practical guide for multivariate statistical techniques-- now updated and revised In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of inte

Advances in statistical models for data analysis

CERN Document Server

Minerva, Tommaso; Vichi, Maurizio

2015-01-01

This edited volume focuses on recent research results in classification, multivariate statistics and machine learning and highlights advances in statistical models for data analysis. The volume provides both methodological developments and contributions to a wide range of application areas such as economics, marketing, education, social sciences and environment. The papers in this volume were first presented at the 9th biannual meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in September 2013 at the University of Modena and Reggio Emilia, Italy.

[On new screening biomarker to evaluate health state in personnel engaged into chemical weapons extinction].

Science.gov (United States)

Voitenko, N G; Garniuk, V V; Prokofieva, D S; Gontcharov, N V

2015-01-01

The work was aimed to find new screeding parameters (biomarkers) for evaluation of health state of workers engaged into enterprises with hazardous work conditions, as exemplified by "Maradykovskyi" object of chemical weapons extinction. Analysis of 27 serum cytokines was conducted in donors and the object personnel with various work conditions. Findings are statistically significant increase of serum eotaxin in the personnel of "dirty" zone, who are regularly exposed to toxic agents in individual filter protective means over the working day. For screening detection of health disorders in the object personnel, the authors suggested new complex biomarker--ratio Eotaxin* IFNγ/TNFα that demonstrates 67.9% sensitivity and 87.5% specificity in differentiating the "dirty" zone personnel and other staffers.

A random-sum Wilcoxon statistic and its application to analysis of ROC and LROC data.

Science.gov (United States)

Tang, Liansheng Larry; Balakrishnan, N

2011-01-01

The Wilcoxon-Mann-Whitney statistic is commonly used for a distribution-free comparison of two groups. One requirement for its use is that the sample sizes of the two groups are fixed. This is violated in some of the applications such as medical imaging studies and diagnostic marker studies; in the former, the violation occurs since the number of correctly localized abnormal images is random, while in the latter the violation is due to some subjects not having observable measurements. For this reason, we propose here a random-sum Wilcoxon statistic for comparing two groups in the presence of ties, and derive its variance as well as its asymptotic distribution for large sample sizes. The proposed statistic includes the regular Wilcoxon rank-sum statistic. Finally, we apply the proposed statistic for summarizing location response operating characteristic data from a liver computed tomography study, and also for summarizing diagnostic accuracy of biomarker data.

Diffusion Entropy: A Potential Neuroimaging Biomarker of Bipolar Disorder in the Temporal Pole.

Science.gov (United States)

Spuhler, Karl; Bartlett, Elizabeth; Ding, Jie; DeLorenzo, Christine; Parsey, Ramin; Huang, Chuan

2018-02-01

Despite much research, bipolar depression remains poorly understood, with no clinically useful biomarkers for its diagnosis. The paralimbic system has become a target for biomarker research, with paralimbic structural connectivity commonly reported to distinguish bipolar patients from controls in tractography-based diffusion MRI studies, despite inconsistent findings in voxel-based studies. The purpose of this analysis was to validate existing findings with traditional diffusion MRI metrics and investigate the utility of a novel diffusion MRI metric, entropy of diffusion, in the search for bipolar depression biomarkers. We performed group-level analysis on 9 un-medicated (6 medication-naïve; 3 medication-free for at least 33 days) bipolar patients in a major depressive episode and 9 matched healthy controls to compare: (1) average mean diffusivity (MD) and fractional anisotropy (FA) and; (2) MD and FA histogram entropy-a statistical measure of distribution homogeneity-in the amygdala, hippocampus, orbitofrontal cortex and temporal pole. We also conducted classification analyses with leave-one-out and separate testing dataset (N = 11) approaches. We did not observe statistically significant differences in average MD or FA between the groups in any region. However, in the temporal pole, we observed significantly lower MD entropy in bipolar patients; this finding suggests a regional difference in MD distributions in the absence of an average difference. This metric allowed us to accurately characterize bipolar patients from controls in leave-one-out (accuracy = 83%) and prediction (accuracy = 73%) analyses. This novel application of diffusion MRI yielded not only an interesting separation between bipolar patients and healthy controls, but also accurately classified bipolar patients from controls. © 2017 Wiley Periodicals, Inc.

The NINDS Parkinson's disease biomarkers program: The Ninds Parkinson's Disease Biomarkers Program

Energy Technology Data Exchange (ETDEWEB)

Rosenthal, Liana S. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Drake, Daniel [Department of Biostatistics, Columbia University, New York New York USA; Alcalay, Roy N. [Department of Neurology, Columbia University, New York New York USA; Babcock, Debra [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Bowman, F. DuBois [Department of Biostatistics, Columbia University, New York New York USA; Chen-Plotkin, Alice [Department of Neurology, University of Pennsylvania, Philadelphia Pennsylvania USA; Dawson, Ted M. [Department of Neurology, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Neuroregeneration and Stem Cell Programs, Institute for Cell Engineering, Solomon H. Snyder Department of Neuroscience, Pharmacology and Molecular Sciences, Johns Hopkins University School of Medicine, Baltimore Maryland USA; Dewey, Richard B. [Department of Neurology and Neurotherapeutics, University of Texas Southwestern Medical Center, Dallas USA; German, Dwight C. [Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas USA; Huang, Xuemei [Department of Neurology, Penn State Hershey Medical Center, Hershey Pennsylvania USA; Landin, Barry [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; McAuliffe, Matthew [Center for Information Technology, National Institutes of Health, Bethesda Maryland USA; Petyuk, Vladislav A. [Biological Sciences Division, Pacific Northwest National Laboratory, Richland Washington USA; Scherzer, Clemens R. [Department of Neurology, Brigham & Women' s Hospital, Harvard Medical School, Cambridge Massachusetts USA; Hillaire-Clarke, Coryse St. [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sieber, Beth-Anne [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Sutherland, Margaret [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA; Tarn, Chi [Coriell Institute for Medical Research, Camden New Jersey USA; West, Andrew [Department of Neurology, University of Alabama at Birmingham, Birmingham USA; Vaillancourt, David [Department of Applied Physiology and Kinesiology, University of Florida, Gainesville Florida USA; Zhang, Jing [Department of Pathology, University of Washington, Seattle Washington USA; Gwinn, Katrina [National Institute of Neurological Diseases and Stroke, National Institutes of Health, Bethesda Maryland USA

2015-10-07

Background: Neuroprotection for Parkinson Disease (PD) remains elusive. Biomarkers hold the promise of removing roadblocks to therapy development. The National Institute of Neurological Disorders and Stroke (NINDS) has therefore established the Parkinson’s Disease Biomarkers Program (PDBP) to promote discovery of biomarkers for use in phase II-III clinical trials in PD. Methods: The PDBP facilitates biomarker development to improve neuroprotective clinical trial design, essential for advancing therapeutics for PD. To date, eleven consortium projects in the PDBP are focused on the development of clinical and laboratory-based PD biomarkers for diagnosis, progression tracking, and/or the prediction of prognosis. Seven of these projects also provide detailed longitudinal data and biospecimens from PD patients and controls, as a resource for all PD researchers. Standardized operating procedures and pooled reference samples have been created in order to allow cross-project comparisons and assessment of batch effects. A web-based Data Management Resource facilitates rapid sharing of data and biosamples across the entire PD research community for additional biomarker projects. Results: Here we describe the PDBP, highlight standard operating procedures for the collection of biospecimens and data, and provide an interim report with quality control analysis on the first 1082 participants and 1033 samples with quality control analysis collected as of October 2014. Conclusions: By making samples and data available to academics and industry, encouraging the adoption of existing standards, and providing a resource which complements existing programs, the PDBP will accelerate the pace of PD biomarker research, with the goal of improving diagnostic methods and treatment.

Polychlorinated biphenyls pattern analysis: Potential nondestructive biomarker in vertebrates for exposure to cytochrome P450-inducing organochlorines

Energy Technology Data Exchange (ETDEWEB)

Brink, N.W. van den; Ruiter-Dijkman, E.M. De; Broekhuizen, S.; Reijnders, P.J.H.; Bosveld, A.T.C.

2000-03-01

Biomarkers are valuable instruments to assess the risks from exposure of organisms to organochlorines. In general, however, these biomarkers are either destructive to the animal of interest or extremely difficult to obtain otherwise. In this paper, the authors present a nondestructive biomarker for exposure to cytochrome P450-inducing organochlorines. This marker is based on a pattern analysis of metabolizable and nonmetabolizable polychlorinated biphenyl (PCB) congeners, which occur in several kinds of tissues (and even blood) that can be obtained without serious effects on the organism involved. The fraction of metabolizable PCB congeners is negatively correlated with exposure to PCBs, which are known to induce specific P450 isoenzymes. This relation can be modeled by a logistic curve, which can be used to define critical levels of exposure. In addition, this method creates an opportunity to analyze biomarker responses in archived tissues stored at standard freezing temperatures ({minus}20 C), at which responses to established biomarkers deteriorate. Furthermore, this method facilitates attribution of the enzyme induction to certain classes of compounds.

MALDI-TOF and SELDI-TOF analysis: “tandem” techniques to identify potential biomarker in fibromyalgia

Directory of Open Access Journals (Sweden)

A. Lucacchini

2011-11-01

Full Text Available Fibromyalgia (FM is characterized by the presence of chronic widespread pain throughout the musculoskeletal system and diffuse tenderness. Unfortunately, no laboratory tests have been appropriately validated for FM and correlated with the subsets and activity. The aim of this study was to apply a proteomic technique in saliva of FM patients: the Surface Enhance Laser Desorption/Ionization Time-of-Flight (SELDI-TOF. For this study, 57 FM patients and 35 HC patients were enrolled. The proteomic analysis of saliva was carried out using SELDI-TOF. The analysis was performed using different chip arrays with different characteristics of binding. The statistical analysis was performed using cluster analysis and the difference between two groups was underlined using Student’s t-test. Spectra analysis highlighted the presence of several peaks differently expressed in FM patients compared with controls. The preliminary results obtained by SELDI-TOF analysis were compared with those obtained in our previous study performed on whole saliva of FM patients by using electrophoresis. The m/z of two peaks, increased in FM patients, seem to overlap well with the molecular weight of calgranulin A and C and Rho GDP-dissociation inhibitor 2, which we had found up-regulated in our previous study. These preliminary results showed the possibility of identifying potential salivary biomarker through salivary proteomic analysis with MALDI-TOF and SELDI-TOF in FM patients. The peaks observed allow us to focus on some of the particular pathogenic aspects of FM, the oxidative stress which contradistinguishes this condition, the involvement of proteins related to the cytoskeletal arrangements, and central sensibilization.

amphibian_biomarker_data

Data.gov (United States)

U.S. Environmental Protection Agency — Amphibian metabolite data used in Snyder, M.N., Henderson, W.M., Glinski, D.G., Purucker, S. T., 2017. Biomarker analysis of american toad (Anaxyrus americanus) and...

Statistical models and methods for reliability and survival analysis

CERN Document Server

Couallier, Vincent; Huber-Carol, Catherine; Mesbah, Mounir; Huber -Carol, Catherine; Limnios, Nikolaos; Gerville-Reache, Leo

2013-01-01

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts - Statistical Models and Methods, Statistical

Classification, (big) data analysis and statistical learning

CERN Document Server

Conversano, Claudio; Vichi, Maurizio

2018-01-01

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data science, including statistical analysis of large datasets, big data analytics, time series clustering, integration of data from different sources, as well as social networks. It covers both methodological aspects as well as applications to a wide range of areas such as economics, marketing, education, social sciences, medicine, environmental sciences and the pharmaceutical industry. In addition, it describes the basic features of the software behind the data analysis results, and provides links to the corresponding codes and data sets where necessary. This book is intended for researchers and practitioners who are interested in the latest developments and applications in the field. The peer-reviewed contributions were presented at the 10th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, held in Santa Margherita di Pul...

Statistical hot spot analysis of reactor cores

International Nuclear Information System (INIS)

Schaefer, H.

1974-05-01

This report is an introduction into statistical hot spot analysis. After the definition of the term 'hot spot' a statistical analysis is outlined. The mathematical method is presented, especially the formula concerning the probability of no hot spots in a reactor core is evaluated. A discussion with the boundary conditions of a statistical hot spot analysis is given (technological limits, nominal situation, uncertainties). The application of the hot spot analysis to the linear power of pellets and the temperature rise in cooling channels is demonstrated with respect to the test zone of KNK II. Basic values, such as probability of no hot spots, hot spot potential, expected hot spot diagram and cumulative distribution function of hot spots, are discussed. It is shown, that the risk of hot channels can be dispersed equally over all subassemblies by an adequate choice of the nominal temperature distribution in the core

The statistical analysis of anisotropies

International Nuclear Information System (INIS)

Webster, A.

1977-01-01

One of the many uses to which a radio survey may be put is an analysis of the distribution of the radio sources on the celestial sphere to find out whether they are bunched into clusters or lie in preferred regions of space. There are many methods of testing for clustering in point processes and since they are not all equally good this contribution is presented as a brief guide to what seems to be the best of them. The radio sources certainly do not show very strong clusering and may well be entirely unclustered so if a statistical method is to be useful it must be both powerful and flexible. A statistic is powerful in this context if it can efficiently distinguish a weakly clustered distribution of sources from an unclustered one, and it is flexible if it can be applied in a way which avoids mistaking defects in the survey for true peculiarities in the distribution of sources. The paper divides clustering statistics into two classes: number density statistics and log N/log S statistics. (Auth.)

Evaluation of Individual and Combined Applications of Serum Biomarkers for Diagnosis of Hepatocellular Carcinoma: A Meta-Analysis

Directory of Open Access Journals (Sweden)

Bin Hu

2013-12-01

Full Text Available The clinical value of Serum alpha-fetoprotein (AFP to detect early hepatocellular carcinoma (HCC has been questioned due to its low sensitivity and specificity found in recent years. Other than AFP, several new serum biomarkers including the circulating AFP isoform AFP-L3, des-gamma-carboxy prothrombin (DCP and Golgi protein-73 (GP73 have been identified as useful HCC markers. In this investigation, we review the current knowledge about these HCC-related biomarkers, and sum up the results of our meta-analysis on studies that have addressed the utility of these biomarkers in early detection and prognostic prediction of HCC. A systematic search in PubMed, Web of Science, and the Cochrane Library was performed for articles published in English from 1999 to 2012, focusing on serum biomarkers for HCC detection. Data on sensitivity and specificity of tests were extracted from 40 articles that met the inclusion criteria, and the summary receiver operating characteristic curve (sROC was obtained. A meta-analysis was carried out in which the area under the curve (AUC for each biomarker or biomarker combinations (AFP, DCP, GP73, AFP-L3, AFP + DCP, AFP + AFP-L3, and AFP + GP73 was used to compare the diagnostic accuracy of different biomarker tests. The AUC of AFP, DCP, GP73, AFP-L3, AFP + DCP, AFP + AFP-L3, and AFP + GP73 are 0.835, 0.797, 0.914, 0.710, 0.874, 0.748, and 0.932 respectively. A combination of AFP + GP73 is superior to AFP in detecting HCC and differentiating HCC patients from non-HCC patients, and may prove to be a useful marker in the diagnosis and screening of HCC. In addition, the AUC of GP73, AFP + DCP and AFP + GP73 are better than that of AFP. The clinical value of GP73, AFP + DCP, or AFP + GP73 as serological markers for HCC diagnosis needs to be addressed further in future studies.

Basic statistical tools in research and data analysis

Directory of Open Access Journals (Sweden)

Zulfiqar Ali

2016-01-01

Full Text Available Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data. The results and inferences are precise only if proper statistical tests are used. This article will try to acquaint the reader with the basic research tools that are utilised while conducting various studies. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and the measures of central tendency. An idea of the sample size estimation, power analysis and the statistical errors is given. Finally, there is a summary of parametric and non-parametric tests used for data analysis.

Low cadmium exposure in males and lactating females–estimation of biomarkers

Energy Technology Data Exchange (ETDEWEB)

Stajnko, Anja [Department of Environmental Sciences, Jožef Stefan Institute, Jamova 39, Ljubljana (Slovenia); Jožef Stefan International Postgraduate School, Jamova 39, Ljubljana (Slovenia); Falnoga, Ingrid, E-mail: ingrid.falnoga@ijs.si [Department of Environmental Sciences, Jožef Stefan Institute, Jamova 39, Ljubljana (Slovenia); Tratnik, Janja Snoj; Mazej, Darja [Department of Environmental Sciences, Jožef Stefan Institute, Jamova 39, Ljubljana (Slovenia); Jagodic, Marta [Department of Environmental Sciences, Jožef Stefan Institute, Jamova 39, Ljubljana (Slovenia); Jožef Stefan International Postgraduate School, Jamova 39, Ljubljana (Slovenia); Krsnik, Mladen [Institute of Clinical Chemistry and Biochemistry, University Medical Centre Ljubljana, Njegoševa 4, Ljubljana (Slovenia); Kobal, Alfred B. [Department of Occupational Health, Idrija Mercury Mine, Arkova 43, Idrija (Slovenia); Prezelj, Marija [Institute of Clinical Chemistry and Biochemistry, University Medical Centre Ljubljana, Njegoševa 4, Ljubljana (Slovenia); Kononenko, Lijana [Chemical Office of RS, Ministry of Health of RS, Ajdovščina 4, Ljubljana (Slovenia); Horvat, Milena [Department of Environmental Sciences, Jožef Stefan Institute, Jamova 39, Ljubljana (Slovenia); Jožef Stefan International Postgraduate School, Jamova 39, Ljubljana (Slovenia)

2017-01-15

Background: Urine cadmium (Cd) and renal function biomarkers, mostly analysed in urine spot samples, are well established biomarkers of occupational exposure. Their use and associations at low environmental level are common, but have recently been questioned, particularly in terms of physiological variability and normalisation bias in the case of urine spot samples. Aim: To determine the appropriateness of spot urine and/or blood Cd exposure biomarkers and their relationships with renal function biomarkers at low levels of exposure. To this end, we used data from Slovenian human biomonitoring program involving 1081 Slovenians (548 males, mean age 31 years; 533 lactating females, mean age 29 years; 2007–2015) who have not been exposed to Cd occupationally. Results: Geometric means (GMs) of Cd in blood and spot urine samples were 0.27 ng/mL (0.28 for males and 0.33 for females) and 0.19 ng/mL (0.21 for males and 0.17 for females), respectively. Differing results were obtained when contrasting normalisation by urine creatinine with specific gravity. GMs of urine albumin (Alb), alpha-1-microglobulin (A1M), N-acetyl-beta-glucosaminidase (NAG), and immunoglobulin G (IgG) were far below their upper reference limits. Statistical analysis of unnormalised or normalised urine data often yielded inconsistent and conflicting results (or trends), so association analyses with unnormalised data were taken as more valid. Relatively weak positive associations were observed between urine Cd (ng/mL) and blood Cd (β=0.11, p=0.002 for males and β=0.33, p<0.001 for females) and for females between urine NAG and blood Cd (β=0.14, p=0.04). No associations were found between other renal function biomarkers and blood Cd. Associations between Cd and renal function biomarkers in urine were stronger (p<0.05, β=0.11–0.63). Mostly, all of the associations stayed significant but weakened after normalisation for diuresis. In the case of A1M, its associations with Cd were influenced by

Low cadmium exposure in males and lactating females–estimation of biomarkers

International Nuclear Information System (INIS)

Stajnko, Anja; Falnoga, Ingrid; Tratnik, Janja Snoj; Mazej, Darja; Jagodic, Marta; Krsnik, Mladen; Kobal, Alfred B.; Prezelj, Marija; Kononenko, Lijana; Horvat, Milena

2017-01-01

Background: Urine cadmium (Cd) and renal function biomarkers, mostly analysed in urine spot samples, are well established biomarkers of occupational exposure. Their use and associations at low environmental level are common, but have recently been questioned, particularly in terms of physiological variability and normalisation bias in the case of urine spot samples. Aim: To determine the appropriateness of spot urine and/or blood Cd exposure biomarkers and their relationships with renal function biomarkers at low levels of exposure. To this end, we used data from Slovenian human biomonitoring program involving 1081 Slovenians (548 males, mean age 31 years; 533 lactating females, mean age 29 years; 2007–2015) who have not been exposed to Cd occupationally. Results: Geometric means (GMs) of Cd in blood and spot urine samples were 0.27 ng/mL (0.28 for males and 0.33 for females) and 0.19 ng/mL (0.21 for males and 0.17 for females), respectively. Differing results were obtained when contrasting normalisation by urine creatinine with specific gravity. GMs of urine albumin (Alb), alpha-1-microglobulin (A1M), N-acetyl-beta-glucosaminidase (NAG), and immunoglobulin G (IgG) were far below their upper reference limits. Statistical analysis of unnormalised or normalised urine data often yielded inconsistent and conflicting results (or trends), so association analyses with unnormalised data were taken as more valid. Relatively weak positive associations were observed between urine Cd (ng/mL) and blood Cd (β=0.11, p=0.002 for males and β=0.33, p<0.001 for females) and for females between urine NAG and blood Cd (β=0.14, p=0.04). No associations were found between other renal function biomarkers and blood Cd. Associations between Cd and renal function biomarkers in urine were stronger (p<0.05, β=0.11–0.63). Mostly, all of the associations stayed significant but weakened after normalisation for diuresis. In the case of A1M, its associations with Cd were influenced by

Data Mining Approaches for Genomic Biomarker Development: Applications Using Drug Screening Data from the Cancer Genome Project and the Cancer Cell Line Encyclopedia.

Directory of Open Access Journals (Sweden)

David G Covell

Full Text Available Developing reliable biomarkers of tumor cell drug sensitivity and resistance can guide hypothesis-driven basic science research and influence pre-therapy clinical decisions. A popular strategy for developing biomarkers uses characterizations of human tumor samples against a range of cancer drug responses that correlate with genomic change; developed largely from the efforts of the Cancer Cell Line Encyclopedia (CCLE and Sanger Cancer Genome Project (CGP. The purpose of this study is to provide an independent analysis of this data that aims to vet existing and add novel perspectives to biomarker discoveries and applications. Existing and alternative data mining and statistical methods will be used to a evaluate drug responses of compounds with similar mechanism of action (MOA, b examine measures of gene expression (GE, copy number (CN and mutation status (MUT biomarkers, combined with gene set enrichment analysis (GSEA, for hypothesizing biological processes important for drug response, c conduct global comparisons of GE, CN and MUT as biomarkers across all drugs screened in the CGP dataset, and d assess the positive predictive power of CGP-derived GE biomarkers as predictors of drug response in CCLE tumor cells. The perspectives derived from individual and global examinations of GEs, MUTs and CNs confirm existing and reveal unique and shared roles for these biomarkers in tumor cell drug sensitivity and resistance. Applications of CGP-derived genomic biomarkers to predict the drug response of CCLE tumor cells finds a highly significant ROC, with a positive predictive power of 0.78. The results of this study expand the available data mining and analysis methods for genomic biomarker development and provide additional support for using biomarkers to guide hypothesis-driven basic science research and pre-therapy clinical decisions.

Urinary Biomarkers of Brain Diseases

Directory of Open Access Journals (Sweden)

Manxia An

2015-12-01

Full Text Available Biomarkers are the measurable changes associated with a physiological or pathophysiological process. Unlike blood, urine is not subject to homeostatic mechanisms. Therefore, greater fluctuations could occur in urine than in blood, better reflecting the changes in human body. The roadmap of urine biomarker era was proposed. Although urine analysis has been attempted for clinical diagnosis, and urine has been monitored during the progression of many diseases, particularly urinary system diseases, whether urine can reflect brain disease status remains uncertain. As some biomarkers of brain diseases can be detected in the body fluids such as cerebrospinal fluid and blood, there is a possibility that urine also contain biomarkers of brain diseases. This review summarizes the clues of brain diseases reflected in the urine proteome and metabolome.

Reproducible statistical analysis with multiple languages

DEFF Research Database (Denmark)

Lenth, Russell; Højsgaard, Søren

2011-01-01

This paper describes the system for making reproducible statistical analyses. differs from other systems for reproducible analysis in several ways. The two main differences are: (1) Several statistics programs can be in used in the same document. (2) Documents can be prepared using OpenOffice or ......Office or \\LaTeX. The main part of this paper is an example showing how to use and together in an OpenOffice text document. The paper also contains some practical considerations on the use of literate programming in statistics....

Proteomic and metabolomic approaches to biomarker discovery

CERN Document Server

Issaq, Haleem J

2013-01-01

Proteomic and Metabolomic Approaches to Biomarker Discovery demonstrates how to leverage biomarkers to improve accuracy and reduce errors in research. Disease biomarker discovery is one of the most vibrant and important areas of research today, as the identification of reliable biomarkers has an enormous impact on disease diagnosis, selection of treatment regimens, and therapeutic monitoring. Various techniques are used in the biomarker discovery process, including techniques used in proteomics, the study of the proteins that make up an organism, and metabolomics, the study of chemical fingerprints created from cellular processes. Proteomic and Metabolomic Approaches to Biomarker Discovery is the only publication that covers techniques from both proteomics and metabolomics and includes all steps involved in biomarker discovery, from study design to study execution.  The book describes methods, and presents a standard operating procedure for sample selection, preparation, and storage, as well as data analysis...

Biomarkers for atopic dermatitis : a systematic review and meta-analysis

NARCIS (Netherlands)

Thijs, Judith; Krastev, Todor; Weidinger, Stephan; Buckens, Constantinus F; de Bruin-Weller, Marjolein; Bruijnzeel-Koomen, Carla; Flohr, Carsten; Hijnen, DirkJan

2015-01-01

PURPOSE OF REVIEW: A large number of studies investigating the correlation between severity of atopic dermatitis and various biomarkers have been published over the past decades. The aim of this review was to identify, evaluate and synthesize the evidence examining the correlation of biomarkers with

Urinary Metabolomic Profiling to Identify Potential Biomarkers for the Diagnosis of Behcet’s Disease by Gas Chromatography/Time-of-Flight−Mass Spectrometry

Directory of Open Access Journals (Sweden)

Joong Kyong Ahn

2017-11-01

Full Text Available Diagnosing Behcet’s disease (BD is challenging because of the lack of a diagnostic biomarker. The purposes of this study were to investigate distinctive metabolic changes in urine samples of BD patients and to identify urinary metabolic biomarkers for diagnosis of BD using gas chromatography/time-of-flight–mass spectrometry (GC/TOF−MS. Metabolomic profiling of urine samples from 44 BD patients and 41 healthy controls (HC were assessed using GC/TOF−MS, in conjunction with multivariate statistical analysis. A total of 110 urinary metabolites were identified. The urine metabolite profiles obtained from GC/TOF−MS analysis could distinguish BD patients from the HC group in the discovery set. The parameter values of the orthogonal partial least squared-discrimination analysis (OPLS-DA model were R2X of 0.231, R2Y of 0.804, and Q2 of 0.598. A biomarker panel composed of guanine, pyrrole-2-carboxylate, 3-hydroxypyridine, mannose, l-citrulline, galactonate, isothreonate, sedoheptuloses, hypoxanthine, and gluconic acid lactone were selected and adequately validated as putative biomarkers of BD (sensitivity 96.7%, specificity 93.3%, area under the curve 0.974. OPLS-DA showed clear discrimination of BD and HC groups by a biomarker panel of ten metabolites in the independent set (accuracy 88%. We demonstrated characteristic urinary metabolic profiles and potential urinary metabolite biomarkers that have clinical value in the diagnosis of BD using GC/TOF−MS.

Common pitfalls in statistical analysis: "P" values, statistical significance and confidence intervals

Directory of Open Access Journals (Sweden)

Priya Ranganathan

2015-01-01

Full Text Available In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ′P′ value, explain the importance of ′confidence intervals′ and clarify the importance of including both values in a paper

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery

Directory of Open Access Journals (Sweden)

Astrid Wachter

2015-11-01

Full Text Available Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.

Occupational exposure to HDI: progress and challenges in biomarker analysis.

Science.gov (United States)

Flack, Sheila L; Ball, Louise M; Nylander-French, Leena A

2010-10-01

1,6-Hexamethylene diisocyanate (HDI) is extensively used in the automotive repair industry and is a commonly reported cause of occupational asthma in industrialized populations. However, the exact pathological mechanism remains uncertain. Characterization and quantification of biomarkers resulting from HDI exposure can fill important knowledge gaps between exposure, susceptibility, and the rise of immunological reactions and sensitization leading to asthma. Here, we discuss existing challenges in HDI biomarker analysis including the quantification of N-acetyl-1,6-hexamethylene diamine (monoacetyl-HDA) and N,N'-diacetyl-1,6-hexamethylene diamine (diacetyl-HDA) in urine samples based on previously established methods for HDA analysis. In addition, we describe the optimization of reaction conditions for the synthesis of monoacetyl-HDA and diacetyl-HDA, and utilize these standards for the quantification of these metabolites in the urine of three occupationally exposed workers. Diacetyl-HDA was present in untreated urine at 0.015-0.060 μg/l. Using base hydrolysis, the concentration range of monoacetyl-HDA in urine was 0.19-2.2 μg/l, 60-fold higher than in the untreated samples on average. HDA was detected only in one sample after base hydrolysis (0.026 μg/l). In contrast, acid hydrolysis yielded HDA concentrations ranging from 0.36 to 10.1 μg/l in these three samples. These findings demonstrate HDI metabolism via N-acetylation metabolic pathway and protein adduct formation resulting from occupational exposure to HDI. Copyright © 2010 Elsevier B.V. All rights reserved.

Statistics and analysis of scientific data

CERN Document Server

Bonamente, Massimiliano

2017-01-01

The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked,to improve the readabili...

Bayesian Inference in Statistical Analysis

CERN Document Server

Box, George E P

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences Rob

Potential serum biomarkers and metabonomic profiling of serum in ischemic stroke patients using UPLC/Q-TOF MS/MS.

Directory of Open Access Journals (Sweden)

Hongxue Sun

Full Text Available Stroke still has a high incidence with a tremendous public health burden and it is a leading cause of mortality and disability. However, biomarkers for early diagnosis are absent and the metabolic alterations associated with ischemic stroke are not clearly understood. The objectives of this case-control study are to identify serum biomarkers and explore the metabolic alterations of ischemic stroke.Metabonomic analysis was performed using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis was employed to study 60 patients with or without ischemic stroke (30 cases and 30 controls.Serum metabolic profiling identified a series of 12 metabolites with significant alterations, and the related metabolic pathways involved glycerophospholipid, sphingolipid, phospholipid, fat acid, acylcarnitine, heme, and purine metabolism. Subsequently, multiple logistic regression analyses of these metabolites showed uric acid, sphinganine and adrenoyl ethanolamide were potential biomarkers of ischemic stroke with an area under the receiver operating characteristic curve of 0.941.These findings provide insights into the early diagnosis and potential pathophysiology of ischemic stroke.

Potential biomarkers of tardive dyskinesia: A multiplex analysis of blood serum

OpenAIRE

Boiko, Anastasia S; Kornetova, Elena G; Ivanova, Svetlana A.; Loonen, Antonius

2017-01-01

Potential biomarkers of tardive dyskinesia: a multiplex analysis of blood serum A.S. Boiko(1), E.G. Kornetova(2), S.A. Ivanova(1), A.J.M. Loonen(3) (1)Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Laboratory of Molecular Genetics and Biochemistry, Tomsk, Russia (2)Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, Department of Endogenous Disorders, Tomsk, Russia (3)Univers...

Substance use in individuals with mild to borderline intellectual disability: A comparison between self-report, collateral-report and biomarker analysis.

Science.gov (United States)

VanDerNagel, Joanneke E L; Kiewik, Marion; van Dijk, Marike; Didden, Robert; Korzilius, Hubert P L M; van der Palen, Job; Buitelaar, Jan K; Uges, Donald R A; Koster, Remco A; de Jong, Cor A J

2017-04-01

Individuals with mild or borderline intellectual disability (MBID) are at risk of substance use (SU). At present, it is unclear which strategy is the best for assessing SU in individuals with MBID. This study compares three strategies, namely self-report, collateral-report, and biomarker analysis. In a sample of 112 participants with MBID from six Dutch facilities providing care to individuals with intellectual disabilities, willingness to participate, SU rates, and agreement between the three strategies were explored. The Substance use and misuse in Intellectual Disability - Questionnaire (SumID-Q; self-report) assesses lifetime use, use in the previous month, and recent use of tobacco, alcohol, cannabis, and stimulants. The Substance use and misuse in Intellectual Disability - Collateral-report questionnaire (SumID-CR; collateral-report) assesses staff members' report of participants' SU over the same reference periods as the SumID-Q. Biomarkers for SU, such as cotinine (metabolite of nicotine), ethanol, tetrahydrocannabinol (THC), and its metabolite THCCOOH, benzoylecgonine (metabolite of cocaine), and amphetamines were assessed in urine, hair, and sweat patches. Willingness to provide biomarker samples was significantly lower compared to willingness to complete the SumID-Q (p<0.001). Most participants reported smoking, drinking alcohol, and using cannabis at least once in their lives, and about a fifth had ever used stimulants. Collateralreported lifetime use was significantly lower. However, self-reported past month and recent SU rates did not differ significantly from the rates from collateral-reports or biomarkers, with the exception of lower alcohol use rates found in biomarker analysis. The agreement between self-report and biomarker analysis was substantial (kappas 0.60-0.89), except for alcohol use (kappa 0.06). Disagreement between SumID-Q and biomarkers concerned mainly over-reporting of the SumID-Q. The agreement between SumID-CR and biomarker

Modeling and Classification of Kinetic Patterns of Dynamic Metabolic Biomarkers in Physical Activity.

Directory of Open Access Journals (Sweden)

Marc Breit

2015-08-01

Full Text Available The objectives of this work were the classification of dynamic metabolic biomarker candidates and the modeling and characterization of kinetic regulatory mechanisms in human metabolism with response to external perturbations by physical activity. Longitudinal metabolic concentration data of 47 individuals from 4 different groups were examined, obtained from a cycle ergometry cohort study. In total, 110 metabolites (within the classes of acylcarnitines, amino acids, and sugars were measured through a targeted metabolomics approach, combining tandem mass spectrometry (MS/MS with the concept of stable isotope dilution (SID for metabolite quantitation. Biomarker candidates were selected by combined analysis of maximum fold changes (MFCs in concentrations and P-values resulting from statistical hypothesis testing. Characteristic kinetic signatures were identified through a mathematical modeling approach utilizing polynomial fitting. Modeled kinetic signatures were analyzed for groups with similar behavior by applying hierarchical cluster analysis. Kinetic shape templates were characterized, defining different forms of basic kinetic response patterns, such as sustained, early, late, and other forms, that can be used for metabolite classification. Acetylcarnitine (C2, showing a late response pattern and having the highest values in MFC and statistical significance, was classified as late marker and ranked as strong predictor (MFC = 1.97, P < 0.001. In the class of amino acids, highest values were shown for alanine (MFC = 1.42, P < 0.001, classified as late marker and strong predictor. Glucose yields a delayed response pattern, similar to a hockey stick function, being classified as delayed marker and ranked as moderate predictor (MFC = 1.32, P < 0.001. These findings coincide with existing knowledge on central metabolic pathways affected in exercise physiology, such as β-oxidation of fatty acids, glycolysis, and glycogenolysis. The presented modeling

Analysis of Variance: What Is Your Statistical Software Actually Doing?

Science.gov (United States)

Li, Jian; Lomax, Richard G.

2011-01-01

Users assume statistical software packages produce accurate results. In this article, the authors systematically examined Statistical Package for the Social Sciences (SPSS) and Statistical Analysis System (SAS) for 3 analysis of variance (ANOVA) designs, mixed-effects ANOVA, fixed-effects analysis of covariance (ANCOVA), and nested ANOVA. For each…

Dynamic of CSF and serum biomarkers in HIV-1 subtype C encephalitis with CNS genetic compartmentalization-case study.

Science.gov (United States)

de Almeida, Sergio M; Rotta, Indianara; Ribeiro, Clea E; Oliveira, Michelli F; Chaillon, Antoine; de Pereira, Ana Paula; Cunha, Ana Paula; Zonta, Marise; Bents, Joao França; Raboni, Sonia M; Smith, Davey; Letendre, Scott; Ellis, Ronald J

2017-06-01

Despite the effective suppression of viremia with antiretroviral therapy, HIV can still replicate in the central nervous system (CNS). This was a longitudinal study of the cerebrospinal fluid (CSF) and serum dynamics of several biomarkers related to inflammation, the blood-brain barrier, neuronal injury, and IgG intrathecal synthesis in serial samples of CSF and serum from a patient infected with HIV-1 subtype C with CNS compartmentalization.The phylogenetic analyses of plasma and CSF samples in an acute phase using next-generation sequencing and F-statistics analysis of C2-V3 haplotypes revealed distinct compartmentalized CSF viruses in paired CSF and peripheral blood mononuclear cell samples. The CSF biomarker analysis in this patient showed that symptomatic CSF escape is accompanied by CNS inflammation, high levels of cell and humoral immune biomarkers, CNS barrier dysfunction, and an increase in neuronal injury biomarkers with demyelization. Independent and isolated HIV replication can occur in the CNS, even in HIV-1 subtype C, leading to compartmentalization and development of quasispecies distinct from the peripheral plasma. These immunological aspects of the HIV CNS escape have not been described previously. To our knowledge, this is the first report of CNS HIV escape and compartmentalization in HIV-1 subtype C.

A HILIC-UHPLC-MS/MS untargeted urinary metabonomics combined with quantitative analysis of five polar biomarkers on osteoporosis rats after oral administration of Gushudan.

Science.gov (United States)

Wu, Xiao; Huang, Yue; Sun, Jinghan; Wen, Yongqing; Qin, Feng; Zhao, Longshan; Xiong, Zhili

2018-01-01

A HILIC-UHPLC-MS/MS untargeted urinary metabonomic method combined with quantitative analysis of five potential polar biomarkers in rat urine was developed and validated, to further understand the anti-osteoporosis effect of Gushudan(GSD) and its mechanism on prednisolone-induced osteoporosis(OP) rats in this study. The metabolites were separated and identified on Waters BEH HILIC (2.1mm×100mm, 1.7μm) column using the Waters ACQUITY™ ultra performance liquid chromatography system (Waters Corporation, Milford, USA) coupled with a Micromass Quattro Micro™ API mass spectrometer (Waters Corp, Milford, MA, USA). Principal component analysis (PCA) was used to identify potential biomarkers. Primary potential polar biomarkers including creatinine, taurine, betaine, hypoxanthine and cytosine, which were related to energy metabolism, lipid metabolism and amino acid metabolism, were found in the untargeted metabonomic research. Moreover, these targeted biomarkers were further separated and quantified in multiple-reaction monitoring (MRM) with positive ionization mode, using tinidazole as internal standard (I.S.). Good linearities (r>0.99) were obtained for all the analytes with the low limit of quantification from 1.00 to 12.8μg/mL. The relative standard deviation (RSD) of the intra-day and inter-day precisions were within 15.0% and the accuracy ranged from -14.3% to 13.5%. The recovery was more than 85.0%. And the validated method was successfully applied to investigate the urine samples of the control group, prednisolone-induced osteoporosis model group and Gushudan-treatment group in rats. Compared to the control group, the level of creatinine, taurine, betaine, hypoxanthine and cytosine in the model group revealed a significant decrease trend (p<0.05), while the Gushudan-treatment group showed no statistically differences by an independent sample t-test. This paper provided a better understanding of the therapeutic effect and mechanism of GSD on prednisolone

Comparing Visual and Statistical Analysis of Multiple Baseline Design Graphs.

Science.gov (United States)

Wolfe, Katie; Dickenson, Tammiee S; Miller, Bridget; McGrath, Kathleen V

2018-04-01

A growing number of statistical analyses are being developed for single-case research. One important factor in evaluating these methods is the extent to which each corresponds to visual analysis. Few studies have compared statistical and visual analysis, and information about more recently developed statistics is scarce. Therefore, our purpose was to evaluate the agreement between visual analysis and four statistical analyses: improvement rate difference (IRD); Tau-U; Hedges, Pustejovsky, Shadish (HPS) effect size; and between-case standardized mean difference (BC-SMD). Results indicate that IRD and BC-SMD had the strongest overall agreement with visual analysis. Although Tau-U had strong agreement with visual analysis on raw values, it had poorer agreement when those values were dichotomized to represent the presence or absence of a functional relation. Overall, visual analysis appeared to be more conservative than statistical analysis, but further research is needed to evaluate the nature of these disagreements.

Looking for biomarkers of Hg exposure by transcriptome analysis in the aquatic plant Elodea nuttallii

Directory of Open Access Journals (Sweden)

Regier N.

2013-04-01

Full Text Available Recently developed genomics tools have a promising potential to identify early biomarkers of exposure to toxicants. In the present work we used transcriptome analysis (RNA-seq of Elodea nuttallii –an invasive rooted macrophyte that is able to accumulate large amounts of metals- to identify biomarkers of Hg exposure. RNA-seq allowed identification of genes affected by Hg exposure and also unraveled plant response to the toxic metal: a change in energy/reserve metabolism caused by the inhibition of photosynthesis, and an adaptation of homeostasis networks to control accumulation of Hg. Data were validated by RT-qPCR and selected genes were further tested as biomarkers. Samples exposed in the field and to natural contaminated sediments clustered well with samples exposed to low metal concentrations under laboratory conditions. Our data suggest that this plant and/or this approach could be useful to develop new tests for water and sediment quality assessment.

Sensitivity analysis and related analysis : A survey of statistical techniques

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This paper reviews the state of the art in five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main question is: when should which type of analysis be applied; which statistical

Chronic Obstructive Pulmonary Disease Biomarkers

Directory of Open Access Journals (Sweden)

Tatsiana Beiko

2016-04-01

Full Text Available Despite significant decreases in morbidity and mortality of cardiovascular diseases (CVD and cancers, morbidity and cost associated with chronic obstructive pulmonary disease (COPD continue to be increasing. Failure to improve disease outcomes has been related to the paucity of interventions improving survival. Insidious onset and slow progression halter research successes in developing disease-modifying therapies. In part, the difficulty in finding new therapies is because of the extreme heterogeneity within recognized COPD phenotypes. Novel biomarkers are necessary to help understand the natural history and pathogenesis of the different COPD subtypes. A more accurate phenotyping and the ability to assess the therapeutic response to new interventions and pharmaceutical agents may improve the statistical power of longitudinal clinical studies. In this study, we will review known candidate biomarkers for COPD, proposed pathways of pathogenesis, and future directions in the field.

Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Biomarkers of Kidney Cancer.

Science.gov (United States)

Song, Yimeng; Zhong, Lijun; Zhou, Juntuo; Lu, Min; Xing, Tianying; Ma, Lulin; Shen, Jing

2017-12-01

Renal cell carcinoma (RCC) is a malignant and metastatic cancer with 95% mortality, and clear cell RCC (ccRCC) is the most observed among the five major subtypes of RCC. Specific biomarkers that can distinguish cancer tissues from adjacent normal tissues should be developed to diagnose this disease in early stages and conduct a reliable prognostic evaluation. Data-independent acquisition (DIA) strategy has been widely employed in proteomic analysis because of various advantages, including enhanced protein coverage and reliable data acquisition. In this study, a DIA workflow is constructed on a quadrupole-Orbitrap LC-MS platform to reveal dysregulated proteins between ccRCC and adjacent normal tissues. More than 4000 proteins are identified, 436 of these proteins are dysregulated in ccRCC tissues. Bioinformatic analysis reveals that multiple pathways and Gene Ontology items are strongly associated with ccRCC. The expression levels of L-lactate dehydrogenase A chain, annexin A4, nicotinamide N-methyltransferase, and perilipin-2 examined through RT-qPCR, Western blot, and immunohistochemistry confirm the validity of the proteomic analysis results. The proposed DIA workflow yields optimum time efficiency and data reliability and provides a good choice for proteomic analysis in biological and clinical studies, and these dysregulated proteins might be potential biomarkers for ccRCC diagnosis. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gene expression analysis of 4 biomarker candidates in Eisenia fetida exposed to an environmental metallic trace elements gradient: A microcosm study

Energy Technology Data Exchange (ETDEWEB)

Brulle, Franck; Lemiere, Sebastien [Univ Lille Nord de France, F-59000 Lille (France); LGCgE, Equipe Ecologie Numerique et Ecotoxicologie, Lille 1, F-59650 Villeneuve d' Ascq (France); Waterlot, Christophe; Douay, Francis [Univ Lille Nord de France, F-59000 Lille (France); LGCgE, Equipe Sols et Environnement, Groupe ISA, 48 boulevard Vauban, F-59046 Lille Cedex (France); Vandenbulcke, Franck, E-mail: franck.vandenbulcke@univ-lille1.fr [Univ Lille Nord de France, F-59000 Lille (France); LGCgE, Equipe Ecologie Numerique et Ecotoxicologie, Lille 1, F-59650 Villeneuve d' Ascq (France)

2011-11-15

Past activities of 2 smelters (Metaleurop Nord and Nyrstar) led to the accumulation of high amounts of Metal Trace Elements (TEs) in top soils of the Noyelles-Godault/Auby area, Northern France. Earthworms were exposed to polluted soils collected in this area to study and better understand the physiological changes, the mechanisms of acclimation, and detoxification resulting from TE exposure. Previously we have cloned and transcriptionally characterized potential biomarkers from immune cells of the ecotoxicologically important earthworm species Eisenia fetida exposed in vivo to TE-spiked standard soils. In the present study, analysis of expression kinetics of four candidate indicator genes (Cadmium-metallothionein, coactosin like protein, phytochelatin synthase and lysenin) was performed in E. fetida after microcosm exposures to natural soils exhibiting an environmental cadmium (Cd) gradient in a kinetic manner. TE body burdens were also measured. This microcosm study provided insights into: (1) the ability of the 4 tested genes to serve as expression biomarkers, (2) detoxification processes through the expression analysis of selected genes, and (3) influence of land uses on the response of potential biomarkers (gene expression or TE uptake). - Highlights: {yields} Expression biomarkers in animals exposed to Cadmium-contaminated field soils. {yields} Expression kinetics to test the ability of genes to serve as expression biomarkers. {yields} Study of detoxification processes through the expression analysis of selected genes.

Identification of EBP50 as a Specific Biomarker for Carcinogens Via the Analysis of Mouse Lymphoma Cellular Proteome

Science.gov (United States)

Lee, Yoen Jung; Choi, In-Kwon; Sheen, Yhun Yhong; Park, Sue Nie; Kwon, Ho Jeong

2012-01-01

To identify specific biomarkers generated upon exposure of L5178Y mouse lymphoma cells to carcinogens, 2-DE and MALDI-TOF MS analysis were conducted using the cellular proteome of L5178Y cells that had been treated with the known carcinogens, 1,2-dibromoethane and O-nitrotoluene and the noncarcinogens, emodin and D-mannitol. Eight protein spots that showed a greater than 1.5-fold increase or decrease in intensity following carcinogen treatment compared with treatment with noncarcinogens were selected. Of the identified proteins, we focused on the candidate biomarker ERM-binding phosphoprotein 50 (EBP50), the expression of which was specifically increased in response to treatment with the carcinogens. The expression level of EBP50 was determined by western analysis using polyclonal rabbit anti-EBP50 antibody. Further, the expression level of EBP50 was increased in cells treated with seven additional carcinogens, verifying that EBP50 could serve as a specific biomarker for carcinogens. PMID:22434383

Biomarkers, Trauma, and Sepsis in Pediatrics: A Review

Directory of Open Access Journals (Sweden)

Marianne Frieri

2016-01-01

Full Text Available Context: There is a logical connection with biomarkers, trauma, and sepsis. This review paper provides new information and clinical practice implications. Biomarkers are very important especially in pediatrics. Procalcitonin and other biomarkers are helpful in identifying neonatal sepsis, defense mechanisms of the immune system. Pediatric trauma and sepsis is very important both in infants and in children. Stress management both in trauma is based upon the notion that stress causes an immune imbalance in susceptible individuals. Evidence Acquisition: Data sources included studies indexed in PubMed, a meta- analysis, predictive values, research strategies, and quality assessments. A recent paper by one of the authors stated marked increase in serum procalcitonin during the course of a septic process often indicates an exacerbation of the illness, and a decreasing level is a sign of improvement. A review of epidemiologic studies on pediatric soccer patients was also addressed. Keywords for searching included biomarkers, immunity, trauma, and sepsis. Results: Of 50 reviewed articles, 34 eligible articles were selected including biomarkers, predictive values for procalcitonin, identifying children at risk for intra-abdominal injuries, blunt trauma, and epidemiology, a meta-analysis. Of neonatal associated sepsis, the NF-kappa B pathway by inflammatory stimuli in human neutrophils, predictive value of gelsolin for the outcomes of preterm neonates, a meta-analysis interleukin-8 for neonatal sepsis diagnosis. Conclusions: Biomarkers are very important especially in pediatrics. Procalcitonin and other biomarkers are helpful in identifying neonatal sepsis, defense mechanisms, and physiological functions of the immune system. Pediatric trauma and sepsis is very important both in infants and in children. Various topics were covered such as biomarkers, trauma, sepsis, inflammation, innate immunity, role of neutrophils and IL-8, reactive oxygen species

Stable isotopes and biomarkers in microbial ecology

NARCIS (Netherlands)

Boschker, H.T.S.; Middelburg, J.J.

2002-01-01

The use of biomarkers in combination with stable isotope analysis is a new approach in microbial ecology and a number of papers on a variety of subjects have appeared. We will first discuss the techniques for analysing stable isotopes in biomarkers, primarily gas chromatography-combustion-isotope

Proteome analysis of body fluids for amyotrophic lateral sclerosis biomarker discovery.

Science.gov (United States)

Krüger, Thomas; Lautenschläger, Janin; Grosskreutz, Julian; Rhode, Heidrun

2013-01-01

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder of motor neurons leading to death of the patients, mostly within 2-5 years after disease onset. The pathomechanism of motor neuron degeneration is only partially understood and therapeutic strategies based on mechanistic insights are largely ineffective. The discovery of reliable biomarkers of disease diagnosis and progression is the sine qua non of both the revelation of insights into the ALS pathomechanism and the assessment of treatment efficacies. Proteomic approaches are an important pillar in ALS biomarker discovery. Cerebrospinal fluid is the most promising body fluid for differential proteome analyses, followed by blood (serum, plasma), and even urine and saliva. The present study provides an overview about reported peptide/protein biomarker candidates that showed significantly altered levels in certain body fluids of ALS patients. These findings have to be discussed according to proposed pathomechanisms to identify modifiers of disease progression and to pave the way for the development of potential therapeutic strategies. Furthermore, limitations and advantages of proteomic approaches for ALS biomarker discovery in different body fluids and reliable validation of biomarker candidates have been addressed. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Online Statistical Modeling (Regression Analysis) for Independent Responses

Science.gov (United States)

Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus

2017-06-01

Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.

Application of Ontology Technology in Health Statistic Data Analysis.

Science.gov (United States)

Guo, Minjiang; Hu, Hongpu; Lei, Xingyun

2017-01-01

Research Purpose: establish health management ontology for analysis of health statistic data. Proposed Methods: this paper established health management ontology based on the analysis of the concepts in China Health Statistics Yearbook, and used protégé to define the syntactic and semantic structure of health statistical data. six classes of top-level ontology concepts and their subclasses had been extracted and the object properties and data properties were defined to establish the construction of these classes. By ontology instantiation, we can integrate multi-source heterogeneous data and enable administrators to have an overall understanding and analysis of the health statistic data. ontology technology provides a comprehensive and unified information integration structure of the health management domain and lays a foundation for the efficient analysis of multi-source and heterogeneous health system management data and enhancement of the management efficiency.

Explorations in Statistics: The Analysis of Change

Science.gov (United States)

Curran-Everett, Douglas; Williams, Calvin L.

2015-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This tenth installment of "Explorations in Statistics" explores the analysis of a potential change in some physiological response. As researchers, we often express absolute change as percent change so we can…

Enhancement of MS Signal Processing For Improved Cancer Biomarker Discovery

Science.gov (United States)

Si, Qian

Technological advances in proteomics have shown great potential in detecting cancer at the earliest stages. One way is to use the time of flight mass spectroscopy to identify biomarkers, or early disease indicators related to the cancer. Pattern analysis of time of flight mass spectra data from blood and tissue samples gives great hope for the identification of potential biomarkers among the complex mixture of biological and chemical samples for the early cancer detection. One of the keys issues is the pre-processing of raw mass spectra data. A lot of challenges need to be addressed: unknown noise character associated with the large volume of data, high variability in the mass spectroscopy measurements, and poorly understood signal background and so on. This dissertation focuses on developing statistical algorithms and creating data mining tools for computationally improved signal processing for mass spectrometry data. I have introduced an advanced accurate estimate of the noise model and a half-supervised method of mass spectrum data processing which requires little knowledge about the data.

Common pitfalls in statistical analysis: “P” values, statistical significance and confidence intervals

Science.gov (United States)

Ranganathan, Priya; Pramesh, C. S.; Buyse, Marc

2015-01-01

In the second part of a series on pitfalls in statistical analysis, we look at various ways in which a statistically significant study result can be expressed. We debunk some of the myths regarding the ‘P’ value, explain the importance of ‘confidence intervals’ and clarify the importance of including both values in a paper PMID:25878958

Predictive inference for best linear combination of biomarkers subject to limits of detection.

Science.gov (United States)

Coolen-Maturi, Tahani

2017-08-15

Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

New Insights into the Evolution of the Human Diet from Faecal Biomarker Analysis in Wild Chimpanzee and Gorilla Faeces.

Directory of Open Access Journals (Sweden)

Ainara Sistiaga

Full Text Available Our understanding of early human diets is based on reconstructed biomechanics of hominin jaws, bone and teeth isotopic data, tooth wear patterns, lithic, taphonomic and zooarchaeological data, which do not provide information about the relative amounts of different types of foods that contributed most to early human diets. Faecal biomarkers are proving to be a valuable tool in identifying relative proportions of plant and animal tissues in Palaeolithic diets. A limiting factor in the application of the faecal biomarker approach is the striking absence of data related to the occurrence of faecal biomarkers in non-human primate faeces. In this study we explored the nature and proportions of sterols and stanols excreted by our closest living relatives. This investigation reports the first faecal biomarker data for wild chimpanzee (Pan troglodytes and mountain gorilla (Gorilla beringei. Our results suggest that the chemometric analysis of faecal biomarkers is a useful tool for distinguishing between NHP and human faecal matter, and hence, it could provide information for palaeodietary research and early human diets.

New Insights into the Evolution of the Human Diet from Faecal Biomarker Analysis in Wild Chimpanzee and Gorilla Faeces.

Science.gov (United States)

Sistiaga, Ainara; Wrangham, Richard; Rothman, Jessica M; Summons, Roger E

2015-01-01

Our understanding of early human diets is based on reconstructed biomechanics of hominin jaws, bone and teeth isotopic data, tooth wear patterns, lithic, taphonomic and zooarchaeological data, which do not provide information about the relative amounts of different types of foods that contributed most to early human diets. Faecal biomarkers are proving to be a valuable tool in identifying relative proportions of plant and animal tissues in Palaeolithic diets. A limiting factor in the application of the faecal biomarker approach is the striking absence of data related to the occurrence of faecal biomarkers in non-human primate faeces. In this study we explored the nature and proportions of sterols and stanols excreted by our closest living relatives. This investigation reports the first faecal biomarker data for wild chimpanzee (Pan troglodytes) and mountain gorilla (Gorilla beringei). Our results suggest that the chemometric analysis of faecal biomarkers is a useful tool for distinguishing between NHP and human faecal matter, and hence, it could provide information for palaeodietary research and early human diets.

Exhaled breath and oral cavity VOCs as potential biomarkers in oral cancer patients.

Science.gov (United States)

Bouza, M; Gonzalez-Soto, J; Pereiro, R; de Vicente, J C; Sanz-Medel, A

2017-03-01

Corporal mechanisms attributed to cancer, such as oxidative stress or the action of cytochrome P450 enzymes, seem to be responsible for the generation of a variety of volatile organic compounds (VOCs) that could be used as non-invasive diagnosis biomarkers. The present work presents an attempt to use VOCs from exhaled breath and oral cavity air as biomarkers for oral squamous cell carcinoma (OSCC) patients. A total of 52 breath samples were collected (in 3 L Tedlar bags) from 26 OSCC patients and 26 cancer-free controls. The samples were analyzed using solid-phase microextraction followed by gas chromatography-mass spectrometry detection. Different statistical strategies (e.g., Icoshift, SIMCA, LDA, etc) were used to classify the analytical data. Results revealed that compounds such as undecane, dodecane, decanal, benzaldehyde, 3,7-dimethyl undecane, 4,5-dimethyl nonane, 1-octene, and hexadecane had relevance as possible biomarkers for OSCC. LDA classification with these compounds showed well-defined clusters for patients and controls (non-smokers and smokers). In addition to breath analysis, preliminary studies were carried out to evaluate the possibility of lesion-surrounded air (analyzed OSCC tumors are in the oral cavity) as a source of biomarkers. The oral cavity location of the squamous cell carcinoma tumors constitutes an opportunity to non-invasively collect the air surrounding the lesion. Small quantities (20 ml) of air collected in the oral cavity were analyzed using the above methodology. Results showed that aldehydes present in the oral cavity might constitute potential OSCC biomarkers.

TECHNIQUE OF THE STATISTICAL ANALYSIS OF INVESTMENT APPEAL OF THE REGION

Directory of Open Access Journals (Sweden)

А. А. Vershinina

2014-01-01

Full Text Available The technique of the statistical analysis of investment appeal of the region is given in scientific article for direct foreign investments. Definition of a technique of the statistical analysis is given, analysis stages reveal, the mathematico-statistical tools are considered.

Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification

Science.gov (United States)

Book Chapter 18, titled Application in pesticide analysis: Liquid chromatography - A review of the state of science for biomarker discovery and identification, will be published in the book titled High Performance Liquid Chromatography in Pesticide Residue Analysis (Part of the C...

Multiple protein biomarker assessment for recombinant bovine somatotropin (rbST abuse in cattle.

Directory of Open Access Journals (Sweden)

Susann K J Ludwig

Full Text Available Biomarker profiling, as a rapid screening approach for detection of hormone abuse, requires well selected candidate biomarkers and a thorough in vivo biomarker evaluation as previously done for detection of growth hormone doping in athletes. The bovine equivalent of growth hormone, called recombinant bovine somatotropin (rbST is (illegally administered to enhance milk production in dairy cows. In this study, first a generic sample pre-treatment and 4-plex flow cytometric immunoassay (FCIA were developed for simultaneous measurement of four candidate biomarkers selected from literature: insulin-like growth factor 1 (IGF-1, its binding protein 2 (IGFBP2, osteocalcin and endogenously produced antibodies against rbST. Next, bovine serum samples from two extensive controlled rbST animal treatment studies were used for in vivo validation and biomarker evaluation. Finally, advanced statistic tools were tested for the assessment of biomarker combination quality aiming to correctly identify rbST-treated animals. The statistical prediction tool k-nearest neighbours using a combination of the biomarkers osteocalcin and endogenously produced antibodies against rbST proved to be very reliable and correctly predicted 95% of the treated samples starting from the second rbST injection until the end of the treatment period and even thereafter. With the same biomarker combination, only 12% of untreated animals appeared false-positive. This reliability meets the requirements of Commission Decision 2002/657/EC for screening methods in veterinary control. From the results of this multidisciplinary study, it is concluded that the osteocalcin - anti-rbST-antibodies combination represent fit-for-purpose biomarkers for screening of rbST abuse in dairy cattle and can be reliably measured in both the developed 4-plex FCIA as well as in a cost-effective 2-plex microsphere-based binding assay. This screening method can be incorporated in routine veterinary monitoring

Statistical analysis of network data with R

CERN Document Server

Kolaczyk, Eric D

2014-01-01

Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Mass spectrum analysis of serum biomarker proteins from patients with schizophrenia.

Science.gov (United States)

Zhou, Na; Wang, Jie; Yu, Yaqin; Shi, Jieping; Li, Xiaokun; Xu, Bin; Yu, Qiong

2014-05-01

Diagnosis of schizophrenia does not have a clear objective test at present, so we aimed to identify the potential biomarkers for the diagnosis of schizophrenia by comparison of serum protein profiling between first-episode schizophrenia patients and healthy controls. The combination of a magnetic bead separation system with matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF-MS) was used to analyze the serum protein spectra of 286 first-episode patients with schizophrenia, 41 chronic disease patients and 304 healthy controls. FlexAnlysis 3.0 and ClinProTools(TM) 2.1 software was used to establish a diagnostic model for schizophrenia. The results demonstrated that 10 fragmented peptides demonstrated an optimal discriminatory performance. Among these fragmented peptides, the peptide with m/z 1206.58 was identified as a fragment of fibrinopeptide A. Receiver operating characteristic analysis for m/z 1206.58 showed that the area under the curve was 0.981 for schizophrenia vs healthy controls, and 0.999 for schizophrenia vs other chronic disease controls. From our result, we consider that the analysis of serum protein spectrum using the magnetic bead separation system and MALDI-TOF/TOF-MS is an objective diagnostic tool. We conclude that fibrinopeptide A has the potential to be a biomarker for diagnosis of schizophrenia. This protein may also help to elucidate schizophrenia disease pathogenesis. Copyright © 2013 John Wiley & Sons, Ltd.

Serum and Plasma Metabolomic Biomarkers for Lung Cancer.

Science.gov (United States)

Kumar, Nishith; Shahjaman, Md; Mollah, Md Nurul Haque; Islam, S M Shahinul; Hoque, Md Aminul

2017-01-01

In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student׳s t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker.

Alzheimer Disease Biomarkers as Outcome Measures for Clinical Trials in MCI.

Science.gov (United States)

Caroli, Anna; Prestia, Annapaola; Wade, Sara; Chen, Kewei; Ayutyanont, Napatkamon; Landau, Susan M; Madison, Cindee M; Haense, Cathleen; Herholz, Karl; Reiman, Eric M; Jagust, William J; Frisoni, Giovanni B

2015-01-01

The aim of this study was to compare the performance and power of the best-established diagnostic biological markers as outcome measures for clinical trials in patients with mild cognitive impairment (MCI). Magnetic resonance imaging, F-18 fluorodeoxyglucose positron emission tomography markers, and Alzheimer's Disease Assessment Scale-cognitive subscale were compared in terms of effect size and statistical power over different follow-up periods in 2 MCI groups, selected from Alzheimer's Disease Neuroimaging Initiative data set based on cerebrospinal fluid (abnormal cerebrospinal fluid Aβ1-42 concentration-ABETA+) or magnetic resonance imaging evidence of Alzheimer disease (positivity to hippocampal atrophy-HIPPO+). Biomarkers progression was modeled through mixed effect models. Scaled slope was chosen as measure of effect size. Biomarkers power was estimated using simulation algorithms. Seventy-four ABETA+ and 51 HIPPO+ MCI patients were included in the study. Imaging biomarkers of neurodegeneration, especially MR measurements, showed highest performance. For all biomarkers and both MCI groups, power increased with increasing follow-up time, irrespective of biomarker assessment frequency. These findings provide information about biomarker enrichment and outcome measurements that could be employed to reduce MCI patient samples and treatment duration in future clinical trials.

Metabolomics of Hydrazine-Induced Hepatotoxicity in Rats for Discovering Potential Biomarkers

Directory of Open Access Journals (Sweden)

Zhuoling An

2018-01-01

Full Text Available Metabolic pathway disturbances associated with drug-induced liver injury remain unsatisfactorily characterized. Diagnostic biomarkers for hepatotoxicity have been used to minimize drug-induced liver injury and to increase the clinical safety. A metabolomics strategy using rapid-resolution liquid chromatography/tandem mass spectrometry (RRLC-MS/MS analyses and multivariate statistics was implemented to identify potential biomarkers for hydrazine-induced hepatotoxicity. The global serum and urine metabolomics of 30 hydrazine-treated rats at 24 or 48 h postdosing and 24 healthy rats were characterized by a metabolomics approach. Multivariate statistical data analyses and receiver operating characteristic (ROC curves were performed to identify the most significantly altered metabolites. The 16 most significant potential biomarkers were identified to be closely related to hydrazine-induced liver injury. The combination of these biomarkers had an area under the curve (AUC > 0.85, with 100% specificity and sensitivity, respectively. This high-quality classification group included amino acids and their derivatives, glutathione metabolites, vitamins, fatty acids, intermediates of pyrimidine metabolism, and lipids. Additionally, metabolomics pathway analyses confirmed that phenylalanine, tyrosine, and tryptophan biosynthesis as well as tyrosine metabolism had great interactions with hydrazine-induced liver injury in rats. These discriminating metabolites might be useful in understanding the pathogenesis mechanisms of liver injury and provide good prospects for drug-induced liver injury diagnosis clinically.

The utility of biomarkers in hepatocellular carcinoma: review of urine-based 1H-NMR studies – what the clinician needs to know

Directory of Open Access Journals (Sweden)

Cartlidge CR

2017-11-01

Full Text Available Caroline R Cartlidge,1 M R Abellona U,2 Alzhraa M A Alkhatib,2 Simon D Taylor-Robinson1 1Department of Surgery and Cancer, Liver Unit, Division of Digestive Health, 2Department of Surgery and Cancer, Division of Computational and Systems Medicine, Faculty of Medicine, Imperial College London, London, UK Abstract: Hepatocellular carcinoma (HCC is the fifth most common malignancy, the third most common cause of cancer death, and the most common primary liver cancer. Overall, there is a need for more reliable biomarkers for HCC, as those currently available lack sensitivity and specificity. For example, the current gold-standard biomarker, serum alpha-fetoprotein, has a sensitivity of roughly only 70%. Cancer cells have different characteristic metabolic signatures in biofluids, compared to healthy cells; therefore, metabolite analysis in blood or urine should lead to the detection of suitable candidates for the detection of HCC. With the advent of metabonomics, this has increased the potential for new biomarker discovery. In this article, we look at approaches used to identify biomarkers of HCC using proton nuclear magnetic resonance (1H-NMR spectroscopy of urine samples. The various multivariate statistical analysis techniques used are explained, and the process of biomarker identification is discussed, with a view to simplifying the knowledge base for the average clinician. Keywords: hepatocellular carcinoma, biomarkers, metabonomics, urine, proton nuclear magnetic resonance spectroscopy, 1H-NMR 

A proteomic analysis identifies candidate early biomarkers to predict ovarian hyperstimulation syndrome in polycystic ovarian syndrome patients.

Science.gov (United States)

Wu, Lan; Sun, Yazhou; Wan, Jun; Luan, Ting; Cheng, Qing; Tan, Yong

2017-07-01

Ovarian hyperstimulation syndrome (OHSS) is a potentially life‑threatening, iatrogenic complication that occurs during assisted reproduction. Polycystic ovarian syndrome (PCOS) significantly increases the risk of OHSS during controlled ovarian stimulation. Therefore, a more effective early prediction technique is required in PCOS patients. Quantitative proteomic analysis of serum proteins indicates the potential diagnostic value for disease. In the present study, the authors revealed the differentially expressed proteins in OHSS patients with PCOS as new diagnostic biomarkers. The promising proteins obtained from liquid chromatography‑mass spectrometry were subjected to ELISA and western blotting assay for further confirmation. A total of 57 proteins were identified with significant difference, of which 29 proteins were upregulated and 28 proteins were downregulated in OHSS patients. Haptoglobin, fibrinogen and lipoprotein lipase were selected as candidate biomarkers. Receiver operating characteristic curve analysis demonstrated all three proteins may have potential as biomarkers to discriminate OHSS in PCOS patients. Haptoglobin, fibrinogen and lipoprotein lipase have never been reported as a predictive marker of OHSS in PCOS patients, and their potential roles in OHSS occurrence deserve further studies. The proteomic results reported in the present study may gain deeper insights into the pathophysiology of OHSS.

Blood Biomarkers of Chronic Inflammation in Gulf War Illness.

Directory of Open Access Journals (Sweden)

Gerhard J Johnson

Full Text Available More than twenty years following the end of the 1990-1991 Gulf War it is estimated that approximately 300,000 veterans of this conflict suffer from an unexplained chronic, multi-system disorder known as Gulf War Illness (GWI. The etiology of GWI may be exposure to chemical toxins, but it remains only partially defined, and its case definition is based only on symptoms. Objective criteria for the diagnosis of GWI are urgently needed for diagnosis and therapeutic research.This study was designed to determine if blood biomarkers could provide objective criteria to assist diagnosis of GWI.A surveillance study of 85 Gulf War Veteran volunteers identified from the Department of Veterans Affairs Minnesota Gulf War registry was performed. All subjects were deployed to the Gulf War. Fifty seven subjects had GWI defined by CDC criteria, and 28 did not have symptomatic criteria for a diagnosis of GWI. Statistical analyses were performed on peripheral blood counts and assays of 61 plasma proteins using the Mann-Whitney rank sum test to compare biomarker distributions and stepwise logistic regression to formulate a diagnostic model.Lymphocyte, monocyte, neutrophil, and platelet counts were higher in GWI subjects. Six serum proteins associated with inflammation were significantly different in GWI subjects. A diagnostic model of three biomarkers-lymphocytes, monocytes, and C reactive protein-had a predicted probability of 90% (CI 76-90% for diagnosing GWI when the probability of having GWI was above 70%.The results of the current study indicate that inflammation is a component of the pathobiology of GWI. Analysis of the data resulted in a model utilizing three readily measurable biomarkers that appears to significantly augment the symptom-based case definition of GWI. These new observations are highly relevant to the diagnosis of GWI, and to therapeutic trials.

Semiclassical analysis, Witten Laplacians, and statistical mechanis

CERN Document Server

Helffer, Bernard

2002-01-01

This important book explains how the technique of Witten Laplacians may be useful in statistical mechanics. It considers the problem of analyzing the decay of correlations, after presenting its origin in statistical mechanics. In addition, it compares the Witten Laplacian approach with other techniques, such as the transfer matrix approach and its semiclassical analysis. The author concludes by providing a complete proof of the uniform Log-Sobolev inequality. Contents: Witten Laplacians Approach; Problems in Statistical Mechanics with Discrete Spins; Laplace Integrals and Transfer Operators; S

A novel statistic for genome-wide interaction analysis.

Directory of Open Access Journals (Sweden)

Xuesen Wu

2010-09-01

Full Text Available Although great progress in genome-wide association studies (GWAS has been made, the significant SNP associations identified by GWAS account for only a few percent of the genetic variance, leading many to question where and how we can find the missing heritability. There is increasing interest in genome-wide interaction analysis as a possible source of finding heritability unexplained by current GWAS. However, the existing statistics for testing interaction have low power for genome-wide interaction analysis. To meet challenges raised by genome-wide interactional analysis, we have developed a novel statistic for testing interaction between two loci (either linked or unlinked. The null distribution and the type I error rates of the new statistic for testing interaction are validated using simulations. Extensive power studies show that the developed statistic has much higher power to detect interaction than classical logistic regression. The results identified 44 and 211 pairs of SNPs showing significant evidence of interactions with FDR<0.001 and 0.001analysis is a valuable tool for finding remaining missing heritability unexplained by the current GWAS, and the developed novel statistic is able to search significant interaction between SNPs across the genome. Real data analysis showed that the results of genome-wide interaction analysis can be replicated in two independent studies.

Systems biology and biomarker discovery

Energy Technology Data Exchange (ETDEWEB)

Rodland, Karin D.

2010-12-01

Medical practitioners have always relied on surrogate markers of inaccessible biological processes to make their diagnosis, whether it was the pallor of shock, the flush of inflammation, or the jaundice of liver failure. Obviously, the current implementation of biomarkers for disease is far more sophisticated, relying on highly reproducible, quantitative measurements of molecules that are often mechanistically associated with the disease in question, as in glycated hemoglobin for the diagnosis of diabetes [1] or the presence of cardiac troponins in the blood for confirmation of myocardial infarcts [2]. In cancer, where the initial symptoms are often subtle and the consequences of delayed diagnosis often drastic for disease management, the impetus to discover readily accessible, reliable, and accurate biomarkers for early detection is compelling. Yet despite years of intense activity, the stable of clinically validated, cost-effective biomarkers for early detection of cancer is pathetically small and still dominated by a handful of markers (CA-125, CEA, PSA) first discovered decades ago. It is time, one could argue, for a fresh approach to the discovery and validation of disease biomarkers, one that takes full advantage of the revolution in genomic technologies and in the development of computational tools for the analysis of large complex datasets. This issue of Disease Markers is dedicated to one such new approach, loosely termed the 'Systems Biology of Biomarkers'. What sets the Systems Biology approach apart from other, more traditional approaches, is both the types of data used, and the tools used for data analysis - and both reflect the revolution in high throughput analytical methods and high throughput computing that has characterized the start of the twenty first century.

Mass Spectrometry-Based Biomarker Discovery.

Science.gov (United States)

Zhou, Weidong; Petricoin, Emanuel F; Longo, Caterina

2017-01-01

The discovery of candidate biomarkers within the entire proteome is one of the most important and challenging goals in proteomic research. Mass spectrometry-based proteomics is a modern and promising technology for semiquantitative and qualitative assessment of proteins, enabling protein sequencing and identification with exquisite accuracy and sensitivity. For mass spectrometry analysis, protein extractions from tissues or body fluids and subsequent protein fractionation represent an important and unavoidable step in the workflow for biomarker discovery. Following extraction of proteins, the protein mixture must be digested, reduced, alkylated, and cleaned up prior to mass spectrometry. The aim of our chapter is to provide comprehensible and practical lab procedures for sample digestion, protein fractionation, and subsequent mass spectrometry analysis.

Biomarkers to Measure Treatment Effects in Alzheimer's Disease: What Should We Look for?

Directory of Open Access Journals (Sweden)

Kenneth Rockwood

2011-01-01

Full Text Available It is often surprisingly difficult to tell whether a treatment for Alzheimer's disease is effective. Biomarkers might offer the potential of a quantifiable objective measure of treatment effectiveness. This paper suggests several criteria by which biomarkers might be evaluated as outcomes measures. These include biological plausibility, statistical significance, dose dependence, convergence across measures, and replicability. If biomarkers can meet these criteria, then, pending regulatory approval, they may have a role in the evaluation of treatment effectiveness in Alzheimer's disease. If not, their usefulness may be in supplementing, but not supplanting, clinical profiles of treatment effects.

A statistical approach to plasma profile analysis

International Nuclear Information System (INIS)

Kardaun, O.J.W.F.; McCarthy, P.J.; Lackner, K.; Riedel, K.S.

1990-05-01

A general statistical approach to the parameterisation and analysis of tokamak profiles is presented. The modelling of the profile dependence on both the radius and the plasma parameters is discussed, and pertinent, classical as well as robust, methods of estimation are reviewed. Special attention is given to statistical tests for discriminating between the various models, and to the construction of confidence intervals for the parameterised profiles and the associated global quantities. The statistical approach is shown to provide a rigorous approach to the empirical testing of plasma profile invariance. (orig.)

Study designs, use of statistical tests, and statistical analysis software choice in 2015: Results from two Pakistani monthly Medline indexed journals.

Science.gov (United States)

Shaikh, Masood Ali

2017-09-01

Assessment of research articles in terms of study designs used, statistical tests applied and the use of statistical analysis programmes help determine research activity profile and trends in the country. In this descriptive study, all original articles published by Journal of Pakistan Medical Association (JPMA) and Journal of the College of Physicians and Surgeons Pakistan (JCPSP), in the year 2015 were reviewed in terms of study designs used, application of statistical tests, and the use of statistical analysis programmes. JPMA and JCPSP published 192 and 128 original articles, respectively, in the year 2015. Results of this study indicate that cross-sectional study design, bivariate inferential statistical analysis entailing comparison between two variables/groups, and use of statistical software programme SPSS to be the most common study design, inferential statistical analysis, and statistical analysis software programmes, respectively. These results echo previously published assessment of these two journals for the year 2014.

Statistical analysis of brake squeal noise

Science.gov (United States)

Oberst, S.; Lai, J. C. S.

2011-06-01

Despite substantial research efforts applied to the prediction of brake squeal noise since the early 20th century, the mechanisms behind its generation are still not fully understood. Squealing brakes are of significant concern to the automobile industry, mainly because of the costs associated with warranty claims. In order to remedy the problems inherent in designing quieter brakes and, therefore, to understand the mechanisms, a design of experiments study, using a noise dynamometer, was performed by a brake system manufacturer to determine the influence of geometrical parameters (namely, the number and location of slots) of brake pads on brake squeal noise. The experimental results were evaluated with a noise index and ranked for warm and cold brake stops. These data are analysed here using statistical descriptors based on population distributions, and a correlation analysis, to gain greater insight into the functional dependency between the time-averaged friction coefficient as the input and the peak sound pressure level data as the output quantity. The correlation analysis between the time-averaged friction coefficient and peak sound pressure data is performed by applying a semblance analysis and a joint recurrence quantification analysis. Linear measures are compared with complexity measures (nonlinear) based on statistics from the underlying joint recurrence plots. Results show that linear measures cannot be used to rank the noise performance of the four test pad configurations. On the other hand, the ranking of the noise performance of the test pad configurations based on the noise index agrees with that based on nonlinear measures: the higher the nonlinearity between the time-averaged friction coefficient and peak sound pressure, the worse the squeal. These results highlight the nonlinear character of brake squeal and indicate the potential of using nonlinear statistical analysis tools to analyse disc brake squeal.

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Analysis of room transfer function and reverberant signal statistics

DEFF Research Database (Denmark)

Georganti, Eleftheria; Mourjopoulos, John; Jacobsen, Finn

2008-01-01

For some time now, statistical analysis has been a valuable tool in analyzing room transfer functions (RTFs). This work examines existing statistical time-frequency models and techniques for RTF analysis (e.g., Schroeder's stochastic model and the standard deviation over frequency bands for the RTF...... magnitude and phase). RTF fractional octave smoothing, as with 1-slash 3 octave analysis, may lead to RTF simplifications that can be useful for several audio applications, like room compensation, room modeling, auralisation purposes. The aim of this work is to identify the relationship of optimal response...... and the corresponding ratio of the direct and reverberant signal. In addition, this work examines the statistical quantities for speech and audio signals prior to their reproduction within rooms and when recorded in rooms. Histograms and other statistical distributions are used to compare RTF minima of typical...

The YKL-40 protein is a potential biomarker for COPD: a meta-analysis and systematic review

Directory of Open Access Journals (Sweden)

Tong X

2018-01-01

Full Text Available Xiang Tong,1 Dongguang Wang,1 Sitong Liu,1 Yao Ma,1,2 Zhenzhen Li,3 Panwen Tian,1,4 Hong Fan1 1Department of Respiratory and Critical Care Medicine, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, People’s Republic of China; 2The Center of Gerontology and Geriatrics, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, People’s Republic of China; 3Health Management Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, People’s Republic of China; 4Lung Cancer Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, People’s Republic of China Background: Many studies have found that YKL-40 may play an important pathogenic role in COPD. However, the results of these studies were inconsistent. Therefore, we performed a systematic review and meta-analysis to investigate the role of YKL-40 in COPD. Methods: We performed a systematic literature search in many database and commercial internet search engines to identify studies involving the role of YKL-40 in patients with COPD. The standardized mean difference (SMD and Fisher’s Z-value with its 95% confidence interval (CI were used to investigate the effect sizes. Results: A total of 15 eligible articles including 16 case–control/cohort groups were included in the meta-analysis. The results indicated that the serum YKL-40 levels in patients with COPD were significantly higher than those in healthy controls (SMD =1.58, 95% CI =0.68–2.49, P=0.001, and it was correlated with lung function (pooled r=-0.32; Z=-0.33; P<0.001. The results of subgroup analysis found that the serum YKL-40 levels were statistically different between the exacerbation group and the stable group in patients with COPD (SMD =1.55, 95% CI =0.81–2.30, P<0.001. Moreover, the results indicated that the sputum YKL-40 levels in patients with COPD were also significantly higher than those in healthy

USE OF MULTIPARAMETER ANALYSIS OF LABORATORY BIOMARKERS TO ASSESS RHEUMATOID ARTHRITIS ACTIVITY

Directory of Open Access Journals (Sweden)

A. A. Novikov

2015-01-01

Full Text Available The key component in the management of patients with rheumatoid arthritis (RA is regular control of RA activity. The quantitative assessment of a patient’s status allows the development of standardized indications for anti-rheumatic therapy.Objective: to identify the laboratory biomarkers able to reflect RA activity.Subjects and methods. Fifty-eight patients with RA and 30 age- and sex-matched healthy donors were examined. The patients were divided into high/moderate and mild disease activity groups according to DAS28. The serum concentrations of 30 biomarkers were measured using immunonephelometric assay, enzyme immunoassay, and xMAP technology.Results and discussion. Multivariate analysis could identify the factors mostly related to high/moderate RA activity according to DAS28, such as fibroblast growth factor-2, monocyte chemoattractant protein-1, interleukins (IL 1α, 6, and 15, and tumor necrosis factor-α and could create a prognostic model for RA activity assessment. ROC analysis has shown that this model has excellent diagnostic efficiency in differentiating high/moderate versus low RA activity.Conclusion. To create a subjective assessment-independent immunological multiparameter index of greater diagnostic accuracy than the laboratory parameters routinely used in clinical practice may be a qualitatively new step in assessing and monitoring RA activity.

Quantitative proteomic analysis by iTRAQ® for the identification of candidate biomarkers in ovarian cancer serum

Directory of Open Access Journals (Sweden)

Higgins LeeAnn

2010-06-01

Full Text Available Abstract Background Ovarian cancer is the most lethal gynecologic malignancy, with the majority of cases diagnosed at an advanced stage when treatments are less successful. Novel serum protein markers are needed to detect ovarian cancer in its earliest stage; when detected early, survival rates are over 90%. The identification of new serum biomarkers is hindered by the presence of a small number of highly abundant proteins that comprise approximately 95% of serum total protein. In this study, we used pooled serum depleted of the most highly abundant proteins to reduce the dynamic range of proteins, and thereby enhance the identification of serum biomarkers using the quantitative proteomic method iTRAQ®. Results Medium and low abundance proteins from 6 serum pools of 10 patients each from women with serous ovarian carcinoma, and 6 non-cancer control pools were labeled with isobaric tags using iTRAQ® to determine the relative abundance of serum proteins identified by MS. A total of 220 unique proteins were identified and fourteen proteins were elevated in ovarian cancer compared to control serum pools, including several novel candidate ovarian cancer biomarkers: extracellular matrix protein-1, leucine-rich alpha-2 glycoprotein-1, lipopolysaccharide binding protein-1, and proteoglycan-4. Western immunoblotting validated the relative increases in serum protein levels for several of the proteins identified. Conclusions This study provides the first analysis of immunodepleted serum in combination with iTRAQ® to measure relative protein expression in ovarian cancer patients for the pursuit of serum biomarkers. Several candidate biomarkers were identified which warrant further development.

Identification of the lipid biomarkers from plasma in idiopathic pulmonary fibrosis by Lipidomics.

Science.gov (United States)

Yan, Feng; Wen, Zhensong; Wang, Rui; Luo, Wenling; Du, Yufeng; Wang, Wenjun; Chen, Xianyang

2017-12-06

Idiopathic pulmonary fibrosis (IPF) is an irreversible interstitial pulmonary disease featured by high mortality, chronic and progressive course, and poor prognosis with unclear etiology. Currently, more studies have been focusing on identifying biomarkers to predict the progression of IPF, such as genes, proteins, and lipids. Lipids comprise diverse classes of molecules and play a critical role in cellular energy storage, structure, and signaling. The role of lipids in respiratory diseases, including cystic fibrosis, asthma and chronic obstructive pulmonary disease (COPD) has been investigated intensely in the recent years. The human serum lipid profiles in IPF patients however, have not been thoroughly understood and it will be very helpful if there are available molecular biomarkers, which can be used to monitor the disease progression or provide prognostic information for IPF disease. In this study, we performed the ultraperformance liquid chromatography coupled with quadrupole time of flight mass spectrometry (UPLC-QTOF/MS) to detect the lipid variation and identify biomarker in plasma of IPF patients. The plasma were from 22 IPF patients before received treatment and 18 controls. A total of 507 individual blood lipid species were determined with lipidomics from the 40 plasma samples including 20 types of fatty acid, 159 types of glycerolipids, 221 types of glycerophospholipids, 47 types of sphingolipids, 46 types of sterol lipids, 7 types of prenol lipids, 3 types of saccharolipids, and 4 types of polyketides. By comparing the variations in the lipid metabolite levels in IPF patients, a total of 62 unique lipids were identified by statistical analysis including 24 kinds of glycerophoslipids, 30 kinds of glycerolipids, 3 kinds of sterol lipids, 4 kinds of sphingolipids and 1 kind of fatty acids. Finally, 6 out of 62 discriminating lipids were selected as the potential biomarkers, which are able to differentiate between IPF disease and controls with ROC

Transit safety & security statistics & analysis 2002 annual report (formerly SAMIS)

Science.gov (United States)

2004-12-01

The Transit Safety & Security Statistics & Analysis 2002 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

Transit safety & security statistics & analysis 2003 annual report (formerly SAMIS)

Science.gov (United States)

2005-12-01

The Transit Safety & Security Statistics & Analysis 2003 Annual Report (formerly SAMIS) is a compilation and analysis of mass transit accident, casualty, and crime statistics reported under the Federal Transit Administrations (FTAs) National Tr...

HPLC-UV Analysis Coupled with Chemometry to Identify Phenolic Biomarkers from Medicinal Plants, used as Ingredients in Two Food Supplement Formulas

Directory of Open Access Journals (Sweden)

Raluca Maria Pop

2013-11-01

Full Text Available . High performance liquid chromatography (HPLC with UV detection is nowadays the reference method to identify and quantify the biomarkers of quality and authenticity of plants and food supplements. Seven medicinal plants were collected from wild flora: Taraxacum officinalis (1, Cynara scolimus (2, Silybum marianum (3, Hypericum perforatum (4,  Chelidonium majus (5, Lycopodium clavatum (6 and  Hippophae rhamnoides (7  leaves and fruits.  Two products (A and B were obtained by mixing individual plant powders. Therefore product A was obtained by mixing dandelion, artichoke and milk thistle, 1:1:1 while product B by mixing St John’s wort, Celandine and Wolf’s claw, 1:1:1. The methanolic extracts of individual plants as well as three different extracts of products A and B (using acidulated water, neutral water and acidulated methanol were analyzed using HPLC-UV for their phenolics’ fingerprint and composition. The qualitative (untargeted analysis and quantitative (targeted analysis results were further compared using Principal Component Analysis (PCA in order to identify their specific biomarkers. Thus, quantitative evaluation of individual phenolics in case of individual plants and products A and B extracts, showed specific and significant differences of composition. Both products A and B contained elagic acid as major compound. For product A, good biomarkers were trans-cinnamic, chlorogenic, caffeic and p-coumaric acids, as well silymarin and silibine originating from milk thistle. For product B, good biomarkers were quercetin and kaempherol, gallic and protocatecuic acids, this product being rich in flavonoids. In conclusion, HPLC-UV coupled with PCA analysis proved to be a rapid and useful way to identify the main biomarkers of plants’ authentication, as well of final products’ quality and safety.

Metabonomics in Ulcerative Colitis: Diagnostics, Biomarker Identification, And Insight into the Pathophysiology

DEFF Research Database (Denmark)

Bjerrum, Jacob T; Nielsen, Ole H; Hao, Fuhua

2010-01-01

Nuclear magnetic resonance (NMR) spectroscopy and appropriate multivariate statistical analyses have been employed on mucosal colonic biopsies, colonocytes, lymphocytes, and urine from patients with ulcerative colitis (UC) and controls in order to explore the diagnostic possibilities, define new...... potential biomarkers, and generate a better understanding of the pathophysiology. Samples were collected from patients with active UC (n = 41), quiescent UC (n = 33), and from controls (n = 25) and analyzed by NMR spectroscopy. Data analysis was carried out by principal component analysis and orthogonal......-projection to latent structure-discriminant analysis using the SIMCA P+11 software package (Umetrics, Umea, Sweden) and Matlab environment. Significant differences between controls and active UC were discovered in the metabolic profiles of biopsies and colonocytes. In the biopsies from patients with active UC higher...

Diagnostic significance of microRNAs as novel biomarkers for bladder cancer: a meta-analysis of ten articles.

Science.gov (United States)

Shi, Hong-Bin; Yu, Jia-Xing; Yu, Jian-Xiu; Feng, Zheng; Zhang, Chao; Li, Guang-Yong; Zhao, Rui-Ning; Yang, Xiao-Bo

2017-08-03

Previous studies have revealed the importance of microRNAs' (miRNAs) function as biomarkers in diagnosing human bladder cancer (BC). However, the results are discordant. Consequently, the possibility of miRNAs to be BC biomarkers was summarized in this meta-analysis. In this study, the relevant articles were systematically searched from CBM, PubMed, EMBASE, and Chinese National Knowledge Infrastructure (CNKI). The bivariate model was used to calculate the pooled diagnostic parameters and summary receiver operator characteristic (SROC) curve in this meta-analysis, thereby estimating the whole predictive performance. STATA software was used during the whole analysis. Thirty-one studies from 10 articles, including 1556 cases and 1347 controls, were explored in this meta-analysis. In short, the pooled sensitivity, area under the SROC curve, specificity, positive likelihood ratio, diagnostic odds ratio, and negative likelihood ratio were 0.72 (95%CI 0.66-0.76), 0.80 (0.77-0.84), 0.76 (0.71-0.81), 3.0 (2.4-3.8), 8 (5.0-12.0), and 0.37 (0.30-0.46) respectively. Additionally, sub-group and meta-regression analyses revealed that there were significant differences between ethnicity, miRNA profiling, and specimen sub-groups. These results suggested that Asian population-based studies, multiple-miRNA profiling, and blood-based assays might yield a higher diagnostic accuracy than their counterparts. This meta-analysis demonstrated that miRNAs, particularly multiple miRNAs in the blood, might be novel, useful biomarkers with relatively high sensitivity and specificity and can be used for the diagnosis of BC. However, further prospective studies with more samples should be performed for further validation.

Sensitive Spectroscopic Analysis of Biomarkers in Exhaled Breath

Science.gov (United States)

Bicer, A.; Bounds, J.; Zhu, F.; Kolomenskii, A. A.; Kaya, N.; Aluauee, E.; Amani, M.; Schuessler, H. A.

2018-06-01

We have developed a novel optical setup which is based on a high finesse cavity and absorption laser spectroscopy in the near-IR spectral region. In pilot experiments, spectrally resolved absorption measurements of biomarkers in exhaled breath, such as methane and acetone, were carried out using cavity ring-down spectroscopy (CRDS). With a 172-cm-long cavity, an efficient optical path of 132 km was achieved. The CRDS technique is well suited for such measurements due to its high sensitivity and good spectral resolution. The detection limits for methane of 8 ppbv and acetone of 2.1 ppbv with spectral sampling of 0.005 cm-1 were achieved, which allowed to analyze multicomponent gas mixtures and to observe absorption peaks of 12CH4 and 13CH4. Further improvements of the technique have the potential to realize diagnostics of health conditions based on a multicomponent analysis of breath samples.

Microarray analysis identifies keratin loci as sensitive biomarkers for thyroid hormone disruption in the salamander Ambystoma mexicanum.

Science.gov (United States)

Page, Robert B; Monaghan, James R; Samuels, Amy K; Smith, Jeramiah J; Beachy, Christopher K; Voss, S Randal

2007-02-01

Ambystomatid salamanders offer several advantages for endocrine disruption research, including genomic and bioinformatics resources, an accessible laboratory model (Ambystoma mexicanum), and natural lineages that are broadly distributed among North American habitats. We used microarray analysis to measure the relative abundance of transcripts isolated from A. mexicanum epidermis (skin) after exogenous application of thyroid hormone (TH). Only one gene had a >2-fold change in transcript abundance after 2 days of TH treatment. However, hundreds of genes showed significantly different transcript levels at days 12 and 28 in comparison to day 0. A list of 123 TH-responsive genes was identified using statistical, BLAST, and fold level criteria. Cluster analysis identified two groups of genes with similar transcription patterns: up-regulated versus down-regulated. Most notably, several keratins exhibited dramatic (1000 fold) increases or decreases in transcript abundance. Keratin gene expression changes coincided with morphological remodeling of epithelial tissues. This suggests that keratin loci can be developed as sensitive biomarkers to assay temporal disruptions of larval-to-adult gene expression programs. Our study has identified the first collection of loci that are regulated during TH-induced metamorphosis in a salamander, thus setting the stage for future investigations of TH disruption in the Mexican axolotl and other salamanders of the genus Ambystoma.

Statistical Modelling of Wind Proles - Data Analysis and Modelling

DEFF Research Database (Denmark)

Jónsson, Tryggvi; Pinson, Pierre

The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles.......The aim of the analysis presented in this document is to investigate whether statistical models can be used to make very short-term predictions of wind profiles....

Urinary metabonomics study on toxicity biomarker discovery in rats treated with Xanthii Fructus.

Science.gov (United States)

Lu, Fang; Cao, Min; Wu, Bin; Li, Xu-zhao; Liu, Hong-yu; Chen, Da-zhong; Liu, Shu-min

2013-08-26

Xanthii Fructus (XF) is commonly called "Cang-Erzi" in traditional Chinese medicine (TCM) and widely used for the treatment of sinusitis, headache, rheumatism, and skin itching. However, the clinical utilization of XF is relatively restricted owing to its toxicity. To discover the characteristic potential biomarkers in rats treated with XF by urinary metabonomics. Ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) was applied in the study. The total ion chromatograms obtained from control and different dosage groups were distinguishable by a multivariate statistical analysis method. The greatest difference in metabolic profile was observed between high dosage group and control group, and the metabolic characters in rats treated with XF were perturbed in a dose-dependent manner. The metabolic changes in response for XF treatment were observed in urinary samples, which were revealed by orthogonal projection to latent structures discriminate analysis (OPLS-DA), and 10 metabolites could be served as the potential toxicity biomarkers. In addition, the mechanism associated with the damages of lipid per-oxidation and the metabolic disturbances of fatty acid oxidation were investigated. These results indicate that metabonomics analysis in urinary samples may be useful for predicting the toxicity induced by XF. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Mining biomarker information in biomedical literature

Directory of Open Access Journals (Sweden)

Younesi Erfan

2012-12-01

://www.scaiview.com/scaiview-academia.html. Conclusions The approach presented in this paper demonstrates that using a dedicated biomarker terminology for automated analysis of the scientific literature maybe helpful as an aid to finding biomarker information in text. Successful extraction of candidate biomarkers information from published resources can be considered as the first step towards developing novel hypotheses. These hypotheses will be valuable for the early decision-making in the drug discovery and development process.

Statistical analysis of long term spatial and temporal trends of ...

Indian Academy of Sciences (India)

Statistical analysis of long term spatial and temporal trends of temperature ... CGCM3; HadCM3; modified Mann–Kendall test; statistical analysis; Sutlej basin. ... Water Resources Systems Division, National Institute of Hydrology, Roorkee 247 ...

Bayesian methods for proteomic biomarker development

Directory of Open Access Journals (Sweden)

Belinda Hernández

2015-12-01

In this review we provide an introduction to Bayesian inference and demonstrate some of the advantages of using a Bayesian framework. We summarize how Bayesian methods have been used previously in proteomics and other areas of bioinformatics. Finally, we describe some popular and emerging Bayesian models from the statistical literature and provide a worked tutorial including code snippets to show how these methods may be applied for the evaluation of proteomic biomarkers.

CORSSA: The Community Online Resource for Statistical Seismicity Analysis

Science.gov (United States)

Michael, Andrew J.; Wiemer, Stefan

2010-01-01

Statistical seismology is the application of rigorous statistical methods to earthquake science with the goal of improving our knowledge of how the earth works. Within statistical seismology there is a strong emphasis on the analysis of seismicity data in order to improve our scientific understanding of earthquakes and to improve the evaluation and testing of earthquake forecasts, earthquake early warning, and seismic hazards assessments. Given the societal importance of these applications, statistical seismology must be done well. Unfortunately, a lack of educational resources and available software tools make it difficult for students and new practitioners to learn about this discipline. The goal of the Community Online Resource for Statistical Seismicity Analysis (CORSSA) is to promote excellence in statistical seismology by providing the knowledge and resources necessary to understand and implement the best practices, so that the reader can apply these methods to their own research. This introduction describes the motivation for and vision of CORRSA. It also describes its structure and contents.

Multivariate statistical analysis a high-dimensional approach

CERN Document Server

Serdobolskii, V

2000-01-01

In the last few decades the accumulation of large amounts of in­ formation in numerous applications. has stimtllated an increased in­ terest in multivariate analysis. Computer technologies allow one to use multi-dimensional and multi-parametric models successfully. At the same time, an interest arose in statistical analysis with a de­ ficiency of sample data. Nevertheless, it is difficult to describe the recent state of affairs in applied multivariate methods as satisfactory. Unimprovable (dominating) statistical procedures are still unknown except for a few specific cases. The simplest problem of estimat­ ing the mean vector with minimum quadratic risk is unsolved, even for normal distributions. Commonly used standard linear multivari­ ate procedures based on the inversion of sample covariance matrices can lead to unstable results or provide no solution in dependence of data. Programs included in standard statistical packages cannot process 'multi-collinear data' and there are no theoretical recommen­ ...

Alpha-1 antitrypsin and granulocyte colony-stimulating factor as serum biomarkers of disease severity in ulcerative colitis

DEFF Research Database (Denmark)

Soendergaard, Christoffer; Nielsen, Ole Haagen; Seidelin, Jakob Benedict

2015-01-01

BACKGROUND: Initial assessment of patients with ulcerative colitis (UC) is challenging and relies on apparent clinical symptoms and measurements of surrogate markers (e.g., C-reactive protein [CRP] or similar acute phase proteins). As CRP only reliably identifies patients with severe disease, novel...... (Mayo score) and from 40 healthy controls were analyzed by multiplex enzyme-linked immunosorbent assay for 78 potential disease biomarkers. Using the statistical software SIMCA-P+ and GraphPad Prism, multivariate statistical analyses were conducted to identify a limited number of biomarkers to assess...

MicroRNAs as biomarkers for early breast cancer diagnosis, prognosis and therapy prediction.

Science.gov (United States)

Nassar, Farah J; Nasr, Rihab; Talhouk, Rabih

2017-04-01

Breast cancer is a major health problem that affects one in eight women worldwide. As such, detecting breast cancer at an early stage anticipates better disease outcome and prolonged patient survival. Extensive research has shown that microRNA (miRNA) are dysregulated at all stages of breast cancer. miRNA are a class of small noncoding RNA molecules that can modulate gene expression and are easily accessible and quantifiable. This review highlights miRNA as diagnostic, prognostic and therapy predictive biomarkers for early breast cancer with an emphasis on the latter. It also examines the challenges that lie ahead in their use as biomarkers. Noteworthy, this review addresses miRNAs reported in patients with early breast cancer prior to chemotherapy, radiotherapy, surgical procedures or distant metastasis (unless indicated otherwise). In this context, miRNA that are mentioned in this review were significantly modulated using more than one statistical test and/or validated by at least two studies. A standardized protocol for miRNA assessment is proposed starting from sample collection to data analysis that ensures comparative analysis of data and reproducibility of results. Copyright © 2016 Elsevier Inc. All rights reserved.

Inference of Causal Relationships between Biomarkers and Outcomes in High Dimensions

Directory of Open Access Journals (Sweden)

Felix Agakov

2011-12-01

Full Text Available We describe a unified computational framework for learning causal dependencies between genotypes, biomarkers, and phenotypic outcomes from large-scale data. In contrast to previous studies, our framework allows for noisy measurements, hidden confounders, missing data, and pleiotropic effects of genotypes on outcomes. The method exploits the use of genotypes as “instrumental variables” to infer causal associations between phenotypic biomarkers and outcomes, without requiring the assumption that genotypic effects are mediated only through the observed biomarkers. The framework builds on sparse linear methods developed in statistics and machine learning and modified here for inferring structures of richer networks with latent variables. Where the biomarkers are gene transcripts, the method can be used for fine mapping of quantitative trait loci (QTLs detected in genetic linkage studies. To demonstrate our method, we examined effects of gene transcript levels in the liver on plasma HDL cholesterol levels in a sample of 260 mice from a heterogeneous stock.

Applied multivariate statistical analysis

CERN Document Server

Härdle, Wolfgang Karl

2015-01-01

Focusing on high-dimensional applications, this 4th edition presents the tools and concepts used in multivariate data analysis in a style that is also accessible for non-mathematicians and practitioners.  It surveys the basic principles and emphasizes both exploratory and inferential statistics; a new chapter on Variable Selection (Lasso, SCAD and Elastic Net) has also been added.  All chapters include practical exercises that highlight applications in different multivariate data analysis fields: in quantitative financial studies, where the joint dynamics of assets are observed; in medicine, where recorded observations of subjects in different locations form the basis for reliable diagnoses and medication; and in quantitative marketing, where consumers’ preferences are collected in order to construct models of consumer behavior.  All of these examples involve high to ultra-high dimensions and represent a number of major fields in big data analysis. The fourth edition of this book on Applied Multivariate ...

Definitions and validation criteria for biomarkers and surrogate endpoints: development and testing of a quantitative hierarchical levels of evidence schema.

Science.gov (United States)

Lassere, Marissa N; Johnson, Kent R; Boers, Maarten; Tugwell, Peter; Brooks, Peter; Simon, Lee; Strand, Vibeke; Conaghan, Philip G; Ostergaard, Mikkel; Maksymowych, Walter P; Landewe, Robert; Bresnihan, Barry; Tak, Paul-Peter; Wakefield, Richard; Mease, Philip; Bingham, Clifton O; Hughes, Michael; Altman, Doug; Buyse, Marc; Galbraith, Sally; Wells, George

2007-03-01

There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Our objective was to review the literature on biomarkers and surrogates to develop a hierarchical schema that systematically evaluates and ranks the surrogacy status of biomarkers and surrogates; and to obtain feedback from stakeholders. After a systematic search of Medline and Embase on biomarkers, surrogate (outcomes, endpoints, markers, indicators), intermediate endpoints, and leading indicators, a quantitative surrogate validation schema was developed and subsequently evaluated at a stakeholder workshop. The search identified several classification schema and definitions. Components of these were incorporated into a new quantitative surrogate validation level of evidence schema that evaluates biomarkers along 4 domains: Target, Study Design, Statistical Strength, and Penalties. Scores derived from 3 domains the Target that the marker is being substituted for, the Design of the (best) evidence, and the Statistical strength are additive. Penalties are then applied if there is serious counterevidence. A total score (0 to 15) determines the level of evidence, with Level 1 the strongest and Level 5 the weakest. It was proposed that the term "surrogate" be restricted to markers attaining Levels 1 or 2 only. Most stakeholders agreed that this operationalization of the National Institutes of Health definitions of biomarker, surrogate endpoint, and clinical endpoint was useful. Further development and application of this schema provides incentives and guidance for effective biomarker and surrogate endpoint research, and more efficient drug discovery, development, and approval.

Statistical evaluation of vibration analysis techniques

Science.gov (United States)

Milner, G. Martin; Miller, Patrice S.

1987-01-01

An evaluation methodology is presented for a selection of candidate vibration analysis techniques applicable to machinery representative of the environmental control and life support system of advanced spacecraft; illustrative results are given. Attention is given to the statistical analysis of small sample experiments, the quantification of detection performance for diverse techniques through the computation of probability of detection versus probability of false alarm, and the quantification of diagnostic performance.

HistFitter software framework for statistical data analysis

CERN Document Server

Baak, M.; Côte, D.; Koutsman, A.; Lorenz, J.; Short, D.

2015-01-01

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fitted to data and interpreted with statistical tests. A key innovation of HistFitter is its design, which is rooted in core analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its very fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with mu...

A GMM-IG framework for selecting genes as expression panel biomarkers.

Science.gov (United States)

Wang, Mingyi; Chen, Jake Y

2010-01-01

The limitation of small sample size of functional genomics experiments has made it necessary to integrate DNA microarray experimental data from different sources. However, experimentation noises and biases of different microarray platforms have made integrated data analysis challenging. In this work, we propose an integrative computational framework to identify candidate biomarker genes from publicly available functional genomics studies. We developed a new framework, Gaussian Mixture Modeling-Coupled Information Gain (GMM-IG). In this framework, we first apply a two-component Gaussian mixture model (GMM) to estimate the conditional probability distributions of gene expression data between two different types of samples, for example, normal versus cancer. An expectation-maximization algorithm is then used to estimate the maximum likelihood parameters of a mixture of two Gaussian models in the feature space and determine the underlying expression levels of genes. Gene expression results from different studies are discretized, based on GMM estimations and then unified. Significantly differentially-expressed genes are filtered and assessed with information gain (IG) measures. DNA microarray experimental data for lung cancers from three different prior studies was processed using the new GMM-IG method. Target gene markers from a gene expression panel were selected and compared with several conventional computational biomarker data analysis methods. GMM-IG showed consistently high accuracy for several classification assessments. A high reproducibility of gene selection results was also determined from statistical validations. Our study shows that the GMM-IG framework can overcome poor reliability issues from single-study DNA microarray experiment while maintaining high accuracies by combining true signals from multiple studies. We present a conceptually simple framework that enables reliable integration of true differential gene expression signals from multiple

Sample preparation and fractionation for proteome analysis and cancer biomarker discovery by mass spectrometry.

Science.gov (United States)

Ahmed, Farid E

2009-03-01

Sample preparation and fractionation technologies are one of the most crucial processes in proteomic analysis and biomarker discovery in solubilized samples. Chromatographic or electrophoretic proteomic technologies are also available for separation of cellular protein components. There are, however, considerable limitations in currently available proteomic technologies as none of them allows for the analysis of the entire proteome in a simple step because of the large number of peptides, and because of the wide concentration dynamic range of the proteome in clinical blood samples. The results of any undertaken experiment depend on the condition of the starting material. Therefore, proper experimental design and pertinent sample preparation is essential to obtain meaningful results, particularly in comparative clinical proteomics in which one is looking for minor differences between experimental (diseased) and control (nondiseased) samples. This review discusses problems associated with general and specialized strategies of sample preparation and fractionation, dealing with samples that are solution or suspension, in a frozen tissue state, or formalin-preserved tissue archival samples, and illustrates how sample processing might influence detection with mass spectrometric techniques. Strategies that dramatically improve the potential for cancer biomarker discovery in minimally invasive, blood-collected human samples are also presented.

Statistical analysis on extreme wave height

Digital Repository Service at National Institute of Oceanography (India)

Teena, N.V.; SanilKumar, V.; Sudheesh, K.; Sajeev, R.

-294. • WAFO (2000) – A MATLAB toolbox for analysis of random waves and loads, Lund University, Sweden, homepage http://www.maths.lth.se/matstat/wafo/,2000. 15    Table 1: Statistical results of data and fitted distribution for cumulative distribution...

Metabolic profiling of potential lung cancer biomarkers using bronchoalveolar lavage fluid and the integrated direct infusion/ gas chromatography mass spectrometry platform.

Science.gov (United States)

Callejón-Leblic, Belén; García-Barrera, Tamara; Grávalos-Guzmán, Jesús; Pereira-Vega, Antonio; Gómez-Ariza, José Luis

2016-08-11

Lung cancer is one of the ten most common causes of death worldwide, so that the search for early diagnosis biomarkers is a very challenging task. Bronchoalveolar lavage fluid (BALF) provides information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and no previous metabolomic studies have been performed with BALF samples from patients with lung cancer. Therefore, this fluid has been explored looking for new contributions in lung cancer metabolism. In this way, two complementary metabolomics techniques based on direct infusion high resolution mass spectrometry (DI-ESI-QTOF-MS) and gas chromatography mass spectrometry (GC-MS) have been applied to compare statistically differences between lung cancer (LC) and control (C) BALF samples, using partial least square discriminant analysis (PLS-DA) in order to find and identify potential biomarkers of the disease. A total of 42 altered metabolites were found in BALF from LC. The metabolic pathway analysis showed that glutamate and glutamine metabolism pathway was mainly altered by this disease. In addition, we assessed the biomarker specificity and sensitivity according to the area under the receiver operator characteristic (ROC) curves, indicating that glycerol and phosphoric acid were potential sensitive and specific biomarkers for lung cancer diagnosis and prognosis. The search for early diagnosis of lung cancer is a very challenging task because of the high mortality associated to this disease and its critical linkage to the initiation of treatment. Bronchoalveolar lavage fluid provides information on cellular and biochemical epithelial surface of the lower respiratory tract constituents and no previous metabolomic studies have been performed with BALF samples from patients with lung cancer. Since BALF is in close interaction with lung tissue it is a more representative sample of lung status than other peripheral biofluids as blood or urine studied in previous works

Biomarkers and asthma management: analysis and potential applications

NARCIS (Netherlands)

Richards, Levi B.; Neerincx, Anne H.; van Bragt, Job J. M. H.; Sterk, Peter J.; Bel, Elisabeth H. D.; Maitland-van der Zee, Anke H.

2018-01-01

Asthma features a high degree of heterogeneity in both pathophysiology and therapeutic response, resulting in many asthma patients being treated inadequately. Biomarkers indicative of underlying pathological processes could be used to identify disease subtypes, determine prognosis and to predict or

Statistical Analysis of Zebrafish Locomotor Response.

Science.gov (United States)

Liu, Yiwen; Carmer, Robert; Zhang, Gaonan; Venkatraman, Prahatha; Brown, Skye Ashton; Pang, Chi-Pui; Zhang, Mingzhi; Ma, Ping; Leung, Yuk Fai

2015-01-01

Zebrafish larvae display rich locomotor behaviour upon external stimulation. The movement can be simultaneously tracked from many larvae arranged in multi-well plates. The resulting time-series locomotor data have been used to reveal new insights into neurobiology and pharmacology. However, the data are of large scale, and the corresponding locomotor behavior is affected by multiple factors. These issues pose a statistical challenge for comparing larval activities. To address this gap, this study has analyzed a visually-driven locomotor behaviour named the visual motor response (VMR) by the Hotelling's T-squared test. This test is congruent with comparing locomotor profiles from a time period. Different wild-type (WT) strains were compared using the test, which shows that they responded differently to light change at different developmental stages. The performance of this test was evaluated by a power analysis, which shows that the test was sensitive for detecting differences between experimental groups with sample numbers that were commonly used in various studies. In addition, this study investigated the effects of various factors that might affect the VMR by multivariate analysis of variance (MANOVA). The results indicate that the larval activity was generally affected by stage, light stimulus, their interaction, and location in the plate. Nonetheless, different factors affected larval activity differently over time, as indicated by a dynamical analysis of the activity at each second. Intriguingly, this analysis also shows that biological and technical repeats had negligible effect on larval activity. This finding is consistent with that from the Hotelling's T-squared test, and suggests that experimental repeats can be combined to enhance statistical power. Together, these investigations have established a statistical framework for analyzing VMR data, a framework that should be generally applicable to other locomotor data with similar structure.

Human movement stochastic variability leads to diagnostic biomarkers In Autism Spectrum Disorders (ASD)

Science.gov (United States)

Wu, Di; Torres, Elizabeth B.; Jose, Jorge V.

2015-03-01

ASD is a spectrum of neurodevelopmental disorders. The high heterogeneity of the symptoms associated with the disorder impedes efficient diagnoses based on human observations. Recent advances with high-resolution MEM wearable sensors enable accurate movement measurements that may escape the naked eye. It calls for objective metrics to extract physiological relevant information from the rapidly accumulating data. In this talk we'll discuss the statistical analysis of movement data continuously collected with high-resolution sensors at 240Hz. We calculated statistical properties of speed fluctuations within the millisecond time range that closely correlate with the subjects' cognitive abilities. We computed the periodicity and synchronicity of the speed fluctuations' from their power spectrum and ensemble averaged two-point cross-correlation function. We built a two-parameter phase space from the temporal statistical analyses of the nearest neighbor fluctuations that provided a quantitative biomarker for ASD and adult normal subjects and further classified ASD severity. We also found age related developmental statistical signatures and potential ASD parental links in our movement dynamical studies. Our results may have direct clinical applications.

Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers

Directory of Open Access Journals (Sweden)

Daniel M Spagnolo

2016-01-01

Full Text Available Background: Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME are key contributors to heterogeneity. Methods: We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI and visually represent heterogeneity with a two-dimensional map. Results: We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. Conclusions: This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI, which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different

Pharmacogenomic Biomarkers

Directory of Open Access Journals (Sweden)

Sandra C. Kirkwood

2002-01-01

Full Text Available Pharmacogenomic biomarkers hold great promise for the future of medicine and have been touted as a means to personalize prescriptions. Genetic biomarkers for disease susceptibility including both Mendelian and complex disease promise to result in improved understanding of the pathophysiology of disease, identification of new potential therapeutic targets, and improved molecular classification of disease. However essential to fulfilling the promise of individualized therapeutic intervention is the identification of drug activity biomarkers that stratify individuals based on likely response to a particular therapeutic, both positive response, efficacy, and negative response, development of side effect or toxicity. Prior to the widespread clinical application of a genetic biomarker multiple scientific studies must be completed to identify the genetic variants and delineate their functional significance in the pathophysiology of a carefully defined phenotype. The applicability of the genetic biomarker in the human population must then be verified through both retrospective studies utilizing stored or clinical trial samples, and through clinical trials prospectively stratifying patients based on the biomarker. The risk conferred by the polymorphism and the applicability in the general population must be clearly understood. Thus, the development and widespread application of a pharmacogenomic biomarker is an involved process and for most disease states we are just at the beginning of the journey towards individualized therapy and improved clinical outcome.

Time Series Analysis Based on Running Mann Whitney Z Statistics

Science.gov (United States)

A sensitive and objective time series analysis method based on the calculation of Mann Whitney U statistics is described. This method samples data rankings over moving time windows, converts those samples to Mann-Whitney U statistics, and then normalizes the U statistics to Z statistics using Monte-...

Sensitivity analysis of ranked data: from order statistics to quantiles

NARCIS (Netherlands)

Heidergott, B.F.; Volk-Makarewicz, W.

2015-01-01

In this paper we provide the mathematical theory for sensitivity analysis of order statistics of continuous random variables, where the sensitivity is with respect to a distributional parameter. Sensitivity analysis of order statistics over a finite number of observations is discussed before

A simulation study on estimating biomarker-treatment interaction effects in randomized trials with prognostic variables.

Science.gov (United States)

Haller, Bernhard; Ulm, Kurt

2018-02-20

To individualize treatment decisions based on patient characteristics, identification of an interaction between a biomarker and treatment is necessary. Often such potential interactions are analysed using data from randomized clinical trials intended for comparison of two treatments. Tests of interactions are often lacking statistical power and we investigated if and how a consideration of further prognostic variables can improve power and decrease the bias of estimated biomarker-treatment interactions in randomized clinical trials with time-to-event outcomes. A simulation study was performed to assess how prognostic factors affect the estimate of the biomarker-treatment interaction for a time-to-event outcome, when different approaches, like ignoring other prognostic factors, including all available covariates or using variable selection strategies, are applied. Different scenarios regarding the proportion of censored observations, the correlation structure between the covariate of interest and further potential prognostic variables, and the strength of the interaction were considered. The simulation study revealed that in a regression model for estimating a biomarker-treatment interaction, the probability of detecting a biomarker-treatment interaction can be increased by including prognostic variables that are associated with the outcome, and that the interaction estimate is biased when relevant prognostic variables are not considered. However, the probability of a false-positive finding increases if too many potential predictors are included or if variable selection is performed inadequately. We recommend undertaking an adequate literature search before data analysis to derive information about potential prognostic variables and to gain power for detecting true interaction effects and pre-specifying analyses to avoid selective reporting and increased false-positive rates.

Consensus Guidelines for CSF and Blood Biobanking for CNS Biomarker Studies

Directory of Open Access Journals (Sweden)

Charlotte E. Teunissen

2011-01-01

Full Text Available There is a long history of research into body fluid biomarkers in neurodegenerative and neuroinflammatory diseases. However, only a few biomarkers in cerebrospinal fluid (CSF are being used in clinical practice. Anti-aquaporin-4 antibodies in serum are currently useful for the diagnosis of neuromyelitis optica (NMO, but we could expect novel CSF biomarkers that help define prognosis and response to treatment for this disease. One of the most critical factors in biomarker research is the inadequate powering of studies performed by single centers. Collaboration between investigators is needed to establish large biobanks of well-defined samples. A key issue in collaboration is to establish standardized protocols for biobanking to ensure that the statistical power gained by increasing the numbers of CSF samples is not compromised by pre-analytical factors. Here, consensus guidelines for CSF collection and biobanking are presented, based on the guidelines that have been published by the BioMS-eu network for CSF biomarker research. We focussed on CSF collection procedures, pre-analytical factors and high quality clinical and paraclinical information. Importantly, the biobanking protocols are applicable for CSF biobanks for research targeting any neurological disease.

Biology and Biomarkers for Wound Healing

Science.gov (United States)

Lindley, Linsey E.; Stojadinovic, Olivera; Pastar, Irena; Tomic-Canic, Marjana

2016-01-01

Background As the population grows older, the incidence and prevalence of conditions which lead to a predisposition for poor wound healing also increases. Ultimately, this increase in non-healing wounds has led to significant morbidity and mortality with subsequent huge economic ramifications. Therefore, understanding specific molecular mechanisms underlying aberrant wound healing is of great importance. It has, and will continue to be the leading pathway to the discovery of therapeutic targets as well as diagnostic molecular biomarkers. Biomarkers may help identify and stratify subsets of non-healing patients for whom biomarker-guided approaches may aid in healing. Methods A series of literature searches were performed using Medline, PubMed, Cochrane Library, and Internet searches. Results Currently, biomarkers are being identified using biomaterials sourced locally, from human wounds and/or systemically using systematic high-throughput “omics” modalities (genomic, proteomic, lipidomic, metabolomic analysis). In this review we highlight the current status of clinically applicable biomarkers and propose multiple steps in validation and implementation spectrum including those measured in tissue specimens e.g. β-catenin and c-myc, wound fluid e.g. MMP’s and interleukins, swabs e.g. wound microbiota and serum e.g. procalcitonin and MMP’s. Conclusions Identification of numerous potential biomarkers utilizing different avenues of sample collection and molecular approaches is currently underway. A focus on simplicity, and consistent implementation of these biomarkers as well as an emphasis on efficacious follow-up therapeutics is necessary for transition of this technology to clinically feasible point-of-care applications. PMID:27556760

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

Science.gov (United States)

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Science.gov (United States)

Bruña, Ricardo; Poza, Jesús; Gómez, Carlos; García, María; Fernández, Alberto; Hornero, Roberto

2012-06-01

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz-Mancini-Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

Improved multimodal biomarkers for Alzheimer's disease and mild cognitive impairment diagnosis: data from ADNI

Science.gov (United States)

Martinez-Torteya, Antonio; Treviño-Alvarado, Víctor; Tamez-Peña, José

2013-02-01

The accurate diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) confers many clinical research and patient care benefits. Studies have shown that multimodal biomarkers provide better diagnosis accuracy of AD and MCI than unimodal biomarkers, but their construction has been based on traditional statistical approaches. The objective of this work was the creation of accurate AD and MCI diagnostic multimodal biomarkers using advanced bioinformatics tools. The biomarkers were created by exploring multimodal combinations of features using machine learning techniques. Data was obtained from the ADNI database. The baseline information (e.g. MRI analyses, PET analyses and laboratory essays) from AD, MCI and healthy control (HC) subjects with available diagnosis up to June 2012 was mined for case/controls candidates. The data mining yielded 47 HC, 83 MCI and 43 AD subjects for biomarker creation. Each subject was characterized by at least 980 ADNI features. A genetic algorithm feature selection strategy was used to obtain compact and accurate cross-validated nearest centroid biomarkers. The biomarkers achieved training classification accuracies of 0.983, 0.871 and 0.917 for HC vs. AD, HC vs. MCI and MCI vs. AD respectively. The constructed biomarkers were relatively compact: from 5 to 11 features. Those multimodal biomarkers included several widely accepted univariate biomarkers and novel image and biochemical features. Multimodal biomarkers constructed from previously and non-previously AD associated features showed improved diagnostic performance when compared to those based solely on previously AD associated features.

Biomarker Analysis of Samples Visually Identified as Microbial in the Eocene Green River Formation: An Analogue for Mars.

Science.gov (United States)

Olcott Marshall, Alison; Cestari, Nicholas A

2015-09-01

One of the major exploration targets for current and future Mars missions are lithofacies suggestive of biotic activity. Although such lithofacies are not confirmation of biotic activity, they provide a way to identify samples for further analyses. To test the efficacy of this approach, we identified carbonate samples from the Eocene Green River Formation as "microbial" or "non-microbial" based on the macroscale morphology of their laminations. These samples were then crushed and analyzed by gas chromatography/mass spectroscopy (GC/MS) to determine their lipid biomarker composition. GC/MS analysis revealed that carbonates visually identified as "microbial" contained a higher concentration of more diverse biomarkers than those identified as "non-microbial," suggesting that this could be a viable detection strategy for selecting samples for further analysis or caching on Mars.

Feature-Based Statistical Analysis of Combustion Simulation Data

Energy Technology Data Exchange (ETDEWEB)

Bennett, J; Krishnamoorthy, V; Liu, S; Grout, R; Hawkes, E; Chen, J; Pascucci, V; Bremer, P T

2011-11-18

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion

Statistical learning methods in high-energy and astrophysics analysis

Energy Technology Data Exchange (ETDEWEB)

Zimmermann, J. [Forschungszentrum Juelich GmbH, Zentrallabor fuer Elektronik, 52425 Juelich (Germany) and Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)]. E-mail: zimmerm@mppmu.mpg.de; Kiesling, C. [Max-Planck-Institut fuer Physik, Foehringer Ring 6, 80805 Munich (Germany)

2004-11-21

We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application.

Statistical learning methods in high-energy and astrophysics analysis

International Nuclear Information System (INIS)

Zimmermann, J.; Kiesling, C.

2004-01-01

We discuss several popular statistical learning methods used in high-energy- and astro-physics analysis. After a short motivation for statistical learning we present the most popular algorithms and discuss several examples from current research in particle- and astro-physics. The statistical learning methods are compared with each other and with standard methods for the respective application

Biomarkers for equine joint injury and osteoarthritis.

Science.gov (United States)

McIlwraith, C Wayne; Kawcak, Christopher E; Frisbie, David D; Little, Christopher B; Clegg, Peter D; Peffers, Mandy J; Karsdal, Morten A; Ekman, Stina; Laverty, Sheila; Slayden, Richard A; Sandell, Linda J; Lohmander, L S; Kraus, Virginia B

2018-03-01

We report the results of a symposium aimed at identifying validated biomarkers that can be used to complement clinical observations for diagnosis and prognosis of joint injury leading to equine osteoarthritis (OA). Biomarkers might also predict pre-fracture change that could lead to catastrophic bone failure in equine athletes. The workshop was attended by leading scientists in the fields of equine and human musculoskeletal biomarkers to enable cross-disciplinary exchange and improve knowledge in both. Detailed proceedings with strategic planning was written, added to, edited and referenced to develop this manuscript. The most recent information from work in equine and human osteoarthritic biomarkers was accumulated, including the use of personalized healthcare to stratify OA phenotypes, transcriptome analysis of anterior cruciate ligament (ACL) and meniscal injuries in the human knee. The spectrum of "wet" biomarker assays that are antibody based that have achieved usefulness in both humans and horses, imaging biomarkers and the role they can play in equine and human OA was discussed. Prediction of musculoskeletal injury in the horse remains a challenge, and the potential usefulness of spectroscopy, metabolomics, proteomics, and development of biobanks to classify biomarkers in different stages of equine and human OA were reviewed. The participants concluded that new information and studies in equine musculoskeletal biomarkers have potential translational value for humans and vice versa. OA is equally important in humans and horses, and the welfare issues associated with catastrophic musculoskeletal injury in horses add further emphasis to the need for good validated biomarkers in the horse. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:823-831, 2018. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Evaluation of the Role of Creatine Phosphokinase as a Biomarker in Acute Myocardial Infarction Patients

Directory of Open Access Journals (Sweden)

Priyal Agrawal

2017-01-01

Full Text Available Introduction: The study hypothesized that salivary creatine phosphokinase (CPK can act as a biomarker in diagnosing acute myocardial infarction (AMI as an alternative to serum CPK, which is a contributory effort towards noninvasive procedures to detect the disease. Aims and Objectives: The main aim of our study was to propose the normal range of salivary CPK in patients with AMI, and to explore the relationship between serum and saliva levels of CPK with comparison of salivary CPK as a biomarker between healthy individuals and patients with AMI. Materials and Methods: A case-control study was carried out including 144 participants who were divided in two main groups – 72 normal healthy individuals and 72 with AMI. CPK levels were assayed in serum and unstimulated whole saliva of AMI and controls by International Federation of Clinical Chemistry (IFCC method. Statistical Analysis: Statistical analysis was performed using unpaired t-test and Pearson correlation coefficient test. Results: The normal proposed range of salivary CPK of patients with AMI was found to range from 11.30 to 184.50 U/L in males and 20.17 to 69.00 U/L in females. The mean salivary and serum CPK was significantly higher in patients with AMI as compared to healthy individuals with P < 0.001. Saliva CPK concentration correlated significantly with serum CPK of AMI and healthy individuals with r = 0.247. Conclusion: Salivary CPK can be used as an alternative to serum CPK in patients with AMI.

The fuzzy approach to statistical analysis

NARCIS (Netherlands)

Coppi, Renato; Gil, Maria A.; Kiers, Henk A. L.

2006-01-01

For the last decades, research studies have been developed in which a coalition of Fuzzy Sets Theory and Statistics has been established with different purposes. These namely are: (i) to introduce new data analysis problems in which the objective involves either fuzzy relationships or fuzzy terms;

Statistical analysis applied to safety culture self-assessment

International Nuclear Information System (INIS)

Macedo Soares, P.P.

2002-01-01

Interviews and opinion surveys are instruments used to assess the safety culture in an organization as part of the Safety Culture Enhancement Programme. Specific statistical tools are used to analyse the survey results. This paper presents an example of an opinion survey with the corresponding application of the statistical analysis and the conclusions obtained. Survey validation, Frequency statistics, Kolmogorov-Smirnov non-parametric test, Student (T-test) and ANOVA means comparison tests and LSD post-hoc multiple comparison test, are discussed. (author)

Early Pregnancy Biomarkers in Pre-Eclampsia: A Systematic Review and Meta-Analysis

Directory of Open Access Journals (Sweden)

Pensée Wu

2015-09-01

Full Text Available Pre-eclampsia (PE complicates 2%–8% of all pregnancies and is an important cause of perinatal morbidity and mortality worldwide. In order to reduce these complications and to develop possible treatment modalities, it is important to identify women at risk of developing PE. The use of biomarkers in early pregnancy would allow appropriate stratification into high and low risk pregnancies for the purpose of defining surveillance in pregnancy and to administer interventions. We used formal methods for a systematic review and meta-analyses to assess the accuracy of all biomarkers that have been evaluated so far during the first and early second trimester of pregnancy to predict PE. We found low predictive values using individual biomarkers which included a disintegrin and metalloprotease 12 (ADAM-12, inhibin-A, pregnancy associated plasma protein A (PAPP-A, placental growth factor (PlGF and placental protein 13 (PP-13. The pooled sensitivity of all single biomarkers was 0.40 (95% CI 0.39–0.41 at a false positive rate of 10%. The area under the Summary of Receiver Operating Characteristics Curve (SROC was 0.786 (SE 0.02. When a combination model was used, the predictive value improved to an area under the SROC of 0.893 (SE 0.03. In conclusion, although there are multiple potential biomarkers for PE their efficacy has been inconsistent and comparisons are difficult because of heterogeneity between different studies. Therefore, there is an urgent need for high quality, large-scale multicentre research in biomarkers for PE so that the best predictive marker(s can be identified in order to improve the management of women destined to develop PE.

Correlations between electrocardiogram and biomarkers in acute pulmonary embolism: Analysis of ZATPOL-2 Registry.

Science.gov (United States)

Kukla, Piotr; Kosior, Dariusz A; Tomaszewski, Andrzej; Ptaszyńska-Kopczyńska, Katarzyna; Widejko, Katarzyna; Długopolski, Robert; Skrzyński, Andrzej; Błaszczak, Piotr; Fijorek, Kamil; Kurzyna, Marcin

2017-07-01

Electrocardiography (ECG) is still one of the first tests performed at admission, mostly in patients (pts) with chest pain or dyspnea. The aim of this study was to assess the correlation between electrocardiographic abnormalities and cardiac biomarkers as well as echocardiographic parameter in patients with acute pulmonary embolism. We performed a retrospective analysis of 614 pts. (F/M 334/280; mean age of 67.9 ± 16.6 years) with confirmed acute pulmonary embolism (APE) who were enrolled to the ZATPOL-2 Registry between 2012 and 2014. Elevated cardiac biomarkers were observed in 358 pts (74.4%). In this group the presence of atrial fibrillation (p = .008), right axis deviation (p = .004), S 1 Q 3 T 3 sign (p electrocardiogram" were as follows: increased heart rate (OR 1.09, 95% CI 1.02-1.17, p = .012), elevated troponin concentration (OR 3.33, 95% CI 1.94-5.72, p = .000), and right ventricular overload (OR 2.30, 95% CI 1.17-4.53, p = .016). Electrocardiographic signs of right ventricular strain are strongly related to elevated cardiac biomarkers and echocardiographic signs of right ventricular overload. ECG may be used in preliminary risk stratification of patient with intermediate- or high-risk forms of APE. © 2017 Wiley Periodicals, Inc.

Foundation of statistical energy analysis in vibroacoustics

CERN Document Server

Le Bot, A

2015-01-01

This title deals with the statistical theory of sound and vibration. The foundation of statistical energy analysis is presented in great detail. In the modal approach, an introduction to random vibration with application to complex systems having a large number of modes is provided. For the wave approach, the phenomena of propagation, group speed, and energy transport are extensively discussed. Particular emphasis is given to the emergence of diffuse field, the central concept of the theory.

Definitions and validation criteria for biomarkers and surrogate endpoints: development and testing of a quantitative hierarchical levels of evidence schema

NARCIS (Netherlands)

Lassere, Marissa N.; Johnson, Kent R.; Boers, Maarten; Tugwell, Peter; Brooks, Peter; Simon, Lee; Strand, Vibeke; Conaghan, Philip G.; Ostergaard, Mikkel; Maksymowych, Walter P.; Landewe, Robert; Bresnihan, Barry; Tak, Paul-Peter; Wakefield, Richard; Mease, Philip; Bingham, Clifton O.; Hughes, Michael; Altman, Doug; Buyse, Marc; Galbraith, Sally; Wells, George

2007-01-01

OBJECTIVE: There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Our objective was to review the literature on biomarkers and surrogates to

Inflammatory Biomarkers of Cardiometabolic Risk in Obese Egyptian Type 2 Diabetics

Directory of Open Access Journals (Sweden)

Lamiaa A. A. Barakat

2017-11-01

Full Text Available Inflammatory biomarkers provide a minimally invasive means for early detection and specific treatment of metabolic syndrome and related disorders. The objective of this work was to search for inflammatory biomarkers of cardiometabolic risk in obese type 2 diabetics. The study was performed on 165 persons attending the medical outpatient clinic of Ismailia General Hospital. Their mean age was (50.69 ± 10.15 years. They were divided into three groups. The control group was composed of 55 non-obese, non-diabetic healthy volunteers, 32 males and 23 females. Two study groups were included in this study: group 2 was composed of 55 obese, non-diabetic subjects, 25 males and 30 females matched for age and gender. All patients including the control were subjected to clinical history taking, a clinical examination for the measurement of body mass index (BMI. Investigations were carried out for fasting blood glucose, fasting serum insulin, insulin resistance (IR, the lipid profile, lipoprotein band lipoprotein phospholipase A2, and non-high-density lipoprotein cholesterol (non-HDL-C. Urea, albumin and creatinine analysis and liver function tests were performed, and a complete blood count (CBC was taken. Hemoglobin A1C (HbA1C, serum high-sensitivity C-reactive protein (hs-CRP, interleukin-6 (IL-6 and tumor necrosis factor-α (TNF-α were tested. There were statistically significant differences among the studied groups in terms of total cholesterol, non-HDL-C, high-density lipoprotein cholesterol (HDL-C, triglycerides (TG, low-density lipoprotein cholesterol (LDL-C, lipoprotein-associated phospholipase A2 and apolipoprotein B. The inflammatory biomarkers hs-CRP, IL-6 and TNF-α were significantly statistically increased in the study groups by (1.62 ± 0.99, 2.32 ± 1.11, (1.73 ± 1.14, 2.53 ± 1.34, and (1.87 ± 1.09, 2.17 ± 0.89 respectively, where p < 0.01. Significant positive correlation was found between Homeostatic Model Assessment (HOMA-IR, hs

Combination of biomarkers

DEFF Research Database (Denmark)

Thurfjell, Lennart; Lötjönen, Jyrki; Lundqvist, Roger

2012-01-01

The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aβ accumulation and neuronal degeneration or injury.......The New National Institute on Aging-Alzheimer's Association diagnostic guidelines for Alzheimer's disease (AD) incorporate biomarkers in the diagnostic criteria and suggest division of biomarkers into two categories: Aβ accumulation and neuronal degeneration or injury....

DDX3X Biomarker Correlates with Poor Survival in Human Gliomas

Directory of Open Access Journals (Sweden)

Dueng-Yuan Hueng

2015-07-01

Full Text Available Primary high-grade gliomas possess invasive growth and lead to unfavorable survival outcome. The investigation of biomarkers for prediction of survival outcome in patients with gliomas is important for clinical assessment. The DEAD (Asp-Glu-Ala-Asp box helicase 3, X-linked (DDX3X controls tumor migration, proliferation, and progression. However, the role of DDX3X in defining the pathological grading and survival outcome in patients with human gliomas is not yet clarified. We analyzed the DDX3X gene expression, WHO pathological grading, and overall survival from de-linked data. Further validation was done using quantitative RT-PCR of cDNA from normal brain and glioma, and immunohistochemical (IHC staining of tissue microarray. Statistical analysis of GEO datasets showed that DDX3X mRNA expression demonstrated statistically higher in WHO grade IV (n = 81 than in non-tumor controls (n = 23, p = 1.13 × 10−10. Moreover, DDX3X level was also higher in WHO grade III (n = 19 than in non-tumor controls (p = 2.43 × 10−5. Kaplan–Meier survival analysis showed poor survival in patients with high DDX3X mRNA levels (n = 24 than in those with low DDX3X expression (n = 53 (median survival, 115 vs. 58 weeks, p = 0.0009, by log-rank test, hazard ratio: 0.3507, 95% CI: 0.1893–0.6496. Furthermore, DDX3X mRNA expression and protein production significantly increased in glioma cells compared with normal brain tissue examined by quantitative RT-PCR, and Western blot. IHC staining showed highly staining of high-grade glioma in comparison with normal brain tissue. Taken together, DDX3X expression level positively correlates with WHO pathologic grading and poor survival outcome, indicating that DDX3X is a valuable biomarker in human gliomas.

HistFitter software framework for statistical data analysis

Energy Technology Data Exchange (ETDEWEB)

Baak, M. [CERN, Geneva (Switzerland); Besjes, G.J. [Radboud University Nijmegen, Nijmegen (Netherlands); Nikhef, Amsterdam (Netherlands); Cote, D. [University of Texas, Arlington (United States); Koutsman, A. [TRIUMF, Vancouver (Canada); Lorenz, J. [Ludwig-Maximilians-Universitaet Muenchen, Munich (Germany); Excellence Cluster Universe, Garching (Germany); Short, D. [University of Oxford, Oxford (United Kingdom)

2015-04-15

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fit to data and interpreted with statistical tests. Internally HistFitter uses the statistics packages RooStats and HistFactory. A key innovation of HistFitter is its design, which is rooted in analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple models at once that describe the data, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication quality style through a simple command-line interface. (orig.)

HistFitter software framework for statistical data analysis

International Nuclear Information System (INIS)

Baak, M.; Besjes, G.J.; Cote, D.; Koutsman, A.; Lorenz, J.; Short, D.

2015-01-01

We present a software framework for statistical data analysis, called HistFitter, that has been used extensively by the ATLAS Collaboration to analyze big datasets originating from proton-proton collisions at the Large Hadron Collider at CERN. Since 2012 HistFitter has been the standard statistical tool in searches for supersymmetric particles performed by ATLAS. HistFitter is a programmable and flexible framework to build, book-keep, fit, interpret and present results of data models of nearly arbitrary complexity. Starting from an object-oriented configuration, defined by users, the framework builds probability density functions that are automatically fit to data and interpreted with statistical tests. Internally HistFitter uses the statistics packages RooStats and HistFactory. A key innovation of HistFitter is its design, which is rooted in analysis strategies of particle physics. The concepts of control, signal and validation regions are woven into its fabric. These are progressively treated with statistically rigorous built-in methods. Being capable of working with multiple models at once that describe the data, HistFitter introduces an additional level of abstraction that allows for easy bookkeeping, manipulation and testing of large collections of signal hypotheses. Finally, HistFitter provides a collection of tools to present results with publication quality style through a simple command-line interface. (orig.)

Robust statistics and geochemical data analysis

International Nuclear Information System (INIS)

Di, Z.

1987-01-01

Advantages of robust procedures over ordinary least-squares procedures in geochemical data analysis is demonstrated using NURE data from the Hot Springs Quadrangle, South Dakota, USA. Robust principal components analysis with 5% multivariate trimming successfully guarded the analysis against perturbations by outliers and increased the number of interpretable factors. Regression with SINE estimates significantly increased the goodness-of-fit of the regression and improved the correspondence of delineated anomalies with known uranium prospects. Because of the ubiquitous existence of outliers in geochemical data, robust statistical procedures are suggested as routine procedures to replace ordinary least-squares procedures

NanoSIMS analysis of Archean fossils and biomarkers

International Nuclear Information System (INIS)

Kilburn, M.R.; Wacey, D.

2008-01-01

The study of fossils and biomarkers from Archean rocks is of vital importance to reveal how life arose on Earth and what we might expect to find on other planets such as Mars. The Cameca NanoSIMS 50 has the unique ability to measure stable isotopes and map biologically relevant elements at the micron-scale, in situ. This makes it the perfect tool for testing the biogenicity of a range of putative biomarkers from early Archean rocks (∼3.50 billion-year-old). NanoSIMS has been used to investigate ambient inclusion trails (AITs) in a 3.43 Ga beach sand deposit from the Pilbara craton, Western Australia. Chemical maps of the light elements necessary for life (C, N and O) and several transition metals commonly associated with biological processing (Ni, Zn and Fe), coupled with 13 C/ 12 C isotope ratios from carbonaceous linings, strongly suggest a biological component in the formation of AITs

Using Pre-Statistical Analysis to Streamline Monitoring Assessments

International Nuclear Information System (INIS)

Reed, J.K.

1999-01-01

A variety of statistical methods exist to aid evaluation of groundwater quality and subsequent decision making in regulatory programs. These methods are applied because of large temporal and spatial extrapolations commonly applied to these data. In short, statistical conclusions often serve as a surrogate for knowledge. However, facilities with mature monitoring programs that have generated abundant data have inherently less uncertainty because of the sheer quantity of analytical results. In these cases, statistical tests can be less important, and ''expert'' data analysis should assume an important screening role.The WSRC Environmental Protection Department, working with the General Separations Area BSRI Environmental Restoration project team has developed a method for an Integrated Hydrogeological Analysis (IHA) of historical water quality data from the F and H Seepage Basins groundwater remediation project. The IHA combines common sense analytical techniques and a GIS presentation that force direct interactive evaluation of the data. The IHA can perform multiple data analysis tasks required by the RCRA permit. These include: (1) Development of a groundwater quality baseline prior to remediation startup, (2) Targeting of constituents for removal from RCRA GWPS, (3) Targeting of constituents for removal from UIC, permit, (4) Targeting of constituents for reduced, (5)Targeting of monitoring wells not producing representative samples, (6) Reduction in statistical evaluation, and (7) Identification of contamination from other facilities

Conjunction analysis and propositional logic in fMRI data analysis using Bayesian statistics.

Science.gov (United States)

Rudert, Thomas; Lohmann, Gabriele

2008-12-01

To evaluate logical expressions over different effects in data analyses using the general linear model (GLM) and to evaluate logical expressions over different posterior probability maps (PPMs). In functional magnetic resonance imaging (fMRI) data analysis, the GLM was applied to estimate unknown regression parameters. Based on the GLM, Bayesian statistics can be used to determine the probability of conjunction, disjunction, implication, or any other arbitrary logical expression over different effects or contrast. For second-level inferences, PPMs from individual sessions or subjects are utilized. These PPMs can be combined to a logical expression and its probability can be computed. The methods proposed in this article are applied to data from a STROOP experiment and the methods are compared to conjunction analysis approaches for test-statistics. The combination of Bayesian statistics with propositional logic provides a new approach for data analyses in fMRI. Two different methods are introduced for propositional logic: the first for analyses using the GLM and the second for common inferences about different probability maps. The methods introduced extend the idea of conjunction analysis to a full propositional logic and adapt it from test-statistics to Bayesian statistics. The new approaches allow inferences that are not possible with known standard methods in fMRI. (c) 2008 Wiley-Liss, Inc.

Oral Metagenomic Biomarkers in Rheumatoid Arthritis

Science.gov (United States)

2017-09-01

individuals with rheumatoid arthritis (RA). The goal is to test the  hypothesis that oral microbiome and metagenomic analyses will allow  us  to identify new...biomarkers  that are  useful  for the diagnosis of early RA and/or biomarkers that help to predict the efficacy of  specific therapeutic interventions... RNA  microbiome analysis as well as whole genome shotgun sequencing.  Upon completion of these aims, any identified bacterial biomarkers may be

Evaluation of miR-122 as a Serum Biomarker for Hepatotoxicity in Investigative Rat Toxicology Studies.

Science.gov (United States)

Sharapova, T; Devanarayan, V; LeRoy, B; Liguori, M J; Blomme, E; Buck, W; Maher, J

2016-01-01

MicroRNAs are short noncoding RNAs involved in regulation of gene expression. Certain microRNAs, including miR-122, seem to have ideal properties as biomarkers due to good stability, high tissue specificity, and ease of detection across multiple species. Recent reports have indicated that miR-122 is a highly liver-specific marker detectable in serum after liver injury. The purpose of the current study was to assess the performance of miR-122 as a serum biomarker for hepatotoxicity in short-term (5-28 days) repeat-dose rat toxicology studies when benchmarked against routine clinical chemistry and histopathology. A total of 23 studies with multiple dose levels of experimental compounds were examined, and they included animals with or without liver injury and with various hepatic histopathologic changes. Serum miR-122 levels were quantified by reverse transcription quantitative polymerase chain reaction. Increases in circulating miR-122 levels highly correlated with serum elevations of liver enzymes, such as alanine aminotransferase (ALT), aspartate aminotransferase (AST) and glutamate dehydrogenase (GLDH). Statistical analysis showed that miR-122 outperformed ALT as a biomarker for histopathologically confirmed liver toxicity and was equivalent in performance to AST and GLDH. Additionally, an increase of 4% in predictive accuracy was obtained using a multiparameter approach incorporating miR-122 with ALT, AST, and GLDH. In conclusion, serum miR-122 levels can be utilized as a biomarker of hepatotoxicity in acute and subacute rat toxicology studies, and its performance can rival or exceed those of standard enzyme biomarkers such as the liver transaminases. © The Author(s) 2015.

Quantitative Tissue Proteomics Analysis Reveals Versican as Potential Biomarker for Early-Stage Hepatocellular Carcinoma.

Science.gov (United States)

Naboulsi, Wael; Megger, Dominik A; Bracht, Thilo; Kohl, Michael; Turewicz, Michael; Eisenacher, Martin; Voss, Don Marvin; Schlaak, Jörg F; Hoffmann, Andreas-Claudius; Weber, Frank; Baba, Hideo A; Meyer, Helmut E; Sitek, Barbara

2016-01-04

Hepatocellular carcinoma (HCC) is one of the most aggressive tumors, and the treatment outcome of this disease is improved when the cancer is diagnosed at an early stage. This requires biomarkers allowing an accurate and early tumor diagnosis. To identify potential markers for such applications, we analyzed a patient cohort consisting of 50 patients (50 HCC and 50 adjacent nontumorous tissue samples as controls) using two independent proteomics approaches. We performed label-free discovery analysis on 19 HCC and corresponding tissue samples. The data were analyzed considering events known to take place in early events of HCC development, such as abnormal regulation of Wnt/b-catenin and activation of receptor tyrosine kinases (RTKs). 31 proteins were selected for verification experiments. For this analysis, the second set of the patient cohort (31 HCC and corresponding tissue samples) was analyzed using selected (multiple) reaction monitoring (SRM/MRM). We present the overexpression of ATP-dependent RNA helicase (DDX39), Fibulin-5 (FBLN5), myristoylated alanine-rich C-kinase substrate (MARCKS), and Serpin H1 (SERPINH1) in HCC for the first time. We demonstrate Versican core protein (VCAN) to be significantly associated with well differentiated and low-stage HCC. We revealed for the first time the evidence of VCAN as a potential biomarker for early-HCC diagnosis.

Analysis of cutin and suberin biomarker patterns in alluvial sedi-ments

Science.gov (United States)

Herschbach, Jennifer; Sesterheim, Anna; König, Frauke; Fuchs, Elmar

2015-04-01

Cutin and suberin are the primary source of hydrolysable aliphatic lipid polyesters in soil organic matter (SOM). They are known as geochemical biomarkers to estimate the contribution of different plant species and tissues to SOM. Despite their potential as biomarkers, cutin and suberin have received less attention as flood plain sediment biomarkers. The objectives of this study were to investigate the efficiency of cutin and suberin as biomarkers in floodplains. Therefore similarities between the lipid pattern in alluvial sediments and in the actual vegetation were considered. Lipids of plant tissues (roots, twigs, leaves) from different species (reed (e.g. Phalaris arun-diacea), Salix alba, Ulmus laevis and grassland (e.g. Carex spec.)) and of the un-derlying soils and sediments were obtained and investigated at four sites in the nature reserve Knoblauchsaue (Hessen, Germany). The four sampling sites differ not only with respect to their vegetation, but also within their distance to the river Rhine. Cutin and suberin monomers of plants and soils were analysed by alkaline hydrolysis, methylation and acetylation and subsequent gas chromatography-mass spectrometry. Resulting lipid patterns were specific for the appropriate plant species and tissues. However, the traceability of single selected lipids was decreasing alongside the soil profile, with the exception of monomers in softwood floodplain soils. Selected tissue specific lipid ratios showed a higher traceability due to strong attributions of lipid ratios in soils and roots of U. laevis and Carex spec. and in leaves of U. laevis and S. alba. In contrast, there was no accordance between the suberin specific lipid ratios in soils and roots of S. alba and P. arundiacea. The most robust interpretations were afforded when a set of multiple biomarkers (i.e. a combination of free lipid ratios and ratios of hydrolysable lipids) was used to collectively reconstruct the source vegetation of different sediment layers.

Sensitivity analysis and optimization of system dynamics models : Regression analysis and statistical design of experiments

NARCIS (Netherlands)

Kleijnen, J.P.C.

1995-01-01

This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for

Multivariate Statistical Methods as a Tool of Financial Analysis of Farm Business

Czech Academy of Sciences Publication Activity Database

Novák, J.; Sůvová, H.; Vondráček, Jiří

2002-01-01

Roč. 48, č. 1 (2002), s. 9-12 ISSN 0139-570X Institutional research plan: AV0Z1030915 Keywords : financial analysis * financial ratios * multivariate statistical methods * correlation analysis * discriminant analysis * cluster analysis Subject RIV: BB - Applied Statistics, Operational Research

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

Directory of Open Access Journals (Sweden)

Antonio Cerasa

2015-01-01

Full Text Available Presently, there are no valid biomarkers to identify individuals with eating disorders (ED. The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa were compared against 17 body mass index-matched healthy controls (HC. Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results

Science.gov (United States)

Cerasa, Antonio; Castiglioni, Isabella; Salvatore, Christian; Funaro, Angela; Martino, Iolanda; Alfano, Stefania; Donzuso, Giulia; Perrotta, Paolo; Gioia, Maria Cecilia; Gilardi, Maria Carla; Quattrone, Aldo

2015-01-01

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice. PMID:26648660

Statistical analysis and interpretation of prenatal diagnostic imaging studies, Part 2: descriptive and inferential statistical methods.

Science.gov (United States)

Tuuli, Methodius G; Odibo, Anthony O

2011-08-01

The objective of this article is to discuss the rationale for common statistical tests used for the analysis and interpretation of prenatal diagnostic imaging studies. Examples from the literature are used to illustrate descriptive and inferential statistics. The uses and limitations of linear and logistic regression analyses are discussed in detail.

Sensitivity analysis for publication bias in meta-analysis of diagnostic studies for a continuous biomarker.

Science.gov (United States)

Hattori, Satoshi; Zhou, Xiao-Hua

2018-02-10

Publication bias is one of the most important issues in meta-analysis. For standard meta-analyses to examine intervention effects, the funnel plot and the trim-and-fill method are simple and widely used techniques for assessing and adjusting for the influence of publication bias, respectively. However, their use may be subjective and can then produce misleading insights. To make a more objective inference for publication bias, various sensitivity analysis methods have been proposed, including the Copas selection model. For meta-analysis of diagnostic studies evaluating a continuous biomarker, the summary receiver operating characteristic (sROC) curve is a very useful method in the presence of heterogeneous cutoff values. To our best knowledge, no methods are available for evaluation of influence of publication bias on estimation of the sROC curve. In this paper, we introduce a Copas-type selection model for meta-analysis of diagnostic studies and propose a sensitivity analysis method for publication bias. Our method enables us to assess the influence of publication bias on the estimation of the sROC curve and then judge whether the result of the meta-analysis is sufficiently confident or should be interpreted with much caution. We illustrate our proposed method with real data. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical analysis of environmental data

International Nuclear Information System (INIS)

Beauchamp, J.J.; Bowman, K.O.; Miller, F.L. Jr.

1975-10-01

This report summarizes the analyses of data obtained by the Radiological Hygiene Branch of the Tennessee Valley Authority from samples taken around the Browns Ferry Nuclear Plant located in Northern Alabama. The data collection was begun in 1968 and a wide variety of types of samples have been gathered on a regular basis. The statistical analysis of environmental data involving very low-levels of radioactivity is discussed. Applications of computer calculations for data processing are described

Validation of New Cancer Biomarkers

DEFF Research Database (Denmark)

Duffy, Michael J; Sturgeon, Catherine M; Söletormos, Georg

2015-01-01

BACKGROUND: Biomarkers are playing increasingly important roles in the detection and management of patients with cancer. Despite an enormous number of publications on cancer biomarkers, few of these biomarkers are in widespread clinical use. CONTENT: In this review, we discuss the key steps...... in advancing a newly discovered cancer candidate biomarker from pilot studies to clinical application. Four main steps are necessary for a biomarker to reach the clinic: analytical validation of the biomarker assay, clinical validation of the biomarker test, demonstration of clinical value from performance...... of the biomarker test, and regulatory approval. In addition to these 4 steps, all biomarker studies should be reported in a detailed and transparent manner, using previously published checklists and guidelines. Finally, all biomarker studies relating to demonstration of clinical value should be registered before...

Biomarkers of necrotising soft tissue infections

DEFF Research Database (Denmark)

Hansen, Marco Bo; Simonsen, Ulf; Garred, Peter

2015-01-01

INTRODUCTION: The mortality and amputation rates are still high in patients with necrotising soft tissue infections (NSTIs). It would be ideal to have a set of biomarkers that enables the clinician to identify high-risk patients with NSTI on admission. The objectives of this study are to evaluate...... and mortality in patients with NSTI and that HBOT reduces the inflammatory response. METHODS AND ANALYSIS: This is a prospective, observational study being conducted in a tertiary referral centre. Biomarkers will be measured in 114 patients who have been operatively diagnosed with NSTI. On admission, baseline...

Biomarkers in Autism

Directory of Open Access Journals (Sweden)

Robert eHendren

2014-08-01

Full Text Available Autism spectrum disorders (ASD are complex, heterogeneous disorders caused by an interaction between genetic vulnerability and environmental factors. In an effort to better target the underlying roots of ASD for diagnosis and treatment, efforts to identify reliable biomarkers in genetics, neuroimaging, gene expression and measures of the body’s metabolism are growing. For this article, we review the published studies of potential biomarkers in autism and conclude that while there is increasing promise of finding biomarkers that can help us target treatment, there are none with enough evidence to support routine clinical use unless medical illness is suspected. Promising biomarkers include those for mitochondrial function, oxidative stress, and immune function. Genetic clusters are also suggesting the potential for useful biomarkers.

Highly Robust Statistical Methods in Medical Image Analysis

Czech Academy of Sciences Publication Activity Database

Kalina, Jan

2012-01-01

Roč. 32, č. 2 (2012), s. 3-16 ISSN 0208-5216 R&D Projects: GA MŠk(CZ) 1M06014 Institutional research plan: CEZ:AV0Z10300504 Keywords : robust statistics * classification * faces * robust image analysis * forensic science Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.208, year: 2012 http://www.ibib.waw.pl/bbe/bbefulltext/BBE_32_2_003_FT.pdf

Cross-population validation of statistical distance as a measure of physiological dysregulation during aging.

Science.gov (United States)

Cohen, Alan A; Milot, Emmanuel; Li, Qing; Legault, Véronique; Fried, Linda P; Ferrucci, Luigi

2014-09-01

Measuring physiological dysregulation during aging could be a key tool both to understand underlying aging mechanisms and to predict clinical outcomes in patients. However, most existing indices are either circular or hard to interpret biologically. Recently, we showed that statistical distance of 14 common blood biomarkers (a measure of how strange an individual's biomarker profile is) was associated with age and mortality in the WHAS II data set, validating its use as a measure of physiological dysregulation. Here, we extend the analyses to other data sets (WHAS I and InCHIANTI) to assess the stability of the measure across populations. We found that the statistical criteria used to determine the original 14 biomarkers produced diverging results across populations; in other words, had we started with a different data set, we would have chosen a different set of markers. Nonetheless, the same 14 markers (or the subset of 12 available for InCHIANTI) produced highly similar predictions of age and mortality. We include analyses of all combinatorial subsets of the markers and show that results do not depend much on biomarker choice or data set, but that more markers produce a stronger signal. We conclude that statistical distance as a measure of physiological dysregulation is stable across populations in Europe and North America. Copyright © 2014 Elsevier Inc. All rights reserved.

Statistical Power Analysis with Missing Data A Structural Equation Modeling Approach

CERN Document Server

Davey, Adam

2009-01-01

Statistical power analysis has revolutionized the ways in which we conduct and evaluate research.  Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling.  It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different amounts and types

Definitions and validation criteria for biomarkers and surrogate endpoints: development and testing of a quantitative hierarchical levels of evidence schema

DEFF Research Database (Denmark)

Lassere, Marissa N; Johnson, Kent R; Boers, Maarten

2007-01-01

endpoints, and leading indicators, a quantitative surrogate validation schema was developed and subsequently evaluated at a stakeholder workshop. RESULTS: The search identified several classification schema and definitions. Components of these were incorporated into a new quantitative surrogate validation...... level of evidence schema that evaluates biomarkers along 4 domains: Target, Study Design, Statistical Strength, and Penalties. Scores derived from 3 domains the Target that the marker is being substituted for, the Design of the (best) evidence, and the Statistical strength are additive. Penalties...... of the National Institutes of Health definitions of biomarker, surrogate endpoint, and clinical endpoint was useful. CONCLUSION: Further development and application of this schema provides incentives and guidance for effective biomarker and surrogate endpoint research, and more efficient drug discovery...

Statistical Analysis of Data for Timber Strengths

DEFF Research Database (Denmark)

Sørensen, John Dalsgaard

2003-01-01

Statistical analyses are performed for material strength parameters from a large number of specimens of structural timber. Non-parametric statistical analysis and fits have been investigated for the following distribution types: Normal, Lognormal, 2 parameter Weibull and 3-parameter Weibull...... fits to the data available, especially if tail fits are used whereas the Log Normal distribution generally gives a poor fit and larger coefficients of variation, especially if tail fits are used. The implications on the reliability level of typical structural elements and on partial safety factors...... for timber are investigated....

Numeric computation and statistical data analysis on the Java platform

CERN Document Server

Chekanov, Sergei V

2016-01-01

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis ...

A Divergence Statistics Extension to VTK for Performance Analysis

Energy Technology Data Exchange (ETDEWEB)

Pebay, Philippe Pierre [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bennett, Janine Camille [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2015-02-01

This report follows the series of previous documents ([PT08, BPRT09b, PT09, BPT09, PT10, PB13], where we presented the parallel descriptive, correlative, multi-correlative, principal component analysis, contingency, k -means, order and auto-correlative statistics engines which we developed within the Visualization Tool Kit ( VTK ) as a scalable, parallel and versatile statistics package. We now report on a new engine which we developed for the calculation of divergence statistics, a concept which we hereafter explain and whose main goal is to quantify the discrepancy, in a stasticial manner akin to measuring a distance, between an observed empirical distribution and a theoretical, "ideal" one. The ease of use of the new diverence statistics engine is illustrated by the means of C++ code snippets. Although this new engine does not yet have a parallel implementation, it has already been applied to HPC performance analysis, of which we provide an example.

Approach to cerebrospinal fluid (CSF) biomarker discovery and evaluation in HIV infection.

Science.gov (United States)

Price, Richard W; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena; Smith, Richard D; Jacobs, Jon M; Brown, Joseph N; Gisslen, Magnus

2013-12-01

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previously-defined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

Energy Technology Data Exchange (ETDEWEB)

Price, Richard W.; Peterson, Julia; Fuchs, Dietmar; Angel, Thomas E.; Zetterberg, Henrik; Hagberg, Lars; Spudich, Serena S.; Smith, Richard D.; Jacobs, Jon M.; Brown, Joseph N.; Gisslen, Magnus

2013-12-13

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

A New Serum Biomarker for Lung Cancer - Transthyretin

Directory of Open Access Journals (Sweden)

Liyun LIU

2009-04-01

Full Text Available Background and objective Lung cancer is the leading cause of cancer death worldwide and very few specific biomarkers could be used in clinical diagnosis at present. The aim of this study is to find novel potential serum biomarkers for lung cancer using Surface Enhanced Laser Desorption/Ionization (SELDI technique. Methods Serumsample of 227 cases including 146 lung cancer, 13 pneumonia, 28 tuberculous pleurisy and 40 normal individuals were analyzed by CM10 chips. The candidate biomarkers were identified by ESI/MS-MS and database searching, and further confirmed by immunoprecipitation. The same sets of serum sample from all groups were re-measured by ELISA assay. Results Three protein peaks with the molecular weight 13.78 kDa, 13.90 kDa and 14.07 kDa were found significantlydecreased in lung cancer serum compared to the other groups and were all automatically selected as specific biomarkers by Biomarker Wizard software. The candidate biomarkers obtained from 1-D SDS gel bands by matching the molecular weight with peaks on CM10 chips were identified by Mass spectrometry as the native transthyretin (nativeTTR, cysTTR and glutTTR, and the identity was further validated by immunoprecipitation using commercial TTR antibodies. Downregulated of TTR was found in both ELISA and SELDI analysis. Conclusion TTRs acted as the potentially useful biomarkers for lung cancer by SELDI technique.

Developments in statistical analysis in quantitative genetics

DEFF Research Database (Denmark)

Sorensen, Daniel

2009-01-01

of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...

On the Statistical Validation of Technical Analysis

Directory of Open Access Journals (Sweden)

Rosane Riera Freire

2007-06-01

Full Text Available Technical analysis, or charting, aims on visually identifying geometrical patterns in price charts in order to antecipate price "trends". In this paper we revisit the issue of thecnical analysis validation which has been tackled in the literature without taking care for (i the presence of heterogeneity and (ii statistical dependence in the analyzed data - various agglutinated return time series from distinct financial securities. The main purpose here is to address the first cited problem by suggesting a validation methodology that also "homogenizes" the securities according to the finite dimensional probability distribution of their return series. The general steps go through the identification of the stochastic processes for the securities returns, the clustering of similar securities and, finally, the identification of presence, or absence, of informatinal content obtained from those price patterns. We illustrate the proposed methodology with a real data exercise including several securities of the global market. Our investigation shows that there is a statistically significant informational content in two out of three common patterns usually found through technical analysis, namely: triangle, rectangle and head and shoulders.

Have biomarkers made their mark? A brief review of dental biomarkers

Directory of Open Access Journals (Sweden)

Mohammed Kaleem Sultan

2014-01-01

Full Text Available Biomarkers are substances that are released into the human body by tumor cells or by other cells in response to tumor. A high level of a tumor marker is considered a sign of certain cancer, which makes biomarker the subject of many testing methods for the diagnosis of cancers. In recent times, these biomarkers have been successfully isolated to diagnose dental-related tumors, benign and malignant conditions. This article is a brief review of literature for various biomarkers used in the field of dentistry.

Neuropathological biomarker candidates in brain tumors: key issues for translational efficiency.

Science.gov (United States)

Hainfellner, J A; Heinzl, H

2010-01-01

Brain tumors comprise a large spectrum of rare malignancies in children and adults that are often associated with severe neurological symptoms and fatal outcome. Neuropathological tumor typing provides both prognostic and predictive tissue information which is the basis for optimal postoperative patient management and therapy. Molecular biomarkers may extend and refine prognostic and predictive information in a brain tumor case, providing more individualized and optimized treatment options. In the recent past a few neuropathological brain tumor biomarkers have translated smoothly into clinical use whereas many candidates show protracted translation. We investigated the causes of protracted translation of candidate brain tumor biomarkers. Considering the research environment from personal, social and systemic perspectives we identified eight determinants of translational success: methodology, funding, statistics, organization, phases of research, cooperation, self-reflection, and scientific progeny. Smoothly translating biomarkers are associated with low degrees of translational complexity whereas biomarkers with protracted translation are associated with high degrees. Key issues for translational efficiency of neuropathological brain tumor biomarker research seem to be related to (i) the strict orientation to the mission of medical research, that is the improval of medical practice as primordial purpose of research, (ii) definition of research priorities according to clinical needs, and (iii) absorption of translational complexities by means of operatively beneficial standards. To this end, concrete actions should comprise adequate scientific education of young investigators, and shaping of integrative diagnostics and therapy research both on the local level and the level of influential international brain tumor research platforms.

Evaluation of direct and indirect ethanol biomarkers using a likelihood ratio approach to identify chronic alcohol abusers for forensic purposes.

Science.gov (United States)

Alladio, Eugenio; Martyna, Agnieszka; Salomone, Alberto; Pirro, Valentina; Vincenti, Marco; Zadora, Grzegorz

2017-02-01

The detection of direct ethanol metabolites, such as ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs), in scalp hair is considered the optimal strategy to effectively recognize chronic alcohol misuses by means of specific cut-offs suggested by the Society of Hair Testing. However, several factors (e.g. hair treatments) may alter the correlation between alcohol intake and biomarkers concentrations, possibly introducing bias in the interpretative process and conclusions. 125 subjects with various drinking habits were subjected to blood and hair sampling to determine indirect (e.g. CDT) and direct alcohol biomarkers. The overall data were investigated using several multivariate statistical methods. A likelihood ratio (LR) approach was used for the first time to provide predictive models for the diagnosis of alcohol abuse, based on different combinations of direct and indirect alcohol biomarkers. LR strategies provide a more robust outcome than the plain comparison with cut-off values, where tiny changes in the analytical results can lead to dramatic divergence in the way they are interpreted. An LR model combining EtG and FAEEs hair concentrations proved to discriminate non-chronic from chronic consumers with ideal correct classification rates, whereas the contribution of indirect biomarkers proved to be negligible. Optimal results were observed using a novel approach that associates LR methods with multivariate statistics. In particular, the combination of LR approach with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) proved successful in discriminating chronic from non-chronic alcohol drinkers. These LR models were subsequently tested on an independent dataset of 43 individuals, which confirmed their high efficiency. These models proved to be less prone to bias than EtG and FAEEs independently considered. In conclusion, LR models may represent an efficient strategy to sustain the diagnosis of chronic alcohol consumption

Metabolomics as a tool in the identification of dietary biomarkers.

Science.gov (United States)

Gibbons, Helena; Brennan, Lorraine

2017-02-01

Current dietary assessment methods including FFQ, 24-h recalls and weighed food diaries are associated with many measurement errors. In an attempt to overcome some of these errors, dietary biomarkers have emerged as a complementary approach to these traditional methods. Metabolomics has developed as a key technology for the identification of new dietary biomarkers and to date, metabolomic-based approaches have led to the identification of a number of putative biomarkers. The three approaches generally employed when using metabolomics in dietary biomarker discovery are: (i) acute interventions where participants consume specific amounts of a test food, (ii) cohort studies where metabolic profiles are compared between consumers and non-consumers of a specific food and (iii) the analysis of dietary patterns and metabolic profiles to identify nutritypes and biomarkers. The present review critiques the current literature in terms of the approaches used for dietary biomarker discovery and gives a detailed overview of the currently proposed biomarkers, highlighting steps needed for their full validation. Furthermore, the present review also evaluates areas such as current databases and software tools, which are needed to advance the interpretation of results and therefore enhance the utility of dietary biomarkers in nutrition research.

Smoking affects diagnostic salivary periodontal disease biomarker levels in adolescents.

Science.gov (United States)

Heikkinen, Anna Maria; Sorsa, Timo; Pitkäniemi, Janne; Tervahartiala, Taina; Kari, Kirsti; Broms, Ulla; Koskenvuo, Markku; Meurman, Jukka H

2010-09-01

The effects of smoking on periodontal biomarkers in adolescents are unknown. This study investigates matrix metalloproteinase (MMP)-8 and polymorphonuclear leukocyte elastase levels in saliva together with periodontal health indices accounting for body mass index and smoking in a birth cohort from Finland. The oral health of boys (n = 258) and girls (n = 243) aged 15 to 16 years was examined clinically. Health habits were assessed by questionnaire. Saliva samples were collected and analyzed by immunofluorometric and peptide assays for MMP-8 levels and polymorphonuclear leukocyte elastase activities, and investigated statistically with the background factors. Median MMP-8 values of male smokers were 112.03 microg/l compared to 176.89 microg/l of non-smokers (P = 0.05). For female smokers corresponding values were 170.88 microg/l versus 177.92 microg/l in non-smokers (not statistically significant). Elastase values in male smokers were 5.88 x 10(-3) Delta OD(405)/h versus 11.0 x 10(-3) Delta OD(405)/h in non-smokers (P = 0.02), and in female smokers 9.16 x 10(-3) Delta OD(405)/h versus 10.88 x 10(-3) Delta OD(405)/h in non-smokers (P = 0.72). The effect was strengthened by high pack-years of smoking (MMP-8, P = 0.04; elastase, P = 0.01). Both biomarkers increased with gingival bleeding. However, statistically significant associations were observed with bleeding on probing and MMP-8 (P = 0.04); MMP-8 was suggestively associated with probing depth (P = 0.09) in non-smoking boys. In smokers with calculus, MMP-8 increased after adjusting with body mass index (P = 0.03). No corresponding differences were seen in girls. Smoking significantly decreased both biomarkers studied. Compared to girls, boys seem to have enhanced susceptibility for periodontitis as reflected in salivary MMP-8 values.

Proteome screening of pleural effusions identifies galectin 1 as a diagnostic biomarker and highlights several prognostic biomarkers for malignant mesothelioma.

Science.gov (United States)

Mundt, Filip; Johansson, Henrik J; Forshed, Jenny; Arslan, Sertaç; Metintas, Muzaffer; Dobra, Katalin; Lehtiö, Janne; Hjerpe, Anders

2014-03-01

Malignant mesothelioma is an aggressive asbestos-induced cancer, and affected patients have a median survival of approximately one year after diagnosis. It is often difficult to reach a conclusive diagnosis, and ancillary measurements of soluble biomarkers could increase diagnostic accuracy. Unfortunately, few soluble mesothelioma biomarkers are suitable for clinical application. Here we screened the effusion proteomes of mesothelioma and lung adenocarcinoma patients to identify novel soluble mesothelioma biomarkers. We performed quantitative mass-spectrometry-based proteomics using isobaric tags for quantification and used narrow-range immobilized pH gradient/high-resolution isoelectric focusing (pH 4-4.25) prior to analysis by means of nano liquid chromatography coupled to MS/MS. More than 1,300 proteins were identified in pleural effusions from patients with malignant mesothelioma (n = 6), lung adenocarcinoma (n = 6), or benign mesotheliosis (n = 7). Data are available via ProteomeXchange with identifier PXD000531. The identified proteins included a set of known mesothelioma markers and proteins that regulate hallmarks of cancer such as invasion, angiogenesis, and immune evasion, plus several new candidate proteins. Seven candidates (aldo-keto reductase 1B10, apolipoprotein C-I, galectin 1, myosin-VIIb, superoxide dismutase 2, tenascin C, and thrombospondin 1) were validated by enzyme-linked immunosorbent assays in a larger group of patients with mesothelioma (n = 37) or metastatic carcinomas (n = 25) and in effusions from patients with benign, reactive conditions (n = 16). Galectin 1 was identified as overexpressed in effusions from lung adenocarcinoma relative to mesothelioma and was validated as an excellent predictor for metastatic carcinomas against malignant mesothelioma. Galectin 1, aldo-keto reductase 1B10, and apolipoprotein C-I were all identified as potential prognostic biomarkers for malignant mesothelioma. This analysis of the effusion proteome

Phthalate metabolites related to infertile biomarkers and infertility in Chinese men.

Science.gov (United States)

Liu, Liangpo; Wang, Heng; Tian, Meiping; Zhang, Jie; Panuwet, Parinya; D'Souza, Priya Esilda; Barr, Dana Boyd; Huang, Qingyu; Xia, Yankai; Shen, Heqing

2017-12-01

Although in vitro and in vivo laboratory studies have demonstrated androgen and anti-androgen effects on male reproduction from phthalate exposures, human studies still remain inconsistent. Therefore, a case-control study (n = 289) was conducted to evaluate the associations between phthalate exposures, male infertility risks, and changes in metabolomic biomarkers. Regional participants consisted of fertile (n = 150) and infertile (n = 139) males were recruited from Nanjing Medical University' affiliated hospitals. Seven urinary phthalate metabolites were measured using HPLC-MS/MS. Associations between levels of phthalate metabolites, infertility risks, and infertility-related biomarkers were statistically evaluated. MEHHP, one of the most abundant DEHP oxidative metabolites was significantly lower in cases than in controls (p = 0.039). When using the 1st quartile range as a reference, although statistically insignificant for odds ratios (ORs) of the 2nd, 3rd, and 4th quartiles (OR (95% CI) = 1.50 (0.34-6.48), 0.70 (0.14-3.52) and 0.42 (0.09-2.00), respectively), the MEHHP dose-dependent trend of infertility risk expressed as OR decreased significantly (p = 0.034). More interestingly, most of the phthalate metabolites, including MEHHP, were either positively associated with fertile prevention metabolic biomarkers or negatively associated with fertile hazard ones. Phthalate metabolism, along with their activated infertility-related biomarkers, may contribute to a decreased risk of male infertility at the subjects' ongoing exposure levels. Our results may be illustrated by the low-dose related androgen effect of phthalates and can improve our understanding of the controversial epidemiological results on this issue. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genetic Biomarkers of Barrett's Esophagus Susceptibility and Progression to Dysplasia and Cancer: A Systematic Review and Meta-Analysis.

Science.gov (United States)

Findlay, John M; Middleton, Mark R; Tomlinson, Ian

2016-01-01

Barrett's esophagus (BE) is a common and important precursor lesion of esophageal adenocarcinoma (EAC). A third of patients with BE are asymptomatic, and our ability to predict the risk of progression of metaplasia to dysplasia and EAC (and therefore guide management) is limited. There is an urgent need for clinically useful biomarkers of susceptibility to both BE and risk of subsequent progression. This study aims to systematically identify, review, and meta-analyze genetic biomarkers reported to predict both. A systematic review of the PubMed and EMBASE databases was performed in May 2014. Study and evidence quality were appraised using the revised American Society of Clinical Oncology guidelines, and modified Recommendations for Tumor Marker Scores. Meta-analysis was performed for all markers assessed by more than one study. A total of 251 full-text articles were reviewed; 52 were included. A total of 33 germline markers of susceptibility were identified (level of evidence II-III); 17 were included. Five somatic markers of progression were identified; meta-analysis demonstrated significant associations for chromosomal instability (level of evidence II). One somatic marker of progression/relapse following photodynamic therapy was identified. However, a number of failings of methodology and reporting were identified. This is the first systematic review and meta-analysis to evaluate genetic biomarkers of BE susceptibility and risk of progression. While a number of limitations of study quality temper the utility of those markers identified, some-in particular, those identified by genome-wide association studies, and chromosomal instability for progression-appear plausible, although robust validation is required.

Data management and statistical analysis for environmental assessment

International Nuclear Information System (INIS)

Wendelberger, J.R.; McVittie, T.I.

1995-01-01

Data management and statistical analysis for environmental assessment are important issues on the interface of computer science and statistics. Data collection for environmental decision making can generate large quantities of various types of data. A database/GIS system developed is described which provides efficient data storage as well as visualization tools which may be integrated into the data analysis process. FIMAD is a living database and GIS system. The system has changed and developed over time to meet the needs of the Los Alamos National Laboratory Restoration Program. The system provides a repository for data which may be accessed by different individuals for different purposes. The database structure is driven by the large amount and varied types of data required for environmental assessment. The integration of the database with the GIS system provides the foundation for powerful visualization and analysis capabilities

Identification of Biomarkers of Exposure to FTOHs and PAPs in Humans Using a Targeted and Nontargeted Analysis Approach.

Science.gov (United States)

Dagnino, Sonia; Strynar, Mark J; McMahen, Rebecca L; Lau, Christopher S; Ball, Carol; Garantziotis, Stavros; Webster, Thomas F; McClean, Michael D; Lindstrom, Andrew B

2016-09-20

Although historic perfluorinated compounds are currently under scrutiny and growing regulatory control in the world, little is known about human exposure to other polyfluorinated compounds presently in use. Fluorotelomer alcohols (FTOHs) and polyfluoroalkyl phosphate esters (PAPs) are known to degrade to terminal perfluorinated acids and toxic reactive intermediates through metabolic pathways. Therefore, it is important to characterize their human exposure by the identification of unique biomarkers. With the use of liquid chromatography-mass spectrometry-time-of-flight analysis (LC-MS-TOF), we developed a workflow for the identification of metabolites for the 8:2 FTOH and 8:2 diPAP. Analysis of serum and urine of dosed rats indicated the 8:2 FTOH-sulfate and the 8:2 diPAP as potential biomarkers. These compounds, as well as 25 other fluorinated compounds and metabolites, were analyzed in human serum and urine samples from the general population (n = 100) and office workers (n = 30). The 8:2 FTOH-sulfate was measured for the first time in human samples in 5 to 10% of the serum samples, ranging from 50 to 80 pg/mL. The 8:2 diPAP was measured in 58% of the samples, ranging from 100 to 800 pg/mL. This study indicates the FTOH-sulfate conjugate as a biomarker of exposure to FTOHs and PAPs in humans.

Compliance strategy for statistically based neutron overpower protection safety analysis methodology

International Nuclear Information System (INIS)

Holliday, E.; Phan, B.; Nainer, O.

2009-01-01

The methodology employed in the safety analysis of the slow Loss of Regulation (LOR) event in the OPG and Bruce Power CANDU reactors, referred to as Neutron Overpower Protection (NOP) analysis, is a statistically based methodology. Further enhancement to this methodology includes the use of Extreme Value Statistics (EVS) for the explicit treatment of aleatory and epistemic uncertainties, and probabilistic weighting of the initial core states. A key aspect of this enhanced NOP methodology is to demonstrate adherence, or compliance, with the analysis basis. This paper outlines a compliance strategy capable of accounting for the statistical nature of the enhanced NOP methodology. (author)

Individual Biomarkers Using Molecular Personalized Medicine Approaches.

Science.gov (United States)

Zenner, Hans P

2017-01-01

Molecular personalized medicine tries to generate individual predictive biomarkers to assist doctors in their decision making. These are thought to improve the efficacy and lower the toxicity of a treatment. The molecular basis of the desired high-precision prediction is modern "omex" technologies providing high-throughput bioanalytical methods. These include genomics and epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, imaging, and functional analyses. In most cases, producing big data also requires a complex biomathematical analysis. Using molecular personalized medicine, the conventional physician's check of biomarker results may no longer be sufficient. By contrast, the physician may need to cooperate with the biomathematician to achieve the desired prediction on the basis of the analysis of individual big data typically produced by omex technologies. Identification of individual biomarkers using molecular personalized medicine approaches is thought to allow a decision-making for the precise use of a targeted therapy, selecting the successful therapeutic tool from a panel of preexisting drugs or medical products. This should avoid the treatment of nonresponders and responders that produces intolerable unwanted effects. © 2017 S. Karger AG, Basel.

Biomarker MicroRNAs for Diagnosis of Oral Squamous Cell Carcinoma Identified Based on Gene Expression Data and MicroRNA-mRNA Network Analysis

Science.gov (United States)

Zhang, Hui; Li, Tangxin; Zheng, Linqing

2017-01-01

Oral squamous cell carcinoma is one of the most malignant tumors with high mortality rate worldwide. Biomarker discovery is critical for early diagnosis and precision treatment of this disease. MicroRNAs are small noncoding RNA molecules which often regulate essential biological processes and are good candidates for biomarkers. By integrative analysis of both the cancer-associated gene expression data and microRNA-mRNA network, miR-148b-3p, miR-629-3p, miR-27a-3p, and miR-142-3p were screened as novel diagnostic biomarkers for oral squamous cell carcinoma based on their unique regulatory abilities in the network structure of the conditional microRNA-mRNA network and their important functions. These findings were confirmed by literature verification and functional enrichment analysis. Future experimental validation is expected for the further investigation of their molecular mechanisms. PMID:29098014

Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

Directory of Open Access Journals (Sweden)

Nauck Matthias

2011-07-01

Full Text Available Abstract Background Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association. Methods Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7% occurred. Subjective health was assessed by SF-12 derived physical (PCS-12 and mental component summaries (MCS-12, and a single-item self-rated health (SRH question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC curves, C-statistics, and reclassification methods. Results In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR, 2.07; 95% CI, 1.34-3.20 and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33 were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883 compared to the selected biomarker panel (0.872, whereas a combined assessment showed the highest C-statistic (0.887 with a highly significant integrated discrimination improvement of 1.5% (p Conclusion Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.

Novel ageing-biomarker discovery using data-intensive technologies

OpenAIRE

Griffiths, H.R.; Augustyniak, E.M.; Bennett, S.J.; Debacq-Chainiaux, F.; Dunston, C.R.; Kristensen, P.; Melchjorsen, C.J.; Navarrete, Santos A.; Simm, A.; Toussaint, O.

2015-01-01

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for bioma...

Diagnosis checking of statistical analysis in RCTs indexed in PubMed.

Science.gov (United States)

Lee, Paul H; Tse, Andy C Y

2017-11-01

Statistical analysis is essential for reporting of the results of randomized controlled trials (RCTs), as well as evaluating their effectiveness. However, the validity of a statistical analysis also depends on whether the assumptions of that analysis are valid. To review all RCTs published in journals indexed in PubMed during December 2014 to provide a complete picture of how RCTs handle assumptions of statistical analysis. We reviewed all RCTs published in December 2014 that appeared in journals indexed in PubMed using the Cochrane highly sensitive search strategy. The 2014 impact factors of the journals were used as proxies for their quality. The type of statistical analysis used and whether the assumptions of the analysis were tested were reviewed. In total, 451 papers were included. Of the 278 papers that reported a crude analysis for the primary outcomes, 31 (27·2%) reported whether the outcome was normally distributed. Of the 172 papers that reported an adjusted analysis for the primary outcomes, diagnosis checking was rarely conducted, with only 20%, 8·6% and 7% checked for generalized linear model, Cox proportional hazard model and multilevel model, respectively. Study characteristics (study type, drug trial, funding sources, journal type and endorsement of CONSORT guidelines) were not associated with the reporting of diagnosis checking. The diagnosis of statistical analyses in RCTs published in PubMed-indexed journals was usually absent. Journals should provide guidelines about the reporting of a diagnosis of assumptions. © 2017 Stichting European Society for Clinical Investigation Journal Foundation.

A κ-generalized statistical mechanics approach to income analysis

Science.gov (United States)

Clementi, F.; Gallegati, M.; Kaniadakis, G.

2009-02-01

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.

A κ-generalized statistical mechanics approach to income analysis

International Nuclear Information System (INIS)

Clementi, F; Gallegati, M; Kaniadakis, G

2009-01-01

This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low–middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful

Ebola hemorrhagic Fever: novel biomarker correlates of clinical outcome.

Science.gov (United States)

McElroy, Anita K; Erickson, Bobbie R; Flietstra, Timothy D; Rollin, Pierre E; Nichol, Stuart T; Towner, Jonathan S; Spiropoulou, Christina F

2014-08-15

Ebola hemorrhagic fever (EHF) outbreaks occur sporadically in Africa and result in high rates of death. The 2000-2001 outbreak of Sudan virus-associated EHF in the Gulu district of Uganda led to 425 cases, of which 216 were laboratory confirmed, making it the largest EHF outbreak on record. Serum specimens from this outbreak had been preserved in liquid nitrogen from the time of collection and were available for analysis. Available samples were tested using a series of multiplex assays to measure the concentrations of 55 biomarkers. The data were analyzed to identify statistically significant associations between the tested biomarkers and hemorrhagic manifestations, viremia, and/or death. Death, hemorrhage, and viremia were independently associated with elevated levels of several chemokines and cytokines. Death and hemorrhage were associated with elevated thrombomodulin and ferritin levels. Hemorrhage was also associated with elevated levels of soluble intracellular adhesion molecule. Viremia was independently associated with elevated levels of tissue factor and tissue plasminogen activator. Finally, samples from nonfatal cases had higher levels of sCD40L. These novel associations provide a better understanding of EHF pathophysiology and a starting point for researching new potential targets for therapeutic interventions. Published by Oxford University Press on behalf of the Infectious Diseases Society of America 2014. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Normality Tests for Statistical Analysis: A Guide for Non-Statisticians

Science.gov (United States)

Ghasemi, Asghar; Zahediasl, Saleh

2012-01-01

Statistical errors are common in scientific literature and about 50% of the published articles have at least one error. The assumption of normality needs to be checked for many statistical procedures, namely parametric tests, because their validity depends on it. The aim of this commentary is to overview checking for normality in statistical analysis using SPSS. PMID:23843808

Atacama Rover Astrobiology Drilling Studies: Roving to Find Subsurface Preserved Biomarkers

Science.gov (United States)

Glass, B.; Davila, A.; Parro, V.; Quinn, R.; Willis, P.; Brinckerhoff, W.; DiRuggiero, J.; Williams, M.; Bergman, D.; Stoker, C.

2016-05-01

The ARADS project is a NASA PSTAR that will drill into a Mars analog site in search of biomarkers. Leading to a field test of an integrated rover-drill system with four prototype in-situ instruments for biomarker detection and analysis.

Prediction of acute coronary syndromes by urinary proteome analysis.

Directory of Open Access Journals (Sweden)

Nay M Htun

Full Text Available Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases. Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75. ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664, which was similar to Framingham risk scoring (c-statistics = 0.644 in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP, combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751 resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001 and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007. In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Science.gov (United States)

Picard de Muller, Gaël; Ait-Belkacem, Rima; Bonnel, David; Longuespée, Rémi; Stauber, Jonathan

2017-12-01

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric - number of objects and total surface - information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. [Figure not available: see fulltext.

COPD association and repeatability of blood biomarkers in the ECLIPSE cohort

Directory of Open Access Journals (Sweden)

Dickens Jennifer A

2011-11-01

Full Text Available Abstract Background There is a need for biomarkers to better characterise individuals with COPD and to aid with the development of therapeutic interventions. A panel of putative blood biomarkers was assessed in a subgroup of the Evaluation of COPD Longitudinally to Identify Surrogate Endpoints (ECLIPSE cohort. Methods Thirty-four blood biomarkers were assessed in 201 subjects with COPD, 37 ex-smoker controls with normal lung function and 37 healthy non-smokers selected from the ECLIPSE cohort. Biomarker repeatability was assessed using baseline and 3-month samples. Intergroup comparisons were made using analysis of variance, repeatability was assessed through Bland-Altman plots, and correlations between biomarkers and clinical characteristics were assessed using Spearman correlation coefficients. Results Fifteen biomarkers were significantly different in individuals with COPD when compared to former or non-smoker controls. Some biomarkers, including tumor necrosis factor-α and interferon-γ, were measurable in only a minority of subjects whilst others such as C-reactive protein showed wide variability over the 3-month replication period. Fibrinogen was the most repeatable biomarker and exhibited a weak correlation with 6-minute walk distance, exacerbation rate, BODE index and MRC dyspnoea score in COPD subjects. 33% (66/201 of the COPD subjects reported at least 1 exacerbation over the 3 month study with 18% (36/201 reporting the exacerbation within 30 days of the 3-month visit. CRP, fibrinogen interleukin-6 and surfactant protein-D were significantly elevated in those COPD subjects with exacerbations within 30 days of the 3-month visit compared with those individuals that did not exacerbate or whose exacerbations had resolved. Conclusions Only a few of the biomarkers assessed may be useful in diagnosis or management of COPD where the diagnosis is based on airflow obstruction (GOLD. Further analysis of more promising biomarkers may reveal

HCC Biomarkers in China and Taiwan

Directory of Open Access Journals (Sweden)

Regina M. Santella

2007-02-01

Full Text Available

A number of different types of biomarkers have been used to understand the etiology and progression of hepatocellular cancer (HCC. Perhaps the most well known are the serum/ plasma markers of HBV or HCV infection. These markers include analysis of viral DNA or proteins or antibodies produced against the viral proteins. HBV surface antigen (HBsAg is most frequently used to determine chronic infection with high or low viral replication, while HBeAg is a measure of chronic infection with high viral replication. Analysis of antibodies includes measurement of anti-HBV core antigen, anti-HBV e antigen and anti-HBsAg. The response to immunization can be monitored by analysis of anti-HBsAg. The other major classes of biomarkers used in studies of HCC are biomarkers of exposure to environmental, lifestyle or dietary carcinogens, biomarkers of oxidative stress and early biologic response. In addition, studies of genetic susceptibility have studied polymorphisms in a number of pathways and their role in HCC risk. The biomarkers of exposure include the measurement of carcinogens in urine and carcinogen-DNA and protein adducts. Examples are measurement of aflatoxin and polycyclic aromatic hydrocarbon metabolites, and DNA and protein adducts.

Biomarkers of oxidative stress include urinary isoprostanes and 8-oxodeoxyguanosine and oxidized plasma proteins. Most of these assays are immunologic although the use of high performance liquid chromatograph (HPLC as well as gas chromatography/mass spectroscopy (GC/MS have been utilized. In nested case-control studies, many of these markers are associated with elevated risk. For example, elevated aflatoxin and polycyclic aromatic hydrocarbon-albumin adducts, aflatoxin metabolites in urine and urinary isoprostanes were observed in baseline samples from

The Role of Urinary Biomarker Levels in Assessing the Presence and Severity of Ureteropelvic Junction Obstruction in Children: A Systematic Review and Meta-Analysis

Directory of Open Access Journals (Sweden)

Abbas Alipour

2016-07-01

Full Text Available Context Ureteropelvic junction obstruction (UPJO is the most common obstructive disease of the urinary tract in infancy and childhood with a prevalence of 15% - 45% in neonates with antenatal hydronephrosis. The diagnosis of UPJO should be confirmed by imaging studies - most of which have a propensity to radiation exposure. Objectives The current study aimed to present a review protocol to assess the role of measuring urinary biomarkers to distinguish severe UPJO from milder forms of the disease. Data Sources The database of UPJO studies was searched and studies that compared the levels of urinary biomarkers with the gold standard (i e, dynamic renal scans for UPJO diagnosis were selected. Severity assessment was done quantitatively. Study Selection Three hundred fifty-eight articles were identified across the electronic databases. Twenty-seven articles were selected for the final analyses. Data Extraction Data were extracted independently by three reviewers and analyzed using STATA software version 12. Results Meta-analysis of studies showed that patients with severe UPJO had significantly higher biomarker levels than those with mild to moderate obstruction, with a pooled standardized mean difference (SMD of 0.5 (confidence interval (CI 95%, 0.34 - 0.67; P < 0.001; and significantly higher biomarker standardized to urinary creatinine levels than those with mild to moderate obstruction, with a pooled SMD of 1.02 (95% CI, 0.88 - 1.16; P < 0.001. Meta-analysis showed that patients with severe UPJO had significantly higher biomarker levels than healthy children, with a pooled SMD of 1.27 (CI 95%, 1.16 - 1.39; P < 0.001; and significantly higher biomarker standardized to urinary creatinine levels than healthy children, with a pooled SMD of 1.14 (CI 95%, 0.95 - 1.32; P < 0.001. Conclusions The assessment of urinary biomarkers is a helpful tool to assess the presence and severity of UPJO, but there is little published data on each of the studied

Some challenges with statistical inference in adaptive designs.

Science.gov (United States)

Hung, H M James; Wang, Sue-Jane; Yang, Peiling

2014-01-01

Adaptive designs have generated a great deal of attention to clinical trial communities. The literature contains many statistical methods to deal with added statistical uncertainties concerning the adaptations. Increasingly encountered in regulatory applications are adaptive statistical information designs that allow modification of sample size or related statistical information and adaptive selection designs that allow selection of doses or patient populations during the course of a clinical trial. For adaptive statistical information designs, a few statistical testing methods are mathematically equivalent, as a number of articles have stipulated, but arguably there are large differences in their practical ramifications. We pinpoint some undesirable features of these methods in this work. For adaptive selection designs, the selection based on biomarker data for testing the correlated clinical endpoints may increase statistical uncertainty in terms of type I error probability, and most importantly the increased statistical uncertainty may be impossible to assess.

Development of computer-assisted instruction application for statistical data analysis android platform as learning resource

Science.gov (United States)

Hendikawati, P.; Arifudin, R.; Zahid, M. Z.

2018-03-01

This study aims to design an android Statistics Data Analysis application that can be accessed through mobile devices to making it easier for users to access. The Statistics Data Analysis application includes various topics of basic statistical along with a parametric statistics data analysis application. The output of this application system is parametric statistics data analysis that can be used for students, lecturers, and users who need the results of statistical calculations quickly and easily understood. Android application development is created using Java programming language. The server programming language uses PHP with the Code Igniter framework, and the database used MySQL. The system development methodology used is the Waterfall methodology with the stages of analysis, design, coding, testing, and implementation and system maintenance. This statistical data analysis application is expected to support statistical lecturing activities and make students easier to understand the statistical analysis of mobile devices.

COPD Exacerbation Biomarkers Validated Using Multiple Reaction Monitoring Mass Spectrometry.

Directory of Open Access Journals (Sweden)

Janice M Leung

Full Text Available Acute exacerbations of chronic obstructive pulmonary disease (AECOPD result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD.We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72. Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate 1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109 using leave-pair-out cross-validation methods.Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p<0.001. The receiver operating characteristic cross-validation area under the curve (CV-AUC statistic was 0.73 in Cohort A, while in the replication cohorts the CV-AUC was 0.77 for Cohort B and 0.79 for Cohort C.A panel of five biomarkers shows promise in distinguishing AECOPD from convalescence and may provide the basis for a clinical blood test to diagnose AECOPD. Further validation in larger cohorts is necessary for future clinical translation.

Biomarker Qualification: Toward a Multiple Stakeholder Framework for Biomarker Development, Regulatory Acceptance, and Utilization.

Science.gov (United States)

Amur, S; LaVange, L; Zineh, I; Buckman-Garner, S; Woodcock, J

2015-07-01

The discovery, development, and use of biomarkers for a variety of drug development purposes are areas of tremendous interest and need. Biomarkers can become accepted for use through submission of biomarker data during the drug approval process. Another emerging pathway for acceptance of biomarkers is via the biomarker qualification program developed by the Center for Drug Evaluation and Research (CDER, US Food and Drug Administration). Evidentiary standards are needed to develop and evaluate various types of biomarkers for their intended use and multiple stakeholders, including academia, industry, government, and consortia must work together to help develop this evidence. The article describes various types of biomarkers that can be useful in drug development and evidentiary considerations that are important for qualification. A path forward for coordinating efforts to identify and explore needed biomarkers is proposed for consideration. © 2015 American Society for Clinical Pharmacology and Therapeutics.

Evaluation of Blood Biomarkers Associated with Risk of Malnutrition in Older Adults: A Systematic Review and Meta-Analysis.

Science.gov (United States)

Zhang, Zhiying; Pereira, Suzette L; Luo, Menghua; Matheson, Eric M

2017-08-03

Malnutrition is a common yet under-recognized problem in hospitalized patients. The aim of this paper was to systematically review and evaluate malnutrition biomarkers among order adults. Eligible studies were identified through Cochrane, PubMed and the ProQuest Dialog. A meta-regression was performed on concentrations of biomarkers according to malnutrition risks classified by validated nutrition assessment tools. A total of 111 studies were included, representing 52,911 participants (55% female, 72 ± 17 years old) from various clinical settings (hospital, community, care homes). The estimated BMI ( p malnutrition risk by MNA were significantly lower than those without a risk. Similar results were observed for malnutrition identified by SGA and NRS-2002. A sensitivity analysis by including patients with acute illness showed that albumin and prealbumin concentrations were dramatically reduced, indicating that they must be carefully interpreted in acute care settings. This review showed that BMI, hemoglobin, and total cholesterol are useful biomarkers of malnutrition in older adults. The reference ranges and cut-offs may need to be updated to avoid underdiagnosis of malnutrition.

Statistical analysis of metallicity in spiral galaxies

Energy Technology Data Exchange (ETDEWEB)

Galeotti, P [Consiglio Nazionale delle Ricerche, Turin (Italy). Lab. di Cosmo-Geofisica; Turin Univ. (Italy). Ist. di Fisica Generale)

1981-04-01

A principal component analysis of metallicity and other integral properties of 33 spiral galaxies is presented; the involved parameters are: morphological type, diameter, luminosity and metallicity. From the statistical analysis it is concluded that the sample has only two significant dimensions and additonal tests, involving different parameters, show similar results. Thus it seems that only type and luminosity are independent variables, being the other integral properties of spiral galaxies correlated with them.

Crevicular fluid biomarkers and periodontal disease progression.

Science.gov (United States)

Kinney, Janet S; Morelli, Thiago; Oh, Min; Braun, Thomas M; Ramseier, Christoph A; Sugai, Jim V; Giannobile, William V

2014-02-01

Assess the ability of a panel of gingival crevicular fluid (GCF) biomarkers as predictors of periodontal disease progression (PDP). In this study, 100 individuals participated in a 12-month longitudinal investigation and were categorized into four groups according to their periodontal status. GCF, clinical parameters and saliva were collected bi-monthly. Subgingival plaque and serum were collected bi-annually. For 6 months, no periodontal treatment was provided. At 6 months, patients received periodontal therapy and continued participation from 6 to 12 months. GCF samples were analysed by ELISA for MMP-8, MMP-9, Osteoprotegerin, C-reactive Protein and IL-1β. Differences in median levels of GCF biomarkers were compared between stable and progressing participants using Wilcoxon Rank Sum test (p = 0.05). Clustering algorithm was used to evaluate the ability of oral biomarkers to classify patients as either stable or progressing. Eighty-three individuals completed the 6-month monitoring phase. With the exception of GCF C-reactive protein, all biomarkers were significantly higher in the PDP group compared to stable patients. Clustering analysis showed highest sensitivity levels when biofilm pathogens and GCF biomarkers were combined with clinical measures, 74% (95% CI = 61, 86). Signature of GCF fluid-derived biomarkers combined with pathogens and clinical measures provides a sensitive measure for discrimination of PDP (ClinicalTrials.gov NCT00277745). © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Identification of CEACAM5 as a Biomarker for Prewarning and Prognosis in Gastric Cancer.

Science.gov (United States)

Zhou, Jinfeng; Fan, Xing; Chen, Ning; Zhou, Fenli; Dong, Jiaqiang; Nie, Yongzhan; Fan, Daiming

2015-12-01

MGd1, a monoclonal antibody raised against gastric cancer cells, possesses a high degree of specificity for gastric cancer (GC). Here we identified that the antigen of MGd1 is CEACAM5, and used MGd1 to investigate the expression of CEACAM5 in non-GC and GC tissues (N=643), as a biomarker for prewarning and prognosis. The expression of CEACAM5 was detected by immunohistochemistry in numerous tissues; its clinicopathological correlation was statistically analyzed. CEACAM5 expression was increased progressively from normal gastric mucosa to chronic atrophic gastritis, intestinal metaplasia, dysplasia and finally to GC (pgastric precancerous lesions (intestinal metaplasia and dysplasia), CEACAM5-positive patients had a higher risk of developing GC as compared with CEACAM5-negative patients (OR = 12.68, pgastric adenocarcinoma (p<0.001). In survival analysis, CEACAM5 was demonstrated to be an independent prognostic predictor for patients with GC of clinical stage IIIA/IV (p=0.033). Our results demonstrate that CEACAM5 is a promising biomarker for GC prewarning and prognostic evaluation. © The Author(s) 2015.

Biomarker identification and pathway analysis of preeclampsia based on serum metabolomics.

Science.gov (United States)

Chen, Tingting; He, Ping; Tan, Yong; Xu, Dongying

2017-03-25

Preeclampsia presents serious risk of both maternal and fetal morbidity and mortality. Biomarkers for the detection of preeclampsia are critical for risk assessment and targeted intervention. The goal of this study is to screen potential biomarkers for the diagnosis of preeclampsia and to illuminate the pathogenesis of preeclampsia development based on the differential expression network. Two groups of subjects, including healthy pregnant women, subjects with preeclampsia, were recruited for this study. The metabolic profiles of all of the subjects' serum were obtained by liquid chromatography quadruple time-of-flight mass spectrometry. Correlation between metabolites was analyzed by bioinformatics technique. Results showed that the PC(14:0/00), proline betaine and proline were potential sensitive and specific biomarkers for preeclampsia diagnosis and prognosis. Perturbation of corresponding biological pathways, such as iNOS signaling, nitric oxide signaling in the cardiovascular system, mitochondrial dysfunction were responsible for the pathogenesis of preeclampsia. This study indicated that the metabolic profiling had a good clinical significance in the diagnosis of preeclampsia as well as in the study of its pathogenesis. Copyright © 2017 Elsevier Inc. All rights reserved.

Metabolomics, Nutrition, and Potential Biomarkers of Food Quality, Intake, and Health Status.

Science.gov (United States)

Sébédio, Jean-Louis

Diet, dietary patterns, and other environmental factors such as exposure to toxins are playing an important role in the prevention/development of many diseases, like obesity, type 2 diabetes, and consequently on the health status of individuals. A major challenge nowadays is to identify novel biomarkers to detect as early as possible metabolic dysfunction and to predict evolution of health status in order to refine nutritional advices to specific population groups. Omics technologies such as genomics, transcriptomics, proteomics, and metabolomics coupled with statistical and bioinformatics tools have already shown great potential in this research field even if so far only few biomarkers have been validated. For the past two decades, important analytical techniques have been developed to detect as many metabolites as possible in human biofluids such as urine, blood, and saliva. In the field of food science and nutrition, many studies have been carried out for food authenticity, quality, and safety, as well as for food processing. Furthermore, metabolomic investigations have been carried out to discover new early biomarkers of metabolic dysfunction and predictive biomarkers of developing pathologies (obesity, metabolic syndrome, type-2 diabetes, etc.). Great emphasis is also placed in the development of methodologies to identify and validate biomarkers of nutrients exposure. © 2017 Elsevier Inc. All rights reserved.

Statistical Analysis of Protein Ensembles

Science.gov (United States)

Máté, Gabriell; Heermann, Dieter

2014-04-01

As 3D protein-configuration data is piling up, there is an ever-increasing need for well-defined, mathematically rigorous analysis approaches, especially that the vast majority of the currently available methods rely heavily on heuristics. We propose an analysis framework which stems from topology, the field of mathematics which studies properties preserved under continuous deformations. First, we calculate a barcode representation of the molecules employing computational topology algorithms. Bars in this barcode represent different topological features. Molecules are compared through their barcodes by statistically determining the difference in the set of their topological features. As a proof-of-principle application, we analyze a dataset compiled of ensembles of different proteins, obtained from the Ensemble Protein Database. We demonstrate that our approach correctly detects the different protein groupings.

The Power of Neuroimaging Biomarkers for Screening Frontotemporal Dementia

OpenAIRE

McMillan, Corey T.; Avants, Brian B.; Cook, Philip; Ungar, Lyle; Trojanowski, John Q.; Grossman, Murray

2014-01-01

Frontotemporal dementia (FTD) is a clinically and pathologically heterogeneous neurodegenerative disease that can result from either frontotemporal lobar degeneration (FTLD) or Alzheimer’s disease (AD) pathology. It is critical to establish statistically powerful biomarkers that can achieve substantial cost-savings and increase feasibility of clinical trials. We assessed three broad categories of neuroimaging methods to screen underlying FTLD and AD pathology in a clinical FTD series: global ...

State analysis of BOP using statistical and heuristic methods

International Nuclear Information System (INIS)

Heo, Gyun Young; Chang, Soon Heung

2003-01-01

Under the deregulation environment, the performance enhancement of BOP in nuclear power plants is being highlighted. To analyze performance level of BOP, we use the performance test procedures provided from an authorized institution such as ASME. However, through plant investigation, it was proved that the requirements of the performance test procedures about the reliability and quantity of sensors was difficult to be satisfied. As a solution of this, state analysis method that are the expanded concept of signal validation, was proposed on the basis of the statistical and heuristic approaches. Authors recommended the statistical linear regression model by analyzing correlation among BOP parameters as a reference state analysis method. Its advantage is that its derivation is not heuristic, it is possible to calculate model uncertainty, and it is easy to apply to an actual plant. The error of the statistical linear regression model is below 3% under normal as well as abnormal system states. Additionally a neural network model was recommended since the statistical model is impossible to apply to the validation of all of the sensors and is sensitive to the outlier that is the signal located out of a statistical distribution. Because there are a lot of sensors need to be validated in BOP, wavelet analysis (WA) were applied as a pre-processor for the reduction of input dimension and for the enhancement of training accuracy. The outlier localization capability of WA enhanced the robustness of the neural network. The trained neural network restored the degraded signals to the values within ±3% of the true signals

Precision Statistical Analysis of Images Based on Brightness Distribution

Directory of Open Access Journals (Sweden)

Muzhir Shaban Al-Ani

2017-07-01

Full Text Available Study the content of images is considered an important topic in which reasonable and accurate analysis of images are generated. Recently image analysis becomes a vital field because of huge number of images transferred via transmission media in our daily life. These crowded media with images lead to highlight in research area of image analysis. In this paper, the implemented system is passed into many steps to perform the statistical measures of standard deviation and mean values of both color and grey images. Whereas the last step of the proposed method concerns to compare the obtained results in different cases of the test phase. In this paper, the statistical parameters are implemented to characterize the content of an image and its texture. Standard deviation, mean and correlation values are used to study the intensity distribution of the tested images. Reasonable results are obtained for both standard deviation and mean value via the implementation of the system. The major issue addressed in the work is concentrated on brightness distribution via statistical measures applying different types of lighting.

Mass spectrometry for protein quantification in biomarker discovery.

Science.gov (United States)

Wang, Mu; You, Jinsam

2012-01-01

Major technological advances have made proteomics an extremely active field for biomarker discovery in recent years due primarily to the development of newer mass spectrometric technologies and the explosion in genomic and protein bioinformatics. This leads to an increased emphasis on larger scale, faster, and more efficient methods for detecting protein biomarkers in human tissues, cells, and biofluids. Most current proteomic methodologies for biomarker discovery, however, are not highly automated and are generally labor-intensive and expensive. More automation and improved software programs capable of handling a large amount of data are essential to reduce the cost of discovery and to increase throughput. In this chapter, we discuss and describe mass spectrometry-based proteomic methods for quantitative protein analysis.

Biomarkers for the Diagnosis of Cholangiocarcinoma: A Systematic Review.

Science.gov (United States)

Tshering, Gyem; Dorji, Palden Wangyel; Chaijaroenkul, Wanna; Na-Bangchang, Kesara

2018-06-01

Cholangiocarcinoma (CCA), a malignant tumor of the bile duct, is a major public health problem in many Southeast Asian countries, particularly Thailand. The slow progression makes it difficult for early diagnosis and most patients are detected in advanced stages. This study aimed to review all relevant articles related to the biomarkers for the diagnosis of CCA and point out potential biomarkers. A thorough search was performed in PubMed and ScienceDirect for CCA biomarker articles. Required data were extracted. A total of 46 articles that fulfilled the inclusion and had none of the exclusion criteria were included in the analysis (17, 22, 3, 4, and 1 articles on blood, tissue, bile, both blood and tissue, and urine biomarkers, respectively). Carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA), either alone or in combination with other biomarkers, are the most commonly studied biomarkers in the serum. Their sensitivity and specificity ranged from 47.2% to 98.2% and 89.7% to 100%, respectively. However, in the tissue, gene methylations and DNA-related markers were the most studied CCA biomarkers. Their sensitivity and specificity ranged from 58% to 87% and 98% to 100%, respectively. Some articles investigated biomarkers both in blood and tissues, particularly CA19-9 and CEA, with sensitivity and specificity ranging from 33% to 100% and 50% to 97.7%, respectively. Although quite a number of biomarkers with a potential role in the early detection of CCA have been established, it is difficult to single out any particular marker that could be used in the routine clinical settings.

Fisher statistics for analysis of diffusion tensor directional information.

Science.gov (United States)

Hutchinson, Elizabeth B; Rutecki, Paul A; Alexander, Andrew L; Sutula, Thomas P

2012-04-30

A statistical approach is presented for the quantitative analysis of diffusion tensor imaging (DTI) directional information using Fisher statistics, which were originally developed for the analysis of vectors in the field of paleomagnetism. In this framework, descriptive and inferential statistics have been formulated based on the Fisher probability density function, a spherical analogue of the normal distribution. The Fisher approach was evaluated for investigation of rat brain DTI maps to characterize tissue orientation in the corpus callosum, fornix, and hilus of the dorsal hippocampal dentate gyrus, and to compare directional properties in these regions following status epilepticus (SE) or traumatic brain injury (TBI) with values in healthy brains. Direction vectors were determined for each region of interest (ROI) for each brain sample and Fisher statistics were applied to calculate the mean direction vector and variance parameters in the corpus callosum, fornix, and dentate gyrus of normal rats and rats that experienced TBI or SE. Hypothesis testing was performed by calculation of Watson's F-statistic and associated p-value giving the likelihood that grouped observations were from the same directional distribution. In the fornix and midline corpus callosum, no directional differences were detected between groups, however in the hilus, significant (pstatistical comparison of tissue structural orientation. Copyright © 2012 Elsevier B.V. All rights reserved.

Statistical analysis of RHIC beam position monitors performance

Science.gov (United States)

Calaga, R.; Tomás, R.

2004-04-01

A detailed statistical analysis of beam position monitors (BPM) performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

Statistical analysis of RHIC beam position monitors performance

Directory of Open Access Journals (Sweden)

R. Calaga

2004-04-01

Full Text Available A detailed statistical analysis of beam position monitors (BPM performance at RHIC is a critical factor in improving regular operations and future runs. Robust identification of malfunctioning BPMs plays an important role in any orbit or turn-by-turn analysis. Singular value decomposition and Fourier transform methods, which have evolved as powerful numerical techniques in signal processing, will aid in such identification from BPM data. This is the first attempt at RHIC to use a large set of data to statistically enhance the capability of these two techniques and determine BPM performance. A comparison from run 2003 data shows striking agreement between the two methods and hence can be used to improve BPM functioning at RHIC and possibly other accelerators.

Statistics Education Research in Malaysia and the Philippines: A Comparative Analysis

Science.gov (United States)

Reston, Enriqueta; Krishnan, Saras; Idris, Noraini

2014-01-01

This paper presents a comparative analysis of statistics education research in Malaysia and the Philippines by modes of dissemination, research areas, and trends. An electronic search for published research papers in the area of statistics education from 2000-2012 yielded 20 for Malaysia and 19 for the Philippines. Analysis of these papers showed…

Statistical analysis of next generation sequencing data

CERN Document Server

Nettleton, Dan

2014-01-01

Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized med...

Selected papers on analysis, probability, and statistics

CERN Document Server

Nomizu, Katsumi

1994-01-01

This book presents papers that originally appeared in the Japanese journal Sugaku. The papers fall into the general area of mathematical analysis as it pertains to probability and statistics, dynamical systems, differential equations and analytic function theory. Among the topics discussed are: stochastic differential equations, spectra of the Laplacian and Schrödinger operators, nonlinear partial differential equations which generate dissipative dynamical systems, fractal analysis on self-similar sets and the global structure of analytic functions.

Biomarkers for Wilms Tumor: a Systematic Review

Science.gov (United States)

Cone, Eugene B.; Dalton, Stewart S.; Van Noord, Megan; Tracy, Elizabeth T.; Rice, Henry E.; Routh, Jonathan C.

2016-01-01

Purpose Wilms tumor is the most common childhood renal malignancy and the fourth most common childhood cancer. Many biomarkers have been studied but there has been no comprehensive summary. We systematically reviewed the literature on biomarkers in Wilms Tumor with the objective of quantifying the prognostic implication of the presence of individual tumor markers. Methods We searched for English language studies from 1980–2015 performed on children with Wilms Tumor under 18 years old with prognostic data. The protocol was conducted as per PRISMA guidelines. Two reviewers abstracted data in duplicate using a standard evaluation form. We performed descriptive statistics, then calculated relative risks and 95% confidence intervals for markers appearing in multiple level 2 or 3 studies. Results 40 studies were included examining 32 biomarkers in 7381 Wilms patients. Studies had a median of 61 patients with 24 biomarker positive patients per study, and a median follow-up of 68.4 months. Median percent of patients in Stage 1, 2, 3, 4, and 5 were 28.5%, 26.4%, 24.5%, 14.1%, and 1.7%, with 10.2% anaplasia. The strongest negative prognostic association was loss of heterozygosity on 11p15, with a risk of recurrence of 5.00, although loss of heterozygosity on 1p and gain of function on 1q were also strongly linked to increased recurrence (2.93 and 2.86 respectively). Conclusions Several tumor markers are associated with an increased risk of recurrence or a decreased risk of overall survival in Wilms Tumor. These data suggest targets for development of diagnostic tests and potential therapies. PMID:27259655

Analysis of statistical misconception in terms of statistical reasoning

Science.gov (United States)

Maryati, I.; Priatna, N.

2018-05-01

Reasoning skill is needed for everyone to face globalization era, because every person have to be able to manage and use information from all over the world which can be obtained easily. Statistical reasoning skill is the ability to collect, group, process, interpret, and draw conclusion of information. Developing this skill can be done through various levels of education. However, the skill is low because many people assume that statistics is just the ability to count and using formulas and so do students. Students still have negative attitude toward course which is related to research. The purpose of this research is analyzing students’ misconception in descriptive statistic course toward the statistical reasoning skill. The observation was done by analyzing the misconception test result and statistical reasoning skill test; observing the students’ misconception effect toward statistical reasoning skill. The sample of this research was 32 students of math education department who had taken descriptive statistic course. The mean value of misconception test was 49,7 and standard deviation was 10,6 whereas the mean value of statistical reasoning skill test was 51,8 and standard deviation was 8,5. If the minimal value is 65 to state the standard achievement of a course competence, students’ mean value is lower than the standard competence. The result of students’ misconception study emphasized on which sub discussion that should be considered. Based on the assessment result, it was found that students’ misconception happen on this: 1) writing mathematical sentence and symbol well, 2) understanding basic definitions, 3) determining concept that will be used in solving problem. In statistical reasoning skill, the assessment was done to measure reasoning from: 1) data, 2) representation, 3) statistic format, 4) probability, 5) sample, and 6) association.

Mass spectrometry for biomarker development

Energy Technology Data Exchange (ETDEWEB)

Wu, Chaochao; Liu, Tao; Baker, Erin Shammel; Rodland, Karin D.; Smith, Richard D.

2015-06-19

Biomarkers potentially play a crucial role in early disease diagnosis, prognosis and targeted therapy. In the past decade, mass spectrometry based proteomics has become increasingly important in biomarker development due to large advances in technology and associated methods. This chapter mainly focuses on the application of broad (e.g. shotgun) proteomics in biomarker discovery and the utility of targeted proteomics in biomarker verification and validation. A range of mass spectrometry methodologies are discussed emphasizing their efficacy in the different stages in biomarker development, with a particular emphasis on blood biomarker development.

The analysis of volatile organic compounds in exhaled breath and biomarkers in exhaled breath condensate in children - clinical tools or scientific toys?

Science.gov (United States)

van Mastrigt, E; de Jongste, J C; Pijnenburg, M W

2015-07-01

Current monitoring strategies for respiratory diseases are mainly based on clinical features, lung function and imaging. As airway inflammation is the hallmark of many respiratory diseases in childhood, noninvasive methods to assess the presence and severity of airway inflammation might be helpful in both diagnosing and monitoring paediatric respiratory diseases. At present, the measurement of fractional exhaled nitric oxide is the only noninvasive method available to assess eosinophilic airway inflammation in clinical practice. We aimed to evaluate whether the analysis of volatile organic compounds (VOCs) in exhaled breath (EB) and biomarkers in exhaled breath condensate (EBC) is helpful in diagnosing and monitoring respiratory diseases in children. An extensive literature search was conducted in Medline, Embase and PubMed on the analysis and applications of VOCs in EB and EBC in children. We retrieved 1165 papers, of which nine contained original data on VOCs in EB and 84 on biomarkers in EBC. These were included in this review. We give an overview of the clinical applications in childhood and summarize the methodological issues. Several VOCs in EB and biomarkers in EBC have the potential to distinguish patients from healthy controls and to monitor treatment responses. Lack of standardization of collection methods and analysis techniques hampers the introduction in clinical practice. The measurement of metabolomic profiles may have important advantages over detecting single markers. There is a lack of longitudinal studies and external validation to reveal whether EB and EBC analysis have added value in the diagnostic process and follow-up of children with respiratory diseases. In conclusion, the use of VOCs in EB and biomarkers in EBC as markers of inflammatory airway diseases in children is still a research tool and not validated for clinical use. © 2014 John Wiley & Sons Ltd.

Comparative analysis of positive and negative attitudes toward statistics

Science.gov (United States)

Ghulami, Hassan Rahnaward; Ab Hamid, Mohd Rashid; Zakaria, Roslinazairimah

2015-02-01

Many statistics lecturers and statistics education researchers are interested to know the perception of their students' attitudes toward statistics during the statistics course. In statistics course, positive attitude toward statistics is a vital because it will be encourage students to get interested in the statistics course and in order to master the core content of the subject matters under study. Although, students who have negative attitudes toward statistics they will feel depressed especially in the given group assignment, at risk for failure, are often highly emotional, and could not move forward. Therefore, this study investigates the students' attitude towards learning statistics. Six latent constructs have been the measurement of students' attitudes toward learning statistic such as affect, cognitive competence, value, difficulty, interest, and effort. The questionnaire was adopted and adapted from the reliable and validate instrument of Survey of Attitudes towards Statistics (SATS). This study is conducted among engineering undergraduate engineering students in the university Malaysia Pahang (UMP). The respondents consist of students who were taking the applied statistics course from different faculties. From the analysis, it is found that the questionnaire is acceptable and the relationships among the constructs has been proposed and investigated. In this case, students show full effort to master the statistics course, feel statistics course enjoyable, have confidence that they have intellectual capacity, and they have more positive attitudes then negative attitudes towards statistics learning. In conclusion in terms of affect, cognitive competence, value, interest and effort construct the positive attitude towards statistics was mostly exhibited. While negative attitudes mostly exhibited by difficulty construct.

Electroencephalography Is a Good Complement to Currently Established Dementia Biomarkers

DEFF Research Database (Denmark)

Ferreira, Daniel; Jelic, Vesna; Cavallin, Lena

2016-01-01

, 135 Alzheimer's disease (AD), 15 dementia with Lewy bodies/Parkinson's disease with dementia (DLB/PDD), 32 other dementias]. The EEG data were recorded in a standardized way. Structural imaging data were visually rated using scales of atrophy in the medial temporal, frontal, and posterior cortex......BACKGROUND/AIMS: Dementia biomarkers that are accessible and easily applicable in nonspecialized clinical settings are urgently needed. Quantitative electroencephalography (qEEG) is a good candidate, and the statistical pattern recognition (SPR) method has recently provided promising results. We......EEG to the diagnostic workup substantially increases the detection of AD pathology even in pre-dementia stages and improves differential diagnosis. EEG could serve as a good complement to currently established dementia biomarkers since it is cheap, noninvasive, and extensively applied outside academic centers....

Biomarker discovery in mass spectrometry-based urinary proteomics.

Science.gov (United States)

Thomas, Samuel; Hao, Ling; Ricke, William A; Li, Lingjun

2016-04-01

Urinary proteomics has become one of the most attractive topics in disease biomarker discovery. MS-based proteomic analysis has advanced continuously and emerged as a prominent tool in the field of clinical bioanalysis. However, only few protein biomarkers have made their way to validation and clinical practice. Biomarker discovery is challenged by many clinical and analytical factors including, but not limited to, the complexity of urine and the wide dynamic range of endogenous proteins in the sample. This article highlights promising technologies and strategies in the MS-based biomarker discovery process, including study design, sample preparation, protein quantification, instrumental platforms, and bioinformatics. Different proteomics approaches are discussed, and progresses in maximizing urinary proteome coverage and standardization are emphasized in this review. MS-based urinary proteomics has great potential in the development of noninvasive diagnostic assays in the future, which will require collaborative efforts between analytical scientists, systems biologists, and clinicians. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Vapor Pressure Data Analysis and Statistics

Science.gov (United States)

2016-12-01

near 8, 2000, and 200, respectively. The A (or a) value is directly related to vapor pressure and will be greater for high vapor pressure materials...1, (10) where n is the number of data points, Yi is the natural logarithm of the i th experimental vapor pressure value, and Xi is the...VAPOR PRESSURE DATA ANALYSIS AND STATISTICS ECBC-TR-1422 Ann Brozena RESEARCH AND TECHNOLOGY DIRECTORATE

Statistical analysis of planktic foraminifera of the surface Continental ...

African Journals Online (AJOL)

Planktic foraminiferal assemblage recorded from selected samples obtained from shallow continental shelf sediments off southwestern Nigeria were subjected to statistical analysis. The Principal Component Analysis (PCA) was used to determine variants of planktic parameters. Values obtained for these parameters were ...

Towards Improved Biomarker Research

DEFF Research Database (Denmark)

Kjeldahl, Karin

This thesis takes a look at the data analytical challenges associated with the search for biomarkers in large-scale biological data such as transcriptomics, proteomics and metabolomics data. These studies aim to identify genes, proteins or metabolites which can be associated with e.g. a diet...... with very specific competencies. In order to optimize the basis of a sound and fruitful data analysis, suggestions are givenwhich focus on (1) collection of good data, (2) preparation of data for the data analysis and (3) a sound data analysis. If these steps are optimized, PLS is a also a very goodmethod...

Novel ageing-biomarker discovery using data-intensive technologies.

Science.gov (United States)

Griffiths, H R; Augustyniak, E M; Bennett, S J; Debacq-Chainiaux, F; Dunston, C R; Kristensen, P; Melchjorsen, C J; Navarrete, Santos A; Simm, A; Toussaint, O

2015-11-01

Ageing is accompanied by many visible characteristics. Other biological and physiological markers are also well-described e.g. loss of circulating sex hormones and increased inflammatory cytokines. Biomarkers for healthy ageing studies are presently predicated on existing knowledge of ageing traits. The increasing availability of data-intensive methods enables deep-analysis of biological samples for novel biomarkers. We have adopted two discrete approaches in MARK-AGE Work Package 7 for biomarker discovery; (1) microarray analyses and/or proteomics in cell systems e.g. endothelial progenitor cells or T cell ageing including a stress model; and (2) investigation of cellular material and plasma directly from tightly-defined proband subsets of different ages using proteomic, transcriptomic and miR array. The first approach provided longitudinal insight into endothelial progenitor and T cell ageing. This review describes the strategy and use of hypothesis-free, data-intensive approaches to explore cellular proteins, miR, mRNA and plasma proteins as healthy ageing biomarkers, using ageing models and directly within samples from adults of different ages. It considers the challenges associated with integrating multiple models and pilot studies as rational biomarkers for a large cohort study. From this approach, a number of high-throughput methods were developed to evaluate novel, putative biomarkers of ageing in the MARK-AGE cohort. Crown Copyright © 2015. Published by Elsevier Ireland Ltd. All rights reserved.

Novel Biomarkers of Physical Activity Maintenance in Midlife Women: Preliminary Investigation

Directory of Open Access Journals (Sweden)

Kelly A. Bosak

2018-04-01

Full Text Available The precision health initiative is leading the discovery of novel biomarkers as important indicators of biological processes or responses to behavior, such as physical activity. Neural biomarkers identified by magnetic resonance imaging (MRI hold promise to inform future research, and ultimately, for transfer to the clinical setting to optimize health outcomes. This study investigated resting-state and functional brain biomarkers between midlife women who were maintaining physical activity in accordance with the current national guidelines and previously acquired age-matched sedentary controls. Approval was obtained from the Human Subjects Committee. Participants included nondiabetic, healthy weight to overweight (body mass index 19–29.9 kg/m2 women (n = 12 aged 40–64 years. Control group data were used from participants enrolled in our previous functional MRI study and baseline resting-state MRI data from a subset of sedentary (<500 kcal of physical activity per week midlife women who were enrolled in a 9-month exercise intervention conducted in our imaging center. Differential activation of the inferior frontal gyrus (IFG and greater connectivity with the dorsolateral prefrontal cortex (dlPFC was identified between physically active women and sedentary controls. After correcting for multiple comparisons, these differences in biomarkers of physical activity maintenance did not reach statistical significance. Preliminary evidence in this small sample suggests that neural biomarkers of physical activity maintenance involve activations in the brain region associated with areas involved in implementing goal-directed behavior. Specifically, activation of the IFG and connectivity with the dlPFC is identified as a neural biomarker to explain and predict long-term physical activity maintenance for healthy aging. Future studies should evaluate these biomarker links with relevant clinical correlations.

Plasma biomarker of dietary phytosterol intake.

Directory of Open Access Journals (Sweden)

Xiaobo Lin

Full Text Available Dietary phytosterols, plant sterols structurally similar to cholesterol, reduce intestinal cholesterol absorption and have many other potentially beneficial biological effects in humans. Due to limited information on phytosterol levels in foods, however, it is difficult to quantify habitual dietary phytosterol intake (DPI. Therefore, we sought to identify a plasma biomarker of DPI.Data were analyzed from two feeding studies with a total of 38 subjects during 94 dietary periods. DPI was carefully controlled at low, intermediate, and high levels. Plasma levels of phytosterols and cholesterol metabolites were assessed at the end of each diet period. Based on simple ordinary least squares regression analysis, the best biomarker for DPI was the ratio of plasma campesterol to the endogenous cholesterol metabolite 5-α-cholestanol (R2 = 0.785, P 0.600; P < 0.01.The ratio of plasma campesterol to the coordinately regulated endogenous cholesterol metabolite 5-α-cholestanol is a biomarker of dietary phytosterol intake. Conversely, plasma phytosterol levels alone are not ideal biomarkers of DPI because they are confounded by large inter-individual variation in absorption and turnover of non-cholesterol sterols. Further work is needed to assess the relation between non-cholesterol sterol metabolism and associated cholesterol transport in the genesis of coronary heart disease.

Biomarkers in cancer screening: a public health perspective.

Science.gov (United States)

Srivastava, Sudhir; Gopal-Srivastava, Rashmi

2002-08-01

The last three decades have witnessed a rapid advancement and diffusion of technology in health services. Technological innovations have given health service providers the means to diagnose and treat an increasing number of illnesses, including cancer. In this effort, research on biomarkers for cancer detection and risk assessment has taken a center stage in our effort to reduce cancer deaths. For the first time, scientists have the technologies to decipher and understand these biomarkers and to apply them to earlier cancer detection. By identifying people at high risk of developing cancer, it would be possible to develop intervention efforts on prevention rather than treatment. Once fully developed and validated, then the regular clinical use of biomarkers in early detection and risk assessment will meet nationally recognized health care needs: detection of cancer at its earliest stage. The dramatic rise in health care costs in the past three decades is partly related to the proliferation of new technologies. More recent analysis indicates that technological change, such as new procedures, products and capabilities, is the primary explanation of the historical increase in expenditure. Biomarkers are the new entrants in this competing environment. Biomarkers are considered as a competing, halfway or add-on technology. Technology such as laboratory tests of biomarkers will cost less compared with computed tomography (CT) scans and other radiographs. However, biomarkers for earlier detection and risk assessment have not achieved the level of confidence required for clinical applications. This paper discusses some issues related to biomarker development, validation and quality assurance. Some data on the trends of diagnostic technologies, proteomics and genomics are presented and discussed in terms of the market share. Eventually, the use of biomarkers in health care could reduce cost by providing noninvasive, sensitive and reliable assays at a fraction of the cost of

Applied Behavior Analysis and Statistical Process Control?

Science.gov (United States)

Hopkins, B. L.

1995-01-01

Incorporating statistical process control (SPC) methods into applied behavior analysis is discussed. It is claimed that SPC methods would likely reduce applied behavior analysts' intimate contacts with problems and would likely yield poor treatment and research decisions. Cases and data presented by Pfadt and Wheeler (1995) are cited as examples.…

Urine Exosomes: An Emerging Trove of Biomarkers.

Science.gov (United States)

Street, J M; Koritzinsky, E H; Glispie, D M; Star, R A; Yuen, P S T

Exosomes are released by most cells and can be isolated from all biofluids including urine. Exosomes are small vesicles formed as part of the endosomal pathway that contain cellular material surrounded by a lipid bilayer that can be traced to the plasma membrane. Exosomes are potentially a more targeted source of material for biomarker discovery than unfractionated urine, and provide diagnostic and pathophysiological information without an invasive tissue biopsy. Cytoplasmic contents including protein, mRNA, miRNA, and lipids have all been studied within the exosomal fraction. Many prospective urinary exosomal biomarkers have been successfully identified for a variety of kidney or genitourinary tract conditions; detection of systemic conditions may also be possible. Isolation and analysis of exosomes can be achieved by several approaches, although many require specialized equipment or involve lengthy protocols. The need for timely analysis in the clinical setting has driven considerable innovation with several promising options recently emerging. Consensus on exosome isolation, characterization, and normalization procedures would resolve critical clinical translational bottlenecks for existing candidate exosomal biomarkers and provide a template for additional discovery studies. 2017 Published by Elsevier Inc.

Matching metal pollution with bioavailability, bioaccumulation and biomarkers response in fish (Centropomus parallelus) resident in neotropical estuaries

International Nuclear Information System (INIS)

Souza, Iara C.; Duarte, Ian D.; Pimentel, Natieli Q.; Rocha, Lívia D.; Morozesk, Mariana; Bonomo, Marina M.; Azevedo, Vinicius C.; Pereira, Camilo D.S.

2013-01-01

Two neotropical estuaries affected by different anthropogenic factors were studied. We report levels of metals and metalloids in water and sediment as well as their influence on genetic, biochemical and morphological biomarkers in the native fish Centropomus parallelus. Biomarkers reflected the fish health status. Multivariate statistics indicated both spatial and temporal changes in both water and sediment, which are linked to the elemental composition and health status of inhabitant fish, showing the biggest influence of surface water, followed by sediments and interstitial water. Bioaccumulation in fish muscle was useful to identify elements that were below detection limits in water, pointing out the risk of consuming fish exceeding allowance limits for some elements (As and Hg in this case). Multivariate statistics, including physical, chemical and biological issues, presents a suitable tool, integrating data from different origin allocated in the same estuary, which could be useful for future studies on estuarine systems. -- Highlights: •C. parallelus is a suitable bioindicator for assessing environmental quality in estuaries. •Biomarkers matched water quality pointing out different pollution scenarios. •Chemometrics allows extrapolating results from field and laboratory. •Chemometrics helps integrating biology and chemistry. -- Chemometrics allows matching pollution with bioaccumulation of metals and biomarkers responses in the fish Centropomus parallelus evidencing differences in estuaries quality

Utility of circulating IGF-I as a biomarker for assessing body composition changes in men during periods of high physical activity superimposed upon energy and sleep restriction.

Science.gov (United States)

Nindl, Bradley C; Alemany, Joseph A; Kellogg, Mark D; Rood, Jennifer; Allison, Steven A; Young, Andrew J; Montain, Scott J

2007-07-01

Insulin-like growth factor (IGF)-I is a biomarker that may have greater utility than other conventional nutritional biomarkers in assessing nutritional, health, and fitness status. We hypothesized that the IGF-I system would directionally track a short-term energy deficit and would be more related to changes in body composition than other nutritional biomarkers. Thirty-five healthy men (24 +/- 0.3 yr) underwent 8 days of exercise and energy imbalance. Total and free IGF-I, IGF binding proteins-1, -2, and -3, the acid labile subunit, transferrin, ferritin, retinol binding protein, prealbumin, testosterone, triiodothyronine, thyroxine, and leptin responses were measured. Dual-energy X-ray absorptiometry assessed changes in body mass and composition. Repeated-measures ANOVA, correlation analysis, and receiver operator characteristic curves were used for statistical analyses (P losing >5% body mass. The IGF-I system is an important adjunct in the overall assessment of adaptation to stress imposed by high levels of physical activity superimposed on energy and sleep restriction and is more closely associated with losses in body mass and fat-free mass than other conventional nutritional biomarkers.

Statistical analysis of JET disruptions

International Nuclear Information System (INIS)

Tanga, A.; Johnson, M.F.

1991-07-01

In the operation of JET and of any tokamak many discharges are terminated by a major disruption. The disruptive termination of a discharge is usually an unwanted event which may cause damage to the structure of the vessel. In a reactor disruptions are potentially a very serious problem, hence the importance of studying them and devising methods to avoid disruptions. Statistical information has been collected about the disruptions which have occurred at JET over a long span of operations. The analysis is focused on the operational aspects of the disruptions rather than on the underlining physics. (Author)

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic

Major depressive disorder: insight into candidate cerebrospinal fluid protein biomarkers from proteomics studies.

Science.gov (United States)

Al Shweiki, Mhd Rami; Oeckl, Patrick; Steinacker, Petra; Hengerer, Bastian; Schönfeldt-Lecuona, Carlos; Otto, Markus

2017-06-01

Major Depressive Disorder (MDD) is the leading cause of global disability, and an increasing body of literature suggests different cerebrospinal fluid (CSF) proteins as biomarkers of MDD. The aim of this review is to summarize the suggested CSF biomarkers and to analyze the MDD proteomics studies of CSF and brain tissues for promising biomarker candidates. Areas covered: The review includes the human studies found by a PubMed search using the following terms: 'depression cerebrospinal fluid biomarker', 'major depression biomarker CSF', 'depression CSF biomarker', 'proteomics depression', 'proteomics biomarkers in depression', 'proteomics CSF biomarker in depression', and 'major depressive disorder CSF'. The literature analysis highlights promising biomarker candidates and demonstrates conflicting results on others. It reveals 42 differentially regulated proteins in MDD that were identified in more than one proteomics study. It discusses the diagnostic potential of the biomarker candidates and their association with the suggested pathologies. Expert commentary: One ultimate goal of finding biomarkers for MDD is to improve the diagnostic accuracy to achieve better treatment outcomes; due to the heterogeneous nature of MDD, using bio-signatures could be a good strategy to differentiate MDD from other neuropsychiatric disorders. Notably, further validation studies of the suggested biomarkers are still needed.

Statistical analysis of the Ft. Calhoun reactor coolant pump system

International Nuclear Information System (INIS)

Patel, Bimal; Heising, C.D.

1997-01-01

In engineering science, statistical quality control techniques have traditionally been applied to control manufacturing processes. An application to commercial nuclear power plant maintenance and control is presented that can greatly improve plant safety. As a demonstration of such an approach, a specific system is analyzed: the reactor coolant pumps (RCPs) of the Ft. Calhoun nuclear power plant. This research uses capability analysis, Shewhart X-bar, R charts, canonical correlation methods, and design of experiments to analyze the process for the state of statistical control. The results obtained show that six out of ten parameters are under control specification limits and four parameters are not in the state of statistical control. The analysis shows that statistical process control methods can be applied as an early warning system capable of identifying significant equipment problems well in advance of traditional control room alarm indicators. Such a system would provide operators with ample time to respond to possible emergency situations and thus improve plant safety and reliability. (Author)

Comparing older and younger Japanese primiparae: fatigue, depression and biomarkers of stress.

Science.gov (United States)

Mori, Emi; Maehara, Kunie; Iwata, Hiroko; Sakajo, Akiko; Tsuchiya, Miyako; Ozawa, Harumi; Morita, Akiko; Maekawa, Tomoko; Saeki, Akiko

2015-03-01

This cohort study of primiparae was conducted to answer the following questions: Do older (≧ 35 years) and younger (20-29 years) Japanese primiparous mothers differ when comparing biomarkers of stress and measures of fatigue and depression? Are there changes in fatigue, depression and stress biomarkers when comparing older and younger mothers during the postpartum period? The Postnatal Accumulated Fatigue Scale and the Edinburgh Postnatal Depression Scale were administered in a time-series method four times: shortly after birth and monthly afterwards. Assays to measure biomarkers of stress, urinary 17-ketosteroids, urinary 17-hydroxycorticosteroids and salivary chromogranin-A, were collected shortly after delivery and at 1 month postpartum in both groups and a third time in older mothers at the 4th month. Statistical testing showed very little difference in fatigue, depression or stress biomarkers between older and younger mothers shortly after birth or 1 month later. Accumulated fatigue and depression scores of older mothers were highest 1 month after delivery. Additional cohort studies are required to characterize physical/psychological well-being of older Japanese primiparae. © 2015 Wiley Publishing Asia Pty Ltd.

Cardiac biomarkers, mortality, and post-traumatic stress disorder in military veterans.

Science.gov (United States)

Xue, Yang; Taub, Pam R; Iqbal, Navaid; Fard, Arrash; Wentworth, Bailey; Redwine, Laura; Clopton, Paul; Stein, Murray; Maisel, Alan

2012-04-15

Post-traumatic stress disorder (PTSD) is gaining increasing recognition as a risk factor for morbidity and mortality. The aim of this study was to examine the impact of PTSD and abnormal cardiovascular biomarkers on mortality in military veterans. Eight hundred ninety-one patients presenting for routine echocardiography were enrolled. Baseline clinical data and serum samples for biomarker measurement were obtained and echocardiography was performed at the time of enrollment. Patients were followed for up to 7.5 years for the end point of all-cause mortality. Ninety-one patients had PTSD at the time of enrollment. There were 33 deaths in patients with PTSD and 221 deaths in those without PTSD. Patients with PTSD had a trend toward worse survival on Kaplan-Meier analysis (p = 0.057). Among patients with elevated B-type natriuretic peptide (>60 pg/ml), those with PTSD had significantly increased mortality (p = 0.024). Among patients with PTSD, midregional proadrenomedullin (MR-proADM), creatinine, and C-terminal proendothelin-1 were significant univariate predictors of mortality (p = 0.006, p = 0.024, and p = 0.003, respectively). In a multivariate model, PTSD, B-type natriuretic peptide, and MR-proADM were independent predictors of mortality. In patients with PTSD, MR-proADM was a significant independent predictor of mortality after adjusting for B-type natriuretic peptide, cardiovascular risk factors, cancer, and sleep apnea. Adding MR-proADM to clinical predictors of mortality increased the C-statistic from 0.572 to 0.697 (p = 0.007). In conclusion, this study demonstrates an association among PTSD, abnormal cardiac biomarker levels, and increased mortality. Copyright © 2012 Elsevier Inc. All rights reserved.

Research and Development of Statistical Analysis Software System of Maize Seedling Experiment

OpenAIRE

Hui Cao

2014-01-01

In this study, software engineer measures were used to develop a set of software system for maize seedling experiments statistics and analysis works. During development works, B/S structure software design method was used and a set of statistics indicators for maize seedling evaluation were established. The experiments results indicated that this set of software system could finish quality statistics and analysis for maize seedling very well. The development of this software system explored a...

An epigenetic biomarker of aging for lifespan and healthspan

Science.gov (United States)

Levine, Morgan E.; Lu, Ake T.; Quach, Austin; Chen, Brian H.; Assimes, Themistocles L.; Bandinelli, Stefania; Hou, Lifang; Baccarelli, Andrea A.; Stewart, James D.; Li, Yun; Whitsel, Eric A.; Wilson, James G; Reiner, Alex P; Aviv, Abraham; Lohman, Kurt; Liu, Yongmei; Ferrucci, Luigi

2018-01-01

Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging. PMID:29676998

Rapid isolation of biomarkers for compound specific radiocarbon dating using high-performance liquid chromatography and flow injection analysis-atmospheric pressure chemical ionisation mass spectrometry

NARCIS (Netherlands)

Sinninghe Damsté, J.S.; Smittenberg, R.H.; Hopmans, E.C.; Schouten, S.

2002-01-01

Repeated semi-preparative normal-phase HPLC was performed to isolate selected biomarkers from sediment extracts for radiocarbon analysis. Flow injection analysis mass spectrometry was used for rapid analysis of collected fractions to evaluate the separation procedure, taking only 1 min per fraction.

Statistical trend analysis methods for temporal phenomena

Energy Technology Data Exchange (ETDEWEB)

Lehtinen, E.; Pulkkinen, U. [VTT Automation, (Finland); Poern, K. [Poern Consulting, Nykoeping (Sweden)

1997-04-01

We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods. 14 refs, 10 figs.

Statistical trend analysis methods for temporal phenomena

International Nuclear Information System (INIS)

Lehtinen, E.; Pulkkinen, U.; Poern, K.

1997-04-01

We consider point events occurring in a random way in time. In many applications the pattern of occurrence is of intrinsic interest as indicating a trend or some other systematic feature in the rate of occurrence. The purpose of this report is to survey briefly different statistical trend analysis methods and illustrate their applicability to temporal phenomena in particular. The trend testing of point events is usually seen as the testing of the hypotheses concerning the intensity of the occurrence of events. When the intensity function is parametrized, the testing of trend is a typical parametric testing problem. In industrial applications the operational experience generally does not suggest any specified model and method in advance. Therefore, and particularly, if the Poisson process assumption is very questionable, it is desirable to apply tests that are valid for a wide variety of possible processes. The alternative approach for trend testing is to use some non-parametric procedure. In this report we have presented four non-parametric tests: The Cox-Stuart test, the Wilcoxon signed ranks test, the Mann test, and the exponential ordered scores test. In addition to the classical parametric and non-parametric approaches we have also considered the Bayesian trend analysis. First we discuss a Bayesian model, which is based on a power law intensity model. The Bayesian statistical inferences are based on the analysis of the posterior distribution of the trend parameters, and the probability of trend is immediately seen from these distributions. We applied some of the methods discussed in an example case. It should be noted, that this report is a feasibility study rather than a scientific evaluation of statistical methods, and the examples can only be seen as demonstrations of the methods

Analysis of complex-type chromosome exchanges in astronauts' lymphocytes after space flight as a biomarker of high-LET exposure

International Nuclear Information System (INIS)

George, K.; Wu, H.; Willingham, V.; Cucinotta, F.A.

2002-01-01

High-linear energy transfer (LET) radiation is moreefficient in producing complex-type chromosome exchanges than sparsely ionizing radiation, and this can potentially be used as a biomarker of radiation quality. To investigate if complex chromosome exchanges are induced by the high-LET component of space radiation exposure, damage was assessed in astronauts' blood lymphocytes before and after longduration missions of 3-4 months. The frequency of simple translocations increased significantly for most of the crewmembers studied. However, there were few complex exchanges detected and only one crewmember had a significant increase after flight. It has been suggested that the yield of complex chromosome damage could be underestimated when analyzing metaphase cellscollected at one time point after irradiation, andanalysis of chemically-induced premature chromosomecondensation (PCC) may be more accurate since problems with complicated cell-cycle delays are avoided.However, in this case the yields of chromosome damage were similar for metaphase and PCC analysis of astronauts' lymphocytes. It appears that the use of complex-type exchanges as biomarkerof radiation quality in vivo after low-dose chronicexposure in mixed radiation fields is hampered by statistical uncertainties. (author)

A meta-analysis of biomarkers related to oxidative stress and nitric oxide pathway in migraine.

Science.gov (United States)

Neri, Monica; Frustaci, Alessandra; Milic, Mirta; Valdiglesias, Vanessa; Fini, Massimo; Bonassi, Stefano; Barbanti, Piero

2015-09-01

Oxidative and nitrosative stress are considered key events in the still unclear pathophysiology of migraine. Studies comparing the level of biomarkers related to nitric oxide (NO) pathway/oxidative stress in the blood/urine of migraineurs vs. unaffected controls were extracted from the PubMed database. Summary estimates of mean ratios (MR) were carried out whenever a minimum of three papers were available. Nineteen studies were included in the meta-analyses, accounting for more than 1000 patients and controls, and compared with existing literature. Most studies measuring superoxide dismutase (SOD) showed lower activity in cases, although the meta-analysis in erythrocytes gave null results. On the contrary, plasma levels of thiobarbituric acid reactive substances (TBARS), an aspecific biomarker of oxidative damage, showed a meta-MR of 2.20 (95% CI: 1.65-2.93). As for NOs, no significant results were found in plasma, serum and urine. However, higher levels were shown during attacks, in patients with aura, and an effect of diet was found. The analysis of glutathione precursor homocysteine and asymmetric dimethylarginine (ADMA), an NO synthase inhibitor, gave inconclusive results. The role of the oxidative pathway in migraine is still uncertain. Interesting evidence emerged for TBARS and SOD, and concerning the possible role of diet in the control of NOx levels. © International Headache Society 2015.

Molecular and phenotypic biomarkers of aging [version 1; referees: 3 approved

Directory of Open Access Journals (Sweden)

Xian Xia

2017-06-01

Full Text Available Individuals of the same age may not age at the same rate. Quantitative biomarkers of aging are valuable tools to measure physiological age, assess the extent of ‘healthy aging’, and potentially predict health span and life span for an individual. Given the complex nature of the aging process, the biomarkers of aging are multilayered and multifaceted. Here, we review the phenotypic and molecular biomarkers of aging. Identifying and using biomarkers of aging to improve human health, prevent age-associated diseases, and extend healthy life span are now facilitated by the fast-growing capacity of multilevel cross-sectional and longitudinal data acquisition, storage, and analysis, particularly for data related to general human populations. Combined with artificial intelligence and machine learning techniques, reliable panels of biomarkers of aging will have tremendous potential to improve human health in aging societies.

StOCNET : Software for the statistical analysis of social networks

NARCIS (Netherlands)

Huisman, M.; van Duijn, M.A.J.

2003-01-01

StOCNET3 is an open software system in a Windows environment for the advanced statistical analysis of social networks. It provides a platform to make a number of recently developed and therefore not (yet) standard statistical methods available to a wider audience. A flexible user interface utilizing

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival

International Nuclear Information System (INIS)

Yuan, Mei; Zhang, Yu-Dong; Pu, Xue-Hui; Zhong, Yan; Yu, Tong-Fu; Li, Hai; Wu, Jiang-Fen

2017-01-01

To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. (orig.)

Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival

Energy Technology Data Exchange (ETDEWEB)

Yuan, Mei; Zhang, Yu-Dong; Pu, Xue-Hui; Zhong, Yan; Yu, Tong-Fu [First Affiliated Hospital of Nanjing Medical University, Department of Radiology, Nanjing, Jiangsu Province (China); Li, Hai [First Affiliated Hospital of Nanjing Medical University, Department of Pathology, Nanjing (China); Wu, Jiang-Fen [GE Healthcare, Shanghai (China)

2017-11-15

To compare a multi-feature-based radiomic biomarker with volumetric analysis in discriminating lung adenocarcinomas with different disease-specific survival on computed tomography (CT) scans. This retrospective study obtained institutional review board approval and was Health Insurance Portability and Accountability Act (HIPAA) compliant. Pathologically confirmed lung adenocarcinoma (n = 431) manifested as subsolid nodules on CT were identified. Volume and percentage solid volume were measured by using a computer-assisted segmentation method. Radiomic features quantifying intensity, texture and wavelet were extracted from the segmented volume of interest (VOI). Twenty best features were chosen by using the Relief method and subsequently fed to a support vector machine (SVM) for discriminating adenocarcinoma in situ (AIS)/minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC). Performance of the radiomic signatures was compared with volumetric analysis via receiver-operating curve (ROC) analysis and logistic regression analysis. The accuracy of proposed radiomic signatures for predicting AIS/MIA from IAC achieved 80.5% with ROC analysis (Az value, 0.829; sensitivity, 72.1%; specificity, 80.9%), which showed significantly higher accuracy than volumetric analysis (69.5%, P = 0.049). Regression analysis showed that radiomic signatures had superior prognostic performance to volumetric analysis, with AIC values of 81.2% versus 70.8%, respectively. The radiomic tumour-phenotypes biomarker exhibited better diagnostic accuracy than traditional volumetric analysis in discriminating lung adenocarcinoma with different disease-specific survival. (orig.)

Identification of potential biomarkers from microarray experiments using multiple criteria optimization

International Nuclear Information System (INIS)

Sánchez-Peña, Matilde L; Isaza, Clara E; Pérez-Morales, Jaileene; Rodríguez-Padilla, Cristina; Castro, José M; Cabrera-Ríos, Mauricio

2013-01-01

Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization

AutoBayes: A System for Generating Data Analysis Programs from Statistical Models

OpenAIRE

Fischer, Bernd; Schumann, Johann

2003-01-01

Data analysis is an important scientific task which is required whenever information needs to be extracted from raw data. Statistical approaches to data analysis, which use methods from probability theory and numerical analysis, are well-founded but dificult to implement: the development of a statistical data analysis program for any given application is time-consuming and requires substantial knowledge and experience in several areas. In this paper, we describe AutoBayes, a program synthesis...

Protein Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: Progress and Challenges.

Science.gov (United States)

Root, Alex; Allen, Peter; Tempst, Paul; Yu, Kenneth

2018-03-07

Approximately 75% of patients with pancreatic ductal adenocarcinoma are diagnosed with advanced cancer, which cannot be safely resected. The most commonly used biomarker CA19-9 has inadequate sensitivity and specificity for early detection, which we define as Stage I/II cancers. Therefore, progress in next-generation biomarkers is greatly needed. Recent reports have validated a number of biomarkers, including combination assays of proteins and DNA mutations; however, the history of translating promising biomarkers to clinical utility suggests that several major hurdles require careful consideration by the medical community. The first set of challenges involves nominating and verifying biomarkers. Candidate biomarkers need to discriminate disease from benign controls with high sensitivity and specificity for an intended use, which we describe as a two-tiered strategy of identifying and screening high-risk patients. Community-wide efforts to share samples, data, and analysis methods have been beneficial and progress meeting this challenge has been achieved. The second set of challenges is assay optimization and validating biomarkers. After initial candidate validation, assays need to be refined into accurate, cost-effective, highly reproducible, and multiplexed targeted panels and then validated in large cohorts. To move the most promising candidates forward, ideally, biomarker panels, head-to-head comparisons, meta-analysis, and assessment in independent data sets might mitigate risk of failure. Much more investment is needed to overcome these challenges. The third challenge is achieving clinical translation. To moonshot an early detection test to the clinic requires a large clinical trial and organizational, regulatory, and entrepreneurial know-how. Additional factors, such as imaging technologies, will likely need to improve concomitant with molecular biomarker development. The magnitude of the clinical translational challenge is uncertain, but interdisciplinary

Protein Biomarkers for Early Detection of Pancreatic Ductal Adenocarcinoma: Progress and Challenges

Directory of Open Access Journals (Sweden)

Alex Root

2018-03-01

Full Text Available Approximately 75% of patients with pancreatic ductal adenocarcinoma are diagnosed with advanced cancer, which cannot be safely resected. The most commonly used biomarker CA19-9 has inadequate sensitivity and specificity for early detection, which we define as Stage I/II cancers. Therefore, progress in next-generation biomarkers is greatly needed. Recent reports have validated a number of biomarkers, including combination assays of proteins and DNA mutations; however, the history of translating promising biomarkers to clinical utility suggests that several major hurdles require careful consideration by the medical community. The first set of challenges involves nominating and verifying biomarkers. Candidate biomarkers need to discriminate disease from benign controls with high sensitivity and specificity for an intended use, which we describe as a two-tiered strategy of identifying and screening high-risk patients. Community-wide efforts to share samples, data, and analysis methods have been beneficial and progress meeting this challenge has been achieved. The second set of challenges is assay optimization and validating biomarkers. After initial candidate validation, assays need to be refined into accurate, cost-effective, highly reproducible, and multiplexed targeted panels and then validated in large cohorts. To move the most promising candidates forward, ideally, biomarker panels, head-to-head comparisons, meta-analysis, and assessment in independent data sets might mitigate risk of failure. Much more investment is needed to overcome these challenges. The third challenge is achieving clinical translation. To moonshot an early detection test to the clinic requires a large clinical trial and organizational, regulatory, and entrepreneurial know-how. Additional factors, such as imaging technologies, will likely need to improve concomitant with molecular biomarker development. The magnitude of the clinical translational challenge is uncertain, but

Prognostic biomarkers in osteoarthritis

Science.gov (United States)

Attur, Mukundan; Krasnokutsky-Samuels, Svetlana; Samuels, Jonathan; Abramson, Steven B.

2013-01-01

Purpose of review Identification of patients at risk for incident disease or disease progression in osteoarthritis remains challenging, as radiography is an insensitive reflection of molecular changes that presage cartilage and bone abnormalities. Thus there is a widely appreciated need for biochemical and imaging biomarkers. We describe recent developments with such biomarkers to identify osteoarthritis patients who are at risk for disease progression. Recent findings The biochemical markers currently under evaluation include anabolic, catabolic, and inflammatory molecules representing diverse biological pathways. A few promising cartilage and bone degradation and synthesis biomarkers are in various stages of development, awaiting further validation in larger populations. A number of studies have shown elevated expression levels of inflammatory biomarkers, both locally (synovial fluid) and systemically (serum and plasma). These chemical biomarkers are under evaluation in combination with imaging biomarkers to predict early onset and the burden of disease. Summary Prognostic biomarkers may be used in clinical knee osteoarthritis to identify subgroups in whom the disease progresses at different rates. This could facilitate our understanding of the pathogenesis and allow us to differentiate phenotypes within a heterogeneous knee osteoarthritis population. Ultimately, such findings may help facilitate the development of disease-modifying osteoarthritis drugs (DMOADs). PMID:23169101

Effects of Exercise Training on Cardiorespiratory Fitness and Biomarkers of Cardiometabolic Health: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Science.gov (United States)

Lin, Xiaochen; Zhang, Xi; Guo, Jianjun; Roberts, Christian K; McKenzie, Steve; Wu, Wen-Chih; Liu, Simin; Song, Yiqing

2015-01-01

Background Guidelines recommend exercise for cardiovascular health, although evidence from trials linking exercise to cardiovascular health through intermediate biomarkers remains inconsistent. We performed a meta-analysis of randomized controlled trials to quantify the impact of exercise on cardiorespiratory fitness and a variety of conventional and novel cardiometabolic biomarkers in adults without cardiovascular disease. Methods and Results Two researchers selected 160 randomized controlled trials (7487 participants) based on literature searches of Medline, Embase, and Cochrane Central (January 1965 to March 2014). Data were extracted using a standardized protocol. A random-effects meta-analysis and systematic review was conducted to evaluate the effects of exercise interventions on cardiorespiratory fitness and circulating biomarkers. Exercise significantly raised absolute and relative cardiorespiratory fitness. Lipid profiles were improved in exercise groups, with lower levels of triglycerides and higher levels of high-density lipoprotein cholesterol and apolipoprotein A1. Lower levels of fasting insulin, homeostatic model assessment–insulin resistance, and glycosylated hemoglobin A1c were found in exercise groups. Compared with controls, exercise groups had higher levels of interleukin-18 and lower levels of leptin, fibrinogen, and angiotensin II. In addition, we found that the exercise effects were modified by age, sex, and health status such that people aged exercise significantly improved cardiorespiratory fitness and some cardiometabolic biomarkers. The effects of exercise were modified by age, sex, and health status. Findings from this study have significant implications for future design of targeted lifestyle interventions. PMID:26116691

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

Directory of Open Access Journals (Sweden)

Claudia Bühnemann

Full Text Available Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases. Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%. The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36 was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality

Quantification of the heterogeneity of prognostic cellular biomarkers in ewing sarcoma using automated image and random survival forest analysis.

Science.gov (United States)

Bühnemann, Claudia; Li, Simon; Yu, Haiyue; Branford White, Harriet; Schäfer, Karl L; Llombart-Bosch, Antonio; Machado, Isidro; Picci, Piero; Hogendoorn, Pancras C W; Athanasou, Nicholas A; Noble, J Alison; Hassan, A Bassim

2014-01-01

Driven by genomic somatic variation, tumour tissues are typically heterogeneous, yet unbiased quantitative methods are rarely used to analyse heterogeneity at the protein level. Motivated by this problem, we developed automated image segmentation of images of multiple biomarkers in Ewing sarcoma to generate distributions of biomarkers between and within tumour cells. We further integrate high dimensional data with patient clinical outcomes utilising random survival forest (RSF) machine learning. Using material from cohorts of genetically diagnosed Ewing sarcoma with EWSR1 chromosomal translocations, confocal images of tissue microarrays were segmented with level sets and watershed algorithms. Each cell nucleus and cytoplasm were identified in relation to DAPI and CD99, respectively, and protein biomarkers (e.g. Ki67, pS6, Foxo3a, EGR1, MAPK) localised relative to nuclear and cytoplasmic regions of each cell in order to generate image feature distributions. The image distribution features were analysed with RSF in relation to known overall patient survival from three separate cohorts (185 informative cases). Variation in pre-analytical processing resulted in elimination of a high number of non-informative images that had poor DAPI localisation or biomarker preservation (67 cases, 36%). The distribution of image features for biomarkers in the remaining high quality material (118 cases, 104 features per case) were analysed by RSF with feature selection, and performance assessed using internal cross-validation, rather than a separate validation cohort. A prognostic classifier for Ewing sarcoma with low cross-validation error rates (0.36) was comprised of multiple features, including the Ki67 proliferative marker and a sub-population of cells with low cytoplasmic/nuclear ratio of CD99. Through elimination of bias, the evaluation of high-dimensionality biomarker distribution within cell populations of a tumour using random forest analysis in quality controlled tumour

A brief review and evaluation of earthworm biomarkers in soil pollution assessment.

Science.gov (United States)

Shi, Zhiming; Tang, Zhiwen; Wang, Congying

2017-05-01

Earthworm biomarker response to pollutants has been widely investigated in the assessment of soil pollution. However, whether and how the earthworm biomarker-approach can be actually applied to soil pollution assessment is still a controversial issue. This review is concerned about the following points: 1. Despite much debate, biomarker is valuable to ecotoxicology and biomarker approach has been properly used in different fields. Earthworm biomarker might be used in different scenarios such as large-scale soil pollution survey and soil pollution risk assessment. Compared with physicochemical analysis, they can provide more comprehensive and straightforward information about soil pollution at low cost. 2. Although many earthworm species from different ecological categories have been tested, Eisenia fetida/andrei is commonly used. Many earthworm biomarkers have been screened from the molecular to the individual level, while only a few biomarkers, such as avoidance behavior and lysosomal membrane stability, have been focused on. Other aspects of the experimental design were critically reviewed. 3. More studies should focus on determining the reliability of various earthworm biomarkers in soil pollution assessment in future research. Besides, establishing a database of a basal level of each biomarker, exploring biomarker response in different region/section/part of earthworm, and other issues are also proposed. 4. A set of research guideline for earthworm biomarker studies was recommended, and the suitability of several earthworm biomarkers was briefly evaluated with respect to their application in soil pollution assessment. This review will help to promote further studies and practical application of earthworm biomarker in soil pollution assessment.

Network similarity and statistical analysis of earthquake seismic data

OpenAIRE

Deyasi, Krishanu; Chakraborty, Abhijit; Banerjee, Anirban

2016-01-01

We study the structural similarity of earthquake networks constructed from seismic catalogs of different geographical regions. A hierarchical clustering of underlying undirected earthquake networks is shown using Jensen-Shannon divergence in graph spectra. The directed nature of links indicates that each earthquake network is strongly connected, which motivates us to study the directed version statistically. Our statistical analysis of each earthquake region identifies the hub regions. We cal...

Biomarkers in volunteers exposed to mobile phone radiation.

Science.gov (United States)

Söderqvist, Fredrik; Carlberg, Michael; Hardell, Lennart

2015-06-01

For some time it has been investigated whether low-intensity non-thermal microwave radiation from mobile phones adversely affects the mammalian blood-brain barrier (BBB). All such studies except one have been either in vitro or experimental animal studies. The one carried out on humans showed a statistically significant increase in serum transthyretin (TTR) 60 min after finishing of a 30-min microwave exposure session. The aim of the present study was to follow up on the finding of the previous one using a better study design. Using biomarkers analyzed in blood serum before and after the exposure this single blinded randomized counterbalanced study, including 24 healthy subjects aged 18-30 years that all underwent three exposure conditions (SAR(10G)=2 W/kg, SAR(10G)=0.2 W/kg, sham), tested whether microwaves from an 890-MHz phone-like signal give acute effects on the integrity of brain-shielding barriers. Over time, statistically significant variations were found for two of the three biomarkers (TTR; β-trace protein); however, no such difference was found between the different exposure conditions nor was there any interaction between exposure condition and time of blood sampling. In conclusion this study failed to show any acute clinically or statistically significant effect of short term microwave exposure on the serum levels of S100β, TTR and β-trace protein with a follow up limited to two hours. The study was hampered by the fact that all study persons were regular wireless phone users and thus not naïve as to microwave exposure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Evaluating the quality of research into a single prognostic biomarker: a systematic review and meta-analysis of 83 studies of C-reactive protein in stable coronary artery disease.

Directory of Open Access Journals (Sweden)

Harry Hemingway

2010-06-01

Full Text Available Systematic evaluations of the quality of research on a single prognostic biomarker are rare. We sought to evaluate the quality of prognostic research evidence for the association of C-reactive protein (CRP with fatal and nonfatal events among patients with stable coronary disease.We searched MEDLINE (1966 to 2009 and EMBASE (1980 to 2009 and selected prospective studies of patients with stable coronary disease, reporting a relative risk for the association of CRP with death and nonfatal cardiovascular events. We included 83 studies, reporting 61,684 patients and 6,485 outcome events. No study reported a prespecified statistical analysis protocol; only two studies reported the time elapsed (in months or years between initial presentation of symptomatic coronary disease and inclusion in the study. Studies reported a median of seven items (of 17 from the REMARK reporting guidelines, with no evidence of change over time. The pooled relative risk for the top versus bottom third of CRP distribution was 1.97 (95% confidence interval [CI] 1.78-2.17, with substantial heterogeneity (I(2 = 79.5. Only 13 studies adjusted for conventional risk factors (age, sex, smoking, obesity, diabetes, and low-density lipoprotein [LDL] cholesterol and these had a relative risk of 1.65 (95% CI 1.39-1.96, I(2 = 33.7. Studies reported ten different ways of comparing CRP values, with weaker relative risks for those based on continuous measures. Adjusting for publication bias (for which there was strong evidence, Egger's p<0.001 using a validated method reduced the relative risk to 1.19 (95% CI 1.13-1.25. Only two studies reported a measure of discrimination (c-statistic. In 20 studies the detection rate for subsequent events could be calculated and was 31% for a 10% false positive rate, and the calculated pooled c-statistic was 0.61 (0.57-0.66.Multiple types of reporting bias, and publication bias, make the magnitude of any independent association between CRP and prognosis

Statistical analysis and interpolation of compositional data in materials science.

Science.gov (United States)

Pesenson, Misha Z; Suram, Santosh K; Gregoire, John M

2015-02-09

Compositional data are ubiquitous in chemistry and materials science: analysis of elements in multicomponent systems, combinatorial problems, etc., lead to data that are non-negative and sum to a constant (for example, atomic concentrations). The constant sum constraint restricts the sampling space to a simplex instead of the usual Euclidean space. Since statistical measures such as mean and standard deviation are defined for the Euclidean space, traditional correlation studies, multivariate analysis, and hypothesis testing may lead to erroneous dependencies and incorrect inferences when applied to compositional data. Furthermore, composition measurements that are used for data analytics may not include all of the elements contained in the material; that is, the measurements may be subcompositions of a higher-dimensional parent composition. Physically meaningful statistical analysis must yield results that are invariant under the number of composition elements, requiring the application of specialized statistical tools. We present specifics and subtleties of compositional data processing through discussion of illustrative examples. We introduce basic concepts, terminology, and methods required for the analysis of compositional data and utilize them for the spatial interpolation of composition in a sputtered thin film. The results demonstrate the importance of this mathematical framework for compositional data analysis (CDA) in the fields of materials science and chemistry.

An Application of Multivariate Statistical Analysis for Query-Driven Visualization

Energy Technology Data Exchange (ETDEWEB)

Gosink, Luke J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Garth, Christoph [Univ. of California, Davis, CA (United States); Anderson, John C. [Univ. of California, Davis, CA (United States); Bethel, E. Wes [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Joy, Kenneth I. [Univ. of California, Davis, CA (United States)

2011-03-01

Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex datasets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates non-parametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they may be used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to datasets from two different scientific domains to demonstrate its broad applicability.

Identification of plasma biomarker candidates in glioblastoma using an antibody-array-based proteomic approach

International Nuclear Information System (INIS)

Zupancic, Klemen; Blejec, Andrej; Herman, Ana; Veber, Matija; Verbovsek, Urska; Korsic, Marjan; Knezevic, Miomir; Rozman, Primoz; Turnsek, Tamara Lah; Gruden, Kristina; Motaln, Helena

2014-01-01

Glioblastoma multiforme (GBM) is a brain tumour with a very high patient mortality rate, with a median survival of 47 weeks. This might be improved by the identification of novel diagnostic, prognostic and predictive therapy-response biomarkers, preferentially through the monitoring of the patient blood. The aim of this study was to define the impact of GBM in terms of alterations of the plasma protein levels in these patients. We used a commercially available antibody array that includes 656 antibodies to analyse blood plasma samples from 17 healthy volunteers in comparison with 17 blood plasma samples from patients with GBM. We identified 11 plasma proteins that are statistically most strongly associated with the presence of GBM. These proteins belong to three functional signalling pathways: T-cell signalling and immune responses; cell adhesion and migration; and cell-cycle control and apoptosis. Thus, we can consider this identified set of proteins as potential diagnostic biomarker candidates for GBM. In addition, a set of 16 plasma proteins were significantly associated with the overall survival of these patients with GBM. Guanine nucleotide binding protein alpha (GNAO1) was associated with both GBM presence and survival of patients with GBM. Antibody array analysis represents a useful tool for the screening of plasma samples for potential cancer biomarker candidates in small-scale exploratory experiments; however, clinical validation of these candidates requires their further evaluation in a larger study on an independent cohort of patients

Aberrantly methylated DNA as a biomarker in breast cancer

DEFF Research Database (Denmark)

Kristiansen, Søren; Jørgensen, Lars Mønster; Guldberg, Per

2013-01-01

hypermethylation events, their use as tumor biomarkers is usually not hampered by analytical signals from normal cells, which is a general problem for existing protein tumor markers used for clinical assessment of breast cancer. There is accumulating evidence that DNA-methylation changes in breast cancer patients...... occur early during tumorigenesis. This may open up for effective screening, and analysis of blood or nipple aspirate may later help in diagnosing breast cancer. As a more detailed molecular characterization of different types of breast cancer becomes available, the ability to divide patients...... as a versatile biomarker tool for screening, diagnosis, prognosis and monitoring of breast cancer. Standardization of methods and biomarker panels will be required to fully exploit this clinical potential....

Quantitative plasma biomarker analysis in HDI exposure assessment.

Science.gov (United States)

Flack, Sheila L; Fent, Kenneth W; Trelles Gaines, Linda G; Thomasen, Jennifer M; Whittaker, Steve; Ball, Louise M; Nylander-French, Leena A

2010-01-01

Quantification of amines in biological samples is important for evaluating occupational exposure to diisocyanates. In this study, we describe the quantification of 1,6-hexamethylene diamine (HDA) levels in hydrolyzed plasma of 46 spray painters applying 1,6-hexamethylene diisocyanate (HDI)-containing paint in vehicle repair shops collected during repeated visits to their workplace and their relationship with dermal and inhalation exposure to HDI monomer. HDA was detected in 76% of plasma samples, as heptafluorobutyryl derivatives, and the range of HDA concentrations was HDA levels and HDI inhalation exposure measured on the same workday was low (N = 108, r = 0.22, P = 0.026) compared with the correlation between plasma HDA levels and inhalation exposure occurring approximately 20 to 60 days before blood collection (N = 29, r = 0.57, P = 0.0014). The correlation between plasma HDA levels and HDI dermal exposure measured on the same workday, although statistically significant, was low (N = 108, r = 0.22, P = 0.040) while the correlation between HDA and dermal exposure occurring approximately 20 to 60 days before blood collection was slightly improved (N = 29, r = 0.36, P = 0.053). We evaluated various workplace factors and controls (i.e. location, personal protective equipment use and paint booth type) as modifiers of plasma HDA levels. Workers using a downdraft-ventilated booth had significantly lower plasma HDA levels relative to semi-downdraft and crossdraft booth types (P = 0.0108); this trend was comparable to HDI inhalation and dermal exposure levels stratified by booth type. These findings indicate that HDA concentration in hydrolyzed plasma may be used as a biomarker of cumulative inhalation and dermal exposure to HDI and for investigating the effectiveness of exposure controls in the workplace.

Accounting for measurement error in biomarker data and misclassification of subtypes in the analysis of tumor data.

Science.gov (United States)

Nevo, Daniel; Zucker, David M; Tamimi, Rulla M; Wang, Molin

2016-12-30

A common paradigm in dealing with heterogeneity across tumors in cancer analysis is to cluster the tumors into subtypes using marker data on the tumor, and then to analyze each of the clusters separately. A more specific target is to investigate the association between risk factors and specific subtypes and to use the results for personalized preventive treatment. This task is usually carried out in two steps-clustering and risk factor assessment. However, two sources of measurement error arise in these problems. The first is the measurement error in the biomarker values. The second is the misclassification error when assigning observations to clusters. We consider the case with a specified set of relevant markers and propose a unified single-likelihood approach for normally distributed biomarkers. As an alternative, we consider a two-step procedure with the tumor type misclassification error taken into account in the second-step risk factor analysis. We describe our method for binary data and also for survival analysis data using a modified version of the Cox model. We present asymptotic theory for the proposed estimators. Simulation results indicate that our methods significantly lower the bias with a small price being paid in terms of variance. We present an analysis of breast cancer data from the Nurses' Health Study to demonstrate the utility of our method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Quantitative proteomic analysis for novel biomarkers of buccal squamous cell carcinoma arising in background of oral submucous fibrosis

International Nuclear Information System (INIS)

Liu, Wen; Zeng, Lijuan; Li, Ning; Wang, Fei; Jiang, Canhua; Guo, Feng; Chen, Xinqun; Su, Tong; Xu, Chunjiao; Zhang, Shanshan; Fang, Changyun

2016-01-01

In South and Southeast Asian, the majority of buccal squamous cell carcinoma (BSCC) can arise from oral submucous fibrosis (OSF). BSCCs develop in OSF that are often not completely resected, causing local relapse. The aim of our study was to find candidate protein biomarkers to detect OSF and predict prognosis in BSCCs by quantitative proteomics approaches. We compared normal oral mucosa (NBM) and paired biopsies of BSCC and OSF by quantitative proteomics using isobaric tags for relative and absolute quantification (iTRAQ) to discover proteins with differential expression. Gene Ontology and KEGG networks were analyzed. The prognostic value of biomarkers was evaluated in 94 BSCCs accompanied with OSF. Significant associations were assessed by Kaplan-Meier survival and Cox-proportional hazards analysis. In total 30 proteins were identified with significantly different expression (false discovery rate < 0.05) among three tissues. Two consistently upregulated proteins, ANXA4 and FLNA, were validated. The disease-free survival was negatively associated with the expression of ANXA4 (hazard ratio, 3.4; P = 0.000), FLNA (hazard ratio, 2.1; P = 0.000) and their combination (hazard ratio, 8.8; P = 0.002) in BSCCs. The present study indicates that iTRAQ quantitative proteomics analysis for tissues of BSCC and OSF is a reliable strategy. A significantly up-regulated ANXA4 and FLNA could be not only candidate biomarkers for BSCC prognosis but also potential targets for its therapy. The online version of this article (doi:10.1186/s12885-016-2650-1) contains supplementary material, which is available to authorized users

Bi-directional gene set enrichment and canonical correlation analysis identify key diet-sensitive pathways and biomarkers of metabolic syndrome

Directory of Open Access Journals (Sweden)

Gaora Peadar Ó

2010-10-01

Full Text Available Abstract Background Currently, a number of bioinformatics methods are available to generate appropriate lists of genes from a microarray experiment. While these lists represent an accurate primary analysis of the data, fewer options exist to contextualise those lists. The development and validation of such methods is crucial to the wider application of microarray technology in the clinical setting. Two key challenges in clinical bioinformatics involve appropriate statistical modelling of dynamic transcriptomic changes, and extraction of clinically relevant meaning from very large datasets. Results Here, we apply an approach to gene set enrichment analysis that allows for detection of bi-directional enrichment within a gene set. Furthermore, we apply canonical correlation analysis and Fisher's exact test, using plasma marker data with known clinical relevance to aid identification of the most important gene and pathway changes in our transcriptomic dataset. After a 28-day dietary intervention with high-CLA beef, a range of plasma markers indicated a marked improvement in the metabolic health of genetically obese mice. Tissue transcriptomic profiles indicated that the effects were most dramatic in liver (1270 genes significantly changed; p Conclusion Bi-directional gene set enrichment analysis more accurately reflects dynamic regulatory behaviour in biochemical pathways, and as such highlighted biologically relevant changes that were not detected using a traditional approach. In such cases where transcriptomic response to treatment is exceptionally large, canonical correlation analysis in conjunction with Fisher's exact test highlights the subset of pathways showing strongest correlation with the clinical markers of interest. In this case, we have identified selenoamino acid metabolism and steroid biosynthesis as key pathways mediating the observed relationship between metabolic health and high-CLA beef. These results indicate that this type of

Metabolomics as a tool for discovery of biomarkers of autism spectrum disorder in the blood plasma of children.

Directory of Open Access Journals (Sweden)

Paul R West

Full Text Available The diagnosis of autism spectrum disorder (ASD at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age.To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment.Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD.A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set.This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1 determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2 gaining new insight into the biochemical mechanisms of various subtypes of ASD 3 identifying biomolecular targets for new modes of therapy, and 4 providing the basis for individualized treatment recommendations.

Metabolomics as a Tool for Discovery of Biomarkers of Autism Spectrum Disorder in the Blood Plasma of Children

Science.gov (United States)

West, Paul R.; Amaral, David G.; Bais, Preeti; Smith, Alan M.; Egnash, Laura A.; Ross, Mark E.; Palmer, Jessica A.; Fontaine, Burr R.; Conard, Kevin R.; Corbett, Blythe A.; Cezar, Gabriela G.; Donley, Elizabeth L. R.; Burrier, Robert E.

2014-01-01

Background The diagnosis of autism spectrum disorder (ASD) at the earliest age possible is important for initiating optimally effective intervention. In the United States the average age of diagnosis is 4 years. Identifying metabolic biomarker signatures of ASD from blood samples offers an opportunity for development of diagnostic tests for detection of ASD at an early age. Objectives To discover metabolic features present in plasma samples that can discriminate children with ASD from typically developing (TD) children. The ultimate goal is to identify and develop blood-based ASD biomarkers that can be validated in larger clinical trials and deployed to guide individualized therapy and treatment. Methods Blood plasma was obtained from children aged 4 to 6, 52 with ASD and 30 age-matched TD children. Samples were analyzed using 5 mass spectrometry-based methods designed to orthogonally measure a broad range of metabolites. Univariate, multivariate and machine learning methods were used to develop models to rank the importance of features that could distinguish ASD from TD. Results A set of 179 statistically significant features resulting from univariate analysis were used for multivariate modeling. Subsets of these features properly classified the ASD and TD samples in the 61-sample training set with average accuracies of 84% and 86%, and with a maximum accuracy of 81% in an independent 21-sample validation set. Conclusions This analysis of blood plasma metabolites resulted in the discovery of biomarkers that may be valuable in the diagnosis of young children with ASD. The results will form the basis for additional discovery and validation research for 1) determining biomarkers to develop diagnostic tests to detect ASD earlier and improve patient outcomes, 2) gaining new insight into the biochemical mechanisms of various subtypes of ASD 3) identifying biomolecular targets for new modes of therapy, and 4) providing the basis for individualized treatment

Metabolomics for Biomarker Discovery: Moving to the Clinic

Science.gov (United States)

Zhang, Aihua; Sun, Hui; Yan, Guangli; Wang, Ping; Wang, Xijun

2015-01-01

To improve the clinical course of diseases, more accurate diagnostic and assessment methods are required as early as possible. In order to achieve this, metabolomics offers new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has become an important tool in clinical research and the diagnosis of human disease and has been applied to discovery and identification of the perturbed pathways. It provides a powerful approach to discover biomarkers in biological systems and offers a holistic approach with the promise to clinically enhance diagnostics. When carried out properly, it could provide insight into the understanding of the underlying mechanisms of diseases, help to identify patients at risk of disease, and predict the response to specific treatments. Currently, metabolomics has become an important tool in clinical research and the diagnosis of human disease and becomes a hot topic. This review will highlight the importance and benefit of metabolomics for identifying biomarkers that accurately screen potential biomarkers of diseases. PMID:26090402

Multi-analyte analysis of saliva biomarkers as predictors of periodontal and pre-implant disease

Science.gov (United States)

Braun, Thomas; Giannobile, William V; Herr, Amy E; Singh, Anup K; Shelburne, Charlie

2015-04-07

The present invention relates to methods of measuring biomarkers to determine the probability of a periodontal and/or peri-implant disease. More specifically, the invention provides a panel of biomarkers that, when used in combination, can allow determination of the probability of a periodontal and/or peri-implant disease state with extremely high accuracy.

Explorations in Statistics: The Analysis of Ratios and Normalized Data

Science.gov (United States)

Curran-Everett, Douglas

2013-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This ninth installment of "Explorations in Statistics" explores the analysis of ratios and normalized--or standardized--data. As researchers, we compute a ratio--a numerator divided by a denominator--to compute a…

Statistical Energy Analysis (SEA) and Energy Finite Element Analysis (EFEA) Predictions for a Floor-Equipped Composite Cylinder

Science.gov (United States)

Grosveld, Ferdinand W.; Schiller, Noah H.; Cabell, Randolph H.

2011-01-01

Comet Enflow is a commercially available, high frequency vibroacoustic analysis software founded on Energy Finite Element Analysis (EFEA) and Energy Boundary Element Analysis (EBEA). Energy Finite Element Analysis (EFEA) was validated on a floor-equipped composite cylinder by comparing EFEA vibroacoustic response predictions with Statistical Energy Analysis (SEA) and experimental results. Statistical Energy Analysis (SEA) predictions were made using the commercial software program VA One 2009 from ESI Group. The frequency region of interest for this study covers the one-third octave bands with center frequencies from 100 Hz to 4000 Hz.

Simulation Experiments in Practice : Statistical Design and Regression Analysis

NARCIS (Netherlands)

Kleijnen, J.P.C.

2007-01-01

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. The goal of this article is to change these traditional, naïve methods of design and analysis, because statistical theory proves that more information is

Towards Discovery and Targeted Peptide Biomarker Detection Using nanoESI-TIMS-TOF MS

Energy Technology Data Exchange (ETDEWEB)

Garabedian, Alyssa; Benigni, Paolo; Ramirez, Cesar; Baker, Erin M.; Liu, Tao; Smith, Richard D.; Fernandez-Lima, Francisco

2018-05-01

Abstract. In the present work, the potential of trapped ion mobility spectrometry coupled to TOF mass spectrometry (TIMS-TOF MS) for discovery and targeted monitoring of peptide biomarkers from human-in-mouse xenograft tumor tissue was evaluated. In particular, a TIMS-MS workflow was developed for the detection and quantification of peptide biomarkers using internal heavy analogs, taking advantage of the high mobility resolution (R = 150–250) prior to mass analysis. Five peptide biomarkers were separated, identified, and quantified using offline nanoESI-TIMSCID- TOF MS; the results were in good agreement with measurements using a traditional LC-ESI-MS/MS proteomics workflow. The TIMS-TOF MS analysis permitted peptide biomarker detection based on accurate mobility, mass measurements, and high sequence coverage for concentrations in the 10–200 nM range, while simultaneously achieving discovery measurements

Statistical trend analysis methodology for rare failures in changing technical systems

International Nuclear Information System (INIS)

Ott, K.O.; Hoffmann, H.J.

1983-07-01

A methodology for a statistical trend analysis (STA) in failure rates is presented. It applies primarily to relatively rare events in changing technologies or components. The formulation is more general and the assumptions are less restrictive than in a previously published version. Relations of the statistical analysis and probabilistic assessment (PRA) are discussed in terms of categorization of decisions for action following particular failure events. The significance of tentatively identified trends is explored. In addition to statistical tests for trend significance, a combination of STA and PRA results quantifying the trend complement is proposed. The STA approach is compared with other concepts for trend characterization. (orig.)

Biomarkers in Prostate Cancer Epidemiology

Directory of Open Access Journals (Sweden)

Mudit Verma

2011-09-01

Full Text Available Understanding the etiology of a disease such as prostate cancer may help in identifying populations at high risk, timely intervention of the disease, and proper treatment. Biomarkers, along with exposure history and clinical data, are useful tools to achieve these goals. Individual risk and population incidence of prostate cancer result from the intervention of genetic susceptibility and exposure. Biochemical, epigenetic, genetic, and imaging biomarkers are used to identify people at high risk for developing prostate cancer. In cancer epidemiology, epigenetic biomarkers offer advantages over other types of biomarkers because they are expressed against a person’s genetic background and environmental exposure, and because abnormal events occur early in cancer development, which includes several epigenetic alterations in cancer cells. This article describes different biomarkers that have potential use in studying the epidemiology of prostate cancer. We also discuss the characteristics of an ideal biomarker for prostate cancer, and technologies utilized for biomarker assays. Among epigenetic biomarkers, most reports indicate GSTP1 hypermethylation as the diagnostic marker for prostate cancer; however, NKX2-5, CLSTN1, SPOCK2, SLC16A12, DPYS, and NSE1 also have been reported to be regulated by methylation mechanisms in prostate cancer. Current challenges in utilization of biomarkers in prostate cancer diagnosis and epidemiologic studies and potential solutions also are discussed.

Biomarkers of adult and developmental neurotoxicity

International Nuclear Information System (INIS)

Slikker, William; Bowyer, John F.

2005-01-01

Neurotoxicity may be defined as any adverse effect on the structure or function of the central and/or peripheral nervous system by a biological, chemical, or physical agent. A multidisciplinary approach is necessary to assess adult and developmental neurotoxicity due to the complex and diverse functions of the nervous system. The overall strategy for understanding developmental neurotoxicity is based on two assumptions: (1) significant differences in the adult versus the developing nervous system susceptibility to neurotoxicity exist and they are often developmental stage dependent; (2) a multidisciplinary approach using neurobiological, including gene expression assays, neurophysiological, neuropathological, and behavioral function is necessary for a precise assessment of neurotoxicity. Application of genomic approaches to developmental studies must use the same criteria for evaluating microarray studies as those in adults including consideration of reproducibility, statistical analysis, homogenous cell populations, and confirmation with non-array methods. A study using amphetamine to induce neurotoxicity supports the following: (1) gene expression data can help define neurotoxic mechanism(s) (2) gene expression changes can be useful biomarkers of effect, and (3) the site-selective nature of gene expression in the nervous system may mandate assessment of selective cell populations

Metabolomics approach for discovering disease biomarkers and understanding metabolic pathway

Directory of Open Access Journals (Sweden)

Jeeyoun Jung

2011-12-01

Full Text Available Metabolomics, the multi-targeted analysis of endogenous metabolites from biological samples, can be efficiently applied to screen disease biomarkers and investigate pathophysiological processes. Metabolites change rapidly in response to physiological perturbations, making them the closest link to disease phenotypes. This study explored the role of metabolomics in gaining mechanistic insight into disease processes and in searching for novel biomarkers of human diseases

Optimised Selection of Stroke Biomarker Based on Svm and Information Theory

Directory of Open Access Journals (Sweden)

Wang Xiang

2017-01-01

Full Text Available With the development of molecular biology and gene-engineering technology, gene diagnosis has been an emerging approach for modern life sciences. Biological marker, recognized as the hot topic in the molecular and gene fields, has important values in early diagnosis, malignant tumor stage, treatment and therapeutic efficacy evaluation. So far, the researcher has not found any effective way to predict and distinguish different type of stroke. In this paper, we aim to optimize stroke biomarker and figure out effective stroke detection index based on SVM (support vector machine and information theory. Through mutual information analysis and principal component analysis to complete the selection of biomarkers and then we use SVM to verify our model. According to the testing data of patients provided by Xuanwu Hospital, we explore the significant markers of the stroke through data analysis. Our model can predict stroke well. Then discuss the effects of each biomarker on the incidence of stroke.

Analysis of thrips distribution: application of spatial statistics and Kriging

Science.gov (United States)

John Aleong; Bruce L. Parker; Margaret Skinner; Diantha Howard

1991-01-01

Kriging is a statistical technique that provides predictions for spatially and temporally correlated data. Observations of thrips distribution and density in Vermont soils are made in both space and time. Traditional statistical analysis of such data assumes that the counts taken over space and time are independent, which is not necessarily true. Therefore, to analyze...

Statistical wind analysis for near-space applications

Science.gov (United States)

Roney, Jason A.

2007-09-01

Statistical wind models were developed based on the existing observational wind data for near-space altitudes between 60 000 and 100 000 ft (18 30 km) above ground level (AGL) at two locations, Akon, OH, USA, and White Sands, NM, USA. These two sites are envisioned as playing a crucial role in the first flights of high-altitude airships. The analysis shown in this paper has not been previously applied to this region of the stratosphere for such an application. Standard statistics were compiled for these data such as mean, median, maximum wind speed, and standard deviation, and the data were modeled with Weibull distributions. These statistics indicated, on a yearly average, there is a lull or a “knee” in the wind between 65 000 and 72 000 ft AGL (20 22 km). From the standard statistics, trends at both locations indicated substantial seasonal variation in the mean wind speed at these heights. The yearly and monthly statistical modeling indicated that Weibull distributions were a reasonable model for the data. Forecasts and hindcasts were done by using a Weibull model based on 2004 data and comparing the model with the 2003 and 2005 data. The 2004 distribution was also a reasonable model for these years. Lastly, the Weibull distribution and cumulative function were used to predict the 50%, 95%, and 99% winds, which are directly related to the expected power requirements of a near-space station-keeping airship. These values indicated that using only the standard deviation of the mean may underestimate the operational conditions.

The clinical role of microRNA-21 as a promising biomarker in the diagnosis and prognosis of colorectal cancer: a systematic review and meta-analysis.

Science.gov (United States)

Peng, Qiliang; Zhang, Xueli; Min, Ming; Zou, Li; Shen, Peipei; Zhu, Yaqun

2017-07-04

This systematic analysis aimed to investigate the value of microRNA-21 (miR-21) in colorectal cancer for multiple purposes, including diagnosis and prognosis, as well as its predictive power in combination biomarkers. Fifty-seven eligible studies were included in our meta-analysis, including 25 studies for diagnostic meta-analysis and 32 for prognostic meta-analysis. For the diagnostic meta-analysis of miR-21 alone, the overall pooled results for sensitivity, specificity, and area under the curve (AUC) were 0.64 (95% CI: 0.53-0.74), 0.85 (0.79-0.90), and 0.85 (0.81-0.87), respectively. Circulating samples presented corresponding values of 0.72 (0.63-0.79), 0.84 (0.78-0.89), and 0.86 (0.83-0.89), respectively. For the diagnostic meta-analysis of miR-21-related combination biomarkers, the above three parameters were 0.79 (0.69-0.86), 0.79 (0.68-0.87), and 0.86 (0.83-0.89), respectively. Notably, subgroup analysis suggested that miRNA combination markers in circulation exhibited high predictive power, with sensitivity of 0.85 (0.70-0.93), specificity of 0.86 (0.77-0.92), and AUC of 0.92 (0.89-0.94). For the prognostic meta-analysis, patients with higher expression of miR-21 had significant shorter disease-free survival [DFS; pooled hazard ratio (HR): 1.60; 95% CI: 1.20-2.15] and overall survival (OS; 1.54; 1.27-1.86). The combined HR in tissues for DFS and OS were 1.76 (1.31-2.36) and 1.58 (1.30-1.93), respectively. Our comprehensive systematic review revealed that circulating miR-21 may be suitable as a diagnostic biomarker, while tissue miR-21 could be a prognostic marker for colorectal cancer. In addition, miRNA combination biomarkers may provide a new approach for clinical application.

Analysis of photon statistics with Silicon Photomultiplier

International Nuclear Information System (INIS)

D'Ascenzo, N.; Saveliev, V.; Wang, L.; Xie, Q.

2015-01-01

The Silicon Photomultiplier (SiPM) is a novel silicon-based photodetector, which represents the modern perspective of low photon flux detection. The aim of this paper is to provide an introduction on the statistical analysis methods needed to understand and estimate in quantitative way the correct features and description of the response of the SiPM to a coherent source of light

Development of statistical analysis code for meteorological data (W-View)

International Nuclear Information System (INIS)

Tachibana, Haruo; Sekita, Tsutomu; Yamaguchi, Takenori

2003-03-01

A computer code (W-View: Weather View) was developed to analyze the meteorological data statistically based on 'the guideline of meteorological statistics for the safety analysis of nuclear power reactor' (Nuclear Safety Commission on January 28, 1982; revised on March 29, 2001). The code gives statistical meteorological data to assess the public dose in case of normal operation and severe accident to get the license of nuclear reactor operation. This code was revised from the original code used in a large office computer code to enable a personal computer user to analyze the meteorological data simply and conveniently and to make the statistical data tables and figures of meteorology. (author)

Statistical analysis of the Ft. Calhoun reactor coolant pump system

International Nuclear Information System (INIS)

Heising, Carolyn D.

1998-01-01

In engineering science, statistical quality control techniques have traditionally been applied to control manufacturing processes. An application to commercial nuclear power plant maintenance and control is presented that can greatly improve plant safety. As a demonstration of such an approach to plant maintenance and control, a specific system is analyzed: the reactor coolant pumps (RCPs) of the Ft. Calhoun nuclear power plant. This research uses capability analysis, Shewhart X-bar, R-charts, canonical correlation methods, and design of experiments to analyze the process for the state of statistical control. The results obtained show that six out of ten parameters are under control specifications limits and four parameters are not in the state of statistical control. The analysis shows that statistical process control methods can be applied as an early warning system capable of identifying significant equipment problems well in advance of traditional control room alarm indicators Such a system would provide operators with ample time to respond to possible emergency situations and thus improve plant safety and reliability. (author)

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

Directory of Open Access Journals (Sweden)

Ujjwal Maulik

Full Text Available Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution. The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

Science.gov (United States)

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data