WorldWideScience

Sample records for bayesian microbial subtyping

  1. Assessing the Differences in Public Health Impact of Salmonella Subtypes Using a Bayesian Microbial Subtyping Approach for Source Attribution

    DEFF Research Database (Denmark)

    Pires, Sara Monteiro; Hald, Tine

    2010-01-01

    Salmonella is a major cause of human gastroenteritis worldwide. To prioritize interventions and assess the effectiveness of efforts to reduce illness, it is important to attribute salmonellosis to the responsible sources. Studies have suggested that some Salmonella subtypes have a higher health...... impact than others. Likewise, some food sources appear to have a higher impact than others. Knowledge of variability in the impact of subtypes and sources may provide valuable added information for research, risk management, and public health strategies. We developed a Bayesian model that attributes...... illness to specific sources and allows for a better estimation of the differences in the ability of Salmonella subtypes and food types to result in reported salmonellosis. The model accommodates data for multiple years and is based on the Danish Salmonella surveillance. The number of sporadic cases caused...

  2. Salmonella source attribution based on microbial subtyping

    DEFF Research Database (Denmark)

    Barco, Lisa; Barrucci, Federica; Olsen, John Elmerdahl

    2013-01-01

    Source attribution of cases of food-borne disease represents a valuable tool for identifying and prioritizing effective food-safety interventions. Microbial subtyping is one of the most common methods to infer potential sources of human food-borne infections. So far, Salmonella microbial subtyping...... source attribution through microbial subtyping approach. It summarizes the available microbial subtyping attribution models and discusses the use of conventional phenotypic typing methods, as well as of the most commonly applied molecular typing methods in the European Union (EU) laboratories...

  3. Salmonella Source Attribution in Japan by a Microbiological Subtyping Approach

    DEFF Research Database (Denmark)

    Toyofuku, Hajime; Pires, Sara Monteiro; Hald, Tine

    2011-01-01

    In order to estimate the number of human Salmonella infections attributable to each of major animal-food source, and help identifying the best Salmonella intervention strategies, a microbial subtyping approach for source attribution was applied. We adapted a Bayesian model that attributes illnesses......-food sources, subtype-related factors, and source-related factors. National-surveillance serotyping data from 1998 to 2007 were applied to the model. Results suggested that the relative contribution of the sources to salmonellosis varied during the 10 year period, and that eggs are the most important source...... to specific sources and allows for the estimation of the differences in the ability of Salmonella subtypes and food types to result in reported salmonellosis. The number of human cases caused by different Salmonella subtypes is estimated as a function of the prevalence of these subtypes in the animal...

  4. New paradigms for Salmonella source attribution based on microbial subtyping.

    NARCIS (Netherlands)

    Mughini-Gras, Lapo; Franz, Eelco; van Pelt, Wilfrid

    Microbial subtyping is the most common approach for Salmonella source attribution. Typically, attributions are computed using frequency-matching models like the Dutch and Danish models based on phenotyping data (serotyping, phage-typing, and antimicrobial resistance profiling). Herewith, we

  5. Bayesian predictive risk modeling of microbial criteria for Campylobacter in broilers

    DEFF Research Database (Denmark)

    Nauta, Maarten; Ranta, J.; Mikkelä, A.

    Microbial Criteria define the acceptability of food products, based on the presence or detected number of microorganisms in samples. The criteria are applied at the level of defined food lots. Generally, these are interpreted as statistical batches representing the production [1]. The batches...... be assessed by computing posterior distribution of the parameters - a Bayesian evidence synthesis. The outcome of a defined Microbial Criterion (MC) for a batch provides additional evidence concerning the batch. Posterior predictive consumer risk (probability of illness) was computed for such batch...

  6. Bayesian analysis of non-linear differential equation models with application to a gut microbial ecosystem.

    Science.gov (United States)

    Lawson, Daniel J; Holtrop, Grietje; Flint, Harry

    2011-07-01

    Process models specified by non-linear dynamic differential equations contain many parameters, which often must be inferred from a limited amount of data. We discuss a hierarchical Bayesian approach combining data from multiple related experiments in a meaningful way, which permits more powerful inference than treating each experiment as independent. The approach is illustrated with a simulation study and example data from experiments replicating the aspects of the human gut microbial ecosystem. A predictive model is obtained that contains prediction uncertainty caused by uncertainty in the parameters, and we extend the model to capture situations of interest that cannot easily be studied experimentally. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. New paradigms for Salmonella source attribution based on microbial subtyping.

    Science.gov (United States)

    Mughini-Gras, Lapo; Franz, Eelco; van Pelt, Wilfrid

    2018-05-01

    Microbial subtyping is the most common approach for Salmonella source attribution. Typically, attributions are computed using frequency-matching models like the Dutch and Danish models based on phenotyping data (serotyping, phage-typing, and antimicrobial resistance profiling). Herewith, we critically review three major paradigms facing Salmonella source attribution today: (i) the use of genotyping data, particularly Multi-Locus Variable Number of Tandem Repeats Analysis (MLVA), which is replacing traditional Salmonella phenotyping beyond serotyping; (ii) the integration of case-control data into source attribution to improve risk factor identification/characterization; (iii) the investigation of non-food sources, as attributions tend to focus on foods of animal origin only. Population genetics models or simplified MLVA schemes may provide feasible options for source attribution, although there is a strong need to explore novel modelling options as we move towards whole-genome sequencing as the standard. Classical case-control studies are enhanced by incorporating source attribution results, as individuals acquiring salmonellosis from different sources have different associated risk factors. Thus, the more such analyses are performed the better Salmonella epidemiology will be understood. Reparametrizing current models allows for inclusion of sources like reptiles, the study of which improves our understanding of Salmonella epidemiology beyond food to tackle the pathogen in a more holistic way. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. HIV-1 subtype A gag variability and epitope evolution.

    Science.gov (United States)

    Abidi, Syed Hani; Kalish, Marcia L; Abbas, Farhat; Rowland-Jones, Sarah; Ali, Syed

    2014-01-01

    The aim of this study was to examine the course of time-dependent evolution of HIV-1 subtype A on a global level, especially with respect to the dynamics of immunogenic HIV gag epitopes. We used a total of 1,893 HIV-1 subtype A gag sequences representing a timeline from 1985 through 2010, and 19 different countries in Africa, Europe and Asia. The phylogenetic relationship of subtype A gag and its epidemic dynamics was analysed through a Maximum Likelihood tree and Bayesian Skyline plot, genomic variability was measured in terms of G → A substitutions and Shannon entropy, and the time-dependent evolution of HIV subtype A gag epitopes was examined. Finally, to confirm observations on globally reported HIV subtype A sequences, we analysed the gag epitope data from our Kenyan, Pakistani, and Afghan cohorts, where both cohort-specific gene epitope variability and HLA restriction profiles of gag epitopes were examined. The most recent common ancestor of the HIV subtype A epidemic was estimated to be 1956 ± 1. A period of exponential growth began about 1980 and lasted for approximately 7 years, stabilized for 15 years, declined for 2-3 years, then stabilized again from about 2004. During the course of evolution, a gradual increase in genomic variability was observed that peaked in 2005-2010. We observed that the number of point mutations and novel epitopes in gag also peaked concurrently during 2005-2010. It appears that as the HIV subtype A epidemic spread globally, changing population immunogenetic pressures may have played a role in steering immune-evolution of this subtype in new directions. This trend is apparent in the genomic variability and epitope diversity of HIV-1 subtype A gag sequences.

  9. HIV-1 subtype A gag variability and epitope evolution.

    Directory of Open Access Journals (Sweden)

    Syed Hani Abidi

    Full Text Available OBJECTIVE: The aim of this study was to examine the course of time-dependent evolution of HIV-1 subtype A on a global level, especially with respect to the dynamics of immunogenic HIV gag epitopes. METHODS: We used a total of 1,893 HIV-1 subtype A gag sequences representing a timeline from 1985 through 2010, and 19 different countries in Africa, Europe and Asia. The phylogenetic relationship of subtype A gag and its epidemic dynamics was analysed through a Maximum Likelihood tree and Bayesian Skyline plot, genomic variability was measured in terms of G → A substitutions and Shannon entropy, and the time-dependent evolution of HIV subtype A gag epitopes was examined. Finally, to confirm observations on globally reported HIV subtype A sequences, we analysed the gag epitope data from our Kenyan, Pakistani, and Afghan cohorts, where both cohort-specific gene epitope variability and HLA restriction profiles of gag epitopes were examined. RESULTS: The most recent common ancestor of the HIV subtype A epidemic was estimated to be 1956 ± 1. A period of exponential growth began about 1980 and lasted for approximately 7 years, stabilized for 15 years, declined for 2-3 years, then stabilized again from about 2004. During the course of evolution, a gradual increase in genomic variability was observed that peaked in 2005-2010. We observed that the number of point mutations and novel epitopes in gag also peaked concurrently during 2005-2010. CONCLUSION: It appears that as the HIV subtype A epidemic spread globally, changing population immunogenetic pressures may have played a role in steering immune-evolution of this subtype in new directions. This trend is apparent in the genomic variability and epitope diversity of HIV-1 subtype A gag sequences.

  10. Evidence of multiple introductions of HIV-1 subtype C in Angola.

    Science.gov (United States)

    Afonso, Joana Morais; Morgado, Mariza G; Bello, Gonzalo

    2012-10-01

    HIV-1 subtype C is the most prevalent group M clade in southern Africa and some eastern African countries. Subtype C is also the most frequent subtype in Angola (southwestern Africa), with an estimated prevalence of 10-20%. In order to better understand the origin of the HIV-1 subtype C strains circulating in Angola, 31 subtype C pol sequences of Angolan origin were compared with 1950 subtype C pol sequences sampled in other African countries. Phylogenetic analyses reveal that the Angolan subtype C sequences were distributed in 16 different lineages that were widely dispersed among other African strains. Ten subtype C Angolan lineages were composed by only one sequence, while the remaining six clades contain between two and seven sequences. Bayesian phylogeographic analysis indicates that most Angolan clades probably originated in different southern African countries with the exception of one lineage that most likely originated in Burundi. Evolutionary analysis suggests that those Angolan subtype C clades composed by ≥ 2 sequences were introduced into the country between the late 1970s and the mid 2000s. The median estimated time frame for the origin of those Angolan lineages coincides with periods of positive migration influx in Angola that were preceded by phases of negative migratory outflow. These results demonstrate that the Angolan subtype C epidemic resulted from multiple introductions of subtype C viruses mainly imported from southern African countries over the last 30years, some of which have been locally disseminated establishing several autochthonous transmission networks. This study also suggests that population mobility between Angola and southern African countries during civil war (1974-2002) may have played a key role in the emergence of the Angolan subtype C epidemic. Copyright © 2012 Elsevier B.V. All rights reserved.

  11. Origin and dynamics of HIV-1 subtype C infection in India.

    Directory of Open Access Journals (Sweden)

    Chengli Shen

    Full Text Available To investigate the geographical origin and evolution dynamics of HIV-1 subtype C infection in India.Ninety HIV-1 subtype C env gp120 subtype C sequences from India were compared with 312 env gp120 reference subtype C sequences from 27 different countries obtained from Los Alamos HIV database. All the HIV-1 subtype C env gp120 sequences from India were used for the geographical origin analysis and 61 subtype C env gp120 sequences with known sampling year (from 1991 to 2008 were employed to determine the origin of HIV infection in India.Phylogenetic analysis of HIV-1 env sequences was used to investigate the geographical origin and tMRCA of Indian HIV-1 subtype C. Evolutionary parameters including origin date and demographic growth patterns of Indian subtype C were estimated using a Bayesian coalescent-based approach under relaxed molecular clock models.The majority of the analyzed Indian and South African HIV-1 subtype C sequences formed a single monophyletic cluster. The most recent common ancestor date was calculated to be 1975.56 (95% HPD, 1968.78-1981.52. Reconstruction of the effective population size revealed three phases of epidemic growth: an initial slow growth, followed by exponential growth, and then a plateau phase approaching present time. Stabilization of the epidemic growth phase correlated with the foundation of National AIDS Control Organization in India.Indian subtype C originated from a single South African lineage in the middle of 1970s. The current study emphasizes not only the utility of HIV-1 sequence data for epidemiological studies but more notably highlights the effectiveness of community or government intervention strategies in controlling the trend of the epidemic.

  12. BAYESIAN BICLUSTERING FOR PATIENT STRATIFICATION.

    Science.gov (United States)

    Khakabimamaghani, Sahand; Ester, Martin

    2016-01-01

    The move from Empirical Medicine towards Personalized Medicine has attracted attention to Stratified Medicine (SM). Some methods are provided in the literature for patient stratification, which is the central task of SM, however, there are still significant open issues. First, it is still unclear if integrating different datatypes will help in detecting disease subtypes more accurately, and, if not, which datatype(s) are most useful for this task. Second, it is not clear how we can compare different methods of patient stratification. Third, as most of the proposed stratification methods are deterministic, there is a need for investigating the potential benefits of applying probabilistic methods. To address these issues, we introduce a novel integrative Bayesian biclustering method, called B2PS, for patient stratification and propose methods for evaluating the results. Our experimental results demonstrate the superiority of B2PS over a popular state-of-the-art method and the benefits of Bayesian approaches. Our results agree with the intuition that transcriptomic data forms a better basis for patient stratification than genomic data.

  13. A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data.

    Science.gov (United States)

    Mo, Qianxing; Shen, Ronglai; Guo, Cui; Vannucci, Marina; Chan, Keith S; Hilsenbeck, Susan G

    2018-01-01

    Identification of clinically relevant tumor subtypes and omics signatures is an important task in cancer translational research for precision medicine. Large-scale genomic profiling studies such as The Cancer Genome Atlas (TCGA) Research Network have generated vast amounts of genomic, transcriptomic, epigenomic, and proteomic data. While these studies have provided great resources for researchers to discover clinically relevant tumor subtypes and driver molecular alterations, there are few computationally efficient methods and tools for integrative clustering analysis of these multi-type omics data. Therefore, the aim of this article is to develop a fully Bayesian latent variable method (called iClusterBayes) that can jointly model omics data of continuous and discrete data types for identification of tumor subtypes and relevant omics features. Specifically, the proposed method uses a few latent variables to capture the inherent structure of multiple omics data sets to achieve joint dimension reduction. As a result, the tumor samples can be clustered in the latent variable space and relevant omics features that drive the sample clustering are identified through Bayesian variable selection. This method significantly improve on the existing integrative clustering method iClusterPlus in terms of statistical inference and computational speed. By analyzing TCGA and simulated data sets, we demonstrate the excellent performance of the proposed method in revealing clinically meaningful tumor subtypes and driver omics features. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  14. The Origin and Evolutionary History of HIV-1 Subtype C in Senegal

    Science.gov (United States)

    Jung, Matthieu; Leye, Nafissatou; Vidal, Nicole; Fargette, Denis; Diop, Halimatou; Toure Kane, Coumba; Gascuel, Olivier; Peeters, Martine

    2012-01-01

    Background The classification of HIV-1 strains in subtypes and Circulating Recombinant Forms (CRFs) has helped in tracking the course of the HIV pandemic. In Senegal, which is located at the tip of West Africa, CRF02_AG predominates in the general population and Female Sex Workers (FSWs). In contrast, 40% of Men having Sex with Men (MSM) in Senegal are infected with subtype C. In this study we analyzed the geographical origins and introduction dates of HIV-1 C in Senegal in order to better understand the evolutionary history of this subtype, which predominates today in the MSM population Methodology/Principal Findings We used a combination of phylogenetic analyses and a Bayesian coalescent-based approach, to study the phylogenetic relationships in pol of 56 subtype C isolates from Senegal with 3,025 subtype C strains that were sampled worldwide. Our analysis shows a significantly well supported cluster which contains all subtype C strains that circulate among MSM in Senegal. The MSM cluster and other strains from Senegal are widely dispersed among the different subclusters of African HIV-1 C strains, suggesting multiple introductions of subtype C in Senegal from many different southern and east African countries. More detailed analyses show that HIV-1 C strains from MSM are more closely related to those from southern Africa. The estimated date of the MRCA of subtype C in the MSM population in Senegal is estimated to be in the early 80's. Conclusions/Significance Our evolutionary reconstructions suggest that multiple subtype C viruses with a common ancestor originating in the early 1970s entered Senegal. There was only one efficient spread in the MSM population, which most likely resulted from a single introduction, underlining the importance of high-risk behavior in spread of viruses. PMID:22470456

  15. Accurate phenotyping: Reconciling approaches through Bayesian model averaging.

    Directory of Open Access Journals (Sweden)

    Carla Chia-Ming Chen

    Full Text Available Genetic research into complex diseases is frequently hindered by a lack of clear biomarkers for phenotype ascertainment. Phenotypes for such diseases are often identified on the basis of clinically defined criteria; however such criteria may not be suitable for understanding the genetic composition of the diseases. Various statistical approaches have been proposed for phenotype definition; however our previous studies have shown that differences in phenotypes estimated using different approaches have substantial impact on subsequent analyses. Instead of obtaining results based upon a single model, we propose a new method, using Bayesian model averaging to overcome problems associated with phenotype definition. Although Bayesian model averaging has been used in other fields of research, this is the first study that uses Bayesian model averaging to reconcile phenotypes obtained using multiple models. We illustrate the new method by applying it to simulated genetic and phenotypic data for Kofendred personality disorder-an imaginary disease with several sub-types. Two separate statistical methods were used to identify clusters of individuals with distinct phenotypes: latent class analysis and grade of membership. Bayesian model averaging was then used to combine the two clusterings for the purpose of subsequent linkage analyses. We found that causative genetic loci for the disease produced higher LOD scores using model averaging than under either individual model separately. We attribute this improvement to consolidation of the cores of phenotype clusters identified using each individual method.

  16. Hierarchical Bayesian mixture modelling for antigen-specific T-cell subtyping in combinatorially encoded flow cytometry studies

    DEFF Research Database (Denmark)

    Lin, Lin; Chan, Cliburn; Hadrup, Sine R

    2013-01-01

    subtype identification in this novel, general model framework, and provide a detailed example using simulated data. We then describe application to a data set from an experimental study of antigen-specific T-cell subtyping using combinatorially encoded assays in human blood samples. Summary comments...... profiling in many biological areas, traditional flow cytometry measures relative levels of abundance of marker proteins using fluorescently labeled tags that identify specific markers by a single-color. One specific and important recent development in this area is the use of combinatorial marker assays...

  17. Phylodynamics of HIV-1 subtype F1 in Angola, Brazil and Romania.

    Science.gov (United States)

    Bello, Gonzalo; Afonso, Joana Morais; Morgado, Mariza G

    2012-07-01

    The HIV-1 subtype F1 is exceptionally prevalent in Angola, Brazil and Romania. The epidemiological context in which the spread of HIV occurred was highly variable from one country to another, mainly due to the existence of a long-term civil war in Angola and the contamination of a large number of children in Romania. Here we apply phylogenetic and Bayesian coalescent-based methods to reconstruct the phylodynamic patterns of HIV-1 subtype F1 in such different epidemiological settings. The phylogenetic analyses of HIV-1 subtype F1 pol sequences sampled worldwide confirmed that most sequences from Angola, Brazil and Romania segregated in country-specific monophyletic groups, while most subtype F1 sequences from Romanian children branched as a monophyletic sub-cluster (Romania-CH) nested within sequences from adults. The inferred time of the most recent common ancestor of the different subtype F1 clades were as follow: Angola=1983 (1978-1989), Brazil=1977 (1972-1981), Romania adults=1980 (1973-1987), and Romania-CH=1985 (1978-1989). All subtype F1 clades showed a demographic history best explained by a model of logistic population growth. Although the expansion phase of subtype F1 epidemic in Angola (mid 1980s to early 2000s) overlaps with the civil war period (1975-2002), the mean estimated growth rate of the Angolan F1 clade (0.49 year(-1)) was not exceptionally high, but quite similar to that estimated for the Brazilian (0.69 year(-1)) and Romanian adult (0.36 year(-1)) subtype F1 clades. The Romania-CH subtype F1 lineage, by contrast, displayed a short and explosive dissemination phase, with a median growth rate (2.47 year(-1)) much higher than that estimated for adult populations. This result supports the idea that the AIDS epidemic that affected the Romanian children was mainly caused by the spread of the HIV through highly efficient parenteral transmission networks, unlike adult populations where HIV is predominantly transmitted through sexual route. Copyright

  18. Emergence of canine parvovirus subtype 2b (CPV-2b) infections in Australian dogs.

    Science.gov (United States)

    Clark, Nicholas J; Seddon, Jennifer M; Kyaw-Tanner, Myat; Al-Alawneh, John; Harper, Gavin; McDonagh, Phillip; Meers, Joanne

    2018-03-01

    Tracing the temporal dynamics of pathogens is crucial for developing strategies to detect and limit disease emergence. Canine parvovirus (CPV-2) is an enteric virus causing morbidity and mortality in dogs around the globe. Previous work in Australia reported that the majority of cases were associated with the CPV-2a subtype, an unexpected finding since CPV-2a was rapidly replaced by another subtype (CPV-2b) in many countries. Using a nine-year dataset of CPV-2 infections from 396 dogs sampled across Australia, we assessed the population dynamics and molecular epidemiology of circulating CPV-2 subtypes. Bayesian phylogenetic Skygrid models and logistic regressions were used to trace the temporal dynamics of CPV-2 infections in dogs sampled from 2007 to 2016. Phylogenetic models indicated that CPV-2a likely emerged in Australia between 1973 and 1988, while CPV-2b likely emerged between 1985 and 1998. Sequences from both subtypes were found in dogs across continental Australia and Tasmania, with no apparent effect of climate variability on subtype occurrence. Both variant subtypes exhibited a classical disease emergence pattern of relatively high rates of evolution during early emergence followed by subsequent decreases in evolutionary rates over time. However, the CPV-2b subtype maintained higher mutation rates than CPV-2a and continued to expand, resulting in an increase in the probability that dogs will carry this subtype over time. Ongoing monitoring programs that provide molecular epidemiology surveillance will be necessary to detect emergence of new variants and make informed recommendations to develop reliable detection and vaccine methods. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Rising prevalence of non-B HIV-1 subtypes in North Carolina and evidence for local onward transmission.

    Science.gov (United States)

    Dennis, Ann M; Hué, Stephane; Learner, Emily; Sebastian, Joseph; Miller, William C; Eron, Joseph J

    2017-01-01

    HIV-1 diversity is increasing in North American and European cohorts which may have public health implications. However, little is known about non-B subtype diversity in the southern United States, despite the region being the epicenter of the nation's epidemic. We characterized HIV-1 diversity and transmission clusters to identify the extent to which non-B strains are transmitted locally. We conducted cross-sectional analyses of HIV-1 partial pol sequences collected from 1997 to 2014 from adults accessing routine clinical care in North Carolina (NC). Subtypes were evaluated using COMET and phylogenetic analysis. Putative transmission clusters were identified using maximum-likelihood trees. Clusters involving non-B strains were confirmed and their dates of origin were estimated using Bayesian phylogenetics. Data were combined with demographic information collected at the time of sample collection and country of origin for a subset of patients. Among 24,972 sequences from 15,246 persons, the non-B subtype prevalence increased from 0% to 3.46% over the study period. Of 325 persons with non-B subtypes, diversity was high with over 15 pure subtypes and recombinants; subtype C (28.9%) and CRF02_AG (24.0%) were most common. While identification of transmission clusters was lower for persons with non-B versus B subtypes, several local transmission clusters (≥3 persons) involving non-B subtypes were identified and all were presumably due to heterosexual transmission. Prevalence of non-B subtype diversity remains low in NC but a statistically significant rise was identified over time which likely reflects multiple importation. However, the combined phylogenetic clustering analysis reveals evidence for local onward transmission. Detection of these non-B clusters suggests heterosexual transmission and may guide diagnostic and prevention interventions.

  20. Bayesian methods for hackers probabilistic programming and Bayesian inference

    CERN Document Server

    Davidson-Pilon, Cameron

    2016-01-01

    Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples a...

  1. HIV-1 subtype D infections among Caucasians from Northwestern Poland--phylogenetic and clinical analysis.

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    Miłosz Parczewski

    Full Text Available BACKGROUND: HIV-1 subtype D infections, which are associated with a faster rate of progression and lymphocyte CD4 decline, cognitive deficit and higher mortality, have rarely been found in native Europeans. In Northwestern Poland, however, infections with this subtype had been identified. This study aimed to analyze the sequence and clinical data for patients with subtype D using molecular phylogeography and identify transmission clusters and ancestry, as well as drug resistance, baseline HIV tropism and antiretroviral treatment efficacy. METHODS: Phylogenetic analyses of local HIV-1 subtype D sequences were performed, with time to the most recent common ancestor inferred using bayesian modeling. Sequence and drug resistance data were linked with the clinical and epidemiological information. RESULTS: Subtype D was found in 24 non-immigrant Caucasian, heterosexually infected patients (75% of females, median age at diagnosis of 49.5 years; IQR: 29-56 years. Partial pol sequences clustered monophyletically with the clades of Ugandan origin and no evidence of transmission from other European countries was found. Time to the most common recent ancestor was 1989.24 (95% HPD: 1968.83-1994.46. Baseline drug resistance to nucleoside reverse transcriptase inhibitors was observed in 54.5% of cases (mutations: M41L, K103N, T215S/D with evidence of clustering, no baseline integrase or protease resistance and infrequent non-R5 tropism (13.6%. Virologic failure was observed in 60% of cases and was associated with poor adherence (p<0.001 and subsequent development of drug resistance (p = 0.008, OR: 20 (95%CI: 1.7-290. CONCLUSIONS: Local subtype D represented an independently transmitted network with probably single index case, high frequency of primary drug resistance and evidence of transmission clusters.

  2. Phylodynamics of HIV-1 subtype B among the men-having-sex-with-men (MSM population in Hong Kong.

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    Jonathan Hon-Kwan Chen

    Full Text Available The men-having-sex-with-men (MSM population has become one of the major risk groups for HIV-1 infection in the Asia Pacific countries. Hong Kong is located in the centre of Asia and the transmission history of HIV-1 subtype B transmission among MSM remained unclear. The aim of this study was to investigate the transmission dynamics of HIV-1 subtype B virus in the Hong Kong MSM population. Samples of 125 HIV-1 subtype B infected MSM patients were recruited in this study. Through this study, the subtype B epidemic in the Hong Kong MSM population was identified spreading mainly among local Chinese who caught infection locally. On the other hand, HIV-1 subtype B infected Caucasian MSM caught infection mainly outside Hong Kong. The Bayesian phylogenetic analysis also indicated that 3 separate subtype B epidemics with divergence dates in the 1990s had occurred. The first and latest epidemics were comparatively small-scaled; spreading among the local Chinese MSM while sauna-visiting was found to be the major sex partner sourcing reservoir for the first subtype B epidemic. However, the second epidemic was spread in a large-scale among local Chinese MSM with a number of them having sourced their sex partners through the internet. The epidemic virus was estimated to have a divergence date in 1987 and the infected population in Hong Kong had a logistic growth throughout the past 20 years. Our study elucidated the evolutionary and demographic history of HIV-1 subtype B virus in Hong Kong MSM population. The understanding of transmission and growth model of the subtype B epidemic provides more information on the HIV-1 transmission among MSM population in other Asia Pacific high-income countries.

  3. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2010-01-01

    Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian networks, the authors discuss the use of Bayesian networks for causal modeling. They also draw on their own applied research to illustrate various applications of the technology.New to the Second EditionNew chapter on Bayesian network classifiersNew section on object-oriente

  4. Bayesian Graphical Models

    DEFF Research Database (Denmark)

    Jensen, Finn Verner; Nielsen, Thomas Dyhre

    2016-01-01

    Mathematically, a Bayesian graphical model is a compact representation of the joint probability distribution for a set of variables. The most frequently used type of Bayesian graphical models are Bayesian networks. The structural part of a Bayesian graphical model is a graph consisting of nodes...

  5. Bayesian artificial intelligence

    CERN Document Server

    Korb, Kevin B

    2003-01-01

    As the power of Bayesian techniques has become more fully realized, the field of artificial intelligence has embraced Bayesian methodology and integrated it to the point where an introduction to Bayesian techniques is now a core course in many computer science programs. Unlike other books on the subject, Bayesian Artificial Intelligence keeps mathematical detail to a minimum and covers a broad range of topics. The authors integrate all of Bayesian net technology and learning Bayesian net technology and apply them both to knowledge engineering. They emphasize understanding and intuition but also provide the algorithms and technical background needed for applications. Software, exercises, and solutions are available on the authors' website.

  6. Spatiotemporal dynamics of the HIV-1 subtype G epidemic in West and Central Africa.

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    Delatorre, Edson; Mir, Daiana; Bello, Gonzalo

    2014-01-01

    The human immunodeficiency virus type 1 (HIV-1) subtype G is the second most prevalent HIV-1 clade in West Africa, accounting for nearly 30% of infections in the region. There is no information about the spatiotemporal dynamics of dissemination of this HIV-1 clade in Africa. To this end, we analyzed a total of 305 HIV-1 subtype G pol sequences isolated from 11 different countries from West and Central Africa over a period of 20 years (1992 to 2011). Evolutionary, phylogeographic and demographic parameters were jointly estimated from sequence data using a Bayesian coalescent-based method. Our analyses indicate that subtype G most probably emerged in Central Africa in 1968 (1956-1976). From Central Africa, the virus was disseminated to West and West Central Africa at multiple times from the middle 1970s onwards. Two subtype G strains probably introduced into Nigeria and Togo between the middle and the late 1970s were disseminated locally and to neighboring countries, leading to the origin of two major western African clades (G WA-I and G WA-II). Subtype G clades circulating in western and central African regions displayed an initial phase of exponential growth followed by a decline in growth rate since the early/middle 1990 s; but the mean epidemic growth rate of G WA-I (0.75 year-1) and G WA-II (0.95 year-1) clades was about two times higher than that estimated for central African lineages (0.47 year-1). Notably, the overall evolutionary and demographic history of G WA-I and G WA-II clades was very similar to that estimated for the CRF06_cpx clade circulating in the same region. These results support the notion that the spatiotemporal dissemination dynamics of major HIV-1 clades circulating in western Africa have probably been shaped by the same ecological factors.

  7. Bayesian Mediation Analysis

    OpenAIRE

    Yuan, Ying; MacKinnon, David P.

    2009-01-01

    This article proposes Bayesian analysis of mediation effects. Compared to conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian mediation analysis, inference is straightforward and exact, which makes it appealing for studies with small samples. Third, the Bayesian approach is conceptua...

  8. Introduction to Bayesian statistics

    CERN Document Server

    Bolstad, William M

    2017-01-01

    There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this Third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian staistics. The author continues to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inferenfe cfor discrete random variables, bionomial proprotion, Poisson, normal mean, and simple linear regression. In addition, newly-developing topics in the field are presented in four new chapters: Bayesian inference with unknown mean and variance; Bayesian inference for Multivariate Normal mean vector; Bayesian inference for Multiple Linear RegressionModel; and Computati...

  9. Subtyping adolescents with bulimia nervosa.

    Science.gov (United States)

    Chen, Eunice Y; Le Grange, Daniel

    2007-12-01

    Cluster analyses of eating disorder patients have yielded a "dietary-depressive" subtype, typified by greater negative affect, and a "dietary" subtype, typified by dietary restraint. This study aimed to replicate these findings in an adolescent sample with bulimia nervosa (BN) from a randomized controlled trial and to examine the validity and reliability of this methodology. In the sample of BN adolescents (N=80), cluster analysis revealed a "dietary-depressive" subtype (37.5%) and a "dietary" subtype (62.5%) using the Beck Depression Inventory, Rosenberg Self-Esteem Scale and Eating Disorder Examination Restraint subscale. The "dietary-depressive" subtype compared to the "dietary" subtype was significantly more likely to: (1) report co-occurring disorders, (2) greater eating and weight concerns, and (3) less vomiting abstinence at post-treatment (all p'sreliability of the subtyping scheme, a larger sample of adolescents with mixed eating and weight disorders in an outpatient eating disorder clinic (N=149) was subtyped, yielding similar subtypes. These results support the validity and reliability of the subtyping strategy in two adolescent samples.

  10. Bayesian biostatistics

    CERN Document Server

    Lesaffre, Emmanuel

    2012-01-01

    The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets. Through examples, exercises and a combination of introd

  11. Morphologic Subtypes of Hepatocellular Carcinoma.

    Science.gov (United States)

    Torbenson, Michael S

    2017-06-01

    Hepatocellular carcinomas can be further divided into distinct subtypes that provide important clinical information and biological insights. These subtypes are distinct from growth patterns and are on based on morphologic and molecular findings. There are 12 reasonably well-defined subtypes as well as 6 provisional subtypes, together making up 35% of all hepatocellular carcinomas. These subtypes are discussed, with an emphasis on their definitions and the key morphologic findings. Copyright © 2017 Elsevier Inc. All rights reserved.

  12. Bayesian data analysis for newcomers.

    Science.gov (United States)

    Kruschke, John K; Liddell, Torrin M

    2018-02-01

    This article explains the foundational concepts of Bayesian data analysis using virtually no mathematical notation. Bayesian ideas already match your intuitions from everyday reasoning and from traditional data analysis. Simple examples of Bayesian data analysis are presented that illustrate how the information delivered by a Bayesian analysis can be directly interpreted. Bayesian approaches to null-value assessment are discussed. The article clarifies misconceptions about Bayesian methods that newcomers might have acquired elsewhere. We discuss prior distributions and explain how they are not a liability but an important asset. We discuss the relation of Bayesian data analysis to Bayesian models of mind, and we briefly discuss what methodological problems Bayesian data analysis is not meant to solve. After you have read this article, you should have a clear sense of how Bayesian data analysis works and the sort of information it delivers, and why that information is so intuitive and useful for drawing conclusions from data.

  13. How to practise Bayesian statistics outside the Bayesian church: What philosophy for Bayesian statistical modelling?

    NARCIS (Netherlands)

    Borsboom, D.; Haig, B.D.

    2013-01-01

    Unlike most other statistical frameworks, Bayesian statistical inference is wedded to a particular approach in the philosophy of science (see Howson & Urbach, 2006); this approach is called Bayesianism. Rather than being concerned with model fitting, this position in the philosophy of science

  14. Bayesian Probability Theory

    Science.gov (United States)

    von der Linden, Wolfgang; Dose, Volker; von Toussaint, Udo

    2014-06-01

    Preface; Part I. Introduction: 1. The meaning of probability; 2. Basic definitions; 3. Bayesian inference; 4. Combinatrics; 5. Random walks; 6. Limit theorems; 7. Continuous distributions; 8. The central limit theorem; 9. Poisson processes and waiting times; Part II. Assigning Probabilities: 10. Transformation invariance; 11. Maximum entropy; 12. Qualified maximum entropy; 13. Global smoothness; Part III. Parameter Estimation: 14. Bayesian parameter estimation; 15. Frequentist parameter estimation; 16. The Cramer-Rao inequality; Part IV. Testing Hypotheses: 17. The Bayesian way; 18. The frequentist way; 19. Sampling distributions; 20. Bayesian vs frequentist hypothesis tests; Part V. Real World Applications: 21. Regression; 22. Inconsistent data; 23. Unrecognized signal contributions; 24. Change point problems; 25. Function estimation; 26. Integral equations; 27. Model selection; 28. Bayesian experimental design; Part VI. Probabilistic Numerical Techniques: 29. Numerical integration; 30. Monte Carlo methods; 31. Nested sampling; Appendixes; References; Index.

  15. Subtypes of nonmedical prescription drug misuse

    Science.gov (United States)

    McCabe, Sean Esteban; Boyd, Carol J.; Teter, Christian J.

    2010-01-01

    This study used three characteristics (i.e., motive, route of administration, and co-ingestion with alcohol) of nonmedical prescription drug misuse across four separate classes (i.e., pain, sedative/anxiety, sleeping and stimulant medications) to examine subtypes and drug related problems. A Web survey was self-administered by a randomly selected sample of 3,639 undergraduate students attending a large Midwestern 4-year U.S. university. Self-treatment subtypes were characterized by motives consistent with the prescription drug's pharmaceutical main indication, oral only routes of administration, and no co-ingestion with alcohol. Recreational subtypes were characterized by recreational motives, oral or non-oral routes, and co-ingestion. Mixed subtypes consisted of other combinations of motives, routes, and co-ingestion. Among those who reported nonmedical prescription drug misuse, approximately 13% were classified into the recreational subtype, while 39% were in the self-treatment subtype, and 48% were in the mixed subtype. There were significant differences in the subtypes in terms of gender, race and prescription drug class. Approximately 50% of those in subtypes other than self-treatment screened positive for drug abuse. The odds of substance use and abuse were generally lower among self-treatment subtypes than other subtypes. The findings indicate subtypes should be considered when examining nonmedical prescription drug misuse, especially for pain medication. PMID:19278795

  16. Bayesian methods for data analysis

    CERN Document Server

    Carlin, Bradley P.

    2009-01-01

    Approaches for statistical inference Introduction Motivating Vignettes Defining the Approaches The Bayes-Frequentist Controversy Some Basic Bayesian Models The Bayes approach Introduction Prior Distributions Bayesian Inference Hierarchical Modeling Model Assessment Nonparametric Methods Bayesian computation Introduction Asymptotic Methods Noniterative Monte Carlo Methods Markov Chain Monte Carlo Methods Model criticism and selection Bayesian Modeling Bayesian Robustness Model Assessment Bayes Factors via Marginal Density Estimation Bayes Factors

  17. Identification of a large, fast-expanding HIV-1 subtype B transmission cluster among MSM in Valencia, Spain.

    Directory of Open Access Journals (Sweden)

    Juan Ángel Patiño-Galindo

    Full Text Available We describe and characterize an exceptionally large HIV-1 subtype B transmission cluster occurring in the Comunidad Valenciana (CV, Spain. A total of 1806 HIV-1 protease-reverse transcriptase (PR/RT sequences from different patients were obtained in the CV between 2004 and 2014. After subtyping and generating a phylogenetic tree with additional HIV-1 subtype B sequences, a very large transmission cluster which included almost exclusively sequences from the CV was detected (n = 143 patients. This cluster was then validated and characterized with further maximum-likelihood phylogenetic analyses and Bayesian coalescent reconstructions. With these analyses, the CV cluster was delimited to 113 patients, predominately men who have sex with men (MSM. Although it was significantly located in the city of Valencia (n = 105, phylogenetic analyses suggested this cluster derives from a larger HIV lineage affecting other Spanish localities (n = 194. Coalescent analyses estimated its expansion in Valencia to have started between 1998 and 2004. From 2004 to 2009, members of this cluster represented only 1.46% of the HIV-1 subtype B samples studied in Valencia (n = 5/143, whereas from 2010 onwards its prevalence raised to 12.64% (n = 100/791. In conclusion, we have detected a very large transmission cluster in the CV where it has experienced a very fast growth in the recent years in the city of Valencia, thus contributing significantly to the HIV epidemic in this locality. Its transmission efficiency evidences shortcomings in HIV control measures in Spain and particularly in Valencia.

  18. Identification of a large, fast-expanding HIV-1 subtype B transmission cluster among MSM in Valencia, Spain.

    Science.gov (United States)

    Patiño-Galindo, Juan Ángel; Torres-Puente, Manoli; Bracho, María Alma; Alastrué, Ignacio; Juan, Amparo; Navarro, David; Galindo, María José; Gimeno, Concepción; Ortega, Enrique; González-Candelas, Fernando

    2017-01-01

    We describe and characterize an exceptionally large HIV-1 subtype B transmission cluster occurring in the Comunidad Valenciana (CV, Spain). A total of 1806 HIV-1 protease-reverse transcriptase (PR/RT) sequences from different patients were obtained in the CV between 2004 and 2014. After subtyping and generating a phylogenetic tree with additional HIV-1 subtype B sequences, a very large transmission cluster which included almost exclusively sequences from the CV was detected (n = 143 patients). This cluster was then validated and characterized with further maximum-likelihood phylogenetic analyses and Bayesian coalescent reconstructions. With these analyses, the CV cluster was delimited to 113 patients, predominately men who have sex with men (MSM). Although it was significantly located in the city of Valencia (n = 105), phylogenetic analyses suggested this cluster derives from a larger HIV lineage affecting other Spanish localities (n = 194). Coalescent analyses estimated its expansion in Valencia to have started between 1998 and 2004. From 2004 to 2009, members of this cluster represented only 1.46% of the HIV-1 subtype B samples studied in Valencia (n = 5/143), whereas from 2010 onwards its prevalence raised to 12.64% (n = 100/791). In conclusion, we have detected a very large transmission cluster in the CV where it has experienced a very fast growth in the recent years in the city of Valencia, thus contributing significantly to the HIV epidemic in this locality. Its transmission efficiency evidences shortcomings in HIV control measures in Spain and particularly in Valencia.

  19. Bayesian benefits with JASP

    NARCIS (Netherlands)

    Marsman, M.; Wagenmakers, E.-J.

    2017-01-01

    We illustrate the Bayesian approach to data analysis using the newly developed statistical software program JASP. With JASP, researchers are able to take advantage of the benefits that the Bayesian framework has to offer in terms of parameter estimation and hypothesis testing. The Bayesian

  20. Bayesian modeling using WinBUGS

    CERN Document Server

    Ntzoufras, Ioannis

    2009-01-01

    A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian Modeling Using WinBUGS provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the principles of Bayesian modeling with detailed guidance on the practical implementation of key principles. The book begins with a basic introduction to Bayesian inference and the WinBUGS software and goes on to cover key topics, including: Markov Chain Monte Carlo algorithms in Bayesian inference Generalized linear models Bayesian hierarchical models Predictive distribution and model checking Bayesian model and variable evaluation Computational notes and screen captures illustrate the use of both WinBUGS as well as R software to apply the discussed techniques. Exercises at the end of each chapter allow readers to test their understanding of the presented concepts and all ...

  1. Bayesian prediction of microbial oxygen requirement [v1; ref status: indexed, http://f1000r.es/1m6

    Directory of Open Access Journals (Sweden)

    Dan B. Jensen

    2013-09-01

    Full Text Available Background: Prediction of the optimal habitat conditions for a given bacterium, based on genome sequence alone would be of value for scientific as well as industrial purposes. One example of such a habitat adaptation is the requirement for oxygen. In spite of good genome data availability, there have been only a few prediction attempts of bacterial oxygen requirements, using genome sequences. Here, we describe a method for distinguishing aerobic, anaerobic and facultative anaerobic bacteria, based on genome sequence-derived input, using naive Bayesian inference. In contrast, other studies found in literature only demonstrate the ability to distinguish two classes at a time. Results: The results shown in the present study are as good as or better than comparable methods previously described in the scientific literature, with an arguably simpler method, when results are directly compared. This method further compares the performance of a single-step naive Bayesian prediction of the three included classifications, compared to a simple Bayesian network with two steps. A two-step network, distinguishing first respiring from non-respiring organisms, followed by the distinction of aerobe and facultative anaerobe organisms within the respiring group, is found to perform best. Conclusions: A simple naive Bayesian network based on the presence or absence of specific protein domains within a genome is an effective and easy way to predict bacterial habitat preferences, such as oxygen requirement.

  2. Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance.

    Science.gov (United States)

    Bokulich, Nicholas A; Bergsveinson, Jordyn; Ziola, Barry; Mills, David A

    2015-03-10

    Distinct microbial ecosystems have evolved to meet the challenges of indoor environments, shaping the microbial communities that interact most with modern human activities. Microbial transmission in food-processing facilities has an enormous impact on the qualities and healthfulness of foods, beneficially or detrimentally interacting with food products. To explore modes of microbial transmission and spoilage-gene frequency in a commercial food-production scenario, we profiled hop-resistance gene frequencies and bacterial and fungal communities in a brewery. We employed a Bayesian approach for predicting routes of contamination, revealing critical control points for microbial management. Physically mapping microbial populations over time illustrates patterns of dispersal and identifies potential contaminant reservoirs within this environment. Habitual exposure to beer is associated with increased abundance of spoilage genes, predicting greater contamination risk. Elucidating the genetic landscapes of indoor environments poses important practical implications for food-production systems and these concepts are translatable to other built environments.

  3. CGBayesNets: conditional Gaussian Bayesian network learning and inference with mixed discrete and continuous data.

    Science.gov (United States)

    McGeachie, Michael J; Chang, Hsun-Hsien; Weiss, Scott T

    2014-06-01

    Bayesian Networks (BN) have been a popular predictive modeling formalism in bioinformatics, but their application in modern genomics has been slowed by an inability to cleanly handle domains with mixed discrete and continuous variables. Existing free BN software packages either discretize continuous variables, which can lead to information loss, or do not include inference routines, which makes prediction with the BN impossible. We present CGBayesNets, a BN package focused around prediction of a clinical phenotype from mixed discrete and continuous variables, which fills these gaps. CGBayesNets implements Bayesian likelihood and inference algorithms for the conditional Gaussian Bayesian network (CGBNs) formalism, one appropriate for predicting an outcome of interest from, e.g., multimodal genomic data. We provide four different network learning algorithms, each making a different tradeoff between computational cost and network likelihood. CGBayesNets provides a full suite of functions for model exploration and verification, including cross validation, bootstrapping, and AUC manipulation. We highlight several results obtained previously with CGBayesNets, including predictive models of wood properties from tree genomics, leukemia subtype classification from mixed genomic data, and robust prediction of intensive care unit mortality outcomes from metabolomic profiles. We also provide detailed example analysis on public metabolomic and gene expression datasets. CGBayesNets is implemented in MATLAB and available as MATLAB source code, under an Open Source license and anonymous download at http://www.cgbayesnets.com.

  4. Bayesian optimization for materials science

    CERN Document Server

    Packwood, Daniel

    2017-01-01

    This book provides a short and concise introduction to Bayesian optimization specifically for experimental and computational materials scientists. After explaining the basic idea behind Bayesian optimization and some applications to materials science in Chapter 1, the mathematical theory of Bayesian optimization is outlined in Chapter 2. Finally, Chapter 3 discusses an application of Bayesian optimization to a complicated structure optimization problem in computational surface science. Bayesian optimization is a promising global optimization technique that originates in the field of machine learning and is starting to gain attention in materials science. For the purpose of materials design, Bayesian optimization can be used to predict new materials with novel properties without extensive screening of candidate materials. For the purpose of computational materials science, Bayesian optimization can be incorporated into first-principles calculations to perform efficient, global structure optimizations. While re...

  5. Understanding Computational Bayesian Statistics

    CERN Document Server

    Bolstad, William M

    2011-01-01

    A hands-on introduction to computational statistics from a Bayesian point of view Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective, Understanding Computational Bayesian Statistics successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula giving its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistic

  6. Bayesian statistics an introduction

    CERN Document Server

    Lee, Peter M

    2012-01-01

    Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as wel

  7. Bayesian networks with examples in R

    CERN Document Server

    Scutari, Marco

    2014-01-01

    Introduction. The Discrete Case: Multinomial Bayesian Networks. The Continuous Case: Gaussian Bayesian Networks. More Complex Cases. Theory and Algorithms for Bayesian Networks. Real-World Applications of Bayesian Networks. Appendices. Bibliography.

  8. Motor subtype changes in early Parkinson's disease.

    Science.gov (United States)

    Eisinger, Robert S; Hess, Christopher W; Martinez-Ramirez, Daniel; Almeida, Leonardo; Foote, Kelly D; Okun, Michael S; Gunduz, Aysegul

    2017-10-01

    Distinct motor subtypes of Parkinson's disease (PD) have been described through both clinical observation and through data-driven approaches. However, the extent to which motor subtypes change during disease progression remains unknown. Our objective was to determine motor subtypes of PD using an unsupervised clustering methodology and evaluate subtype changes with disease duration. The Parkinson's Progression Markers Initiative database of 423 newly diagnosed PD patients was utilized to retrospectively identify unique motor subtypes through a data-driven, hierarchical correlational clustering approach. For each patient, we assigned a subtype to each motor assessment at each follow-up visit (time points) and by using published criteria. We examined changes in PD subtype with disease duration using both qualitative and quantitative methods. Five distinct motor subtypes were identified based on the motor assessment items and these included: Tremor Dominant (TD), Axial Dominant, Appendicular Dominant, Rigidity Dominant, and Postural and Instability Gait Disorder Dominant. About half of the patients had consistent subtypes at all time points. Most patients met criteria for TD subtype soon after diagnosis. For patients with inconsistent subtypes, there was an overall trend to shift away from a TD phenotype with disease duration, as shown by chi-squared test, p motor subtypes in PD can shift with increasing disease duration. Shifting subtypes is a factor that should be accounted for in clinical practice or in clinical trials. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Motoric subtypes of delirium in geriatric patients

    Directory of Open Access Journals (Sweden)

    Sandeep Grover

    2014-01-01

    Results: On amended DMSS, hyperactive subtype (N = 45; 45.9% was the most common motoric subtype of delirium, followed by hypoactive subtype (N = 23; 23.5%, and mixed subtype (N = 21; 21.4%. On DRS-R-98, all patients fulfilled the criteria of ′acute (temporal onset of symptoms′, ′presence of an underlying physical disorder′ and ′difficulty in attention′. In the total sample, >90% of the patients had disturbances in sleep-wake cycle, orientation and fluctuation of symptoms. The least common symptoms were delusions, visuospatial disturbances and motor retardation. When compared to hypoactive group, significantly higher proportion of patients with hyperactive subtype had delusions, perceptual disturbances, and motor agitation. Whereas, compared to hyperactive subtype, significantly higher proportion of patients with hypoactive subtype had thought process abnormality and motor retardation. When the hyperactive and mixed motoric subtype groups were compared, patients with mixed subtype group had significantly higher prevalence of thought process abnormality and motor retardation. Comparison of hypoactive and mixed subtype revealed significant differences in the frequency of perceptual disturbances, delusions and motor agitation and all these symptoms being found more commonly in patients with the mixed subtype. Severity of symptoms were found to be significantly different across the various motoric subtypes for some of the non-cognitive symptoms, but significant differences were not seen for the cognitive symptoms as assessed on DRS-R-98. Conclusion: In elderly patients, motor subtypes of delirium differ from each other on non-cognitive symptom profile in terms of frequency and severity.

  10. Bayesian Mediation Analysis

    Science.gov (United States)

    Yuan, Ying; MacKinnon, David P.

    2009-01-01

    In this article, we propose Bayesian analysis of mediation effects. Compared with conventional frequentist mediation analysis, the Bayesian approach has several advantages. First, it allows researchers to incorporate prior information into the mediation analysis, thus potentially improving the efficiency of estimates. Second, under the Bayesian…

  11. Bayesian Inference on Gravitational Waves

    Directory of Open Access Journals (Sweden)

    Asad Ali

    2015-12-01

    Full Text Available The Bayesian approach is increasingly becoming popular among the astrophysics data analysis communities. However, the Pakistan statistics communities are unaware of this fertile interaction between the two disciplines. Bayesian methods have been in use to address astronomical problems since the very birth of the Bayes probability in eighteenth century. Today the Bayesian methods for the detection and parameter estimation of gravitational waves have solid theoretical grounds with a strong promise for the realistic applications. This article aims to introduce the Pakistan statistics communities to the applications of Bayesian Monte Carlo methods in the analysis of gravitational wave data with an  overview of the Bayesian signal detection and estimation methods and demonstration by a couple of simplified examples.

  12. Bayesian analysis in plant pathology.

    Science.gov (United States)

    Mila, A L; Carriquiry, A L

    2004-09-01

    ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.

  13. Classification of alpha 1-adrenoceptor subtypes

    NARCIS (Netherlands)

    Michel, M. C.; Kenny, B.; Schwinn, D. A.

    1995-01-01

    Two alpha 1-adrenoceptor subtypes (alpha 1A and alpha 1B) have been detected in various tissues by pharmacological techniques, and three distinct cDNAs encoding alpha 1-adrenoceptor subtypes have been cloned. The profile of an increasing number of subtype-selective compounds at cloned and endogenous

  14. HLA-Driven Convergence of HIV-1 Viral Subtypes B and F Toward the Adaptation to Immune Responses in Human Populations

    Science.gov (United States)

    Dilernia, Dario Alberto; Jones, Leandro; Rodriguez, Sabrina; Turk, Gabriela; Rubio, Andrea E.; Pampuro, Sandra; Gomez-Carrillo, Manuel; Bautista, Christian; Deluchi, Gabriel; Benetucci, Jorge; Lasala, María Beatriz; Lourtau, Leonardo; Losso, Marcelo Horacio; Perez, Héctor; Cahn, Pedro; Salomón, Horacio

    2008-01-01

    Background Cytotoxic T-Lymphocyte (CTL) response drives the evolution of HIV-1 at a host-level by selecting HLA-restricted escape mutations. Dissecting the dynamics of these escape mutations at a population-level would help to understand how HLA-mediated selection drives the evolution of HIV-1. Methodology/Principal Findings We undertook a study of the dynamics of HIV-1 CTL-escape mutations by analyzing through statistical approaches and phylogenetic methods the viral gene gag sequenced in plasma samples collected between the years 1987 and 2006 from 302 drug-naïve HIV-positive patients. By applying logistic regression models and after performing correction for multiple test, we identified 22 potential CTL-escape mutations (p-value<0.05; q-value<0.2); 10 of these associations were confirmed in samples biologically independent by a Bayesian Markov Chain Monte-Carlo method. Analyzing their prevalence back in time we found that escape mutations that are the consensus residue in samples collected after 2003 have actually significantly increased in time in one of either B or F subtype until becoming the most frequent residue, while dominating the other viral subtype. Their estimated prevalence in the viral subtype they did not dominate was lower than 30% for the majority of samples collected at the end of the 80's. In addition, when screening the entire viral region, we found that the 75% of positions significantly changing in time (p<0.05) were located within known CTL epitopes. Conclusions Across HIV Gag protein, the rise of polymorphisms from independent origin during the last twenty years of epidemic in our setting was related to an association with an HLA allele. The fact that these mutations accumulated in one of either B or F subtypes have also dominated the other subtype shows how this selection might be causing a convergence of viral subtypes to variants which are more likely to evade the immune response of the population where they circulate. PMID:18941505

  15. Automated Bayesian model development for frequency detection in biological time series

    Directory of Open Access Journals (Sweden)

    Oldroyd Giles ED

    2011-06-01

    Full Text Available Abstract Background A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. Results In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Conclusions Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and

  16. Automated Bayesian model development for frequency detection in biological time series.

    Science.gov (United States)

    Granqvist, Emma; Oldroyd, Giles E D; Morris, Richard J

    2011-06-24

    A first step in building a mathematical model of a biological system is often the analysis of the temporal behaviour of key quantities. Mathematical relationships between the time and frequency domain, such as Fourier Transforms and wavelets, are commonly used to extract information about the underlying signal from a given time series. This one-to-one mapping from time points to frequencies inherently assumes that both domains contain the complete knowledge of the system. However, for truncated, noisy time series with background trends this unique mapping breaks down and the question reduces to an inference problem of identifying the most probable frequencies. In this paper we build on the method of Bayesian Spectrum Analysis and demonstrate its advantages over conventional methods by applying it to a number of test cases, including two types of biological time series. Firstly, oscillations of calcium in plant root cells in response to microbial symbionts are non-stationary and noisy, posing challenges to data analysis. Secondly, circadian rhythms in gene expression measured over only two cycles highlights the problem of time series with limited length. The results show that the Bayesian frequency detection approach can provide useful results in specific areas where Fourier analysis can be uninformative or misleading. We demonstrate further benefits of the Bayesian approach for time series analysis, such as direct comparison of different hypotheses, inherent estimation of noise levels and parameter precision, and a flexible framework for modelling the data without pre-processing. Modelling in systems biology often builds on the study of time-dependent phenomena. Fourier Transforms are a convenient tool for analysing the frequency domain of time series. However, there are well-known limitations of this method, such as the introduction of spurious frequencies when handling short and noisy time series, and the requirement for uniformly sampled data. Biological time

  17. Characterization of Nucleoside Reverse Transcriptase Inhibitor-Associated Mutations in the RNase H Region of HIV-1 Subtype C Infected Individuals.

    Science.gov (United States)

    Ngcapu, Sinaye; Theys, Kristof; Libin, Pieter; Marconi, Vincent C; Sunpath, Henry; Ndung'u, Thumbi; Gordon, Michelle L

    2017-11-08

    The South African national treatment programme includes nucleoside reverse transcriptase inhibitors (NRTIs) in both first and second line highly active antiretroviral therapy regimens. Mutations in the RNase H domain have been associated with resistance to NRTIs but primarily in HIV-1 subtype B studies. Here, we investigated the prevalence and association of RNase H mutations with NRTI resistance in sequences from HIV-1 subtype C infected individuals. RNase H sequences from 112 NRTI treated but virologically failing individuals and 28 antiretroviral therapy (ART)-naive individuals were generated and analysed. In addition, sequences from 359 subtype C ART-naive sequences were downloaded from Los Alamos database to give a total of 387 sequences from ART-naive individuals for the analysis. Fisher's exact test was used to identify mutations and Bayesian network learning was applied to identify novel NRTI resistance mutation pathways in RNase H domain. The mutations A435L, S468A, T470S, L484I, A508S, Q509L, L517I, Q524E and E529D were more prevalent in sequences from treatment-experienced compared to antiretroviral treatment naive individuals, however, only the E529D mutation remained significant after correction for multiple comparison. Our findings suggest a potential interaction between E529D and NRTI-treatment; however, site-directed mutagenesis is needed to understand the impact of this RNase H mutation.

  18. A Bayesian method for detecting pairwise associations in compositional data.

    Directory of Open Access Journals (Sweden)

    Emma Schwager

    2017-11-01

    Full Text Available Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.

  19. Effect of HIV type 1 subtype on virological and immunological response to combination antiretroviral therapy: evidence for a more rapid viral suppression for subtype A than subtype B-infected Greek individuals.

    Science.gov (United States)

    Paraskevis, Dimirios; Touloumi, Giota; Bakoyannis, Giorgos; Paparizos, Vassilios; Lazanas, Marios; Gargalianos, Panagiotis; Chryssos, Georgios; Antoniadou, Anastasia; Psichogiou, Mina; Panos, Georgios; Katsarou, Olga; Sambatakou, Helen; Kordossis, Theodoros; Hatzakis, Angelos

    2013-03-01

    Whether response to combination antiretroviral therapy (cART) differs between those infected with HIV-1 subtype A or B remains unclear. We compared virological and immunological response to cART in individuals infected with subtype A or B in an ethnically homogeneous population. Data derived from the Athens Multicenter AIDS Cohort Study (AMACS) and analysis were restricted to those of Greek origin. Time to virological response (confirmed HIV-RNA 500 copies/ml at any time or no response by month 6) were analyzed using survival models and CD4 changes after cART initiation using piecewise linear mixed effects models. Of the 571 subjects included in the analysis, 412 (72.2%) were infected with subtype B and 159 (27.8%) with subtype A. After adjusting for various prognostic factors, the rate of virological response was higher for those infected with subtype A versus B (adjusted HR: 1.35; 95% CI: 1.08-1.68; p=0.009). Subtype A was also marginally associated with a lower hazard of virological failure compared to subtype B (HR=0.73; 95% CI: 0.53-1.02; p=0.062). Further adjustment for treatment adherence did not substantially changed the main results. No significant differences were observed in the rates of CD4 increases by subtype. The overall median (95% CI) CD4 increase at 2 years of cART was 193 (175, 212) cells/μl. Our study, based on one of the largest homogeneous groups of subtype A and B infections in Europe, showed that individuals infected with subtype A had an improved virological but similar immunological response to cART compared to those infected with subtype B.

  20. Basics of Bayesian methods.

    Science.gov (United States)

    Ghosh, Sujit K

    2010-01-01

    Bayesian methods are rapidly becoming popular tools for making statistical inference in various fields of science including biology, engineering, finance, and genetics. One of the key aspects of Bayesian inferential method is its logical foundation that provides a coherent framework to utilize not only empirical but also scientific information available to a researcher. Prior knowledge arising from scientific background, expert judgment, or previously collected data is used to build a prior distribution which is then combined with current data via the likelihood function to characterize the current state of knowledge using the so-called posterior distribution. Bayesian methods allow the use of models of complex physical phenomena that were previously too difficult to estimate (e.g., using asymptotic approximations). Bayesian methods offer a means of more fully understanding issues that are central to many practical problems by allowing researchers to build integrated models based on hierarchical conditional distributions that can be estimated even with limited amounts of data. Furthermore, advances in numerical integration methods, particularly those based on Monte Carlo methods, have made it possible to compute the optimal Bayes estimators. However, there is a reasonably wide gap between the background of the empirically trained scientists and the full weight of Bayesian statistical inference. Hence, one of the goals of this chapter is to bridge the gap by offering elementary to advanced concepts that emphasize linkages between standard approaches and full probability modeling via Bayesian methods.

  1. Avian metapneumovirus subtype A in China and subtypes A and B in Nigeria.

    Science.gov (United States)

    Owoade, A A; Ducatez, M F; Hübschen, J M; Sausy, A; Chen, H; Guan, Y; Muller, C P

    2008-09-01

    In order to detect and characterize avian metapneumovirus, organs or swabs were collected from 697 chicken and 110 turkeys from commercial farms in Southwestern Nigeria and from 107 chickens from live bird markets in Southeastern China. In Nigeria, 15% and 6% of the chicken and turkey samples, respectively, and 39% of the chicken samples from China, were positive for aMPV genome by PCR. The sequence of a 400 nt fragment of the attachment protein gene (G gene) revealed the presence of aMPV subtype A in both Nigeria and Southeastern China. Essentially identical subtype A viruses were found in both countries and were also previously reported from Brazil and the United Kingdom, suggesting a link between these countries or a common source of this subtype. In Nigeria, subtype B was also found, which may be a reflection of chicken importations from most major poultry-producing countries in Europe and Asia. In order to justify countermeasures, further studies are warranted to better understand the metapneumoviruses and their impact on poultry production.

  2. Bayesian computation with R

    CERN Document Server

    Albert, Jim

    2009-01-01

    There has been a dramatic growth in the development and application of Bayesian inferential methods. Some of this growth is due to the availability of powerful simulation-based algorithms to summarize posterior distributions. There has been also a growing interest in the use of the system R for statistical analyses. R's open source nature, free availability, and large number of contributor packages have made R the software of choice for many statisticians in education and industry. Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The earl

  3. The Bayesian Score Statistic

    NARCIS (Netherlands)

    Kleibergen, F.R.; Kleijn, R.; Paap, R.

    2000-01-01

    We propose a novel Bayesian test under a (noninformative) Jeffreys'priorspecification. We check whether the fixed scalar value of the so-calledBayesian Score Statistic (BSS) under the null hypothesis is aplausiblerealization from its known and standardized distribution under thealternative. Unlike

  4. 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.

  5. Bayesian inference with ecological applications

    CERN Document Server

    Link, William A

    2009-01-01

    This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context. The advent of fast personal computers and easily available software has simplified the use of Bayesian and hierarchical models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approach for students and research ecologists. Engagingly written text specifically designed to demystify a complex subject Examples drawn from ecology and wildlife research An essential grounding for graduate and research ecologists in the increasingly prevalent Bayesian approach to inference Companion website with analyt...

  6. Origin and Population Dynamics of a Novel HIV-1 Subtype G Clade Circulating in Cape Verde and Portugal.

    Science.gov (United States)

    de Pina-Araujo, Isabel Inês M; Delatorre, Edson; Guimarães, Monick L; Morgado, Mariza G; Bello, Gonzalo

    2015-01-01

    The human immunodeficiency virus type 1 (HIV-1) subtype G is the most prevalent and second most prevalent HIV-1 clade in Cape Verde and Portugal, respectively; but there is no information about the origin and spatiotemporal dispersal pattern of this HIV-1 clade circulating in those countries. To this end, we used Maximum Likelihood and Bayesian coalescent-based methods to analyze a collection of 578 HIV-1 subtype G pol sequences sampled throughout Portugal, Cape Verde and 11 other countries from West and Central Africa over a period of 22 years (1992 to 2013). Our analyses indicate that most subtype G sequences from Cape Verde (80%) and Portugal (95%) branched together in a distinct monophyletic cluster (here called G(CV-PT)). The G(CV-PT) clade probably emerged after a single migration of the virus out of Central Africa into Cape Verde between the late 1970s and the middle 1980s, followed by a rapid dissemination to Portugal a couple of years later. Reconstruction of the demographic history of the G(CV-PT) clade circulating in Cape Verde and Portugal indicates that this viral clade displayed an initial phase of exponential growth during the 1980s and 1990s, followed by a decline in growth rate since the early 2000s. Our data also indicate that during the exponential growth phase the G(CV-PT) clade recombined with a preexisting subtype B viral strain circulating in Portugal, originating the CRF14_BG clade that was later disseminated to Spain and Cape Verde. Historical and recent human population movements between Angola, Cape Verde and Portugal probably played a key role in the origin and dispersal of the G(CV-PT )and CRF14_BG clades.

  7. Comparing two basic subtypes in OCD across three large community samples: a pure compulsive versus a mixed obsessive-compulsive subtype.

    Science.gov (United States)

    Rodgers, Stephanie; Ajdacic-Gross, Vladeta; Kawohl, Wolfram; Müller, Mario; Rössler, Wulf; Hengartner, Michael P; Castelao, Enrique; Vandeleur, Caroline; Angst, Jules; Preisig, Martin

    2015-12-01

    Due to its heterogeneous phenomenology, obsessive-compulsive disorder (OCD) has been subtyped. However, these subtypes are not mutually exclusive. This study presents an alternative subtyping approach by deriving non-overlapping OCD subtypes. A pure compulsive and a mixed obsessive-compulsive subtype (including subjects manifesting obsessions with/without compulsions) were analyzed with respect to a broad pattern of psychosocial risk factors and comorbid syndromes/diagnoses in three representative Swiss community samples: the Zurich Study (n = 591), the ZInEP sample (n = 1500), and the PsyCoLaus sample (n = 3720). A selection of comorbidities was examined in a pooled database. Odds ratios were derived from logistic regressions and, in the analysis of pooled data, multilevel models. The pure compulsive subtype showed a lower age of onset and was characterized by few associations with psychosocial risk factors. The higher social popularity of the pure compulsive subjects and their families was remarkable. Comorbidities within the pure compulsive subtype were mainly restricted to phobias. In contrast, the mixed obsessive-compulsive subtype had a higher prevalence and was associated with various childhood adversities, more familial burden, and numerous comorbid disorders, including disorders characterized by high impulsivity. The current comparison study across three representative community surveys presented two basic, distinct OCD subtypes associated with differing psychosocial impairment. Such highly specific subtypes offer the opportunity to learn about pathophysiological mechanisms specifically involved in OCD.

  8. Current trends in Bayesian methodology with applications

    CERN Document Server

    Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia

    2015-01-01

    Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on

  9. A Bayesian framework for risk perception

    NARCIS (Netherlands)

    van Erp, H.R.N.

    2017-01-01

    We present here a Bayesian framework of risk perception. This framework encompasses plausibility judgments, decision making, and question asking. Plausibility judgments are modeled by way of Bayesian probability theory, decision making is modeled by way of a Bayesian decision theory, and relevancy

  10. Bayesian flood forecasting methods: A review

    Science.gov (United States)

    Han, Shasha; Coulibaly, Paulin

    2017-08-01

    Over the past few decades, floods have been seen as one of the most common and largely distributed natural disasters in the world. If floods could be accurately forecasted in advance, then their negative impacts could be greatly minimized. It is widely recognized that quantification and reduction of uncertainty associated with the hydrologic forecast is of great importance for flood estimation and rational decision making. Bayesian forecasting system (BFS) offers an ideal theoretic framework for uncertainty quantification that can be developed for probabilistic flood forecasting via any deterministic hydrologic model. It provides suitable theoretical structure, empirically validated models and reasonable analytic-numerical computation method, and can be developed into various Bayesian forecasting approaches. This paper presents a comprehensive review on Bayesian forecasting approaches applied in flood forecasting from 1999 till now. The review starts with an overview of fundamentals of BFS and recent advances in BFS, followed with BFS application in river stage forecasting and real-time flood forecasting, then move to a critical analysis by evaluating advantages and limitations of Bayesian forecasting methods and other predictive uncertainty assessment approaches in flood forecasting, and finally discusses the future research direction in Bayesian flood forecasting. Results show that the Bayesian flood forecasting approach is an effective and advanced way for flood estimation, it considers all sources of uncertainties and produces a predictive distribution of the river stage, river discharge or runoff, thus gives more accurate and reliable flood forecasts. Some emerging Bayesian forecasting methods (e.g. ensemble Bayesian forecasting system, Bayesian multi-model combination) were shown to overcome limitations of single model or fixed model weight and effectively reduce predictive uncertainty. In recent years, various Bayesian flood forecasting approaches have been

  11. Molecular subtypes and imaging phenotypes of breast cancer

    Directory of Open Access Journals (Sweden)

    Nariya Cho

    2016-10-01

    Full Text Available During the last 15 years, traditional breast cancer classifications based on histopathology have been reorganized into the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2, and basal-like subtypes based on gene expression profiling. Each molecular subtype has shown varying risk for progression, response to treatment, and survival outcomes. Research linking the imaging phenotype with the molecular subtype has revealed that non-calcified, relatively circumscribed masses with posterior acoustic enhancement are common in the basal-like subtype, spiculated masses with a poorly circumscribed margin and posterior acoustic shadowing in the luminal subtype, and pleomorphic calcifications in the HER2-enriched subtype. Understanding the clinical implications of the molecular subtypes and imaging phenotypes could help radiologists guide precision medicine, tailoring medical treatment to patients and their tumor characteristics.

  12. Molecular subtypes and imaging phenotypes of breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Nariya [Dept. of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)

    2016-08-15

    During the last 15 years, traditional breast cancer classifications based on histopathology have been reorganized into the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2), and basal-like subtypes based on gene expression profiling. Each molecular subtype has shown varying risk for progression, response to treatment, and survival outcomes. Research linking the imaging phenotype with the molecular subtype has revealed that non-calcified, relatively circumscribed masses with posterior acoustic enhancement are common in the basal-like subtype, spiculated masses with a poorly circumscribed margin and posterior acoustic shadowing in the luminal subtype, and pleomorphic calcifications in the HER2-enriched subtype. Understanding the clinical implications of the molecular subtypes and imaging phenotypes could help radiologists guide precision medicine, tailoring medical treatment to patients and their tumor characteristics.

  13. Molecular subtypes and imaging phenotypes of breast cancer

    International Nuclear Information System (INIS)

    Cho, Nariya

    2016-01-01

    During the last 15 years, traditional breast cancer classifications based on histopathology have been reorganized into the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2), and basal-like subtypes based on gene expression profiling. Each molecular subtype has shown varying risk for progression, response to treatment, and survival outcomes. Research linking the imaging phenotype with the molecular subtype has revealed that non-calcified, relatively circumscribed masses with posterior acoustic enhancement are common in the basal-like subtype, spiculated masses with a poorly circumscribed margin and posterior acoustic shadowing in the luminal subtype, and pleomorphic calcifications in the HER2-enriched subtype. Understanding the clinical implications of the molecular subtypes and imaging phenotypes could help radiologists guide precision medicine, tailoring medical treatment to patients and their tumor characteristics

  14. Topics in Bayesian statistics and maximum entropy

    International Nuclear Information System (INIS)

    Mutihac, R.; Cicuttin, A.; Cerdeira, A.; Stanciulescu, C.

    1998-12-01

    Notions of Bayesian decision theory and maximum entropy methods are reviewed with particular emphasis on probabilistic inference and Bayesian modeling. The axiomatic approach is considered as the best justification of Bayesian analysis and maximum entropy principle applied in natural sciences. Particular emphasis is put on solving the inverse problem in digital image restoration and Bayesian modeling of neural networks. Further topics addressed briefly include language modeling, neutron scattering, multiuser detection and channel equalization in digital communications, genetic information, and Bayesian court decision-making. (author)

  15. Book review: Bayesian analysis for population ecology

    Science.gov (United States)

    Link, William A.

    2011-01-01

    Brian Dennis described the field of ecology as “fertile, uncolonized ground for Bayesian ideas.” He continued: “The Bayesian propagule has arrived at the shore. Ecologists need to think long and hard about the consequences of a Bayesian ecology. The Bayesian outlook is a successful competitor, but is it a weed? I think so.” (Dennis 2004)

  16. Influenza A Subtyping

    Science.gov (United States)

    Kaul, Karen L.; Mangold, Kathy A.; Du, Hongyan; Pesavento, Kristen M.; Nawrocki, John; Nowak, Jan A.

    2010-01-01

    Influenza virus subtyping has emerged as a critical tool in the diagnosis of influenza. Antiviral resistance is present in the majority of seasonal H1N1 influenza A infections, with association of viral strain type and antiviral resistance. Influenza A virus subtypes can be reliably distinguished by examining conserved sequences in the matrix protein gene. We describe our experience with an assay for influenza A subtyping based on matrix gene sequences. Viral RNA was prepared from nasopharyngeal swab samples, and real-time RT-PCR detection of influenza A and B was performed using a laboratory developed analyte-specific reagent-based assay that targets a conserved region of the influenza A matrix protein gene. FluA-positive samples were analyzed using a second RT-PCR assay targeting the matrix protein gene to distinguish seasonal influenza subtypes based on differential melting of fluorescence resonance energy transfer probes. The novel H1N1 influenza strain responsible for the 2009 pandemic showed a melting profile distinct from that of seasonal H1N1 or H3N2 and compatible with the predicted melting temperature based on the published novel H1N1 matrix gene sequence. Validation by comparison with the Centers for Disease Control and Prevention real-time RT-PCR for swine influenza A (novel H1N1) test showed this assay to be both rapid and reliable (>99% sensitive and specific) in the identification of the novel H1N1 influenza A virus strain. PMID:20595627

  17. Comparison of symptoms of delirium across various motoric subtypes.

    Science.gov (United States)

    Grover, Sandeep; Sharma, Akhilesh; Aggarwal, Munish; Mattoo, Surendra K; Chakrabarti, Subho; Malhotra, Savita; Avasthi, Ajit; Kulhara, Parmanand; Basu, Debasish

    2014-04-01

    The aim of this study was to determine the correlation between delirium motor subtypes and other symptoms of delirium. Three hundred and twenty-one (n = 321) consecutive patients referred to consultation-liaison psychiatry services were evaluated on Delirium Rating scale-Revised-98 version and amended Delirium Motor Symptom Scale. Half of the patients had hyperactive subtype (n = 161; 50.15%) delirium. One-quarter of the study sample met the criteria for mixed subtype (n = 79; 24.61%), about one-fifth of the study sample met the criteria for hypoactive delirium subtype (n = 64; 19.93%), and only very few patients (n = 17; 5.29%) did not meet the required criteria for any of these three subtypes and were categorized as 'no subtype'. When the hyperactive and hypoactive subtypes were compared, significant differences were seen in the prevalence of perceptual disturbances, delusions, lability of affect, thought process abnormality, motor agitation and motor retardation. All the symptoms were more common in the hyperactive subtype except for thought process abnormality and motor retardation. Compared to hyperactive subtype, the mixed subtype had significantly higher prevalence of thought process abnormality and motor retardation. Significant differences emerged with regard to perceptual disturbances, delusions, lability of affect and motor agitation when comparing the patients with mixed subtype with those with hypoactive subtype. All these symptoms were found to be more common in the mixed subtype. No significant differences emerged for the cognitive symptoms as assessed on Delirium Rating scale-Revised-98 across the different motoric subtypes. Different motoric subtypes of delirium differ on non-cognitive symptoms. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

  18. Pure type systems with subtyping

    NARCIS (Netherlands)

    Zwanenburg, J.; Girard, J.-Y.

    1999-01-01

    We extend the framework of Pure Type Systems with subtyping, as found in F = ¿ . This leads to a concise description of many existing systems with subtyping, and also to some new interesting systems. We develop the meta-theory for this framework, including Subject Reduction and Minimal Typing. The

  19. Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

    Science.gov (United States)

    Jones, Matt; Love, Bradley C

    2011-08-01

    The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls

  20. 3rd Bayesian Young Statisticians Meeting

    CERN Document Server

    Lanzarone, Ettore; Villalobos, Isadora; Mattei, Alessandra

    2017-01-01

    This book is a selection of peer-reviewed contributions presented at the third Bayesian Young Statisticians Meeting, BAYSM 2016, Florence, Italy, June 19-21. The meeting provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and postdocs dealing with Bayesian statistics to connect with the Bayesian community at large, to exchange ideas, and to network with others working in the same field. The contributions develop and apply Bayesian methods in a variety of fields, ranging from the traditional (e.g., biostatistics and reliability) to the most innovative ones (e.g., big data and networks).

  1. Robust bayesian analysis of an autoregressive model with ...

    African Journals Online (AJOL)

    In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...

  2. Plug & Play object oriented Bayesian networks

    DEFF Research Database (Denmark)

    Bangsø, Olav; Flores, J.; Jensen, Finn Verner

    2003-01-01

    been shown to be quite suitable for dynamic domains as well. However, processing object oriented Bayesian networks in practice does not take advantage of their modular structure. Normally the object oriented Bayesian network is transformed into a Bayesian network and, inference is performed...... dynamic domains. The communication needed between instances is achieved by means of a fill-in propagation scheme....

  3. The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective.

    Science.gov (United States)

    Kruschke, John K; Liddell, Torrin M

    2018-02-01

    In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty on the other. Among frequentists in psychology, a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

  4. 2nd Bayesian Young Statisticians Meeting

    CERN Document Server

    Bitto, Angela; Kastner, Gregor; Posekany, Alexandra

    2015-01-01

    The Second Bayesian Young Statisticians Meeting (BAYSM 2014) and the research presented here facilitate connections among researchers using Bayesian Statistics by providing a forum for the development and exchange of ideas. WU Vienna University of Business and Economics hosted BAYSM 2014 from September 18th to 19th. The guidance of renowned plenary lecturers and senior discussants is a critical part of the meeting and this volume, which follows publication of contributions from BAYSM 2013. The meeting's scientific program reflected the variety of fields in which Bayesian methods are currently employed or could be introduced in the future. Three brilliant keynote lectures by Chris Holmes (University of Oxford), Christian Robert (Université Paris-Dauphine), and Mike West (Duke University), were complemented by 24 plenary talks covering the major topics Dynamic Models, Applications, Bayesian Nonparametrics, Biostatistics, Bayesian Methods in Economics, and Models and Methods, as well as a lively poster session ...

  5. Bayesian natural language semantics and pragmatics

    CERN Document Server

    Zeevat, Henk

    2015-01-01

    The contributions in this volume focus on the Bayesian interpretation of natural languages, which is widely used in areas of artificial intelligence, cognitive science, and computational linguistics. This is the first volume to take up topics in Bayesian Natural Language Interpretation and make proposals based on information theory, probability theory, and related fields. The methodologies offered here extend to the target semantic and pragmatic analyses of computational natural language interpretation. Bayesian approaches to natural language semantics and pragmatics are based on methods from signal processing and the causal Bayesian models pioneered by especially Pearl. In signal processing, the Bayesian method finds the most probable interpretation by finding the one that maximizes the product of the prior probability and the likelihood of the interpretation. It thus stresses the importance of a production model for interpretation as in Grice's contributions to pragmatics or in interpretation by abduction.

  6. Interferon α subtypes in HIV infection.

    Science.gov (United States)

    Sutter, Kathrin; Dickow, Julia; Dittmer, Ulf

    2018-02-13

    Type I interferons (IFN), which are immediately induced after most virus infections, are central for direct antiviral immunity and link innate and adaptive immune responses. However, several viruses have evolved strategies to evade the IFN response by preventing IFN induction or blocking IFN signaling pathways. Thus, therapeutic application of exogenous type I IFN or agonists inducing type I IFN responses are a considerable option for future immunotherapies against chronic viral infections. An important part of the type I IFN family are 12 IFNα subtypes, which all bind the same receptor, but significantly differ in their biological activities. Up to date only one IFNα subtype (IFNα2) is being used in clinical treatment against chronic virus infections, however its therapeutic success rate is rather limited, especially during Human Immunodeficiency Virus (HIV) infection. Recent studies addressed the important question if other IFNα subtypes would be more potent against retroviral infections in in vitro and in vivo experiments. Indeed, very potent IFNα subtypes were defined and their antiviral and immunomodulatory properties were characterized. In this review we summarize the recent findings on the role of individual IFNα subtypes during HIV and Simian Immunodeficiency Virus infection. This includes their induction during HIV/SIV infection, their antiretroviral activity and the regulation of immune response against HIV by different IFNα subtypes. The findings might facilitate novel strategies for HIV cure or functional cure studies. Copyright © 2018 Elsevier Ltd. All rights reserved.

  7. Bayesian methods in reliability

    Science.gov (United States)

    Sander, P.; Badoux, R.

    1991-11-01

    The present proceedings from a course on Bayesian methods in reliability encompasses Bayesian statistical methods and their computational implementation, models for analyzing censored data from nonrepairable systems, the traits of repairable systems and growth models, the use of expert judgment, and a review of the problem of forecasting software reliability. Specific issues addressed include the use of Bayesian methods to estimate the leak rate of a gas pipeline, approximate analyses under great prior uncertainty, reliability estimation techniques, and a nonhomogeneous Poisson process. Also addressed are the calibration sets and seed variables of expert judgment systems for risk assessment, experimental illustrations of the use of expert judgment for reliability testing, and analyses of the predictive quality of software-reliability growth models such as the Weibull order statistics.

  8. Bayesian networks and food security - An introduction

    NARCIS (Netherlands)

    Stein, A.

    2004-01-01

    This paper gives an introduction to Bayesian networks. Networks are defined and put into a Bayesian context. Directed acyclical graphs play a crucial role here. Two simple examples from food security are addressed. Possible uses of Bayesian networks for implementation and further use in decision

  9. Hypertension Subtypes among Hypertensive Patients in Ibadan

    OpenAIRE

    Abiodun M. Adeoye; Adewole Adebiyi; Bamidele O. Tayo; Babatunde L. Salako; Adesola Ogunniyi; Richard S. Cooper

    2014-01-01

    Background. Certain hypertension subtypes have been shown to increase the risk for cardiovascular morbidity and mortality and may be related to specific underlying genetic determinants. Inappropriate characterization of subtypes of hypertension makes efforts at elucidating the genetic contributions to the etiology of hypertension largely vapid. We report the hypertension subtypes among patients with hypertension from South-Western Nigeria. Methods. A total of 1858 subjects comprising 76% fema...

  10. Using Campylobacter spp. and Escherichia coli data and Bayesian microbial risk assessment to examine public health risks in agricultural watersheds under tile drainage management.

    Science.gov (United States)

    Schmidt, P J; Pintar, K D M; Fazil, A M; Flemming, C A; Lanthier, M; Laprade, N; Sunohara, M D; Simhon, A; Thomas, J L; Topp, E; Wilkes, G; Lapen, D R

    2013-06-15

    Human campylobacteriosis is the leading bacterial gastrointestinal illness in Canada; environmental transmission has been implicated in addition to transmission via consumption of contaminated food. Information about Campylobacter spp. occurrence at the watershed scale will enhance our understanding of the associated public health risks and the efficacy of source water protection strategies. The overriding purpose of this study is to provide a quantitative framework to assess and compare the relative public health significance of watershed microbial water quality associated with agricultural BMPs. A microbial monitoring program was expanded from fecal indicator analyses and Campylobacter spp. presence/absence tests to the development of a novel, 11-tube most probable number (MPN) method that targeted Campylobacter jejuni, Campylobacter coli, and Campylobacter lari. These three types of data were used to make inferences about theoretical risks in a watershed in which controlled tile drainage is widely practiced, an adjacent watershed with conventional (uncontrolled) tile drainage, and reference sites elsewhere in the same river basin. E. coli concentrations (MPN and plate count) in the controlled tile drainage watershed were statistically higher (2008-11), relative to the uncontrolled tile drainage watershed, but yearly variation was high as well. Escherichia coli loading for years 2008-11 combined were statistically higher in the controlled watershed, relative to the uncontrolled tile drainage watershed, but Campylobacter spp. loads for 2010-11 were generally higher for the uncontrolled tile drainage watershed (but not statistically significant). Using MPN data and a Bayesian modelling approach, higher mean Campylobacter spp. concentrations were found in the controlled tile drainage watershed relative to the uncontrolled tile drainage watershed (2010, 2011). A second-order quantitative microbial risk assessment (QMRA) was used, in a relative way, to identify

  11. 12th Brazilian Meeting on Bayesian Statistics

    CERN Document Server

    Louzada, Francisco; Rifo, Laura; Stern, Julio; Lauretto, Marcelo

    2015-01-01

    Through refereed papers, this volume focuses on the foundations of the Bayesian paradigm; their comparison to objectivistic or frequentist Statistics counterparts; and the appropriate application of Bayesian foundations. This research in Bayesian Statistics is applicable to data analysis in biostatistics, clinical trials, law, engineering, and the social sciences. EBEB, the Brazilian Meeting on Bayesian Statistics, is held every two years by the ISBrA, the International Society for Bayesian Analysis, one of the most active chapters of the ISBA. The 12th meeting took place March 10-14, 2014 in Atibaia. Interest in foundations of inductive Statistics has grown recently in accordance with the increasing availability of Bayesian methodological alternatives. Scientists need to deal with the ever more difficult choice of the optimal method to apply to their problem. This volume shows how Bayes can be the answer. The examination and discussion on the foundations work towards the goal of proper application of Bayesia...

  12. Kernel Bayesian ART and ARTMAP.

    Science.gov (United States)

    Masuyama, Naoki; Loo, Chu Kiong; Dawood, Farhan

    2018-02-01

    Adaptive Resonance Theory (ART) is one of the successful approaches to resolving "the plasticity-stability dilemma" in neural networks, and its supervised learning model called ARTMAP is a powerful tool for classification. Among several improvements, such as Fuzzy or Gaussian based models, the state of art model is Bayesian based one, while solving the drawbacks of others. However, it is known that the Bayesian approach for the high dimensional and a large number of data requires high computational cost, and the covariance matrix in likelihood becomes unstable. This paper introduces Kernel Bayesian ART (KBA) and ARTMAP (KBAM) by integrating Kernel Bayes' Rule (KBR) and Correntropy Induced Metric (CIM) to Bayesian ART (BA) and ARTMAP (BAM), respectively, while maintaining the properties of BA and BAM. The kernel frameworks in KBA and KBAM are able to avoid the curse of dimensionality. In addition, the covariance-free Bayesian computation by KBR provides the efficient and stable computational capability to KBA and KBAM. Furthermore, Correntropy-based similarity measurement allows improving the noise reduction ability even in the high dimensional space. The simulation experiments show that KBA performs an outstanding self-organizing capability than BA, and KBAM provides the superior classification ability than BAM, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Bayesian networks improve causal environmental ...

    Science.gov (United States)

    Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the problem, using this knowledge with probabilistic calculus to combine multiple lines of evidence, and minimizing biases in predicting or diagnosing causal relationships. Too often, sources of uncertainty in conventional weight of evidence approaches are ignored that can be accounted for with Bayesian networks. Specifying and propagating uncertainties improve the ability of models to incorporate strength of the evidence in the risk management phase of an assessment. Probabilistic inference from a Bayesian network allows evaluation of changes in uncertainty for variables from the evidence. The network structure and probabilistic framework of a Bayesian approach provide advantages over qualitative approaches in weight of evidence for capturing the impacts of multiple sources of quantifiable uncertainty on predictions of ecological risk. Bayesian networks can facilitate the development of evidence-based policy under conditions of uncertainty by incorporating analytical inaccuracies or the implications of imperfect information, structuring and communicating causal issues through qualitative directed graph formulations, and quantitatively comparing the causal power of multiple stressors on value

  14. Bayesian Latent Class Analysis Tutorial.

    Science.gov (United States)

    Li, Yuelin; Lord-Bessen, Jennifer; Shiyko, Mariya; Loeb, Rebecca

    2018-01-01

    This article is a how-to guide on Bayesian computation using Gibbs sampling, demonstrated in the context of Latent Class Analysis (LCA). It is written for students in quantitative psychology or related fields who have a working knowledge of Bayes Theorem and conditional probability and have experience in writing computer programs in the statistical language R . The overall goals are to provide an accessible and self-contained tutorial, along with a practical computation tool. We begin with how Bayesian computation is typically described in academic articles. Technical difficulties are addressed by a hypothetical, worked-out example. We show how Bayesian computation can be broken down into a series of simpler calculations, which can then be assembled together to complete a computationally more complex model. The details are described much more explicitly than what is typically available in elementary introductions to Bayesian modeling so that readers are not overwhelmed by the mathematics. Moreover, the provided computer program shows how Bayesian LCA can be implemented with relative ease. The computer program is then applied in a large, real-world data set and explained line-by-line. We outline the general steps in how to extend these considerations to other methodological applications. We conclude with suggestions for further readings.

  15. Bayesian policy reuse

    CSIR Research Space (South Africa)

    Rosman, Benjamin

    2016-02-01

    Full Text Available Keywords Policy Reuse · Reinforcement Learning · Online Learning · Online Bandits · Transfer Learning · Bayesian Optimisation · Bayesian Decision Theory. 1 Introduction As robots and software agents are becoming more ubiquitous in many applications.... The agent has access to a library of policies (pi1, pi2 and pi3), and has previously experienced a set of task instances (τ1, τ2, τ3, τ4), as well as samples of the utilities of the library policies on these instances (the black dots indicate the means...

  16. Inverse problems in the Bayesian framework

    International Nuclear Information System (INIS)

    Calvetti, Daniela; Somersalo, Erkki; Kaipio, Jari P

    2014-01-01

    The history of Bayesian methods dates back to the original works of Reverend Thomas Bayes and Pierre-Simon Laplace: the former laid down some of the basic principles on inverse probability in his classic article ‘An essay towards solving a problem in the doctrine of chances’ that was read posthumously in the Royal Society in 1763. Laplace, on the other hand, in his ‘Memoirs on inverse probability’ of 1774 developed the idea of updating beliefs and wrote down the celebrated Bayes’ formula in the form we know today. Although not identified yet as a framework for investigating inverse problems, Laplace used the formalism very much in the spirit it is used today in the context of inverse problems, e.g., in his study of the distribution of comets. With the evolution of computational tools, Bayesian methods have become increasingly popular in all fields of human knowledge in which conclusions need to be drawn based on incomplete and noisy data. Needless to say, inverse problems, almost by definition, fall into this category. Systematic work for developing a Bayesian inverse problem framework can arguably be traced back to the 1980s, (the original first edition being published by Elsevier in 1987), although articles on Bayesian methodology applied to inverse problems, in particular in geophysics, had appeared much earlier. Today, as testified by the articles in this special issue, the Bayesian methodology as a framework for considering inverse problems has gained a lot of popularity, and it has integrated very successfully with many traditional inverse problems ideas and techniques, providing novel ways to interpret and implement traditional procedures in numerical analysis, computational statistics, signal analysis and data assimilation. The range of applications where the Bayesian framework has been fundamental goes from geophysics, engineering and imaging to astronomy, life sciences and economy, and continues to grow. There is no question that Bayesian

  17. Bayesian models: A statistical primer for ecologists

    Science.gov (United States)

    Hobbs, N. Thompson; Hooten, Mevin B.

    2015-01-01

    Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods—in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach.Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals.This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management.Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticiansCovers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and moreDeemphasizes computer coding in favor of basic principlesExplains how to write out properly factored statistical expressions representing Bayesian models

  18. Transsexual subtypes : Clinical and theoretical significance

    NARCIS (Netherlands)

    Smith, YLS; van Goozen, SHM; Kuiper, AJ; Cohen-Kettenis, PT

    2005-01-01

    The present study was designed to investigate whether transsexuals can be validly subdivided into subtypes on the basis of sexual orientation, and whether differences between subtypes of transsexuals are similar for male-to-female (ME) and female-to-male transsexuals (FMs). Within a large

  19. The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group.

    Science.gov (United States)

    Natanegara, Fanni; Neuenschwander, Beat; Seaman, John W; Kinnersley, Nelson; Heilmann, Cory R; Ohlssen, David; Rochester, George

    2014-01-01

    Bayesian applications in medical product development have recently gained popularity. Despite many advances in Bayesian methodology and computations, increase in application across the various areas of medical product development has been modest. The DIA Bayesian Scientific Working Group (BSWG), which includes representatives from industry, regulatory agencies, and academia, has adopted the vision to ensure Bayesian methods are well understood, accepted more broadly, and appropriately utilized to improve decision making and enhance patient outcomes. As Bayesian applications in medical product development are wide ranging, several sub-teams were formed to focus on various topics such as patient safety, non-inferiority, prior specification, comparative effectiveness, joint modeling, program-wide decision making, analytical tools, and education. The focus of this paper is on the recent effort of the BSWG Education sub-team to administer a Bayesian survey to statisticians across 17 organizations involved in medical product development. We summarize results of this survey, from which we provide recommendations on how to accelerate progress in Bayesian applications throughout medical product development. The survey results support findings from the literature and provide additional insight on regulatory acceptance of Bayesian methods and information on the need for a Bayesian infrastructure within an organization. The survey findings support the claim that only modest progress in areas of education and implementation has been made recently, despite substantial progress in Bayesian statistical research and software availability. Copyright © 2013 John Wiley & Sons, Ltd.

  20. Verified Subtyping with Traits and Mixins

    Directory of Open Access Journals (Sweden)

    Asankhaya Sharma

    2014-07-01

    Full Text Available Traits allow decomposing programs into smaller parts and mixins are a form of composition that resemble multiple inheritance. Unfortunately, in the presence of traits, programming languages like Scala give up on subtyping relation between objects. In this paper, we present a method to check subtyping between objects based on entailment in separation logic. We implement our method as a domain specific language in Scala and apply it on the Scala standard library. We have verified that 67% of mixins used in the Scala standard library do indeed conform to subtyping between the traits that are used to build them.

  1. Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome

    DEFF Research Database (Denmark)

    Boonstra, Philip S; Mukherjee, Bhramar; Taylor, Jeremy M G

    2011-01-01

    Summary Genetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation subtypes known to be associated with the disease phenotype....... In this article, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect...... to cause hereditary nonpolyposis colorectal cancer, also called Lynch syndrome (LS). We find evidence for a decrease in AOO between generations in this article. Our model predicts family-level anticipation effects that are potentially useful in genetic counseling clinics for high-risk families....

  2. Bayesian Alternation During Tactile Augmentation

    Directory of Open Access Journals (Sweden)

    Caspar Mathias Goeke

    2016-10-01

    Full Text Available A large number of studies suggest that the integration of multisensory signals by humans is well described by Bayesian principles. However, there are very few reports about cue combination between a native and an augmented sense. In particular, we asked the question whether adult participants are able to integrate an augmented sensory cue with existing native sensory information. Hence for the purpose of this study we build a tactile augmentation device. Consequently, we compared different hypotheses of how untrained adult participants combine information from a native and an augmented sense. In a two-interval forced choice (2 IFC task, while subjects were blindfolded and seated on a rotating platform, our sensory augmentation device translated information on whole body yaw rotation to tactile stimulation. Three conditions were realized: tactile stimulation only (augmented condition, rotation only (native condition, and both augmented and native information (bimodal condition. Participants had to choose one out of two consecutive rotations with higher angular rotation. For the analysis, we fitted the participants’ responses with a probit model and calculated the just notable difference (JND. Then we compared several models for predicting bimodal from unimodal responses. An objective Bayesian alternation model yielded a better prediction (χred2 = 1.67 than the Bayesian integration model (χred2= 4.34. Slightly higher accuracy showed a non-Bayesian winner takes all model (χred2= 1.64, which either used only native or only augmented values per subject for prediction. However the performance of the Bayesian alternation model could be substantially improved (χred2= 1.09 utilizing subjective weights obtained by a questionnaire. As a result, the subjective Bayesian alternation model predicted bimodal performance most accurately among all tested models. These results suggest that information from augmented and existing sensory modalities in

  3. An introduction to Bayesian statistics in health psychology.

    Science.gov (United States)

    Depaoli, Sarah; Rus, Holly M; Clifton, James P; van de Schoot, Rens; Tiemensma, Jitske

    2017-09-01

    The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of health psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation models, latent growth curve (and mixture) models, and hierarchical linear models. Likewise, Bayesian methods can be used with small sample sizes since they do not rely on large sample theory. In this article, we discuss several important components of Bayesian statistics as they relate to health-based inquiries. We discuss the incorporation and impact of prior knowledge into the estimation process and the different components of the analysis that should be reported in an article. We present an example implementing Bayesian estimation in the context of blood pressure changes after participants experienced an acute stressor. We conclude with final thoughts on the implementation of Bayesian statistics in health psychology, including suggestions for reviewing Bayesian manuscripts and grant proposals. We have also included an extensive amount of online supplementary material to complement the content presented here, including Bayesian examples using many different software programmes and an extensive sensitivity analysis examining the impact of priors.

  4. Bayesian Network Induction via Local Neighborhoods

    National Research Council Canada - National Science Library

    Margaritis, Dimitris

    1999-01-01

    .... We present an efficient algorithm for learning Bayesian networks from data. Our approach constructs Bayesian networks by first identifying each node's Markov blankets, then connecting nodes in a consistent way...

  5. A Bayesian encourages dropout

    OpenAIRE

    Maeda, Shin-ichi

    2014-01-01

    Dropout is one of the key techniques to prevent the learning from overfitting. It is explained that dropout works as a kind of modified L2 regularization. Here, we shed light on the dropout from Bayesian standpoint. Bayesian interpretation enables us to optimize the dropout rate, which is beneficial for learning of weight parameters and prediction after learning. The experiment result also encourages the optimization of the dropout.

  6. Integrative subtype discovery in glioblastoma using iCluster.

    Directory of Open Access Journals (Sweden)

    Ronglai Shen

    Full Text Available Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA project, are comprehensive molecular characterization efforts to accelerate our understanding of cancer biology and the discovery of new therapeutic targets. The accumulating wealth of multidimensional data provides a new paradigm for important research problems including cancer subtype discovery. The current standard approach relies on separate clustering analyses followed by manual integration. Results can be highly data type dependent, restricting the ability to discover new insights from multidimensional data. In this study, we present an integrative subtype analysis of the TCGA glioblastoma (GBM data set. Our analysis revealed new insights through integrated subtype characterization. We found three distinct integrated tumor subtypes. Subtype 1 lacks the classical GBM events of chr 7 gain and chr 10 loss. This subclass is enriched for the G-CIMP phenotype and shows hypermethylation of genes involved in brain development and neuronal differentiation. The tumors in this subclass display a Proneural expression profile. Subtype 2 is characterized by a near complete association with EGFR amplification, overrepresentation of promoter methylation of homeobox and G-protein signaling genes, and a Classical expression profile. Subtype 3 is characterized by NF1 and PTEN alterations and exhibits a Mesenchymal-like expression profile. The data analysis workflow we propose provides a unified and computationally scalable framework to harness the full potential of large-scale integrated cancer genomic data for integrative subtype discovery.

  7. Bayesian Data Analysis (lecture 2)

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.

  8. Bayesian Data Analysis (lecture 1)

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    framework but we will also go into more detail and discuss for example the role of the prior. The second part of the lecture will cover further examples and applications that heavily rely on the bayesian approach, as well as some computational tools needed to perform a bayesian analysis.

  9. Bulimia nervosa-nonpurging subtype: closer to the bulimia nervosa-purging subtype or to binge eating disorder?

    Science.gov (United States)

    Jordan, Jennifer; McIntosh, Virginia V W; Carter, Janet D; Rowe, Sarah; Taylor, Kathryn; Frampton, Christopher M A; McKenzie, Janice M; Latner, Janet; Joyce, Peter R

    2014-04-01

    DSM-5 has dropped subtyping of bulimia nervosa (BN), opting to continue inclusion of the somewhat contentious diagnosis of BN-nonpurging subtype (BN-NP) within a broad BN category. Some contend however that BN-NP is more like binge eating disorder (BED) than BN-P. This study examines clinical characteristics, eating disorder symptomatology, and Axis I comorbidity in BN-NP, BN-P, and BED groups to establish whether BN-NP more closely resembles BN-P or BED. Women with BN-P (n = 29), BN-NP (n = 29), and BED (n = 54) were assessed at baseline in an outpatient psychotherapy trial for those with binge eating. Measures included the Structured Clinical Interviews for DSM-IV, Eating Disorder Examination, and Eating Disorder Inventory-2. The BN-NP subtype had BMIs between those with BN-P and BED. Both BN subtypes had higher Restraint and Drive for Thinness scores than BED. Body Dissatisfaction was highest in BN-NP and predicted BN-NP compared to BN-P. Higher Restraint and lower BMI predicted BN-NP relative to BED. BN-NP resembled BED with higher lifetime BMIs; and weight-loss clinic than eating disorder clinic attendances relative to the BN-P subtype. Psychiatric comorbidity was comparable except for higher lifetime cannabis use disorder in the BN-NP than BN-P subtype These results suggest that BN-NP sits between BN-P and BED however the high distress driving inappropriate compensatory behaviors in BN-P requires specialist eating disorder treatment. These results support retaining the BN-NP group within the BN category. Further research is needed to determine whether there are meaningful differences in outcome over follow-up. Copyright © 2013 Wiley Periodicals, Inc.

  10. Learning Local Components to Understand Large Bayesian Networks

    DEFF Research Database (Denmark)

    Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

    2009-01-01

    (domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

  11. Spreading of hepatitis C virus subtypes 1a and 1b through the central region of Argentina.

    Science.gov (United States)

    Culasso, Andrés Carlos Alberto; Farías, Adrián; Di Lello, Federico Alejandro; Golemba, Marcelo Darío; Ré, Viviana; Barbini, Luciana; Campos, Rodolfo

    2014-08-01

    The recent history of the hepatitis C virus (HCV) subtypes 1a and 1b in the central region of Argentina is hypothesized by phylogeographic reconstruction using coalescent based Bayesian analyses. Direct partial E2 sequences from HCV 1a and 1b infected patients attending different health-care centers of the country were analyzed. The inferred date of the most recent common ancestor (tMRCA) for HCV-1a was: 1962 (between 1943 and 1977) and for HCV-1b was earlier: 1929 (between 1895 and 1953). Diverse ancestral populations were inferred from both subtypes in Córdoba and in Buenos Aires cities and after that, HCV spread within and between larger cities and to other smaller cities. The analyses suggested that HCV-1b was dispersed first and it is currently in a stationary phase whereas HCV-1a was dispersed latter and it is still in a growth phase. Finally, as it was observed in the developed countries, while the transmission of HCV-1b appears to have been somehow prevented, the HCV-1a may still represent a concern in the public health. Further work should be carried out to address their current transmission rate (and its main transmission route) in the Argentinean population. Copyright © 2014 Elsevier B.V. All rights reserved.

  12. Philosophy and the practice of Bayesian statistics.

    Science.gov (United States)

    Gelman, Andrew; Shalizi, Cosma Rohilla

    2013-02-01

    A substantial school in the philosophy of science identifies Bayesian inference with inductive inference and even rationality as such, and seems to be strengthened by the rise and practical success of Bayesian statistics. We argue that the most successful forms of Bayesian statistics do not actually support that particular philosophy but rather accord much better with sophisticated forms of hypothetico-deductivism. We examine the actual role played by prior distributions in Bayesian models, and the crucial aspects of model checking and model revision, which fall outside the scope of Bayesian confirmation theory. We draw on the literature on the consistency of Bayesian updating and also on our experience of applied work in social science. Clarity about these matters should benefit not just philosophy of science, but also statistical practice. At best, the inductivist view has encouraged researchers to fit and compare models without checking them; at worst, theorists have actively discouraged practitioners from performing model checking because it does not fit into their framework. © 2012 The British Psychological Society.

  13. Appreciating HIV-1 diversity: subtypic differences in ENV

    Energy Technology Data Exchange (ETDEWEB)

    Gnanakaran, S [Los Alamos National Laboratory; Shen, Tongye [Los Alamos National Laboratory; Lynch, Rebecca M [NON LANL; Derdeyn, Cynthia A [NON LANL

    2008-01-01

    Human immunodeficiency virus type 1 (HIV-1) group M is responsible for the current AIDS pandemic and exhibits exceedingly high levels of viral genetic diversity around the world, necessitating categorization of viruses into distinct lineages, or subtypes. These subtypes can differ by around 35% in the envelope (Env) glycoproteins of the virus, which are displayed on the surface of the virion and are targets for both neutralizing antibody and cell-mediated immune responses. This diversity reflects the remarkable ability of the virus to adapt to selective pressures, the bulk of which is applied by the host immune response, and represents a serious obstacle for developing an effective vaccine with broad coverage. Thus, it is important to understand the underlying biological consequences of inter-subtype diversity. Recent studies have revealed that the HIV-1 subtypes exhibit phenotypic differences that result from subtle differences in Env structure, particularly within the highly immunogenic V3 domain, which participates directly in viral entry. This review will therefore explore current research that describes subtypic differences in Env at the genetic and phenotypic level, focusing in particular on V3, and highlighting recent discoveries about the unique features of subtype C Env, which is the most prevalent subtype globally.

  14. On Predicting lung cancer subtypes using ‘omic’ data from tumor and tumor-adjacent histologically-normal tissue

    International Nuclear Information System (INIS)

    Pineda, Arturo López; Ogoe, Henry Ato; Balasubramanian, Jeya Balaji; Rangel Escareño, Claudia; Visweswaran, Shyam; Herman, James Gordon; Gopalakrishnan, Vanathi

    2016-01-01

    Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue. Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis. All Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our

  15. Persistence of low-pathogenic H5N7 and H7N1 avian influenza subtypes in filtered natural waters

    DEFF Research Database (Denmark)

    Nielsen, Anne Ahlmann; Jensen, Trine Hammer; Stockmarr, Anders

    2013-01-01

    knowledge on the influence of environmental factors on the persistence of AIV in natural habitats would be valuable for risk assessments. The presented work investigated the persistence of two low-pathogenic AIV subtypes in natural water samples. The study included two AIVs formerly isolated from wild ducks......, the persistence of infectivity was negatively affected by increased temperature, salinity as well as presence of natural microbial flora. The study provides insight on impact of essential physical, chemical and biological parameters on persistence of AIV in aquatic environments. Studies determining the importance...

  16. Hypertension Subtypes among Hypertensive Patients in Ibadan

    Directory of Open Access Journals (Sweden)

    Abiodun M. Adeoye

    2014-01-01

    Full Text Available Background. Certain hypertension subtypes have been shown to increase the risk for cardiovascular morbidity and mortality and may be related to specific underlying genetic determinants. Inappropriate characterization of subtypes of hypertension makes efforts at elucidating the genetic contributions to the etiology of hypertension largely vapid. We report the hypertension subtypes among patients with hypertension from South-Western Nigeria. Methods. A total of 1858 subjects comprising 76% female, hypertensive, aged 18 and above were recruited into the study from two centers in Ibadan, Nigeria. Hypertension was identified using JNCVII definition and was further grouped into four subtypes: controlled hypertension (CH, isolated systolic hypertension (ISH, isolated diastolic hypertension (IDH, and systolic-diastolic hypertension (SDH. Results. Systolic-diastolic hypertension was the most prevalent. Whereas SDH (77.6% versus 73.5% and IDH (4.9% versus 4.7% were more prevalent among females, ISH (10.1% versus 6.2% was higher among males (P=0.048. Female subjects were more obese (P<0.0001 and SDH was prevalent among the obese group. Conclusion. Gender and obesity significantly influenced the distribution of the hypertension subtypes. Characterization of hypertension by subtypes in genetic association studies could lead to identification of previously unknown genetic variants involved in the etiology of hypertension. Large-scale studies among various ethnic groups may be needed to confirm these observations.

  17. Bayesian Utilitarianism

    OpenAIRE

    ZHOU, Lin

    1996-01-01

    In this paper I consider social choices under uncertainty. I prove that any social choice rule that satisfies independence of irrelevant alternatives, translation invariance, and weak anonymity is consistent with ex post Bayesian utilitarianism

  18. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming; Zhang, Jian

    2009-01-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly

  19. Searching Algorithm Using Bayesian Updates

    Science.gov (United States)

    Caudle, Kyle

    2010-01-01

    In late October 1967, the USS Scorpion was lost at sea, somewhere between the Azores and Norfolk Virginia. Dr. Craven of the U.S. Navy's Special Projects Division is credited with using Bayesian Search Theory to locate the submarine. Bayesian Search Theory is a straightforward and interesting application of Bayes' theorem which involves searching…

  20. Bayesian estimates of linkage disequilibrium

    Directory of Open Access Journals (Sweden)

    Abad-Grau María M

    2007-06-01

    Full Text Available Abstract Background The maximum likelihood estimator of D' – a standard measure of linkage disequilibrium – is biased toward disequilibrium, and the bias is particularly evident in small samples and rare haplotypes. Results This paper proposes a Bayesian estimation of D' to address this problem. The reduction of the bias is achieved by using a prior distribution on the pair-wise associations between single nucleotide polymorphisms (SNPs that increases the likelihood of equilibrium with increasing physical distances between pairs of SNPs. We show how to compute the Bayesian estimate using a stochastic estimation based on MCMC methods, and also propose a numerical approximation to the Bayesian estimates that can be used to estimate patterns of LD in large datasets of SNPs. Conclusion Our Bayesian estimator of D' corrects the bias toward disequilibrium that affects the maximum likelihood estimator. A consequence of this feature is a more objective view about the extent of linkage disequilibrium in the human genome, and a more realistic number of tagging SNPs to fully exploit the power of genome wide association studies.

  1. Post-fire spatial patterns of soil nitrogen mineralization and microbial abundance.

    Directory of Open Access Journals (Sweden)

    Erica A H Smithwick

    Full Text Available Stand-replacing fires influence soil nitrogen availability and microbial community composition, which may in turn mediate post-fire successional dynamics and nutrient cycling. However, fires create patchiness at both local and landscape scales and do not result in consistent patterns of ecological dynamics. The objectives of this study were to (1 quantify the spatial structure of microbial communities in forest stands recently affected by stand-replacing fire and (2 determine whether microbial variables aid predictions of in situ net nitrogen mineralization rates in recently burned stands. The study was conducted in lodgepole pine (Pinus contorta var. latifolia and Engelmann spruce/subalpine fir (Picea engelmannii/Abies lasiocarpa forest stands that burned during summer 2000 in Greater Yellowstone (Wyoming, USA. Using a fully probabilistic spatial process model and Bayesian kriging, the spatial structure of microbial lipid abundance and fungi-to-bacteria ratios were found to be spatially structured within plots two years following fire (for most plots, autocorrelation range varied from 1.5 to 10.5 m. Congruence of spatial patterns among microbial variables, in situ net N mineralization, and cover variables was evident. Stepwise regression resulted in significant models of in situ net N mineralization and included variables describing fungal and bacterial abundance, although explained variance was low (R²<0.29. Unraveling complex spatial patterns of nutrient cycling and the biotic factors that regulate it remains challenging but is critical for explaining post-fire ecosystem function, especially in Greater Yellowstone, which is projected to experience increased fire frequencies by mid 21(st Century.

  2. Muscarinic acetylcholine receptor subtypes: localization and structure/function

    DEFF Research Database (Denmark)

    Brann, M R; Ellis, J; Jørgensen, H

    1993-01-01

    Based on the sequence of the five cloned muscarinic receptor subtypes (m1-m5), subtype selective antibody and cDNA probes have been prepared. Use of these probes has demonstrated that each of the five subtypes has a markedly distinct distribution within the brain and among peripheral tissues...... are described, as well as the implied structures of these functional domains....

  3. Learning Bayesian networks for discrete data

    KAUST Repository

    Liang, Faming

    2009-02-01

    Bayesian networks have received much attention in the recent literature. In this article, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. Firstly, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Secondly, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches. © 2008 Elsevier B.V. All rights reserved.

  4. Molecular Subtyping of Tumors from Patients with Familial Glioma.

    Science.gov (United States)

    Ruiz, Vanessa Y; Praska, Corinne E; Armstrong, Georgina; Kollmeyer, Thomas M; Yamada, Seiji; Decker, Paul A; Kosel, Matthew L; Eckel-Passow, Jeanette E; Consortium, The Gliogene; Lachance, Daniel H; Bainbridge, Matthew N; Melin, Beatrice S; Bondy, Melissa L; Jenkins, Robert B

    2017-10-10

    Single-gene mutation syndromes account for some familial glioma (FG); however, they make up only a small fraction of glioma families. Gliomas can be classified into 3 major molecular subtypes based on IDH mutation and 1p/19q co-deletion. We hypothesized that the prevalence of molecular subtypes might differ in familial versus sporadic gliomas, and that tumors in the same family should have the same molecular subtype. Participants in the FG study (Gliogene) provided samples for germline DNA analysis. Formalin-fixed, paraffin-embedded (FFPE) tumor was obtained for a subset of FG cases, and DNA was extracted. We analyzed tissue from 75 families, including 10 families containing a second affected family member. Copy number variation (CNV) data was obtained using a first-generation Affymetrix molecular inversion probe (MIP) array. Samples from 62 of 75 (83%) FG cases could be classified into the 3 subtypes. The prevalence of the molecular subtypes was: 30 (48%) IDH-wild type, 21 (34%) IDH-mutant non-codeleted, and 11 (19%) IDH-mutant and 1p/19q-codeleted. This distribution of molecular subtypes was not statistically different from that of sporadic gliomas (p=0.54). Of 10 paired FG samples, molecular subtypes were concordant for 7 (κ=0.59): 3 IDH-mutant non-codeleted, 2 IDH-wild type, and 2 IDH-mutant and 1p/19q-codeleted gliomas. Our data suggest that within individual families, patients develop gliomas of the same molecular subtype. However, we did not observe differences in the prevalence of the molecular subtypes in FG compared with sporadic gliomas. These observations provide further insight about the distribution of molecular subtypes in FG. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com

  5. Frequent intra-subtype recombination among HIV-1 circulating in Tanzania.

    Directory of Open Access Journals (Sweden)

    Ireen E Kiwelu

    Full Text Available The study estimated the prevalence of HIV-1 intra-subtype recombinant variants among female bar and hotel workers in Tanzania. While intra-subtype recombination occurs in HIV-1, it is generally underestimated. HIV-1 env gp120 V1-C5 quasispecies from 45 subjects were generated by single-genome amplification and sequencing (median (IQR of 38 (28-50 sequences per subject. Recombination analysis was performed using seven methods implemented within the recombination detection program version 3, RDP3. HIV-1 sequences were considered recombinant if recombination signals were detected by at least three methods with p-values of ≤0.05 after Bonferroni correction for multiple comparisons. HIV-1 in 38 (84% subjects showed evidence for intra-subtype recombination including 22 with HIV-1 subtype A1, 13 with HIV-1 subtype C, and 3 with HIV-1 subtype D. The distribution of intra-patient recombination breakpoints suggested ongoing recombination and showed selective enrichment of recombinant variants in 23 (60% subjects. The number of subjects with evidence of intra-subtype recombination increased from 29 (69% to 36 (82% over one year of follow-up, although the increase did not reach statistical significance. Adjustment for intra-subtype recombination is important for the analysis of multiplicity of HIV infection. This is the first report of high prevalence of intra-subtype recombination in the HIV/AIDS epidemic in Tanzania, a region where multiple HIV-1 subtypes co-circulate. HIV-1 intra-subtype recombination increases viral diversity and presents additional challenges for HIV-1 vaccine design.

  6. A default Bayesian hypothesis test for ANOVA designs

    NARCIS (Netherlands)

    Wetzels, R.; Grasman, R.P.P.P.; Wagenmakers, E.J.

    2012-01-01

    This article presents a Bayesian hypothesis test for analysis of variance (ANOVA) designs. The test is an application of standard Bayesian methods for variable selection in regression models. We illustrate the effect of various g-priors on the ANOVA hypothesis test. The Bayesian test for ANOVA

  7. Bayesian Networks An Introduction

    CERN Document Server

    Koski, Timo

    2009-01-01

    Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout. Features include:.: An introduction to Dirichlet Distribution, Exponential Families and their applications.; A detailed description of learni

  8. A default Bayesian hypothesis test for mediation.

    Science.gov (United States)

    Nuijten, Michèle B; Wetzels, Ruud; Matzke, Dora; Dolan, Conor V; Wagenmakers, Eric-Jan

    2015-03-01

    In order to quantify the relationship between multiple variables, researchers often carry out a mediation analysis. In such an analysis, a mediator (e.g., knowledge of a healthy diet) transmits the effect from an independent variable (e.g., classroom instruction on a healthy diet) to a dependent variable (e.g., consumption of fruits and vegetables). Almost all mediation analyses in psychology use frequentist estimation and hypothesis-testing techniques. A recent exception is Yuan and MacKinnon (Psychological Methods, 14, 301-322, 2009), who outlined a Bayesian parameter estimation procedure for mediation analysis. Here we complete the Bayesian alternative to frequentist mediation analysis by specifying a default Bayesian hypothesis test based on the Jeffreys-Zellner-Siow approach. We further extend this default Bayesian test by allowing a comparison to directional or one-sided alternatives, using Markov chain Monte Carlo techniques implemented in JAGS. All Bayesian tests are implemented in the R package BayesMed (Nuijten, Wetzels, Matzke, Dolan, & Wagenmakers, 2014).

  9. A Bayesian model for binary Markov chains

    Directory of Open Access Journals (Sweden)

    Belkheir Essebbar

    2004-02-01

    Full Text Available This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.

  10. Inference in hybrid Bayesian networks

    DEFF Research Database (Denmark)

    Lanseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael

    2009-01-01

    Since the 1980s, Bayesian Networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability-techniques (like fault trees...... decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability....

  11. Bayesian theory and applications

    CERN Document Server

    Dellaportas, Petros; Polson, Nicholas G; Stephens, David A

    2013-01-01

    The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and devel...

  12. Universal Darwinism As a Process of Bayesian Inference.

    Science.gov (United States)

    Campbell, John O

    2016-01-01

    Many of the mathematical frameworks describing natural selection are equivalent to Bayes' Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus, natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians). As Bayesian inference can always be cast in terms of (variational) free energy minimization, natural selection can be viewed as comprising two components: a generative model of an "experiment" in the external world environment, and the results of that "experiment" or the "surprise" entailed by predicted and actual outcomes of the "experiment." Minimization of free energy implies that the implicit measure of "surprise" experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

  13. Subtypes of Patients Experiencing Exacerbations of COPD and Associations with Outcomes

    Science.gov (United States)

    Arostegui, Inmaculada; Esteban, Cristobal; García-Gutierrez, Susana; Bare, Marisa; Fernández-de-Larrea, Nerea; Briones, Eduardo; Quintana, José M.

    2014-01-01

    Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous condition characterized by occasional exacerbations. Identifying clinical subtypes among patients experiencing COPD exacerbations (ECOPD) could help better understand the pathophysiologic mechanisms involved in exacerbations, establish different strategies of treatment, and improve the process of care and patient prognosis. The objective of this study was to identify subtypes of ECOPD patients attending emergency departments using clinical variables and to validate the results using several outcomes. We evaluated data collected as part of the IRYSS-COPD prospective cohort study conducted in 16 hospitals in Spain. Variables collected from ECOPD patients attending one of the emergency departments included arterial blood gases, presence of comorbidities, previous COPD treatment, baseline severity of COPD, and previous hospitalizations for ECOPD. Patient subtypes were identified by combining results from multiple correspondence analysis and cluster analysis. Results were validated using key outcomes of ECOPD evolution. Four ECOPD subtypes were identified based on the severity of the current exacerbation and general health status (largely a function of comorbidities): subtype A (n = 934), neither high comorbidity nor severe exacerbation; subtype B (n = 682), moderate comorbidities; subtype C (n = 562), severe comorbidities related to mortality; and subtype D (n = 309), very severe process of exacerbation, significantly related to mortality and admission to an intensive care unit. Subtype D experienced the highest rate of mortality, admission to an intensive care unit and need for noninvasive mechanical ventilation, followed by subtype C. Subtypes A and B were primarily related to other serious complications. Hospitalization rate was more than 50% for all the subtypes, although significantly higher for subtypes C and D than for subtypes A and B. These results could help identify

  14. Daniel Goodman’s empirical approach to Bayesian statistics

    Science.gov (United States)

    Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina

    2016-01-01

    Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

  15. Structure of the Unbound Form of HIV-1 Subtype A Protease: Comparison with Unbound Forms of Proteases from other HIV Subtypes

    Energy Technology Data Exchange (ETDEWEB)

    Robbins, Arthur H.; Coman, Roxana M.; Bracho-Sanchez, Edith; Fernandez, Marty A.; Gilliland, C.Taylor; Li, Mi; Agbandje-McKenna, Mavis; Wlodawer, Alexander; Dunn, Ben M.; McKenna, Robert (NCI); (Florida)

    2010-03-12

    The crystal structure of the unbound form of HIV-1 subtype A protease (PR) has been determined to 1.7 {angstrom} resolution and refined as a homodimer in the hexagonal space group P6{sub 1} to an R{sub cryst} of 20.5%. The structure is similar in overall shape and fold to the previously determined subtype B, C and F PRs. The major differences lie in the conformation of the flap region. The flaps in the crystal structures of the unbound subtype B and C PRs, which were crystallized in tetragonal space groups, are either semi-open or wide open. In the present structure of subtype A PR the flaps are found in the closed position, a conformation that would be more anticipated in the structure of HIV protease complexed with an inhibitor. The amino-acid differences between the subtypes and their respective crystal space groups are discussed in terms of the differences in the flap conformations.

  16. Integrative Analysis of Prognosis Data on Multiple Cancer Subtypes

    Science.gov (United States)

    Liu, Jin; Huang, Jian; Zhang, Yawei; Lan, Qing; Rothman, Nathaniel; Zheng, Tongzhang; Ma, Shuangge

    2014-01-01

    Summary In cancer research, profiling studies have been extensively conducted, searching for genes/SNPs associated with prognosis. Cancer is diverse. Examining the similarity and difference in the genetic basis of multiple subtypes of the same cancer can lead to a better understanding of their connections and distinctions. Classic meta-analysis methods analyze each subtype separately and then compare analysis results across subtypes. Integrative analysis methods, in contrast, analyze the raw data on multiple subtypes simultaneously and can outperform meta-analysis methods. In this study, prognosis data on multiple subtypes of the same cancer are analyzed. An AFT (accelerated failure time) model is adopted to describe survival. The genetic basis of multiple subtypes is described using the heterogeneity model, which allows a gene/SNP to be associated with prognosis of some subtypes but not others. A compound penalization method is developed to identify genes that contain important SNPs associated with prognosis. The proposed method has an intuitive formulation and is realized using an iterative algorithm. Asymptotic properties are rigorously established. Simulation shows that the proposed method has satisfactory performance and outperforms a penalization-based meta-analysis method and a regularized thresholding method. An NHL (non-Hodgkin lymphoma) prognosis study with SNP measurements is analyzed. Genes associated with the three major subtypes, namely DLBCL, FL, and CLL/SLL, are identified. The proposed method identifies genes that are different from alternatives and have important implications and satisfactory prediction performance. PMID:24766212

  17. Approximation methods for efficient learning of Bayesian networks

    CERN Document Server

    Riggelsen, C

    2008-01-01

    This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. The topics discussed are: basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and, the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.

  18. Bayesian community detection

    DEFF Research Database (Denmark)

    Mørup, Morten; Schmidt, Mikkel N

    2012-01-01

    Many networks of scientific interest naturally decompose into clusters or communities with comparatively fewer external than internal links; however, current Bayesian models of network communities do not exert this intuitive notion of communities. We formulate a nonparametric Bayesian model...... for community detection consistent with an intuitive definition of communities and present a Markov chain Monte Carlo procedure for inferring the community structure. A Matlab toolbox with the proposed inference procedure is available for download. On synthetic and real networks, our model detects communities...... consistent with ground truth, and on real networks, it outperforms existing approaches in predicting missing links. This suggests that community structure is an important structural property of networks that should be explicitly modeled....

  19. Inverse Problems in a Bayesian Setting

    KAUST Repository

    Matthies, Hermann G.

    2016-02-13

    In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.

  20. Inverse Problems in a Bayesian Setting

    KAUST Repository

    Matthies, Hermann G.; Zander, Elmar; Rosić, Bojana V.; Litvinenko, Alexander; Pajonk, Oliver

    2016-01-01

    In a Bayesian setting, inverse problems and uncertainty quantification (UQ)—the propagation of uncertainty through a computational (forward) model—are strongly connected. In the form of conditional expectation the Bayesian update becomes computationally attractive. We give a detailed account of this approach via conditional approximation, various approximations, and the construction of filters. Together with a functional or spectral approach for the forward UQ there is no need for time-consuming and slowly convergent Monte Carlo sampling. The developed sampling-free non-linear Bayesian update in form of a filter is derived from the variational problem associated with conditional expectation. This formulation in general calls for further discretisation to make the computation possible, and we choose a polynomial approximation. After giving details on the actual computation in the framework of functional or spectral approximations, we demonstrate the workings of the algorithm on a number of examples of increasing complexity. At last, we compare the linear and nonlinear Bayesian update in form of a filter on some examples.

  1. Implementing the Bayesian paradigm in risk analysis

    International Nuclear Information System (INIS)

    Aven, T.; Kvaloey, J.T.

    2002-01-01

    The Bayesian paradigm comprises a unified and consistent framework for analyzing and expressing risk. Yet, we see rather few examples of applications where the full Bayesian setting has been adopted with specifications of priors of unknown parameters. In this paper, we discuss some of the practical challenges of implementing Bayesian thinking and methods in risk analysis, emphasizing the introduction of probability models and parameters and associated uncertainty assessments. We conclude that there is a need for a pragmatic view in order to 'successfully' apply the Bayesian approach, such that we can do the assignments of some of the probabilities without adopting the somewhat sophisticated procedure of specifying prior distributions of parameters. A simple risk analysis example is presented to illustrate ideas

  2. Molecular Subtyping of Treponema pallidum subsp. pallidum in Lisbon, Portugal▿

    Science.gov (United States)

    Castro, R.; Prieto, E.; Águas, M. J.; Manata, M. J.; Botas, J.; Martins Pereira, F.

    2009-01-01

    The objectives of this study were to evaluate the reproducibility of a molecular method for the subtyping of Treponema pallidum subsp. pallidum and to discriminate strains of this microorganism from strains from patients with syphilis. We studied 212 specimens from a total of 82 patients with different stages of syphilis (14 primary, 7 secondary and 61 latent syphilis). The specimens were distributed as follows: genital ulcers (n = 9), skin and mucosal lesions (n = 7), blood (n = 82), plasma (n = 82), and ear lobe scrapings (n = 32). The samples were assayed by a PCR technique to amplify a segment of the polymerase gene I (polA). Positive samples were typed on the basis of the analysis of two variable genes, tpr and arp. Sixty-two of the 90 samples positive for polA yielded typeable Treponema pallidum DNA. All skin lesions in which T. pallidum was identified (six of six [100%]) were found to contain enough DNA for typing of the organism. It was also possible to type DNA from 7/9 (77.7%) genital ulcer samples, 13/22 (59.1%) blood samples, 20/32 (62.5%) plasma samples, and 16/21 (76.2%) ear lobe scrapings. The same subtype was identified in all samples from the same patient. Five molecular subtypes (subtypes 10a, 14a, 14c, 14f, and 14g) were identified, with the most frequently found subtype being subtype 14a and the least frequently found subtype being subtype 10a. In conclusion, the subtyping technique used in this study seems to have good reproducibility. To our knowledge, subtype 10a was identified for the first time. Further studies are needed to explain the presence of this subtype in Portugal, namely, its relationship to the Treponema pallidum strains circulating in the African countries where Portuguese is spoken. PMID:19494073

  3. Interactive Instruction in Bayesian Inference

    DEFF Research Database (Denmark)

    Khan, Azam; Breslav, Simon; Hornbæk, Kasper

    2018-01-01

    An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction. These pri......An instructional approach is presented to improve human performance in solving Bayesian inference problems. Starting from the original text of the classic Mammography Problem, the textual expression is modified and visualizations are added according to Mayer’s principles of instruction....... These principles concern coherence, personalization, signaling, segmenting, multimedia, spatial contiguity, and pretraining. Principles of self-explanation and interactivity are also applied. Four experiments on the Mammography Problem showed that these principles help participants answer the questions...... that an instructional approach to improving human performance in Bayesian inference is a promising direction....

  4. Universal Darwinism as a process of Bayesian inference

    Directory of Open Access Journals (Sweden)

    John Oberon Campbell

    2016-06-01

    Full Text Available Many of the mathematical frameworks describing natural selection are equivalent to Bayes’ Theorem, also known as Bayesian updating. By definition, a process of Bayesian Inference is one which involves a Bayesian update, so we may conclude that these frameworks describe natural selection as a process of Bayesian inference. Thus natural selection serves as a counter example to a widely-held interpretation that restricts Bayesian Inference to human mental processes (including the endeavors of statisticians. As Bayesian inference can always be cast in terms of (variational free energy minimization, natural selection can be viewed as comprising two components: a generative model of an ‘experiment’ in the external world environment, and the results of that 'experiment' or the 'surprise' entailed by predicted and actual outcomes of the ‘experiment’. Minimization of free energy implies that the implicit measure of 'surprise' experienced serves to update the generative model in a Bayesian manner. This description closely accords with the mechanisms of generalized Darwinian process proposed both by Dawkins, in terms of replicators and vehicles, and Campbell, in terms of inferential systems. Bayesian inference is an algorithm for the accumulation of evidence-based knowledge. This algorithm is now seen to operate over a wide range of evolutionary processes, including natural selection, the evolution of mental models and cultural evolutionary processes, notably including science itself. The variational principle of free energy minimization may thus serve as a unifying mathematical framework for universal Darwinism, the study of evolutionary processes operating throughout nature.

  5. Bayesian analysis of magnetic island dynamics

    International Nuclear Information System (INIS)

    Preuss, R.; Maraschek, M.; Zohm, H.; Dose, V.

    2003-01-01

    We examine a first order differential equation with respect to time used to describe magnetic islands in magnetically confined plasmas. The free parameters of this equation are obtained by employing Bayesian probability theory. Additionally, a typical Bayesian change point is solved in the process of obtaining the data

  6. Epidemic dynamics of two coexisting hepatitis C virus subtypes.

    Science.gov (United States)

    Jiménez-Hernández, Nuria; Torres-Puente, Manuela; Bracho, Maria Alma; García-Robles, Inmaculada; Ortega, Enrique; del Olmo, Juan; Carnicer, Fernando; González-Candelas, Fernando; Moya, Andrés

    2007-01-01

    Hepatitis C virus (HCV) infection affects about 3% of the human population. Phylogenetic analyses have grouped its variants into six major genotypes, which have a star-like distribution and several minor subtypes. The most abundant genotype in Europe is the so-called genotype 1, with two prevalent subtypes, 1a and 1b. In order to explain the higher prevalence of subtype 1b over 1a, a large-scale sequence analysis (100 virus clones) has been carried out over 25 patients of both subtypes in two regions of the HCV genome: one comprising hypervariable region 1 and another including the interferon sensitivity-determining region. Neither polymorphism analysis nor molecular variance analysis (attending to intra- and intersubtype differences, age, sex and previous history of antiviral treatment) was able to show any particular difference between subtypes that might account for their different prevalence. Only the demographic history of the populations carrying both subtypes and analysis of molecular variance (AMOVA) for risk practice suggested that the route of transmission may be the most important factor to explain the observed difference.

  7. Heterogeneity of muscarinic receptor subtypes in cerebral blood vessels

    International Nuclear Information System (INIS)

    Garcia-Villalon, A.L.; Krause, D.N.; Ehlert, F.J.; Duckles, S.P.

    1991-01-01

    The identity and distribution of muscarinic cholinergic receptor subtypes and associated signal transduction mechanisms was characterized for the cerebral circulation using correlated functional and biochemical investigations. Subtypes were distinguished by the relative affinities of a panel of muscarinic antagonists, pirenzepine, AF-DX 116 [11-2-[[2-[diethylaminomethyl]- 1-piperidinyl]acetyl]-5,11-dihydro-6H- pyrido[2,3-b][1,4]benzodiazepine-6-one], hexahydrosiladifenidol, methoctramine, 4-diphenylacetoxy-N-methylpiperidine methobromide, dicyclomine, para-fluoro-hexahydrosiladifenidol and atropine. Muscarinic receptors characterized by inhibition of [3H]quinuclidinylbenzilate binding in membranes of bovine pial arteries were of the M2 subtype. In contrast pharmacological analysis of [3H]-quinuclidinylbenzilate binding in bovine intracerebral microvessels suggests the presence of an M4 subtype. Receptors mediating endothelium-dependent vasodilation in rabbit pial arteries were of the M3 subtype, whereas muscarinic receptors stimulating endothelium-independent phosphoinositide hydrolysis in bovine pial arteries were of the M1 subtype. These findings suggest that characteristics of muscarinic receptors in cerebral blood vessels vary depending on the type of vessel, cellular location and function mediated

  8. Retinal Ganglion Cell Diversity and Subtype Specification from Human Pluripotent Stem Cells

    Directory of Open Access Journals (Sweden)

    Kirstin B. Langer

    2018-04-01

    Full Text Available Summary: Retinal ganglion cells (RGCs are the projection neurons of the retina and transmit visual information to postsynaptic targets in the brain. While this function is shared among nearly all RGCs, this class of cell is remarkably diverse, comprised of multiple subtypes. Previous efforts have identified numerous RGC subtypes in animal models, but less attention has been paid to human RGCs. Thus, efforts of this study examined the diversity of RGCs differentiated from human pluripotent stem cells (hPSCs and characterized defined subtypes through the expression of subtype-specific markers. Further investigation of these subtypes was achieved using single-cell transcriptomics, confirming the combinatorial expression of molecular markers associated with these subtypes, and also provided insight into more subtype-specific markers. Thus, the results of this study describe the derivation of RGC subtypes from hPSCs and will support the future exploration of phenotypic and functional diversity within human RGCs. : In this article, Langer and colleagues present extensive characterization of RGC subtypes derived from human pluripotent stem cells, with multiple subtypes identified by subtype-specific molecular markers. Their results present a more detailed analysis of RGC diversity in human cells and yield the use of different markers to identify RGC subtypes. Keywords: iPSC, retina, retinal ganglion cell, RGC subtype, stem cell, ipRGC, alpha RGC, direction selective RGC, RNA-seq

  9. Bayesian ensemble refinement by replica simulations and reweighting

    Science.gov (United States)

    Hummer, Gerhard; Köfinger, Jürgen

    2015-12-01

    We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

  10. Bayesian Decision Theoretical Framework for Clustering

    Science.gov (United States)

    Chen, Mo

    2011-01-01

    In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…

  11. [Human immunodeficiency virus type 1 subtypes in Djibouti].

    Science.gov (United States)

    Abar, A Elmi; Jlizi, A; Darar, H Youssouf; Ben Nasr, M; Abid, S; Kacem, M Ali Ben Hadj; Slim, A

    2012-01-01

    The authors had for aim to study the distribution of HIV-1 subtypes in a cohort of HIV positive patients in the hospital General Peltier of Djibouti. An epidemiological study was made on 40 HIV-1 positive patients followed up in the Infectious Diseases Department over three months. All patients sample were subtyped by genotyping. Thirty-five patients (15 men and 20 women) were found infected by an HIV-1 strain belonging to the M group. Genotyping revealed that - 66% of samples were infected with subtype C, 20% with CRF02_AG, 8.5% with B, 2.9% with CRF02_AG/C and 2.9% with K/C. In fact, Subtype C prevalence has been described in the Horn of Africa and a similar prevalence was previously reported in Djibouti. However our study describes the subtype B in Djibouti for the first time. It is the predominant subtype in the Western world. The detection of CRF02_AG strains indicates that they are still circulating in Djibouti, the only country in East Africa in which this recombinant virus was found. CRF02_AG recombinant isolates were primarily described in West and Central Africa. The presence of this viral heterogeneity, probably coming from the mixing of populations in Djibouti, which is an essential economic and geographical crossroads, incites us to vigilance in the surveillance of this infection.

  12. Quantum-Like Representation of Non-Bayesian Inference

    Science.gov (United States)

    Asano, M.; Basieva, I.; Khrennikov, A.; Ohya, M.; Tanaka, Y.

    2013-01-01

    This research is related to the problem of "irrational decision making or inference" that have been discussed in cognitive psychology. There are some experimental studies, and these statistical data cannot be described by classical probability theory. The process of decision making generating these data cannot be reduced to the classical Bayesian inference. For this problem, a number of quantum-like coginitive models of decision making was proposed. Our previous work represented in a natural way the classical Bayesian inference in the frame work of quantum mechanics. By using this representation, in this paper, we try to discuss the non-Bayesian (irrational) inference that is biased by effects like the quantum interference. Further, we describe "psychological factor" disturbing "rationality" as an "environment" correlating with the "main system" of usual Bayesian inference.

  13. Correct Bayesian and frequentist intervals are similar

    International Nuclear Information System (INIS)

    Atwood, C.L.

    1986-01-01

    This paper argues that Bayesians and frequentists will normally reach numerically similar conclusions, when dealing with vague data or sparse data. It is shown that both statistical methodologies can deal reasonably with vague data. With sparse data, in many important practical cases Bayesian interval estimates and frequentist confidence intervals are approximately equal, although with discrete data the frequentist intervals are somewhat longer. This is not to say that the two methodologies are equally easy to use: The construction of a frequentist confidence interval may require new theoretical development. Bayesians methods typically require numerical integration, perhaps over many variables. Also, Bayesian can easily fall into the trap of over-optimism about their amount of prior knowledge. But in cases where both intervals are found correctly, the two intervals are usually not very different. (orig.)

  14. Pulmonary Emphysema Subtypes on Computed Tomography in Smokers

    Science.gov (United States)

    Smith, Benjamin M.; Austin, John H.M.; Newell, John D.; D’Souza, Belinda M.; Rozenshtein, Anna; Hoffman, Eric A.; Ahmed, Firas; Barr, R. Graham

    2013-01-01

    Background Pulmonary emphysema is divided into three major subtypes at autopsy: centrilobular, paraseptal and panlobular emphysema. These subtypes can be defined by visual assessment on computed tomography (CT); however, clinical characteristics of emphysema subtypes on CT are not well-defined. We developed a reliable approach to visual assessment of emphysema subtypes on CT and examined if emphysema subtypes have distinct characteristics. Methods The Multi-Ethnic Study of Atherosclerosis COPD Study recruited smokers with COPD and controls age 50–79 years with ≥10 pack-years. Participants underwent CT following a standardized protocol. Definitions of centrilobular, paraseptal and panlobular emphysema were obtained by literature review. Six-minute walk distance and pulmonary function were performed following guidelines. Results Twenty-seven percent of 318 smokers had emphysema on CT. Inter-rater reliability of emphysema subtype was substantial (K:0.70). Compared to participants without emphysema, individuals with centrilobular or panlobular emphysema had greater dyspnea, reduced walk distance, greater hyperinflation, and lower diffusing capacity. In contrast, individuals with PSE were similar to controls, except for male predominance. Centrilobular but not panlobular or paraseptal emphysema was associated with greater smoking history (+21 pack-years Pemphysema was associated with reduced body mass index (−5 kg/m2;P=0.01). Other than for dyspnea, these findings were independent of the forced expiratory volume in one second. Seventeen percent of smokers without COPD on spirometry had emphysema, which was independently associated with reduced walk distance. Conclusions Emphysema subtypes on CT are common in smokers with and without COPD. Centrilobular and panlobular emphysema but not paraseptal emphysema have considerable symptomatic and physiological consequences. PMID:24384106

  15. Using consensus bayesian network to model the reactive oxygen species regulatory pathway.

    Directory of Open Access Journals (Sweden)

    Liangdong Hu

    Full Text Available Bayesian network is one of the most successful graph models for representing the reactive oxygen species regulatory pathway. With the increasing number of microarray measurements, it is possible to construct the bayesian network from microarray data directly. Although large numbers of bayesian network learning algorithms have been developed, when applying them to learn bayesian networks from microarray data, the accuracies are low due to that the databases they used to learn bayesian networks contain too few microarray data. In this paper, we propose a consensus bayesian network which is constructed by combining bayesian networks from relevant literatures and bayesian networks learned from microarray data. It would have a higher accuracy than the bayesian networks learned from one database. In the experiment, we validated the bayesian network combination algorithm on several classic machine learning databases and used the consensus bayesian network to model the Escherichia coli's ROS pathway.

  16. Bayesian analysis of rare events

    Energy Technology Data Exchange (ETDEWEB)

    Straub, Daniel, E-mail: straub@tum.de; Papaioannou, Iason; Betz, Wolfgang

    2016-06-01

    In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

  17. Subtypes of depression in cancer patients : An empirically driven approach

    NARCIS (Netherlands)

    Zhu, Lei; Ranchor, Adelita V; van der Lee, Marije; Garssen, Bert; Sanderman, Robbert; Schroevers, Maya J

    PURPOSE: This study aimed to (1) identify subgroups of cancer patients with distinct subtypes of depression before the start of psychological care, (2) examine whether socio-demographic and medical characteristics distinguished these subtypes, and (3) examine whether people with distinct subtypes

  18. Subtypes of depression in cancer patients: an empirically driven approach

    NARCIS (Netherlands)

    Zhu, Lei; Ranchor, A.V.; van der Lee, Marije; Garssen, Bert; Sanderman, Robbert; Schroevers, Maya J.

    2016-01-01

    Purpose This study aimed to (1) identify subgroups of cancer patients with distinct subtypes of depression before the start of psychological care, (2) examine whether socio-demographic and medical characteristics distinguished these subtypes, and (3) examine whether people with distinct subtypes

  19. Bayesian models a statistical primer for ecologists

    CERN Document Server

    Hobbs, N Thompson

    2015-01-01

    Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods-in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probabili

  20. Reviewing the history of HIV-1: spread of subtype B in the Americas.

    Directory of Open Access Journals (Sweden)

    Dennis Maletich Junqueira

    Full Text Available The dispersal of HIV-1 subtype B (HIV-1B is a reflection of the movement of human populations in response to social, political, and geographical issues. The initial dissemination of HIV-1B outside Africa seems to have included the passive involvement of human populations from the Caribbean in spreading the virus to the United States. However, the exact pathways taken during the establishment of the pandemic in the Americas remain unclear. Here, we propose a geographical scenario for the dissemination of HIV-1B in the Americas, based on phylogenetic and genetic statistical analyses of 313 available sequences of the pol gene from 27 countries. Maximum likelihood and bayesian inference methods were used to explore the phylogenetic relationships between HIV-1B sequences, and molecular variance estimates were analyzed to infer the genetic structure of the viral population. We found that the initial dissemination and subsequent spread of subtype B in the Americas occurred via a single introduction event in the Caribbean around 1964 (1950-1967. Phylogenetic trees present evidence of several primary outbreaks in countries in South America, directly seeded by the Caribbean epidemic. Cuba is an exception insofar as its epidemic seems to have been introduced from South America. One clade comprising isolates from different countries emerged in the most-derived branches, reflecting the intense circulation of the virus throughout the American continents. Statistical analysis supports the genetic compartmentalization of the virus among the Americas, with a close relationship between the South American and Caribbean epidemics. These findings reflect the complex establishment of the HIV-1B pandemic and contribute to our understanding between the migration process of human populations and virus diffusion.

  1. Robust Bayesian detection of unmodelled bursts

    International Nuclear Information System (INIS)

    Searle, Antony C; Sutton, Patrick J; Tinto, Massimo; Woan, Graham

    2008-01-01

    We develop a Bayesian treatment of the problem of detecting unmodelled gravitational wave bursts using the new global network of interferometric detectors. We also compare this Bayesian treatment with existing coherent methods, and demonstrate that the existing methods make implicit assumptions on the distribution of signals that make them sub-optimal for realistic signal populations

  2. BAYESIAN ESTIMATION OF THERMONUCLEAR REACTION RATES

    Energy Technology Data Exchange (ETDEWEB)

    Iliadis, C.; Anderson, K. S. [Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3255 (United States); Coc, A. [Centre de Sciences Nucléaires et de Sciences de la Matière (CSNSM), CNRS/IN2P3, Univ. Paris-Sud, Université Paris–Saclay, Bâtiment 104, F-91405 Orsay Campus (France); Timmes, F. X.; Starrfield, S., E-mail: iliadis@unc.edu [School of Earth and Space Exploration, Arizona State University, Tempe, AZ 85287-1504 (United States)

    2016-11-01

    The problem of estimating non-resonant astrophysical S -factors and thermonuclear reaction rates, based on measured nuclear cross sections, is of major interest for nuclear energy generation, neutrino physics, and element synthesis. Many different methods have been applied to this problem in the past, almost all of them based on traditional statistics. Bayesian methods, on the other hand, are now in widespread use in the physical sciences. In astronomy, for example, Bayesian statistics is applied to the observation of extrasolar planets, gravitational waves, and Type Ia supernovae. However, nuclear physics, in particular, has been slow to adopt Bayesian methods. We present astrophysical S -factors and reaction rates based on Bayesian statistics. We develop a framework that incorporates robust parameter estimation, systematic effects, and non-Gaussian uncertainties in a consistent manner. The method is applied to the reactions d(p, γ ){sup 3}He, {sup 3}He({sup 3}He,2p){sup 4}He, and {sup 3}He( α , γ ){sup 7}Be, important for deuterium burning, solar neutrinos, and Big Bang nucleosynthesis.

  3. Prior approval: the growth of Bayesian methods in psychology.

    Science.gov (United States)

    Andrews, Mark; Baguley, Thom

    2013-02-01

    Within the last few years, Bayesian methods of data analysis in psychology have proliferated. In this paper, we briefly review the history or the Bayesian approach to statistics, and consider the implications that Bayesian methods have for the theory and practice of data analysis in psychology.

  4. Oppositional defiant disorder dimensions and subtypes among detained male adolescent offenders.

    Science.gov (United States)

    Aebi, Marcel; Barra, Steffen; Bessler, Cornelia; Steinhausen, Hans-Christoph; Walitza, Susanne; Plattner, Belinda

    2016-06-01

    In adolescent offenders, oppositional defiant disorder (ODD) and its dimensions/subtypes have been frequently ignored due to the stronger focus on criminal behaviours. The revised criteria of the DSM-5 now allow diagnosing ODD in older youths independent of conduct disorder (CD). This study aimed at analysing ODD dimensions/subtypes and their relation to suicidality, comorbid psychiatric disorders, and criminal behaviours after release from detention in a sample of detained male adolescents. Suicidality and psychiatric disorders (including ODD symptoms) were assessed in a consecutive sample of 158 male adolescents (Mage  = 16.89 years) from the Zurich Juvenile Detention Centre. Based on previous research findings, an irritable ODD dimension and a defiant/vindictive ODD dimension based on ODD symptoms were defined. Latent Class Analysis (LCA) was used to identify distinct subtypes of adolescent offenders according to their ODD symptom profiles. Logistic regression and Cox regression were used to analyse the relations of ODD dimensions/ODD subtypes to comorbid psychopathology and criminal reoffenses from official data. The ODD-irritable dimension, but not the ODD defiant/vindictive dimension predicted comorbid anxiety, suicidality and violent reoffending. LCA identified four subtypes, namely, a no-ODD subtype, a severe ODD subtype and two moderate ODD subtypes with either defiant or irritable symptoms. The irritable ODD subtype and the severe ODD subtype were related to suicidality and comorbid affective/anxiety disorders. The irritable ODD subtype was the strongest predictor of criminal (violent) reoffending even when controlling for CD. The present findings confirm the presence of ODD dimensions/subtypes in a highly disturbed adolescent offender sample. Irritable youths were at risk of suicide and persistent criminal behaviours. Due to the severe consequences of irritability, a standardized assessment approach and a specific treatment is needed in prison to

  5. Can a significance test be genuinely Bayesian?

    OpenAIRE

    Pereira, Carlos A. de B.; Stern, Julio Michael; Wechsler, Sergio

    2008-01-01

    The Full Bayesian Significance Test, FBST, is extensively reviewed. Its test statistic, a genuine Bayesian measure of evidence, is discussed in detail. Its behavior in some problems of statistical inference like testing for independence in contingency tables is discussed.

  6. Strategies for subtyping influenza viruses circulating in the Danish pig population

    DEFF Research Database (Denmark)

    Breum, Solvej Østergaard; Hjulsager, Charlotte Kristiane; Trebbien, Ramona

    2010-01-01

    in the Danish pig population functional and rapid subtyping assays are required. The conventional RT-PCR influenza subtyping assays developed by Chiapponi et al. (2003) have been implemented and used for typing of influenza viruses found positive in a pan influenza A real time RT-PCR assay. The H1 and N1 assays......Influenza viruses are endemic in the Danish pig population and the dominant circulating subtypes are H1N1, a Danish H1N2 reassortant, and H3N2. Here we present our current and future strategies for influenza virus subtyping. For diagnostic and surveillance of influenza subtypes circulating...... were specific when applied on Danish influenza positive samples, whereas the N2 assay consistently showed several unspecific PCR products. A subset of positive influenza samples detected by the real time RT-PCR screening assay could not be subtyped using these assays. Therefore, new influenza subtyping...

  7. Bayesian image restoration, using configurations

    OpenAIRE

    Thorarinsdottir, Thordis

    2006-01-01

    In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the re...

  8. Bayesian Networks and Influence Diagrams

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders Læsø

     Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification......, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended...

  9. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Chavira, Mark; Darwiche, Adnan

    2004-01-01

    We describe a system for exact inference with relational Bayesian networks as defined in the publicly available \\primula\\ tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference by evaluating...

  10. Bayesian analysis of CCDM models

    Science.gov (United States)

    Jesus, J. F.; Valentim, R.; Andrade-Oliveira, F.

    2017-09-01

    Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3αH0 model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

  11. Bayesian analysis of CCDM models

    Energy Technology Data Exchange (ETDEWEB)

    Jesus, J.F. [Universidade Estadual Paulista (Unesp), Câmpus Experimental de Itapeva, Rua Geraldo Alckmin 519, Vila N. Sra. de Fátima, Itapeva, SP, 18409-010 Brazil (Brazil); Valentim, R. [Departamento de Física, Instituto de Ciências Ambientais, Químicas e Farmacêuticas—ICAQF, Universidade Federal de São Paulo (UNIFESP), Unidade José Alencar, Rua São Nicolau No. 210, Diadema, SP, 09913-030 Brazil (Brazil); Andrade-Oliveira, F., E-mail: jfjesus@itapeva.unesp.br, E-mail: valentim.rodolfo@unifesp.br, E-mail: felipe.oliveira@port.ac.uk [Institute of Cosmology and Gravitation—University of Portsmouth, Burnaby Road, Portsmouth, PO1 3FX United Kingdom (United Kingdom)

    2017-09-01

    Creation of Cold Dark Matter (CCDM), in the context of Einstein Field Equations, produces a negative pressure term which can be used to explain the accelerated expansion of the Universe. In this work we tested six different spatially flat models for matter creation using statistical criteria, in light of SNe Ia data: Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Bayesian Evidence (BE). These criteria allow to compare models considering goodness of fit and number of free parameters, penalizing excess of complexity. We find that JO model is slightly favoured over LJO/ΛCDM model, however, neither of these, nor Γ = 3α H {sub 0} model can be discarded from the current analysis. Three other scenarios are discarded either because poor fitting or because of the excess of free parameters. A method of increasing Bayesian evidence through reparameterization in order to reducing parameter degeneracy is also developed.

  12. Sparse Event Modeling with Hierarchical Bayesian Kernel Methods

    Science.gov (United States)

    2016-01-05

    SECURITY CLASSIFICATION OF: The research objective of this proposal was to develop a predictive Bayesian kernel approach to model count data based on...several predictive variables. Such an approach, which we refer to as the Poisson Bayesian kernel model, is able to model the rate of occurrence of... kernel methods made use of: (i) the Bayesian property of improving predictive accuracy as data are dynamically obtained, and (ii) the kernel function

  13. Neurocognitive Impairments Are More Severe in the Binge-Eating/Purging Anorexia Nervosa Subtype Than in the Restricting Subtype.

    Science.gov (United States)

    Tamiya, Hiroko; Ouchi, Atushi; Chen, Runshu; Miyazawa, Shiho; Akimoto, Yoritaka; Kaneda, Yasuhiro; Sora, Ichiro

    2018-01-01

    Objective: To evaluate cognitive function impairment in patients with anorexia nervosa (AN) of either the restricting (ANR) or binge-eating/purging (ANBP) subtype. Method: We administered the Japanese version of the MATRICS Consensus Cognitive Battery to 22 patients with ANR, 18 patients with ANBP, and 69 healthy control subjects. Our participants were selected from among the patients at the Kobe University Hospital and community residents. Results: Compared to the healthy controls, the ANR group had significantly lower visual learning and social cognition scores, and the ANBP group had significantly lower processing speed, attention/vigilance, visual learning, reasoning/problem-solving, and social cognition scores. Compared to the ANR group, the ANBP group had significantly lower attention/vigilance scores. Discussion: The AN subtypes differed in cognitive function impairments. Participants with ANBP, which is associated with higher mortality rates than ANR, exhibited greater impairment severities, especially in the attention/vigilance domain, confirming the presence of impairments in continuous concentration. This may relate to the impulsivity, an ANBP characteristic reported in the personality research. Future studies can further clarify the cognitive impairments of each subtype by addressing the subtype cognitive functions and personality characteristics.

  14. Superficial basal cell carcinoma: A comparison of superficial only subtype with superficial combined with other subtypes by age, sex and anatomic site in 3150 cases.

    Science.gov (United States)

    Pyne, John H; Myint, Esther; Barr, Elizabeth M; Clark, Simon P; David, Michael; Na, Renua; Hou, Ruihang

    2017-08-01

    Basal cell carcinoma (BCC) may present as superficial subtype alone (sBCC) or superficial combined with other subtypes. The objective of this study was to compare sBCC without or with other BCC subtypes by age, sex and anatomic site. We retrospectively collected superficial BCC with the above characteristics from an Australian center during 2009 to 2014. We recorded 1528 sBCC and 1622 superficial BCC combined with other BCC subtype cases. Males numbered 2007 and females 1140. On males, head sites (forehead, cheek, nose and ear combined) compared to limb plus trunk sites displayed a higher incidence of superficial BCC combined with either nodular and or aggressive BCC subtypes (OR 13.15 CI 95% 8.9-19.5 P < .0001). On females a similar comparison also found a higher incidence of superficial BCC combined with solid subtype BCC on head sites compared to trunk and limb sites (OR 9.66 CI 95% 5.8-16.1 P < .0001). Superficial BCC alone is more likely on younger females on trunk and limb sites. Small partial biopsies reported as sBCC may miss other BCC subtypes present with higher risk on facial sites for males and females. Males had smaller proportions of superficial only subtype BCC on facial and ear sites compared to females. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  15. The Region of Difference Four is a Robust Genetic Marker for Subtyping Mycobacterium caprae Isolates and is Linked to Spatial Distribution of Three Subtypes.

    Science.gov (United States)

    Rettinger, A; Broeckl, S; Fink, M; Prodinger, W M; Blum, H; Krebs, S; Domogalla, J; Just, F; Gellert, S; Straubinger, R K; Büttner, M

    2017-06-01

    Alpine Mycobacterium caprae isolates found in cattle and red deer display at least three genetic variations in the region of difference four (RD4) that can be used for further differentiation of the isolates into the subtypes 'Allgäu', 'Karwendel' and 'Lechtal'. Each genomic subtype is thereby characterized by a specific nucleotide deletion pattern in the 12.7-kb RD4 region. Even though M. caprae infections are frequently documented in cattle and red deer, little is known about the transmission routes. Hence, robust markers for M. caprae subtyping are needed to gain insight into the molecular epidemiology. For this reason, a rapid and robust multiplex PCR was developed for the simultaneous detection of three M. caprae RD4 subtypes and was used to subtype a total number of 241 M. caprae isolates from animals (145 cattle, 95 red deer and one fox) from Bavaria and Austria. All three subtypes occur spatially distributed and are found in cattle and in red deer suggesting transmission between the two species. As subtypes are genetically stable in both species it is hypothesized that the described genetic variations developed within the host due to 'within-host replication'. The results of this study recommend the genomic RD4 region as a reliable diagnostic marker for M. caprae subtype differentiation. © 2015 Blackwell Verlag GmbH.

  16. Bayesian Inference for Functional Dynamics Exploring in fMRI Data

    Directory of Open Access Journals (Sweden)

    Xuan Guo

    2016-01-01

    Full Text Available This paper aims to review state-of-the-art Bayesian-inference-based methods applied to functional magnetic resonance imaging (fMRI data. Particularly, we focus on one specific long-standing challenge in the computational modeling of fMRI datasets: how to effectively explore typical functional interactions from fMRI time series and the corresponding boundaries of temporal segments. Bayesian inference is a method of statistical inference which has been shown to be a powerful tool to encode dependence relationships among the variables with uncertainty. Here we provide an introduction to a group of Bayesian-inference-based methods for fMRI data analysis, which were designed to detect magnitude or functional connectivity change points and to infer their functional interaction patterns based on corresponding temporal boundaries. We also provide a comparison of three popular Bayesian models, that is, Bayesian Magnitude Change Point Model (BMCPM, Bayesian Connectivity Change Point Model (BCCPM, and Dynamic Bayesian Variable Partition Model (DBVPM, and give a summary of their applications. We envision that more delicate Bayesian inference models will be emerging and play increasingly important roles in modeling brain functions in the years to come.

  17. HIV subtype, epidemiological and mutational correlations in patients from Paraná, Brazil.

    Science.gov (United States)

    Silva, Monica Maria Gomes da; Telles, Flavio Queiroz; da Cunha, Clovis Arns; Rhame, Frank S

    2010-01-01

    Analyze patients with HIV infection from Curitiba, Paraná, their epidemiological characteristics and HIV RAM. Patients regularly followed in an ID Clinic had their medical data evaluated and cases of virological failure were analyzed with genotypic report. Patients with complete medical charts were selected (n = 191). Demographic and clinical characteristics were compared. One hundred thirty two patients presented with subtype B infection (69.1%), 41 subtype C (21.5%), 10 subtype F (5.2%), 7 BF (3.7%) and 1 CF (0.5%). Patients with subtype B infection had been diagnosed earlier than patients with subtype non-B. Also, subtype B infection was more frequent in men who have sex with men, while non-B subtypes occurred more frequently in heterosexuals and women. Patients with previous history of three classes of ARVs (n = 161) intake were selected to evaluate resistance. For RT inhibitors, 41L and 210W were more frequently observed in subtype B than in non-B strains. No differences between subtypes and mutations were observed to NNTRIs. Mutations at 10, 32 and 63 position of protease were more observed in subtype B viruses than non-B, while positions 20 and 36 of showed more amino acid substitutions in subtype non-B viruses. Patients with history of NFV intake were evaluated to resistance pathway. The 90M pathway was more frequent in subtypes B and non-B. Mutations previously reported as common in non-B viruses, such as 65R and 106M, were uncommon in our study. Mutations 63P and 36I, previously reported as common in HIV-1 subtypes B and C from Brazil, respectively, were common. There is a significant frequency of HIV-1 non-B infections in Paraná state, with isolates classified as subtypes C, F, BF and BC. Patients with subtype C infection were more frequently female, heterosexual and had a longer average time of HIV diagnosis.

  18. HIV subtype, epidemiological and mutational correlations in patients from Paraná, Brazil

    Directory of Open Access Journals (Sweden)

    Monica Maria Gomes da Silva

    Full Text Available OBJECTIVE: Analyze patients with HIV infection from Curitiba, Paraná, their epidemiological characteristics and HIV RAM. METHODS: Patients regularly followed in an ID Clinic had their medical data evaluated and cases of virological failure were analyzed with genotypic report. RESULTS: Patients with complete medical charts were selected (n = 191. Demographic and clinical characteristics were compared. One hundred thirty two patients presented with subtype B infection (69.1%, 41 subtype C (21.5%, 10 subtype F (5.2%, 7 BF (3.7% and 1 CF (0.5%. Patients with subtype B infection had been diagnosed earlier than patients with subtype non-B. Also, subtype B infection was more frequent in men who have sex with men, while non-B subtypes occurred more frequently in heterosexuals and women. Patients with previous history of three classes of ARVs (n = 161 intake were selected to evaluate resistance. For RT inhibitors, 41L and 210W were more frequently observed in subtype B than in non-B strains. No differences between subtypes and mutations were observed to NNTRIs. Mutations at 10, 32 and 63 position of protease were more observed in subtype B viruses than non-B, while positions 20 and 36 of showed more amino acid substitutions in subtype non-B viruses. Patients with history of NFV intake were evaluated to resistance pathway. The 90M pathway was more frequent in subtypes B and non-B. Mutations previously reported as common in non-B viruses, such as 65R and 106M, were uncommon in our study. Mutations 63P and 36I, previously reported as common in HIV-1 subtypes B and C from Brazil, respectively, were common. CONCLUSION: There is a significant frequency of HIV-1 non-B infections in Paraná state, with isolates classified as subtypes C, F, BF and BC. Patients with subtype C infection were more frequently female, heterosexual and had a longer average time of HIV diagnosis

  19. Assessing the genetic architecture of epithelial ovarian cancer histological subtypes

    DEFF Research Database (Denmark)

    Cuellar-Partida, Gabriel; Lu, Yi; Dixon, Suzanne C

    2016-01-01

    studies show that certain genetic variants confer susceptibility to all subtypes while other variants are subtype-specific. Here, we perform an extensive analysis of the genetic architecture of EOC subtypes. To this end, we used data of 10,014 invasive EOC patients and 21,233 controls from the Ovarian...

  20. Sensory Subtypes in Preschool Aged Children with Autism Spectrum Disorder.

    Science.gov (United States)

    Tomchek, Scott D; Little, Lauren M; Myers, John; Dunn, Winnie

    2018-06-01

    Given the heterogeneity of autism spectrum disorder (ASD), research has investigated how sensory features elucidate subtypes that enhance our understanding of etiology and tailored treatment approaches. Previous studies, however, have not integrated core developmental behaviors with sensory features in investigations of subtypes in ASD. Therefore, we used latent profile analysis to examine subtypes in a preschool aged sample considering sensory processing patterns in combination with social-communication skill, motor performance, and adaptive behavior. Results showed four subtypes that differed by degree and quality of sensory features, age and differential presentation of developmental skills. Findings partially align with previous literature on sensory subtypes and extends our understanding of how sensory processing aligns with other developmental domains in young children with ASD.

  1. Pathological Gambling Subtypes

    Science.gov (United States)

    Vachon, David D.; Bagby, R. Michael

    2009-01-01

    Although pathological gambling (PG) is regarded in the 4th edition of the "Diagnostic and Statistical Manual of Mental Disorders" (American Psychiatric Association, 1994) as a unitary diagnostic construct, it is likely composed of distinct subtypes. In the current report, the authors used cluster analyses of personality traits with a…

  2. Particle identification in ALICE: a Bayesian approach

    NARCIS (Netherlands)

    Adam, J.; Adamova, D.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Agrawal, N.; Ahammed, Z.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alexandre, D.; Alfaro Molina, R.; Alici, A.; Alkin, A.; Almaraz, J. R. M.; Alme, J.; Alt, T.; Altinpinar, S.; Altsybeev, I.; Alves Garcia Prado, C.; Andrei, C.; Andronic, A.; Anguelov, V.; Anticic, T.; Antinori, F.; Antonioli, P.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arnold, O. W.; Arsene, I. C.; Arslandok, M.; Audurier, B.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Balasubramanian, S.; Baldisseri, A.; Baral, R. C.; Barbano, A. M.; Barbera, R.; Barile, F.; Barnafoeldi, G. G.; Barnby, L. S.; Barret, V.; Bartalini, P.; Barth, K.; Bartke, J.; Bartsch, E.; Basile, M.; Bastid, N.; Bathen, B.; Batigne, G.; Camejo, A. Batista; Batyunya, B.; Batzing, P. C.; Bearden, I. G.; Beck, H.; Bedda, C.; Behera, N. K.; Belikov, I.; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Belmont, R.; Belmont-Moreno, E.; Belyaev, V.; Benacek, P.; Bencedi, G.; Beole, S.; Berceanu, I.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhati, A. K.; Bhattacharjee, B.; Bhom, J.; Bianchi, L.; Bianchi, N.; Bianchin, C.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Bjelogrlic, S.; Blair, J. T.; Blau, D.; Blume, C.; Bock, F.; Bogdanov, A.; Boggild, H.; Boldizsar, L.; Bombara, M.; Book, J.; Borel, H.; Borissov, A.; Borri, M.; Bossu, F.; Botta, E.; Bourjau, C.; Braun-Munzinger, P.; Bregant, M.; Breitner, T.; Broker, T. A.; Browning, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Cabala, J.; Caffarri, D.; Cai, X.; Caines, H.; Diaz, L. Calero; Caliva, A.; Calvo Villar, E.; Camerini, P.; Carena, F.; Carena, W.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Cepila, J.; Cerello, P.; Cerkala, J.; Chang, B.; Chapeland, S.; Chartier, M.; Charvet, J. L.; Chattopadhyay, S.; Chattopadhyay, S.; Chauvin, A.; Chelnokov, V.; Cherney, M.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Choi, K.; Chojnacki, M.; Choudhury, S.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Connors, M. E.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortes Maldonado, I.; Cortese, P.; Cosentino, M. R.; Costa, F.; Crochet, P.; Cruz Albino, R.; Cuautle, E.; Cunqueiro, L.; Dahms, T.; Dainese, A.; Danisch, M. C.; Danu, A.; Das, I.; Das, S.; Dash, A.; Dash, S.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; Deisting, A.; Deloff, A.; Denes, E.; Deplano, C.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Di Nezza, P.; Corchero, M. A. Diaz; Dietel, T.; Dillenseger, P.; Divia, R.; Djuvsland, O.; Dobrin, A.; Gimenez, D. Domenicis; Doenigus, B.; Dordic, O.; Drozhzhova, T.; Dubey, A. K.; Dubla, A.; Ducroux, L.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Endress, E.; Engel, H.; Epple, E.; Erazmus, B.; Erdemir, I.; Erhardt, F.; Espagnon, B.; Estienne, M.; Esumi, S.; Eum, J.; Evans, D.; Evdokimov, S.; Eyyubova, G.; Fabbietti, L.; Fabris, D.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Ferencei, J.; Fernandez Tellez, A.; Ferreiro, E. G.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Figueredo, M. A. S.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiore, E. M.; Fleck, M. G.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francescon, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gallio, M.; Gangadharan, D. R.; Ganoti, P.; Gao, C.; Garabatos, C.; Garcia-Solis, E.; Gargiulo, C.; Gasik, P.; Gauger, E. F.; Germain, M.; Gheata, A.; Gheata, M.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Gladysz-Dziadus, E.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, A. S.; Gonzalez, V.; Gonzalez-Zamora, P.; Gorbunov, S.; Goerlich, L.; Gotovac, S.; Grabski, V.; Grachov, O. A.; Graczykowski, L. K.; Graham, K. L.; Grelli, A.; Grigoras, A.; Grigoras, C.; Grigoriev, V.; Grigoryan, A.; Grigoryan, S.; Grinyov, B.; Grion, N.; Gronefeld, J. M.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guber, F.; Guernane, R.; Guerzoni, B.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Haake, R.; Haaland, O.; Hadjidakis, C.; Haiduc, M.; Hamagaki, H.; Hamar, G.; Hamon, J. C.; Harris, J. W.; Harton, A.; Hatzifotiadou, D.; Hayashi, S.; Heckel, S. T.; Hellbaer, E.; Helstrup, H.; Herghelegiu, A.; Herrera Corral, G.; Hess, B. A.; Hetland, K. F.; Hillemanns, H.; Hippolyte, B.; Horak, D.; Hosokawa, R.; Hristov, P.; Humanic, T. J.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Ilkaev, R.; Inaba, M.; Incani, E.; Ippolitov, M.; Irfan, M.; Ivanov, M.; Ivanov, V.; Izucheev, V.; Jacazio, N.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jahnke, C.; Jakubowska, M. J.; Jang, H. J.; Janik, M. A.; Jayarathna, P. H. S. Y.; Jena, C.; Jena, S.; Bustamante, R. T. Jimenez; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kamin, J.; Kaplin, V.; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karayan, L.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keijdener, D. L. D.; Keil, M.; Khan, M. Mohisin; Khan, P.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Kileng, B.; Kim, D. W.; Kim, D. J.; Kim, D.; Kim, J. S.; Kim, M.; Kim, T.; Kirsch, S.; Kisel, I.; Kiselev, S.; Kisiel, A.; Kiss, G.; Klay, J. L.; Klein, C.; Klein-Boesing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Kollegger, T.; Kolojvari, A.; Kondratiev, V.; Kondratyeva, N.; Kondratyuk, E.; Konevskikh, A.; Kopcik, M.; Kostarakis, P.; Kour, M.; Kouzinopoulos, C.; Kovalenko, O.; Kovalenko, V.; Kowalski, M.; Meethaleveedu, G. Koyithatta; Kralik, I.; Kravcakova, A.; Krivda, M.; Krizek, F.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kucera, V.; Kuijer, P. G.; Kumar, J.; Kumar, L.; Kumar, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kuryakin, A.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Ladron de Guevara, P.; Lagana Fernandes, C.; Lakomov, I.; Langoy, R.; Lara, C.; Lardeux, A.; Lattuca, A.; Laudi, E.; Lea, R.; Leardini, L.; Lee, G. R.; Lee, S.; Lehas, F.; Lemmon, R. C.; Lenti, V.; Leogrande, E.; Monzon, I. Leon; Leon Vargas, H.; Leoncino, M.; Levai, P.; Lien, J.; Lietava, R.; Lindal, S.; Lindenstruth, V.; Lippmann, C.; Lisa, M. A.; Ljunggren, H. M.; Lodato, D. F.; Loenne, P. I.; Loginov, V.; Loizides, C.; Lopez, X.; Torres, E. Lopez; Lowe, A.; Luettig, P.; Lunardon, M.; Luparello, G.; Lutz, T. H.; Maevskaya, A.; Mager, M.; Mahajan, S.; Mahmood, S. M.; Maire, A.; Majka, R. D.; Malaev, M.; Maldonado Cervantes, I.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V.; Manso, F.; Manzari, V.; Marchisone, M.; Mares, J.; Margagliotti, G. V.; Margotti, A.; Margutti, J.; Marin, A.; Markert, C.; Marquard, M.; Martin, N. A.; Blanco, J. Martin; Martinengo, P.; Martinez, M. I.; Garcia, G. Martinez; Pedreira, M. Martinez; Mas, A.; Masciocchi, S.; Masera, M.; Masoni, A.; Mastroserio, A.; Matyja, A.; Mayer, C.; Mazer, J.; Mazzoni, M. A.; Mcdonald, D.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Perez, J. Mercado; Meres, M.; Miake, Y.; Mieskolainen, M. M.; Mikhaylov, K.; Milano, L.; Milosevic, J.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, B.; Molnar, L.; Montano Zetina, L.; Montes, E.; De Godoy, D. A. Moreira; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Muccifora, V.; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; Nedosekin, A.; Nellen, L.; Ng, F.; Nicassio, M.; Niculescu, M.; Niedziela, J.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V.; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Oeschler, H.; Oh, S.; Oh, S. K.; Ohlson, A.; Okatan, A.; Okubo, T.; Olah, L.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Oravec, M.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Ozdemir, M.; Pachmayer, Y.; Pagano, D.; Pagano, P.; Paic, G.; Pal, S. K.; Pan, J.; Papikyan, V.; Pappalardo, G. S.; Pareek, P.; Park, W. J.; Parmar, S.; Passfeld, A.; Paticchio, V.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Da Costa, H. Pereira; Peresunko, D.; Lara, C. E. Perez; Lezama, E. Perez; Peskov, V.; Pestov, Y.; Petracek, V.; Petrov, V.; Petrovici, M.; Petta, C.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Porteboeuf-Houssais, S.; Porter, J.; Pospisil, J.; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I.; Puccio, M.; Puddu, G.; Pujahari, P.; Punin, V.; Putschke, J.; Qvigstad, H.; Rachevski, A.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Raesaenen, S. S.; Rascanu, B. T.; Rathee, D.; Read, K. F.; Redlich, K.; Reed, R. J.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reolon, A. R.; Reshetin, A.; Reygers, K.; Riabov, V.; Ricci, R. A.; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rocco, E.; Rodriguez Cahuantzi, M.; Manso, A. Rodriguez; Roed, K.; Rogochaya, E.; Rohr, D.; Roehrich, D.; Ronchetti, F.; Ronflette, L.; Rosnet, P.; Rossi, A.; Roukoutakis, F.; Roy, A.; Roy, C.; Roy, P.; Montero, A. J. Rubio; Rui, R.; Russo, R.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Safarik, K.; Sahlmuller, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Saleh, M. A.; Salzwedel, J.; Sambyal, S.; Samsonov, V.; Sandor, L.; Sandoval, A.; Sano, M.; Sarkar, D.; Sarkar, N.; Sarma, P.; Scapparone, E.; Scarlassara, F.; Schiaua, C.; Schicker, R.; Schmidt, C.; Schmidt, H. R.; Schuchmann, S.; Schukraft, J.; Schulc, M.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Scott, R.; Sefcik, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I.; Senosi, K.; Senyukov, S.; Serradilla, E.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shadura, O.; Shahoyan, R.; Shahzad, M. I.; Shangaraev, A.; Sharma, M.; Sharma, M.; Sharma, N.; Sheikh, A. I.; Shigaki, K.; Shou, Q.; Shtejer, K.; Sibiriak, Y.; Siddhanta, S.; Sielewicz, K. M.; Siemiarczuk, T.; Silvermyr, D.; Silvestre, C.; Simatovic, G.; Simonetti, G.; Singaraju, R.; Singh, R.; Singha, S.; Singhal, V.; Sinha, B. C.; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. W.; Song, J.; Song, M.; Song, Z.; Soramel, F.; Sorensen, S.; de Souza, R. D.; Sozzi, F.; Spacek, M.; Spiriti, E.; Sputowska, I.; Spyropoulou-Stassinaki, M.; Stachel, J.; Stan, I.; Stankus, P.; Stenlund, E.; Steyn, G.; Stiller, J. H.; Stocco, D.; Strmen, P.; Suaide, A. A. P.; Sugitate, T.; Suire, C.; Suleymanov, M.; Suljic, M.; Sultanov, R.; Sumbera, M.; Sumowidagdo, S.; Szabo, A.; Szanto de Toledo, A.; Szarka, I.; Szczepankiewicz, A.; Szymanski, M.; Tabassam, U.; Takahashi, J.; Tambave, G. J.; Tanaka, N.; Tarhini, M.; Tariq, M.; Tarzila, M. G.; Tauro, A.; Tejeda Munoz, G.; Telesca, A.; Terasaki, K.; Terrevoli, C.; Teyssier, B.; Thaeder, J.; Thakur, D.; Thomas, D.; Tieulent, R.; Timmins, A. R.; Toia, A.; Trogolo, S.; Trombetta, G.; Trubnikov, V.; Trzaska, W. H.; Tsuji, T.; Tumkin, A.; Turrisi, R.; Tveter, T. S.; Ullaland, K.; Uras, A.; Usai, G. L.; Utrobicic, A.; Vala, M.; Palomo, L. Valencia; Vallero, S.; Van Der Maarel, J.; Van Hoorne, J. W.; van Leeuwen, M.; Vanat, T.; Vyvre, P. Vande; Varga, D.; Vargas, A.; Vargyas, M.; Varma, R.; Vasileiou, M.; Vasiliev, A.; Vauthier, A.; Vechernin, V.; Veen, A. M.; Veldhoen, M.; Velure, A.; Vercellin, E.; Vergara Limon, S.; Vernet, R.; Verweij, M.; Vickovic, L.; Viesti, G.; Viinikainen, J.; Vilakazi, Z.; Baillie, O. Villalobos; Villatoro Tello, A.; Vinogradov, A.; Vinogradov, L.; Vinogradov, Y.; Virgili, T.; Vislavicius, V.; Viyogi, Y. P.; Vodopyanov, A.; Voelkl, M. A.; Voloshin, K.; Voloshin, S. A.; Volpe, G.; von Haller, B.; Vorobyev, I.; Vranic, D.; Vrlakova, J.; Vulpescu, B.; Wagner, B.; Wagner, J.; Wang, H.; Watanabe, D.; Watanabe, Y.; Weiser, D. F.; Westerhoff, U.; Whitehead, A. M.; Wiechula, J.; Wikne, J.; Wilk, G.; Wilkinson, J.; Williams, M. C. S.; Windelband, B.; Winn, M.; Yang, H.; Yano, S.; Yasin, Z.; Yokoyama, H.; Yoo, I. -K.; Yoon, J. H.; Yurchenko, V.; Yushmanov, I.; Zaborowska, A.; Zaccolo, V.; Zaman, A.; Zampolli, C.; Zanoli, H. J. C.; Zaporozhets, S.; Zardoshti, N.; Zarochentsev, A.; Zavada, P.; Zaviyalov, N.; Zbroszczyk, H.; Zgura, I. S.; Zhalov, M.; Zhang, C.; Zhao, C.; Zhigareva, N.; Zhou, Y.; Zhou, Z.; Zhu, H.; Zichichi, A.; Zimmermann, A.; Zimmermann, M. B.; Zinovjev, G.; Zyzak, M.

    2016-01-01

    We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian

  3. Compiling Relational Bayesian Networks for Exact Inference

    DEFF Research Database (Denmark)

    Jaeger, Manfred; Darwiche, Adnan; Chavira, Mark

    2006-01-01

    We describe in this paper a system for exact inference with relational Bayesian networks as defined in the publicly available PRIMULA tool. The system is based on compiling propositional instances of relational Bayesian networks into arithmetic circuits and then performing online inference...

  4. A Bayesian Justification for Random Sampling in Sample Survey

    Directory of Open Access Journals (Sweden)

    Glen Meeden

    2012-07-01

    Full Text Available In the usual Bayesian approach to survey sampling the sampling design, plays a minimal role, at best. Although a close relationship between exchangeable prior distributions and simple random sampling has been noted; how to formally integrate simple random sampling into the Bayesian paradigm is not clear. Recently it has been argued that the sampling design can be thought of as part of a Bayesian's prior distribution. We will show here that under this scenario simple random sample can be given a Bayesian justification in survey sampling.

  5. Source attribution of human campylobacteriosis at the point of exposure by combining comparative exposure assessment and subtype comparison based on comparative genomic fingerprinting.

    Directory of Open Access Journals (Sweden)

    André Ravel

    Full Text Available Human campylobacteriosis is a common zoonosis with a significant burden in many countries. Its prevention is difficult because humans can be exposed to Campylobacter through various exposures: foodborne, waterborne or by contact with animals. This study aimed at attributing campylobacteriosis to sources at the point of exposure. It combined comparative exposure assessment and microbial subtype comparison with subtypes defined by comparative genomic fingerprinting (CGF. It used isolates from clinical cases and from eight potential exposure sources (chicken, cattle and pig manure, retail chicken, beef, pork and turkey meat, and surface water collected within a single sentinel site of an integrated surveillance system for enteric pathogens in Canada. Overall, 1518 non-human isolates and 250 isolates from domestically-acquired human cases were subtyped and their subtype profiles analyzed for source attribution using two attribution models modified to include exposure. Exposure values were obtained from a concurrent comparative exposure assessment study undertaken in the same area. Based on CGF profiles, attribution was possible for 198 (79% human cases. Both models provide comparable figures: chicken meat was the most important source (65-69% of attributable cases whereas exposure to cattle (manure ranked second (14-19% of attributable cases, the other sources being minor (including beef meat. In comparison with other attributions conducted at the point of production, the study highlights the fact that Campylobacter transmission from cattle to humans is rarely meat borne, calling for a closer look at local transmission from cattle to prevent campylobacteriosis, in addition to increasing safety along the chicken supply chain.

  6. Source attribution of human campylobacteriosis at the point of exposure by combining comparative exposure assessment and subtype comparison based on comparative genomic fingerprinting.

    Science.gov (United States)

    Ravel, André; Hurst, Matt; Petrica, Nicoleta; David, Julie; Mutschall, Steven K; Pintar, Katarina; Taboada, Eduardo N; Pollari, Frank

    2017-01-01

    Human campylobacteriosis is a common zoonosis with a significant burden in many countries. Its prevention is difficult because humans can be exposed to Campylobacter through various exposures: foodborne, waterborne or by contact with animals. This study aimed at attributing campylobacteriosis to sources at the point of exposure. It combined comparative exposure assessment and microbial subtype comparison with subtypes defined by comparative genomic fingerprinting (CGF). It used isolates from clinical cases and from eight potential exposure sources (chicken, cattle and pig manure, retail chicken, beef, pork and turkey meat, and surface water) collected within a single sentinel site of an integrated surveillance system for enteric pathogens in Canada. Overall, 1518 non-human isolates and 250 isolates from domestically-acquired human cases were subtyped and their subtype profiles analyzed for source attribution using two attribution models modified to include exposure. Exposure values were obtained from a concurrent comparative exposure assessment study undertaken in the same area. Based on CGF profiles, attribution was possible for 198 (79%) human cases. Both models provide comparable figures: chicken meat was the most important source (65-69% of attributable cases) whereas exposure to cattle (manure) ranked second (14-19% of attributable cases), the other sources being minor (including beef meat). In comparison with other attributions conducted at the point of production, the study highlights the fact that Campylobacter transmission from cattle to humans is rarely meat borne, calling for a closer look at local transmission from cattle to prevent campylobacteriosis, in addition to increasing safety along the chicken supply chain.

  7. Hierarchical Bayesian Modeling of Fluid-Induced Seismicity

    Science.gov (United States)

    Broccardo, M.; Mignan, A.; Wiemer, S.; Stojadinovic, B.; Giardini, D.

    2017-11-01

    In this study, we present a Bayesian hierarchical framework to model fluid-induced seismicity. The framework is based on a nonhomogeneous Poisson process with a fluid-induced seismicity rate proportional to the rate of injected fluid. The fluid-induced seismicity rate model depends upon a set of physically meaningful parameters and has been validated for six fluid-induced case studies. In line with the vision of hierarchical Bayesian modeling, the rate parameters are considered as random variables. We develop both the Bayesian inference and updating rules, which are used to develop a probabilistic forecasting model. We tested the Basel 2006 fluid-induced seismic case study to prove that the hierarchical Bayesian model offers a suitable framework to coherently encode both epistemic uncertainty and aleatory variability. Moreover, it provides a robust and consistent short-term seismic forecasting model suitable for online risk quantification and mitigation.

  8. Distinct molecular subtypes of uterine leiomyosarcoma respond differently to chemotherapy treatment.

    Science.gov (United States)

    An, Yang; Wang, Shuzhen; Li, Songlin; Zhang, Lulu; Wang, Dayong; Wang, Haojie; Zhu, Shibai; Zhu, Wan; Li, Yongqiang; Chen, Wenwu; Ji, Shaoping; Guo, Xiangqian

    2017-09-11

    Uterine leiomyosarcoma (ULMS) is an aggressive form of soft tissue tumors. The molecular heterogeneity and pathogenesis of ULMS are not well understood. Expression profiling data were used to determine the possibility and optimal number of ULMS molecular subtypes. Next, clinicopathological characters and molecular pathways were analyzed in each subtype to prospect the clinical applications and progression mechanisms of ULMS. Two distinct molecular subtypes of ULMS were defined based on different gene expression signatures. Subtype I ULMS recapitulated low-grade ULMS, the gene expression pattern of which resembled normal smooth muscle cells, characterized by overexpression of smooth muscle function genes such as LMOD1, SLMAP, MYLK, MYH11. In contrast, subtype II ULMS recapitulated high-grade ULMS with higher tumor weight and invasion rate, and was characterized by overexpression of genes involved in the pathway of epithelial to mesenchymal transition and tumorigenesis, such as CDK6, MAPK13 and HOXA1. We identified two distinct molecular subtypes of ULMS responding differently to chemotherapy treatment. Our findings provide a better understanding of ULMS intrinsic molecular subtypes, and will potentially facilitate the development of subtype-specific diagnosis biomarkers and therapy strategies for these tumors.

  9. Difference of achalasia subtypes based on clinical symptoms, radiographic findings, and stasis scores

    Directory of Open Access Journals (Sweden)

    A. Meillier

    2018-01-01

    Conclusions: Achalasia subtypes had similar clinical symptoms, except for increased vomiting severity in subtype i. The maximum esophageal diameter in subtype ii was significantly greater than in subtype iii. Esophageal stasis scores were similar. Thus, high-resolution esophageal manometry remains essential in assessing achalasia subtypes.

  10. The Development of Bayesian Theory and Its Applications in Business and Bioinformatics

    Science.gov (United States)

    Zhang, Yifei

    2018-03-01

    Bayesian Theory originated from an Essay of a British mathematician named Thomas Bayes in 1763, and after its development in 20th century, Bayesian Statistics has been taking a significant part in statistical study of all fields. Due to the recent breakthrough of high-dimensional integral, Bayesian Statistics has been improved and perfected, and now it can be used to solve problems that Classical Statistics failed to solve. This paper summarizes Bayesian Statistics’ history, concepts and applications, which are illustrated in five parts: the history of Bayesian Statistics, the weakness of Classical Statistics, Bayesian Theory and its development and applications. The first two parts make a comparison between Bayesian Statistics and Classical Statistics in a macroscopic aspect. And the last three parts focus on Bayesian Theory in specific -- from introducing some particular Bayesian Statistics’ concepts to listing their development and finally their applications.

  11. Empirical Bayesian inference and model uncertainty

    International Nuclear Information System (INIS)

    Poern, K.

    1994-01-01

    This paper presents a hierarchical or multistage empirical Bayesian approach for the estimation of uncertainty concerning the intensity of a homogeneous Poisson process. A class of contaminated gamma distributions is considered to describe the uncertainty concerning the intensity. These distributions in turn are defined through a set of secondary parameters, the knowledge of which is also described and updated via Bayes formula. This two-stage Bayesian approach is an example where the modeling uncertainty is treated in a comprehensive way. Each contaminated gamma distributions, represented by a point in the 3D space of secondary parameters, can be considered as a specific model of the uncertainty about the Poisson intensity. Then, by the empirical Bayesian method each individual model is assigned a posterior probability

  12. Tubal ligation and risk of ovarian cancer subtypes

    DEFF Research Database (Denmark)

    Sieh, Weiva; Salvador, Shannon; McGuire, Valerie

    2013-01-01

    Tubal ligation is a protective factor for ovarian cancer, but it is unknown whether this protection extends to all invasive histological subtypes or borderline tumors. We undertook an international collaborative study to examine the association between tubal ligation and ovarian cancer subtypes....

  13. Subtyping pathological gamblers based on impulsivity, depression, and anxiety.

    Science.gov (United States)

    Ledgerwood, David M; Petry, Nancy M

    2010-12-01

    This study examined putative subtypes of pathological gamblers (PGs) based on the Pathways model, and it also evaluated whether the subtypes would benefit differentially from treatment. Treatment-seeking PGs (N = 229) were categorized into Pathways subtypes based on scores from questionnaires assessing anxiety, depression, and impulsivity. The Addiction Severity Index-Gambling assessed severity of gambling problems at baseline, posttreatment, and 12-month follow-up. Compared with behaviorally conditioned (BC) gamblers, emotionally vulnerable (EV) gamblers had higher psychiatric and gambling severity, and were more likely to have a parent with a psychiatric history. Antisocial impulsive (AI) gamblers also had elevated gambling and psychiatric severity relative to BC gamblers. They were more likely to have antisocial personality disorder and had the highest legal and family/social severity scores. They were also most likely to have a history of substance abuse treatment, history of inpatient psychiatric treatment, and a parent with a substance use or gambling problem. AI and EV gamblers experienced greater gambling severity throughout treatment than BC gamblers, but all three subtypes demonstrated similar patterns of treatment response. Thus, the three Pathways subtypes differ on some baseline characteristics, but subtyping did not predict treatment outcomes beyond a simple association with problem gambling severity. (PsycINFO Database Record (c) 2010 APA, all rights reserved).

  14. Diabetes and Breast Cancer Subtypes.

    Directory of Open Access Journals (Sweden)

    Heleen K Bronsveld

    Full Text Available Women with diabetes have a worse survival after breast cancer diagnosis compared to women without diabetes. This may be due to a different etiological profile, leading to the development of more aggressive breast cancer subtypes. Our aim was to investigate whether insulin and non-insulin treated women with diabetes develop specific clinicopathological breast cancer subtypes compared to women without diabetes.This cross-sectional study included randomly selected patients with invasive breast cancer diagnosed in 2000-2010. Stratified by age at breast cancer diagnosis (≤50 and >50 years, women with diabetes were 2:1 frequency-matched on year of birth and age at breast cancer diagnosis (both in 10-year categories to women without diabetes, to select ~300 patients with tumor tissue available. Tumor MicroArrays were stained by immunohistochemistry for estrogen and progesterone receptor (ER, PR, HER2, Ki67, CK5/6, CK14, and p63. A pathologist scored all stains and revised morphology and grade. Associations between diabetes/insulin treatment and clinicopathological subtypes were analyzed using multivariable logistic regression. Morphology and grade were not significantly different between women with diabetes (n = 211 and women without diabetes (n = 101, irrespective of menopausal status. Premenopausal women with diabetes tended to have more often PR-negative (OR = 2.44(95%CI:1.07-5.55, HER2-negative (OR = 2.84(95%CI:1.11-7.22, and basal-like (OR = 3.14(95%CI:1.03-9.60 tumors than the women without diabetes, with non-significantly increased frequencies of ER-negative (OR = 2.48(95%CI:0.95-6.45 and triple negative (OR = 2.60(95%CI:0.88-7.67 tumors. After adjustment for age and BMI, the associations remained similar in size but less significant. We observed no evidence for associations of clinicopathological subtypes with diabetes in postmenopausal women, or with insulin treatment in general.We found no compelling evidence that women with diabetes

  15. Diabetes and Breast Cancer Subtypes.

    Science.gov (United States)

    Bronsveld, Heleen K; Jensen, Vibeke; Vahl, Pernille; De Bruin, Marie L; Cornelissen, Sten; Sanders, Joyce; Auvinen, Anssi; Haukka, Jari; Andersen, Morten; Vestergaard, Peter; Schmidt, Marjanka K

    2017-01-01

    Women with diabetes have a worse survival after breast cancer diagnosis compared to women without diabetes. This may be due to a different etiological profile, leading to the development of more aggressive breast cancer subtypes. Our aim was to investigate whether insulin and non-insulin treated women with diabetes develop specific clinicopathological breast cancer subtypes compared to women without diabetes. This cross-sectional study included randomly selected patients with invasive breast cancer diagnosed in 2000-2010. Stratified by age at breast cancer diagnosis (≤50 and >50 years), women with diabetes were 2:1 frequency-matched on year of birth and age at breast cancer diagnosis (both in 10-year categories) to women without diabetes, to select ~300 patients with tumor tissue available. Tumor MicroArrays were stained by immunohistochemistry for estrogen and progesterone receptor (ER, PR), HER2, Ki67, CK5/6, CK14, and p63. A pathologist scored all stains and revised morphology and grade. Associations between diabetes/insulin treatment and clinicopathological subtypes were analyzed using multivariable logistic regression. Morphology and grade were not significantly different between women with diabetes (n = 211) and women without diabetes (n = 101), irrespective of menopausal status. Premenopausal women with diabetes tended to have more often PR-negative (OR = 2.44(95%CI:1.07-5.55)), HER2-negative (OR = 2.84(95%CI:1.11-7.22)), and basal-like (OR = 3.14(95%CI:1.03-9.60) tumors than the women without diabetes, with non-significantly increased frequencies of ER-negative (OR = 2.48(95%CI:0.95-6.45)) and triple negative (OR = 2.60(95%CI:0.88-7.67) tumors. After adjustment for age and BMI, the associations remained similar in size but less significant. We observed no evidence for associations of clinicopathological subtypes with diabetes in postmenopausal women, or with insulin treatment in general. We found no compelling evidence that women with diabetes, treated

  16. Advances in Bayesian Modeling in Educational Research

    Science.gov (United States)

    Levy, Roy

    2016-01-01

    In this article, I provide a conceptually oriented overview of Bayesian approaches to statistical inference and contrast them with frequentist approaches that currently dominate conventional practice in educational research. The features and advantages of Bayesian approaches are illustrated with examples spanning several statistical modeling…

  17. Bacterially-Associated Transcriptional Remodelling in a Distinct Genomic Subtype of Colorectal Cancer Provides a Plausible Molecular Basis for Disease Development.

    Directory of Open Access Journals (Sweden)

    Katie S Lennard

    Full Text Available The relevance of specific microbial colonisation to colorectal cancer (CRC disease pathogenesis is increasingly recognised, but our understanding of possible underlying molecular mechanisms that may link colonisation to disease in vivo remains limited. Here, we investigate the relationships between the most commonly studied CRC-associated bacteria (Enterotoxigenic Bacteroides fragilis, pks+ Escherichia coli, Fusobacterium spp., afaC+ E. coli, Enterococcus faecalis & Enteropathogenic E. coli and altered transcriptomic and methylation profiles of CRC patients, in order to gain insight into the potential contribution of these bacteria in the aetiopathogenesis of CRC. We show that colonisation by E. faecalis and high levels of Fusobacterium is associated with a specific transcriptomic subtype of CRC that is characterised by CpG island methylation, microsatellite instability and a significant increase in inflammatory and DNA damage pathways. Analysis of the significant, bacterially-associated changes in host gene expression, both at the level of individual genes as well as pathways, revealed a transcriptional remodeling that provides a plausible mechanistic link between specific bacterial colonisation and colorectal cancer disease development and progression in this subtype; these included upregulation of REG3A, REG1A and REG1P in the case of high-level colonization by Fusobacterium, and CXCL10 and BMI1 in the case of colonisation by E. faecalis. The enrichment of both E. faecalis and Fusobacterium in this CRC subtype suggests that polymicrobial colonisation of the colonic epithelium may well be an important aspect of colonic tumourigenesis.

  18. Subtype distribution of Blastocystis isolates from synanthropic and zoo animals and identification of a new subtype

    DEFF Research Database (Denmark)

    Stensvold, C. R.; Alfellani, M. A.; Nørskov-Lauritsen, S.

    2009-01-01

    Blastocystis isolates from 56 Danish synanthropic and zoo animals, 62 primates primarily from United Kingdom (UK) collections and 16 UK primate handlers were subtyped by PCR, sequencing and phylogenetic analysis. A new subtype (ST) from primates and artiodactyls was identified and designated...... infections from primates by their handlers had occurred in these cases. Data from published studies of non-human primates, other mammals and birds were collected and interpreted to generate a comprehensive overview on the ST distribution in such animals. On the basis of information on 438 samples...

  19. Objective Bayesianism and the Maximum Entropy Principle

    Directory of Open Access Journals (Sweden)

    Jon Williamson

    2013-09-01

    Full Text Available Objective Bayesian epistemology invokes three norms: the strengths of our beliefs should be probabilities; they should be calibrated to our evidence of physical probabilities; and they should otherwise equivocate sufficiently between the basic propositions that we can express. The three norms are sometimes explicated by appealing to the maximum entropy principle, which says that a belief function should be a probability function, from all those that are calibrated to evidence, that has maximum entropy. However, the three norms of objective Bayesianism are usually justified in different ways. In this paper, we show that the three norms can all be subsumed under a single justification in terms of minimising worst-case expected loss. This, in turn, is equivalent to maximising a generalised notion of entropy. We suggest that requiring language invariance, in addition to minimising worst-case expected loss, motivates maximisation of standard entropy as opposed to maximisation of other instances of generalised entropy. Our argument also provides a qualified justification for updating degrees of belief by Bayesian conditionalisation. However, conditional probabilities play a less central part in the objective Bayesian account than they do under the subjective view of Bayesianism, leading to a reduced role for Bayes’ Theorem.

  20. Classifying emotion in Twitter using Bayesian network

    Science.gov (United States)

    Surya Asriadie, Muhammad; Syahrul Mubarok, Mohamad; Adiwijaya

    2018-03-01

    Language is used to express not only facts, but also emotions. Emotions are noticeable from behavior up to the social media statuses written by a person. Analysis of emotions in a text is done in a variety of media such as Twitter. This paper studies classification of emotions on twitter using Bayesian network because of its ability to model uncertainty and relationships between features. The result is two models based on Bayesian network which are Full Bayesian Network (FBN) and Bayesian Network with Mood Indicator (BNM). FBN is a massive Bayesian network where each word is treated as a node. The study shows the method used to train FBN is not very effective to create the best model and performs worse compared to Naive Bayes. F1-score for FBN is 53.71%, while for Naive Bayes is 54.07%. BNM is proposed as an alternative method which is based on the improvement of Multinomial Naive Bayes and has much lower computational complexity compared to FBN. Even though it’s not better compared to FBN, the resulting model successfully improves the performance of Multinomial Naive Bayes. F1-Score for Multinomial Naive Bayes model is 51.49%, while for BNM is 52.14%.

  1. Probability biases as Bayesian inference

    Directory of Open Access Journals (Sweden)

    Andre; C. R. Martins

    2006-11-01

    Full Text Available In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them. Previous results show that the weight functions and the observed violations of coalescing and stochastic dominance can be understood from a Bayesian point of view. We will review those results and see that Bayesian methods should also be used as part of the explanation behind other known biases. That means that, although the observed errors are still errors under the be understood as adaptations to the solution of real life problems. Heuristics that allow fast evaluations and mimic a Bayesian inference would be an evolutionary advantage, since they would give us an efficient way of making decisions. %XX In that sense, it should be no surprise that humans reason with % probability as it has been observed.

  2. Is PIGD a legitimate motor subtype in Parkinson disease?

    Science.gov (United States)

    Kotagal, Vikas

    2016-06-01

    Parkinson disease is a chronic progressive syndrome with a broad array of clinical features. Different investigators have suggested the heterogeneous motor manifestations of early Parkinson disease can be conceptualized through a taxonomy of clinical subtypes including tremor-predominant and postural instability and gait difficulty-predominant subtypes. Although it is theoretically valuable to distinguish subtypes of Parkinson disease, the reality is that few patients fit these discrete categories well and many transition from exhibiting elements of one subtype to elements of another. In the time since the initial description of the postural instability and gait difficulty-predominant subtype, Parkinson disease clinical research has blossomed in many ways - including an increased emphasis on the role of medical comorbidities and extranigral pathologies in Parkinson disease as markers of prognostic significance. By conceptualizing the pathogenesis of an expansive disease process in the limited terms of categorical motor subtypes, we run the risk of overlooking or misclassifying clinically significant pathogenic risk factors that lead to the development of motor milestones such as falls and related axial motor disability. Given its critical influence on quality of life and overall prognosis, we are in need of a model of postural instability and gait difficulty-predominant features in Parkinson disease that emphasizes the overlooked pathological influence of aging and medical comorbidities on the development of axial motor burden and postural instability and gait difficulty-predominant features. This Point of View proposes thinking of postural instability and gait difficulties in Parkinson disease not as a discrete subtype, but rather as multidimensional continuum influenced by several overlapping age-related pathologies.

  3. Racial difference in histologic subtype of renal cell carcinoma

    International Nuclear Information System (INIS)

    Olshan, Andrew F; Kuo, Tzy-Mey; Meyer, Anne-Marie; Nielsen, Matthew E; Purdue, Mark P; Rathmell, W Kimryn

    2013-01-01

    In the United States, renal cell carcinoma (RCC) has rapidly increased in incidence for over two decades. The most common histologic subtypes of RCC, clear cell, papillary, and chromophobe have distinct genetic and clinical characteristics; however, epidemiologic features of these subtypes have not been well characterized, particularly regarding any associations between race, disease subtypes, and recent incidence trends. Using data from the Surveillance, Epidemiology, and End Results (SEER) Program, we examined differences in the age-adjusted incidence rates and trends of RCC subtypes, including analysis focusing on racial differences. Incidence rates increased over time (2001–2009) for all three subtypes. However, the proportion of white cases with clear cell histology was higher than among blacks (50% vs. 31%, respectively), whereas black cases were more likely than white cases to have papillary RCC (23% vs. 9%, respectively). Moreover, papillary RCC incidence increased more rapidly for blacks than whites (P < 0.01) over this period. We also observed that increased incidence of papillary histology among blacks is not limited to the smallest size strata. We observed racial differences in proportionate incidence of RCC subtypes, which appear to be increasing over time; this novel finding motivates further etiologic, clinical, molecular, and genetic studies. Using national data, we observed a higher proportion of black renal cell carcinoma (RCC) cases with papillary histology compared to Caucasian cases. We also observed time trends in black-white incidence differences in histologic RCC subtypes, with rapid increases in the disproportionate share of black cases with papillary histology

  4. Bayesian data analysis in population ecology: motivations, methods, and benefits

    Science.gov (United States)

    Dorazio, Robert

    2016-01-01

    During the 20th century ecologists largely relied on the frequentist system of inference for the analysis of their data. However, in the past few decades ecologists have become increasingly interested in the use of Bayesian methods of data analysis. In this article I provide guidance to ecologists who would like to decide whether Bayesian methods can be used to improve their conclusions and predictions. I begin by providing a concise summary of Bayesian methods of analysis, including a comparison of differences between Bayesian and frequentist approaches to inference when using hierarchical models. Next I provide a list of problems where Bayesian methods of analysis may arguably be preferred over frequentist methods. These problems are usually encountered in analyses based on hierarchical models of data. I describe the essentials required for applying modern methods of Bayesian computation, and I use real-world examples to illustrate these methods. I conclude by summarizing what I perceive to be the main strengths and weaknesses of using Bayesian methods to solve ecological inference problems.

  5. Genetic recombination and Cryptosporidium hominis virulent subtype IbA10G2.

    Science.gov (United States)

    Li, Na; Xiao, Lihua; Cama, Vitaliano A; Ortega, Ynes; Gilman, Robert H; Guo, Meijin; Feng, Yaoyu

    2013-10-01

    Little is known about the emergence and spread of virulent subtypes of Cryptosporidium hominis, the predominant species responsible for human cryptosporidiosis. We conducted sequence analyses of 32 genetic loci of 53 C. hominis specimens isolated from a longitudinally followed cohort of children living in a small community. We identified by linkage disequilibrium and recombination analyses only limited genetic recombination, which occurred exclusively within the 60-kDa glycoprotein gene subtype IbA10G2, a predominant subtype for outbreaks in industrialized nations and a virulent subtype in the study community. Intensive transmission of virulent subtype IbA10G2 in the study area might have resulted in genetic recombination with other subtypes. Moreover, we identified selection for IbA10G2 at a 129-kb region around the 60-kDa glycoprotein gene in chromosome 6. These findings improve our understanding of the origin and evolution of C. hominis subtypes and the spread of virulent subtypes.

  6. Bayesian psychometric scaling

    NARCIS (Netherlands)

    Fox, Gerardus J.A.; van den Berg, Stéphanie Martine; Veldkamp, Bernard P.; Irwing, P.; Booth, T.; Hughes, D.

    2015-01-01

    In educational and psychological studies, psychometric methods are involved in the measurement of constructs, and in constructing and validating measurement instruments. Assessment results are typically used to measure student proficiency levels and test characteristics. Recently, Bayesian item

  7. Diagnosis and subtypes of adolescent antisocial personality disorder.

    Science.gov (United States)

    Jones, Meredith; Westen, Drew

    2010-04-01

    The present study examined the application of the Antisocial Personality Disorder (APD) diagnosis to adolescents and investigated the possibility of subtypes of APD adolescents. As part of a broader study of adolescent personality in clinically-referred patients, experienced clinicians provided personality data on a randomly selected patient in their care using the SWAP-II-A personality pathology instrument. Three hundred thirteen adolescents met adult DSM-IV diagnostic criteria for APD. To characterize adolescents with the disorder, we aggregated the data to identify the items most descriptive and distinctive of APD adolescents relative to other teenagers in the sample (N = 950). Q-factor analysis identified five personality subtypes: psychopathic-like, socially withdrawn, impulsive-histrionic, emotionally dysregulated, and attentionally dysregulated. The five subtypes differed in predictable ways on a set of external criteria related to global adaptive functioning, childhood family environment, and family history of psychiatric illness. Both the APD diagnosis and the empirically derived APD subtypes provided incremental validity over and above the DSM-IV disruptive behavior disorders in predicting global adaptive functioning, number of arrests, early-onset severe externalizing pathology, and quality of peer relationships. Although preliminary, these results provide support for the use of both APD and personality-based subtyping systems in adolescents.

  8. Learning Bayesian Networks with Incomplete Data by Augmentation

    OpenAIRE

    Adel, Tameem; de Campos, Cassio P.

    2016-01-01

    We present new algorithms for learning Bayesian networks from data with missing values using a data augmentation approach. An exact Bayesian network learning algorithm is obtained by recasting the problem into a standard Bayesian network learning problem without missing data. To the best of our knowledge, this is the first exact algorithm for this problem. As expected, the exact algorithm does not scale to large domains. We build on the exact method to create an approximate algorithm using a ...

  9. Precise subtyping for synchronous multiparty sessions

    Directory of Open Access Journals (Sweden)

    Mariangiola Dezani-Ciancaglini

    2016-02-01

    Full Text Available The notion of subtyping has gained an important role both in theoretical and applicative domains: in lambda and concurrent calculi as well as in programming languages. The soundness and the completeness, together referred to as the preciseness of subtyping, can be considered from two different points of view: operational and denotational. The former preciseness has been recently developed with respect to type safety, i.e. the safe replacement of a term of a smaller type when a term of a bigger type is expected. The latter preciseness is based on the denotation of a type which is a mathematical object that describes the meaning of the type in accordance with the denotations of other expressions from the language. The result of this paper is the operational and denotational preciseness of the subtyping for a synchronous multiparty session calculus. The novelty of this paper is the introduction of characteristic global types to prove the operational completeness.

  10. Bayesian optimization for computationally extensive probability distributions.

    Science.gov (United States)

    Tamura, Ryo; Hukushima, Koji

    2018-01-01

    An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique. A key idea of the proposed method is to use extreme values of acquisition functions by Gaussian processes for the next training phase, which should be located near a local maximum or a global maximum of the probability distribution. Our Bayesian optimization technique is applied to the posterior distribution in the effective physical model estimation, which is a computationally extensive probability distribution. Even when the number of sampling points on the posterior distributions is fixed to be small, the Bayesian optimization provides a better maximizer of the posterior distributions in comparison to those by the random search method, the steepest descent method, or the Monte Carlo method. Furthermore, the Bayesian optimization improves the results efficiently by combining the steepest descent method and thus it is a powerful tool to search for a better maximizer of computationally extensive probability distributions.

  11. An Intuitive Dashboard for Bayesian Network Inference

    International Nuclear Information System (INIS)

    Reddy, Vikas; Farr, Anna Charisse; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K D V

    2014-01-01

    Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++

  12. An Intuitive Dashboard for Bayesian Network Inference

    Science.gov (United States)

    Reddy, Vikas; Charisse Farr, Anna; Wu, Paul; Mengersen, Kerrie; Yarlagadda, Prasad K. D. V.

    2014-03-01

    Current Bayesian network software packages provide good graphical interface for users who design and develop Bayesian networks for various applications. However, the intended end-users of these networks may not necessarily find such an interface appealing and at times it could be overwhelming, particularly when the number of nodes in the network is large. To circumvent this problem, this paper presents an intuitive dashboard, which provides an additional layer of abstraction, enabling the end-users to easily perform inferences over the Bayesian networks. Unlike most software packages, which display the nodes and arcs of the network, the developed tool organises the nodes based on the cause-and-effect relationship, making the user-interaction more intuitive and friendly. In addition to performing various types of inferences, the users can conveniently use the tool to verify the behaviour of the developed Bayesian network. The tool has been developed using QT and SMILE libraries in C++.

  13. Bayesian disease mapping: hierarchical modeling in spatial epidemiology

    National Research Council Canada - National Science Library

    Lawson, Andrew

    2013-01-01

    .... Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications...

  14. Distribution And Clinicopathological Features Of Breast Cancer Histological Subtypes In Latvia

    Directory of Open Access Journals (Sweden)

    Srebnijs Andrejs

    2015-04-01

    Full Text Available Breast cancer is a heterogenous disease. It consists of several histological subtypes that can be separated by morphology and immunohistochemistry. The aim of our study was to determine the distribution of breast cancer histological and molecular subtypes, and their relationship with clinical and pathological characteristics. A total of 561 patients who underwent breast carcinoma surgical treatment from January 2003 till December 2012 were enrolled in the study. In total, invasive ductal carcinomas not otherwise specified (IDC-NOS plus invasive ductal carcinomas no special type (IDC-NST were observed in 430 patients (76.65% of cases, medullar carcinoma in 14 patients (2.45%, other rare ductal carcinoma subtypes in 13 patients (2.31%, lobular carcinoma in 81 patients (14.4% and tubulolobular carcinoma in 23 patients (4.19%. Ductal carcinoma, lobular and tubulolobular carcinoma had predominantly luminal A and B subtype, whereas medullar carcinoma had HER2-positive and triple-negative (TN subtype. Tubular, cribriform, mucinous, papillary, and apocrine carcinomas had predominantly luminal A subtype. Significant differences between breast cancer histological subtypes and clinicopathological characteristics were observed. Our study for the first time reported the distribution and characteristics of breast cancer histological subtypes in Latvian women and relationship to clinical and tumour histopathological characteristics.

  15. The validity and utility of subtyping bulimia nervosa

    NARCIS (Netherlands)

    van Hoeken, Daphne; Veling, Wim; Sinke, Sjoukje; Mitchell, James E.; Hoek, Hans W.

    Objective: To review the evidence for the validity and utility of subtyping bulimia nervosa (BN) into a purging (BN-P) and a nonpurging subtype (BN-NP), and of distinguishing BN-NP from binge eating disorder (BED), by comparing course, complications, and treatment. Method: A literature search of

  16. The validity and utility of subtyping bulimia nervosa

    NARCIS (Netherlands)

    van Hoeken, Daphne; Veling, Wim; Sinke, Sjoukje; Mitchell, James E.; Hoek, Hans W.

    2009-01-01

    Objective: To review the evidence for the validity and utility of subtyping bulimia nervosa (BN) into a purging (BN-P) and a nonpurging subtype (BN-NP), and of distinguishing BN-NP from binge eating disorder (BED), by comparing course, complications, and treatment. Method: A literature search of

  17. Using Bayesian Networks to Improve Knowledge Assessment

    Science.gov (United States)

    Millan, Eva; Descalco, Luis; Castillo, Gladys; Oliveira, Paula; Diogo, Sandra

    2013-01-01

    In this paper, we describe the integration and evaluation of an existing generic Bayesian student model (GBSM) into an existing computerized testing system within the Mathematics Education Project (PmatE--Projecto Matematica Ensino) of the University of Aveiro. This generic Bayesian student model had been previously evaluated with simulated…

  18. Learning dynamic Bayesian networks with mixed variables

    DEFF Research Database (Denmark)

    Bøttcher, Susanne Gammelgaard

    This paper considers dynamic Bayesian networks for discrete and continuous variables. We only treat the case, where the distribution of the variables is conditional Gaussian. We show how to learn the parameters and structure of a dynamic Bayesian network and also how the Markov order can be learned...

  19. Bayesian Statistics: Concepts and Applications in Animal Breeding – A Review

    Directory of Open Access Journals (Sweden)

    Lsxmikant-Sambhaji Kokate

    2011-07-01

    Full Text Available Statistics uses two major approaches- conventional (or frequentist and Bayesian approach. Bayesian approach provides a complete paradigm for both statistical inference and decision making under uncertainty. Bayesian methods solve many of the difficulties faced by conventional statistical methods, and extend the applicability of statistical methods. It exploits the use of probabilistic models to formulate scientific problems. To use Bayesian statistics, there is computational difficulty and secondly, Bayesian methods require specifying prior probability distributions. Markov Chain Monte-Carlo (MCMC methods were applied to overcome the computational difficulty, and interest in Bayesian methods was renewed. In Bayesian statistics, Bayesian structural equation model (SEM is used. It provides a powerful and flexible approach for studying quantitative traits for wide spectrum problems and thus it has no operational difficulties, with the exception of some complex cases. In this method, the problems are solved at ease, and the statisticians feel it comfortable with the particular way of expressing the results and employing the software available to analyze a large variety of problems.

  20. Cardiac potassium channel subtypes

    DEFF Research Database (Denmark)

    Schmitt, Nicole; Grunnet, Morten; Olesen, Søren-Peter

    2014-01-01

    About 10 distinct potassium channels in the heart are involved in shaping the action potential. Some of the K(+) channels are primarily responsible for early repolarization, whereas others drive late repolarization and still others are open throughout the cardiac cycle. Three main K(+) channels...... drive the late repolarization of the ventricle with some redundancy, and in atria this repolarization reserve is supplemented by the fairly atrial-specific KV1.5, Kir3, KCa, and K2P channels. The role of the latter two subtypes in atria is currently being clarified, and several findings indicate...... that they could constitute targets for new pharmacological treatment of atrial fibrillation. The interplay between the different K(+) channel subtypes in both atria and ventricle is dynamic, and a significant up- and downregulation occurs in disease states such as atrial fibrillation or heart failure...

  1. Bayesian non- and semi-parametric methods and applications

    CERN Document Server

    Rossi, Peter

    2014-01-01

    This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics and marketing applications involve arbitrary distributional assumptions. As more data becomes available, a natural desire to provide methods that relax these assumptions arises. Peter Rossi advocates a Bayesian approach in which specific distributional assumptions are replaced with more flexible distributions based on mixtures of normals. The Bayesian approach can use either a large but fixed number

  2. Non-linear Bayesian update of PCE coefficients

    KAUST Repository

    Litvinenko, Alexander

    2014-01-06

    Given: a physical system modeled by a PDE or ODE with uncertain coefficient q(?), a measurement operator Y (u(q), q), where u(q, ?) uncertain solution. Aim: to identify q(?). The mapping from parameters to observations is usually not invertible, hence this inverse identification problem is generally ill-posed. To identify q(!) we derived non-linear Bayesian update from the variational problem associated with conditional expectation. To reduce cost of the Bayesian update we offer a unctional approximation, e.g. polynomial chaos expansion (PCE). New: We apply Bayesian update to the PCE coefficients of the random coefficient q(?) (not to the probability density function of q).

  3. Non-linear Bayesian update of PCE coefficients

    KAUST Repository

    Litvinenko, Alexander; Matthies, Hermann G.; Pojonk, Oliver; Rosic, Bojana V.; Zander, Elmar

    2014-01-01

    Given: a physical system modeled by a PDE or ODE with uncertain coefficient q(?), a measurement operator Y (u(q), q), where u(q, ?) uncertain solution. Aim: to identify q(?). The mapping from parameters to observations is usually not invertible, hence this inverse identification problem is generally ill-posed. To identify q(!) we derived non-linear Bayesian update from the variational problem associated with conditional expectation. To reduce cost of the Bayesian update we offer a unctional approximation, e.g. polynomial chaos expansion (PCE). New: We apply Bayesian update to the PCE coefficients of the random coefficient q(?) (not to the probability density function of q).

  4. Therapeutic response to benzodiazepine in panic disorder subtypes

    Directory of Open Access Journals (Sweden)

    Alexandre Martins Valença

    Full Text Available CONTEXT: This study makes a comparison between two subtypes of panic disorder regarding the clinical efficacy of clonazepam, a benzodiazepine. OBJECTIVES: To evaluate the clinical efficacy of clonazepam in a fixed dosage (2 mg/day, compared to placebo, in the treatment of panic disorder patients and to verify whether there are any differences in the responses to clonazepam between panic disorder patients with the respiratory and non-respiratory subtypes. TYPE OF STUDY: Randomized study with clonazepam and placebo. SETTING: Outpatient Anxiety and Depression Unit of the Institute of Psychiatry, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil. PARTICIPANTS: 34 patients with a diagnosis of panic disorder with agoraphobia, between 18 and 55 years old. PROCEDURES: Administration of clonazepam or placebo for 6 weeks, in panic disorder patients, after they were classified within two subtypes of panic disorder: respiratory and non-respiratory. MAIN MEASUREMENTS: Changes in the number of panic attacks in comparison with the period before the beginning of the study; Hamilton Anxiety Scale; Global Clinical Impression Scale; and Patient's Global Impression scale. RESULTS: In the group that received clonazepam, by the end of the 6th week there was a statistically significant clinical improvement, shown by the remission of panic attacks (p < 0.001 and decrease in anxiety (p = 0.024. In the group that received clonazepam there was no significant difference between the respiratory and non-respiratory subtypes of panic disorder, regarding the therapeutic response to clonazepam. CONCLUSION: Clonazepam was equally effective in the treatment of the respiratory and non-respiratory subtypes of panic disorder, suggesting there is no difference in the therapeutic response between the two subtypes.

  5. A Fast Iterative Bayesian Inference Algorithm for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri

    2013-01-01

    representation of the Bessel K probability density function; a highly efficient, fast iterative Bayesian inference method is then applied to the proposed model. The resulting estimator outperforms other state-of-the-art Bayesian and non-Bayesian estimators, either by yielding lower mean squared estimation error...

  6. A Gentle Introduction to Bayesian Analysis : Applications to Developmental Research

    NARCIS (Netherlands)

    Van de Schoot, Rens; Kaplan, David; Denissen, Jaap; Asendorpf, Jens B.; Neyer, Franz J.; van Aken, Marcel A G

    2014-01-01

    Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,

  7. A gentle introduction to Bayesian analysis : Applications to developmental research

    NARCIS (Netherlands)

    van de Schoot, R.; Kaplan, D.; Denissen, J.J.A.; Asendorpf, J.B.; Neyer, F.J.; van Aken, M.A.G.

    2014-01-01

    Bayesian statistical methods are becoming ever more popular in applied and fundamental research. In this study a gentle introduction to Bayesian analysis is provided. It is shown under what circumstances it is attractive to use Bayesian estimation, and how to interpret properly the results. First,

  8. Molecular and epidemiological characterization of HIV-1 subtypes among Libyan patients.

    Science.gov (United States)

    Daw, Mohamed A; El-Bouzedi, Abdallah; Ahmed, Mohamed O; Dau, Aghnyia A

    2017-04-28

    The epidemiological and clinical aspects of human immunodeficiency virus subtypes are of great interest worldwide. These subtypes are rarely studied in North African countries. Libya is a large country with the longest coast on the Mediterranean Sea, facing the Southern European countries. Studies on the characterization of HIV-1 subtypes are limited in Libya. This study aimed to determine the magnitude of the HIV problem among the Libyan population and to better understand the genetic diversity and the epidemiologic dynamics of HIV 1, as well as to correlate that with the risk factors involved. A total of 159 HIV-1 strains were collected from 814 HIV positive patients from the four Libyan regions during a 16-year period (1995-2010). To determine the HIV-1 subtypes, genetic analysis and molecular sequencing were carried out using provirus polygene. Epidemiologic and demographic information was obtained from each participant and correlated with HIV-1 subtypes using logistic regression. The overall prevalence of HIV among Libyans ranged from 5 to 10 per 100,000 during the study period. It was higher among intravenous drug users (IVDUs) (53.9%), blood recipients (25.9%) and heterosexuals (17.6%) than by vertical transmission (2.6%). Prevalence was higher among males aged 20-40 years (M:F 1:6, P > 0.001). Among the 159 strains of HIV-1 available for typing, 117 strains (73.6%) were subtype B, 29 (18.2%) were CRF02_AG, and 13 (8.2%) were subtype A. HIV-1 subtype B was the most prevalent all over the country, and it was more prevalent in the Northern region, particularly among IVDUs (P HIV-1 infection is emerging in Libya with a shifting prevalence of subtypes associated with the changing epidemiology of HIV-1 among risk groups. A genetic analysis of HIV-1 strains demonstrated low subtype heterogeneity with the evolution of subtype B, and CRF_20 AG, as well as HIV-1 subtype A. Our study highlights the importance of expanded surveillance programs to control HIV

  9. Progranulin expression in breast cancer with different intrinsic subtypes.

    Science.gov (United States)

    Li, Li Qin; Min, Li Shan; Jiang, Qun; Ping, Jin Liang; Li, Jing; Dai, Li Cheng

    2012-04-15

    Progranulin is a newly discovered 88-kDa glycoprotein originally purified from the highly tumorigenic mouse teratoma-derived cell line PC. We found that high progranulin expression was associated with higher breast carcinoma angiogenesis, reflected by increased vascular endothelial growth factor expression and higher microvessel density. However, no immunohistochemical evidence currently exists to correlate progranulin expression with clinicopathological features in different intrinsic subtypes of breast carcinoma biopsies. The aim of this study was to investigate the progranulin expression profiles in the intrinsic subtypes of breast carcinomas and their relevance to histopathological and clinicopathological features. Tissue blocks containing 264 cases of breast carcinomas from 2006 to 2009 were classified as different intrinsic subtypes. Tissues of four intrinsic subtypes were immunostained for progranulin, vascular endothelial growth factor and CD105. Their relevance to histopathological and clinicopathological features was also analyzed. Twenty tissue samples from breast fibroadenomas were included in this study. Progranulin expression showed no significant differences in different intrinsic subtypes, although an increasing tendency could be found in the triple-negative breast cancer (TNBC) subgroup (χ(2)=5.00, df=3, p=0.17). However, differences were significant when pathologically node metastasis-positive (pN(+)) TNBC were excluded (χ(2)=17.84, df=3, pprogranulin in pathologically node metastasis-negative (pN(-)) TNBC. It was noted that the EGFR expression level of the pN(-) TNBC subtype was significantly higher in cases with strong progranulin expression than in cases with weak progranulin expression (χ(2)=11.26, df=1, pprogranulin in pN(-) TNBC suggests that progranulin is a promising new target for pN(-) TNBC treatment. Strong expression of progranulin correlates with positive EGFR expression in the pN(-) TNBC subtype. The close relationship between

  10. A nonparametric Bayesian approach for genetic evaluation in ...

    African Journals Online (AJOL)

    South African Journal of Animal Science ... the Bayesian and Classical models, a Bayesian procedure is provided which allows these random ... data from the Elsenburg Dormer sheep stud and data from a simulation experiment are utilized. >

  11. 3D Bayesian contextual classifiers

    DEFF Research Database (Denmark)

    Larsen, Rasmus

    2000-01-01

    We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours.......We extend a series of multivariate Bayesian 2-D contextual classifiers to 3-D by specifying a simultaneous Gaussian distribution for the feature vectors as well as a prior distribution of the class variables of a pixel and its 6 nearest 3-D neighbours....

  12. Bayesian probability theory and inverse problems

    International Nuclear Information System (INIS)

    Kopec, S.

    1994-01-01

    Bayesian probability theory is applied to approximate solving of the inverse problems. In order to solve the moment problem with the noisy data, the entropic prior is used. The expressions for the solution and its error bounds are presented. When the noise level tends to zero, the Bayesian solution tends to the classic maximum entropy solution in the L 2 norm. The way of using spline prior is also shown. (author)

  13. Variations on Bayesian Prediction and Inference

    Science.gov (United States)

    2016-05-09

    inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

  14. Bayesian inference for psychology. Part II: Example applications with JASP.

    Science.gov (United States)

    Wagenmakers, Eric-Jan; Love, Jonathon; Marsman, Maarten; Jamil, Tahira; Ly, Alexander; Verhagen, Josine; Selker, Ravi; Gronau, Quentin F; Dropmann, Damian; Boutin, Bruno; Meerhoff, Frans; Knight, Patrick; Raj, Akash; van Kesteren, Erik-Jan; van Doorn, Johnny; Šmíra, Martin; Epskamp, Sacha; Etz, Alexander; Matzke, Dora; de Jong, Tim; van den Bergh, Don; Sarafoglou, Alexandra; Steingroever, Helen; Derks, Koen; Rouder, Jeffrey N; Morey, Richard D

    2018-02-01

    Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely. An important impediment to the widespread adoption of Bayesian tests is arguably the lack of user-friendly software for the run-of-the-mill statistical problems that confront psychologists for the analysis of almost every experiment: the t-test, ANOVA, correlation, regression, and contingency tables. In Part II of this series we introduce JASP ( http://www.jasp-stats.org ), an open-source, cross-platform, user-friendly graphical software package that allows users to carry out Bayesian hypothesis tests for standard statistical problems. JASP is based in part on the Bayesian analyses implemented in Morey and Rouder's BayesFactor package for R. Armed with JASP, the practical advantages of Bayesian hypothesis testing are only a mouse click away.

  15. Competing endogenous RNA network analysis identifies critical genes among the different breast cancer subtypes.

    Science.gov (United States)

    Chen, Juan; Xu, Juan; Li, Yongsheng; Zhang, Jinwen; Chen, Hong; Lu, Jianping; Wang, Zishan; Zhao, Xueying; Xu, Kang; Li, Yixue; Li, Xia; Zhang, Yan

    2017-02-07

    Although competing endogenous RNAs (ceRNAs) have been implicated in many solid tumors, their roles in breast cancer subtypes are not well understood. We therefore generated a ceRNA network for each subtype based on the significance of both, positive co-expression and the shared miRNAs, in the corresponding subtype miRNA dys-regulatory network, which was constructed based on negative regulations between differentially expressed miRNAs and targets. All four subtype ceRNA networks exhibited scale-free architecture and showed that the common ceRNAs were at the core of the networks. Furthermore, the common ceRNA hubs had greater connectivity than the subtype-specific hubs. Functional analysis of the common subtype ceRNA hubs highlighted factors involved in proliferation, MAPK signaling pathways and tube morphogenesis. Subtype-specific ceRNA hubs highlighted unique subtype-specific pathways, like the estrogen response and inflammatory pathways in the luminal subtypes or the factors involved in the coagulation process that participates in the basal-like subtype. Ultimately, we identified 29 critical subtype-specific ceRNA hubs that characterized the different breast cancer subtypes. Our study thus provides new insight into the common and specific subtype ceRNA interactions that define the different categories of breast cancer and enhances our understanding of the pathology underlying the different breast cancer subtypes, which can have prognostic and therapeutic implications in the future.

  16. Improving Transparency and Replication in Bayesian Statistics : The WAMBS-Checklist

    NARCIS (Netherlands)

    Depaoli, Sarah; van de Schoot, Rens

    2017-01-01

    Bayesian statistical methods are slowly creeping into all fields of science and are becoming ever more popular in applied research. Although it is very attractive to use Bayesian statistics, our personal experience has led us to believe that naively applying Bayesian methods can be dangerous for at

  17. An introduction to using Bayesian linear regression with clinical data.

    Science.gov (United States)

    Baldwin, Scott A; Larson, Michael J

    2017-11-01

    Statistical training psychology focuses on frequentist methods. Bayesian methods are an alternative to standard frequentist methods. This article provides researchers with an introduction to fundamental ideas in Bayesian modeling. We use data from an electroencephalogram (EEG) and anxiety study to illustrate Bayesian models. Specifically, the models examine the relationship between error-related negativity (ERN), a particular event-related potential, and trait anxiety. Methodological topics covered include: how to set up a regression model in a Bayesian framework, specifying priors, examining convergence of the model, visualizing and interpreting posterior distributions, interval estimates, expected and predicted values, and model comparison tools. We also discuss situations where Bayesian methods can outperform frequentist methods as well has how to specify more complicated regression models. Finally, we conclude with recommendations about reporting guidelines for those using Bayesian methods in their own research. We provide data and R code for replicating our analyses. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Bayesian ARTMAP for regression.

    Science.gov (United States)

    Sasu, L M; Andonie, R

    2013-10-01

    Bayesian ARTMAP (BA) is a recently introduced neural architecture which uses a combination of Fuzzy ARTMAP competitive learning and Bayesian learning. Training is generally performed online, in a single-epoch. During training, BA creates input data clusters as Gaussian categories, and also infers the conditional probabilities between input patterns and categories, and between categories and classes. During prediction, BA uses Bayesian posterior probability estimation. So far, BA was used only for classification. The goal of this paper is to analyze the efficiency of BA for regression problems. Our contributions are: (i) we generalize the BA algorithm using the clustering functionality of both ART modules, and name it BA for Regression (BAR); (ii) we prove that BAR is a universal approximator with the best approximation property. In other words, BAR approximates arbitrarily well any continuous function (universal approximation) and, for every given continuous function, there is one in the set of BAR approximators situated at minimum distance (best approximation); (iii) we experimentally compare the online trained BAR with several neural models, on the following standard regression benchmarks: CPU Computer Hardware, Boston Housing, Wisconsin Breast Cancer, and Communities and Crime. Our results show that BAR is an appropriate tool for regression tasks, both for theoretical and practical reasons. Copyright © 2013 Elsevier Ltd. All rights reserved.

  19. Using Bayesian belief networks in adaptive management.

    Science.gov (United States)

    J.B. Nyberg; B.G. Marcot; R. Sulyma

    2006-01-01

    Bayesian belief and decision networks are relatively new modeling methods that are especially well suited to adaptive-management applications, but they appear not to have been widely used in adaptive management to date. Bayesian belief networks (BBNs) can serve many purposes for practioners of adaptive management, from illustrating system relations conceptually to...

  20. Functional characteristics of HIV-1 subtype C compatible with increased heterosexual transmissibility

    DEFF Research Database (Denmark)

    Walter, Brandon L; Armitage, Andrew E; Graham, Stephen C

    2009-01-01

    BACKGROUND: Despite the existence of over 50 subtypes and circulating recombinant forms of HIV-1, subtype C dominates the heterosexual pandemic causing approximately 56% of all infections. OBJECTIVE: To evaluate whether viral genetic factors may contribute to the observed subtype-C predominance. ....... CONCLUSION: As CD4-CCR5-T cells are key targets for genital HIV infection and cervical selection can favor compact V1-V2 loops and 316T, which increase viral infectivity, we propose that these conserved subtype-C motifs may contribute to transmission and spread of this subtype....

  1. Doing bayesian data analysis a tutorial with R and BUGS

    CERN Document Server

    Kruschke, John K

    2011-01-01

    There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis obtainable to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS provides an accessible approach to Bayesian data analysis, as material is explained clearly with concrete examples. The book begins with the basics, including essential concepts of probability and random sampling, and gradually progresses to advanced hierarchical modeling methods for realistic data. The text delivers comprehensive coverage of all

  2. Bayesian estimation of dose rate effectiveness

    International Nuclear Information System (INIS)

    Arnish, J.J.; Groer, P.G.

    2000-01-01

    A Bayesian statistical method was used to quantify the effectiveness of high dose rate 137 Cs gamma radiation at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice. The Bayesian approach considers both the temporal and dose dependence of radiation carcinogenesis and total mortality. This paper provides the first direct estimation of dose rate effectiveness using Bayesian statistics. This statistical approach provides a quantitative description of the uncertainty of the factor characterising the dose rate in terms of a probability density function. The results show that a fixed dose from 137 Cs gamma radiation delivered at a high dose rate is more effective at inducing fatal mammary tumours and increasing the overall mortality rate in BALB/c female mice than the same dose delivered at a low dose rate. (author)

  3. BATSE gamma-ray burst line search. 2: Bayesian consistency methodology

    Science.gov (United States)

    Band, D. L.; Ford, L. A.; Matteson, J. L.; Briggs, M.; Paciesas, W.; Pendleton, G.; Preece, R.; Palmer, D.; Teegarden, B.; Schaefer, B.

    1994-01-01

    We describe a Bayesian methodology to evaluate the consistency between the reported Ginga and Burst and Transient Source Experiment (BATSE) detections of absorption features in gamma-ray burst spectra. Currently no features have been detected by BATSE, but this methodology will still be applicable if and when such features are discovered. The Bayesian methodology permits the comparison of hypotheses regarding the two detectors' observations and makes explicit the subjective aspects of our analysis (e.g., the quantification of our confidence in detector performance). We also present non-Bayesian consistency statistics. Based on preliminary calculations of line detectability, we find that both the Bayesian and non-Bayesian techniques show that the BATSE and Ginga observations are consistent given our understanding of these detectors.

  4. Bayesian signal processing classical, modern, and particle filtering methods

    CERN Document Server

    Candy, James V

    2016-01-01

    This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters" as well as an expansion of Case Studies that detail Bayesian solutions for a variety of applications. These studies illustrate Bayesian approaches to real-world problems incorporating detailed particle filter designs, adaptive particle filters and sequential Bayesian detectors. In addition to these major developments a variety of sections are expanded to "fill-in-the gaps" of the first edition. Here metrics for particle filter (PF) designs with emphasis on classical "sanity testing" lead to ensemble techniques as a basic requirement for performance analysis. The expansion of information theory metrics and their application to PF designs is fully developed an...

  5. Bayesian Networks for Modeling Dredging Decisions

    Science.gov (United States)

    2011-10-01

    years, that algorithms have been developed to solve these problems efficiently. Most modern Bayesian network software uses junction tree (a.k.a. join... software was used to develop the network . This is by no means an exhaustive list of Bayesian network applications, but it is representative of recent...characteristic node (SCN), state- defining node ( SDN ), effect node (EFN), or value node. The five types of nodes can be described as follows: ERDC/EL TR-11

  6. A Bayesian classifier for symbol recognition

    OpenAIRE

    Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

    2007-01-01

    URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

  7. Sparse reconstruction using distribution agnostic bayesian matching pursuit

    KAUST Repository

    Masood, Mudassir; Al-Naffouri, Tareq Y.

    2013-01-01

    A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics

  8. The spectrum of aphasia subtypes and etiology in subacute stroke.

    Science.gov (United States)

    Hoffmann, Michael; Chen, Ren

    2013-11-01

    Aphasia is one of the most common stroke syndrome presentations, yet little is known about the spectrum of different subtypes or their stroke mechanisms. Yet, subtypes and etiology are known to influence the prognosis and recovery. Our aim is to analyze aphasia subtypes and etiology in a large subacute stroke population. Consecutive patients from a dedicated cognitive stroke registry were accrued. A validated cognitive screening examination was administered during the first month of stroke presentation, which enabled a diagnosis of 14 different aphasic subtypes. The evolution from one subtype to another in the acute and subacute period, at times, resulted in more than 1 subtype being diagnosed. Etiology of stroke was determined by the modified Trial of Org 10172 in Acute Stroke Treatment criteria that included intracerebral hemorrhage. Exclusions included dementia, chronic medical illness, substance abuse, and severe depression. Of 2389 stroke patients, after exclusions (n=593), aphasias numbered 625 (625 of 1796; 34.8%), and the subtype frequencies included Broca aphasia (n=170; 27.2%), anomic aphasia (n=165; 26.4%), global aphasia (n=119; 19.04%), and subcortical aphasia (n=57; 9.12%). Less frequent subtypes (total n=40; 6.7%) included transcortical aphasia (n=11), Wernicke aphasia (n=10), conduction aphasia (n=7), aphemia (n=3), semantic aphasia (n=3), crossed aphasia (n=3), pure word deafness (n=2), and foreign accent syndrome (n=1). Aphasia subtypes and etiologies had some significant associations (chi-square: 855.8, P valueaphasia had a significant association with small-vessel disease (SVD) (odds ratio [OR]=2.0254, 95% confidence interval [CI]: 1.3820-2.9681), and global aphasia patients mostly had cardioembolic (CE) causes (OR=2.3589, 95% CI: 1.5506-3.5885) and less likely SVD (OR=.2583, 95% CI: .1444-.4654). Other notable inferences were included. Wernicke aphasia was caused by either CE (6 of 12; 50%) or hemorrhage (4 of 12; 33.3%) in a combined 83% of

  9. Bayesian emulation for optimization in multi-step portfolio decisions

    OpenAIRE

    Irie, Kaoru; West, Mike

    2016-01-01

    We discuss the Bayesian emulation approach to computational solution of multi-step portfolio studies in financial time series. "Bayesian emulation for decisions" involves mapping the technical structure of a decision analysis problem to that of Bayesian inference in a purely synthetic "emulating" statistical model. This provides access to standard posterior analytic, simulation and optimization methods that yield indirect solutions of the decision problem. We develop this in time series portf...

  10. Bayesian Analysis for Penalized Spline Regression Using WinBUGS

    Directory of Open Access Journals (Sweden)

    Ciprian M. Crainiceanu

    2005-09-01

    Full Text Available Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.

  11. Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation

    DEFF Research Database (Denmark)

    Brouwer, Thomas; Frellsen, Jes; Liò, Pietro

    2017-01-01

    In this paper, we study the trade-offs of different inference approaches for Bayesian matrix factorisation methods, which are commonly used for predicting missing values, and for finding patterns in the data. In particular, we consider Bayesian nonnegative variants of matrix factorisation and tri......-factorisation, and compare non-probabilistic inference, Gibbs sampling, variational Bayesian inference, and a maximum-a-posteriori approach. The variational approach is new for the Bayesian nonnegative models. We compare their convergence, and robustness to noise and sparsity of the data, on both synthetic and real...

  12. Bayesian Source Attribution of Salmonellosis in South Australia.

    Science.gov (United States)

    Glass, K; Fearnley, E; Hocking, H; Raupach, J; Veitch, M; Ford, L; Kirk, M D

    2016-03-01

    Salmonellosis is a significant cause of foodborne gastroenteritis in Australia, and rates of illness have increased over recent years. We adopt a Bayesian source attribution model to estimate the contribution of different animal reservoirs to illness due to Salmonella spp. in South Australia between 2000 and 2010, together with 95% credible intervals (CrI). We excluded known travel associated cases and those of rare subtypes (fewer than 20 human cases or fewer than 10 isolates from included sources over the 11-year period), and the remaining 76% of cases were classified as sporadic or outbreak associated. Source-related parameters were included to allow for different handling and consumption practices. We attributed 35% (95% CrI: 20-49) of sporadic cases to chicken meat and 37% (95% CrI: 23-53) of sporadic cases to eggs. Of outbreak-related cases, 33% (95% CrI: 20-62) were attributed to chicken meat and 59% (95% CrI: 29-75) to eggs. A comparison of alternative model assumptions indicated that biases due to possible clustering of samples from sources had relatively minor effects on these estimates. Analysis of source-related parameters showed higher risk of illness from contaminated eggs than from contaminated chicken meat, suggesting that consumption and handling practices potentially play a bigger role in illness due to eggs, considering low Salmonella prevalence on eggs. Our results strengthen the evidence that eggs and chicken meat are important vehicles for salmonellosis in South Australia. © 2015 Society for Risk Analysis.

  13. Molecular subtypes of glioblastoma are relevant to lower grade glioma.

    Directory of Open Access Journals (Sweden)

    Xiaowei Guan

    Full Text Available Gliomas are the most common primary malignant brain tumors in adults with great heterogeneity in histopathology and clinical course. The intent was to evaluate the relevance of known glioblastoma (GBM expression and methylation based subtypes to grade II and III gliomas (ie. lower grade gliomas.Gene expression array, single nucleotide polymorphism (SNP array and clinical data were obtained for 228 GBMs and 176 grade II/II gliomas (GII/III from the publically available Rembrandt dataset. Two additional datasets with IDH1 mutation status were utilized as validation datasets (one publicly available dataset and one newly generated dataset from MD Anderson. Unsupervised clustering was performed and compared to gene expression subtypes assigned using the Verhaak et al 840-gene classifier. The glioma-CpG Island Methylator Phenotype (G-CIMP was assigned using prediction models by Fine et al.Unsupervised clustering by gene expression aligned with the Verhaak 840-gene subtype group assignments. GII/IIIs were preferentially assigned to the proneural subtype with IDH1 mutation and G-CIMP. GBMs were evenly distributed among the four subtypes. Proneural, IDH1 mutant, G-CIMP GII/III s had significantly better survival than other molecular subtypes. Only 6% of GBMs were proneural and had either IDH1 mutation or G-CIMP but these tumors had significantly better survival than other GBMs. Copy number changes in chromosomes 1p and 19q were associated with GII/IIIs, while these changes in CDKN2A, PTEN and EGFR were more commonly associated with GBMs.GBM gene-expression and methylation based subtypes are relevant for GII/III s and associate with overall survival differences. A better understanding of the association between these subtypes and GII/IIIs could further knowledge regarding prognosis and mechanisms of glioma progression.

  14. Achalasia symptom response after Heller myotomy segregated by high-resolution manometry subtypes.

    Science.gov (United States)

    Patel, Amit; Patel, Ami; Mirza, Faiz A; Soudagar, Samad; Sayuk, Gregory S; Gyawali, C Prakash

    2016-02-01

    Achalasia is classified into three HRM subtypes that predict outcomes from diverse management strategies. We assessed if symptomatic response varied when a single management strategy-Heller myotomy (HM)-is employed. Treatment-naive subjects with achalasia referred for HM were followed in this observational cohort study. Chicago criteria designated achalasia subtypes (subtype I: no esophageal pressurization; subtype II: panesophageal pressurization in ≥20 % swallows; subtype III: premature contractions in ≥20 % swallows). Symptom questionnaires assessed symptom burden before and after HM on five-point Likert scales (0 = no symptoms, 4 = severe symptoms) and on 10-cm visual analog scales (global symptom severity, GSS); satisfaction with HM was recorded similarly. Data were analyzed to determine predictors of GSS change across subtypes. Sixty achalasia subjects (56.1 ± 2.4 years, 55 % female) fulfilled inclusion criteria, 15 % with subtype I, 58 % with subtype II, and 27 % with subtype III achalasia. Baseline symptoms included dysphagia (solids: 85 %, liquids: 73 %), regurgitation (84 %), and chest pain (35 %); mean GSS was 7.1 ± 0.3. Upon follow-up 2.1 ± 0.2 years after HM, GSS declined to 1.9 ± 0.4 (p < 0.001), with surgical satisfaction score of 8.7 ± 0.3 out of 10; these were similar across achalasia subtypes. On univariate analysis, female gender, Eckardt score, severity of transit symptoms, and maximal IRP predicted linear GSS improvement; female gender (p = 0.003) and dysphagia for liquids (p = 0.043) remained predictive on multivariate analysis. When a uniform surgical approach is utilized, symptomatic outcome and satisfaction with therapy are similar across achalasia subtypes. Female gender and severity of dysphagia for solids may predict better HM outcome.

  15. Robust stratification of breast cancer subtypes using differential patterns of transcript isoform expression.

    Directory of Open Access Journals (Sweden)

    Thomas P Stricker

    2017-03-01

    Full Text Available Breast cancer, the second leading cause of cancer death of women worldwide, is a heterogenous disease with multiple different subtypes. These subtypes carry important implications for prognosis and therapy. Interestingly, it is known that these different subtypes not only have different biological behaviors, but also have distinct gene expression profiles. However, it has not been rigorously explored whether particular transcriptional isoforms are also differentially expressed among breast cancer subtypes, or whether transcript isoforms from the same sets of genes can be used to differentiate subtypes. To address these questions, we analyzed the patterns of transcript isoform expression using a small set of RNA-sequencing data for eleven Estrogen Receptor positive (ER+ subtype and fourteen triple negative (TN subtype tumors. We identified specific sets of isoforms that distinguish these tumor subtypes with higher fidelity than standard mRNA expression profiles. We found that alternate promoter usage, alternative splicing, and alternate 3'UTR usage are differentially regulated in breast cancer subtypes. Profiling of isoform expression in a second, independent cohort of 68 tumors confirmed that expression of splice isoforms differentiates breast cancer subtypes. Furthermore, analysis of RNAseq data from 594 cases from the TCGA cohort confirmed the ability of isoform usage to distinguish breast cancer subtypes. Also using our expression data, we identified several RNA processing factors that were differentially expressed between tumor subtypes and/or regulated by estrogen receptor, including YBX1, YBX2, MAGOH, MAGOHB, and PCBP2. RNAi knock-down of these RNA processing factors in MCF7 cells altered isoform expression. These results indicate that global dysregulation of splicing in breast cancer occurs in a subtype-specific and reproducible manner and is driven by specific differentially expressed RNA processing factors.

  16. Parkinson's disease motor subtypes and mood.

    Science.gov (United States)

    Burn, David J; Landau, Sabine; Hindle, John V; Samuel, Michael; Wilson, Kenneth C; Hurt, Catherine S; Brown, Richard G

    2012-03-01

    Parkinson's disease is heterogeneous, both in terms of motor symptoms and mood. Identifying associations between phenotypic variants of motor and mood subtypes may provide clues to understand mechanisms underlying mood disorder and symptoms in Parkinson's disease. A total of 513 patients were assessed using the Hospital Anxiety and Depression Scale, and separately classified into anxious, depressed, and anxious-depressed mood classes based on latent class analysis of a semistructured interview. Motor subtypes assessed related to age-of-onset, rate of progression, presence of motor fluctuations, lateralization of motor symptoms, tremor dominance, and the presence of postural instability and gait symptoms and falls. The directions of observed associations tended to support previous findings with the exception of lateralization of symptoms, for which there were no consistent or significant results. Regression models examining a range of motor subtypes together indicated increased risk of anxiety in patients with younger age-of-onset and motor fluctuations. In contrast, depression was most strongly related to axial motor symptoms. Different risk factors were observed for depressed patients with and without anxiety, suggesting heterogeneity within Parkinson's disease depression. Such association data may suggest possible underlying common risk factors for motor subtype and mood. Combined with convergent evidence from other sources, possible mechanisms may include cholinergic system damage and white matter changes contributing to non-anxious depression in Parkinson's disease, while situational factors related to threat and unpredictability may contribute to the exacerbation and maintenance of anxiety in susceptible individuals. Copyright © 2011 Movement Disorder Society.

  17. Optimizing Prediction Using Bayesian Model Averaging: Examples Using Large-Scale Educational Assessments.

    Science.gov (United States)

    Kaplan, David; Lee, Chansoon

    2018-01-01

    This article provides a review of Bayesian model averaging as a means of optimizing the predictive performance of common statistical models applied to large-scale educational assessments. The Bayesian framework recognizes that in addition to parameter uncertainty, there is uncertainty in the choice of models themselves. A Bayesian approach to addressing the problem of model uncertainty is the method of Bayesian model averaging. Bayesian model averaging searches the space of possible models for a set of submodels that satisfy certain scientific principles and then averages the coefficients across these submodels weighted by each model's posterior model probability (PMP). Using the weighted coefficients for prediction has been shown to yield optimal predictive performance according to certain scoring rules. We demonstrate the utility of Bayesian model averaging for prediction in education research with three examples: Bayesian regression analysis, Bayesian logistic regression, and a recently developed approach for Bayesian structural equation modeling. In each case, the model-averaged estimates are shown to yield better prediction of the outcome of interest than any submodel based on predictive coverage and the log-score rule. Implications for the design of large-scale assessments when the goal is optimal prediction in a policy context are discussed.

  18. Can natural selection encode Bayesian priors?

    Science.gov (United States)

    Ramírez, Juan Camilo; Marshall, James A R

    2017-08-07

    The evolutionary success of many organisms depends on their ability to make decisions based on estimates of the state of their environment (e.g., predation risk) from uncertain information. These decision problems have optimal solutions and individuals in nature are expected to evolve the behavioural mechanisms to make decisions as if using the optimal solutions. Bayesian inference is the optimal method to produce estimates from uncertain data, thus natural selection is expected to favour individuals with the behavioural mechanisms to make decisions as if they were computing Bayesian estimates in typically-experienced environments, although this does not necessarily imply that favoured decision-makers do perform Bayesian computations exactly. Each individual should evolve to behave as if updating a prior estimate of the unknown environment variable to a posterior estimate as it collects evidence. The prior estimate represents the decision-maker's default belief regarding the environment variable, i.e., the individual's default 'worldview' of the environment. This default belief has been hypothesised to be shaped by natural selection and represent the environment experienced by the individual's ancestors. We present an evolutionary model to explore how accurately Bayesian prior estimates can be encoded genetically and shaped by natural selection when decision-makers learn from uncertain information. The model simulates the evolution of a population of individuals that are required to estimate the probability of an event. Every individual has a prior estimate of this probability and collects noisy cues from the environment in order to update its prior belief to a Bayesian posterior estimate with the evidence gained. The prior is inherited and passed on to offspring. Fitness increases with the accuracy of the posterior estimates produced. Simulations show that prior estimates become accurate over evolutionary time. In addition to these 'Bayesian' individuals, we also

  19. Mechanistic curiosity will not kill the Bayesian cat

    NARCIS (Netherlands)

    Borsboom, D.; Wagenmakers, E.-J.; Romeijn, J.-W.

    2011-01-01

    Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer

  20. Mechanistic curiosity will not kill the Bayesian cat

    NARCIS (Netherlands)

    Borsboom, Denny; Wagenmakers, Eric-Jan; Romeijn, Jan-Willem

    Jones & Love (J&L) suggest that Bayesian approaches to the explanation of human behavior should be constrained by mechanistic theories. We argue that their proposal misconstrues the relation between process models, such as the Bayesian model, and mechanisms. While mechanistic theories can answer

  1. Non-homogeneous dynamic Bayesian networks for continuous data

    NARCIS (Netherlands)

    Grzegorczyk, Marco; Husmeier, Dirk

    Classical dynamic Bayesian networks (DBNs) are based on the homogeneous Markov assumption and cannot deal with non-homogeneous temporal processes. Various approaches to relax the homogeneity assumption have recently been proposed. The present paper presents a combination of a Bayesian network with

  2. Statistics: a Bayesian perspective

    National Research Council Canada - National Science Library

    Berry, Donald A

    1996-01-01

    ...: it is the only introductory textbook based on Bayesian ideas, it combines concepts and methods, it presents statistics as a means of integrating data into the significant process, it develops ideas...

  3. Embedding the results of focussed Bayesian fusion into a global context

    Science.gov (United States)

    Sander, Jennifer; Heizmann, Michael

    2014-05-01

    Bayesian statistics offers a well-founded and powerful fusion methodology also for the fusion of heterogeneous information sources. However, except in special cases, the needed posterior distribution is not analytically derivable. As consequence, Bayesian fusion may cause unacceptably high computational and storage costs in practice. Local Bayesian fusion approaches aim at reducing the complexity of the Bayesian fusion methodology significantly. This is done by concentrating the actual Bayesian fusion on the potentially most task relevant parts of the domain of the Properties of Interest. Our research on these approaches is motivated by an analogy to criminal investigations where criminalists pursue clues also only locally. This publication follows previous publications on a special local Bayesian fusion technique called focussed Bayesian fusion. Here, the actual calculation of the posterior distribution gets completely restricted to a suitably chosen local context. By this, the global posterior distribution is not completely determined. Strategies for using the results of a focussed Bayesian analysis appropriately are needed. In this publication, we primarily contrast different ways of embedding the results of focussed Bayesian fusion explicitly into a global context. To obtain a unique global posterior distribution, we analyze the application of the Maximum Entropy Principle that has been shown to be successfully applicable in metrology and in different other areas. To address the special need for making further decisions subsequently to the actual fusion task, we further analyze criteria for decision making under partial information.

  4. A Bayesian Optimal Design for Sequential Accelerated Degradation Testing

    Directory of Open Access Journals (Sweden)

    Xiaoyang Li

    2017-07-01

    Full Text Available When optimizing an accelerated degradation testing (ADT plan, the initial values of unknown model parameters must be pre-specified. However, it is usually difficult to obtain the exact values, since many uncertainties are embedded in these parameters. Bayesian ADT optimal design was presented to address this problem by using prior distributions to capture these uncertainties. Nevertheless, when the difference between a prior distribution and actual situation is large, the existing Bayesian optimal design might cause some over-testing or under-testing issues. For example, the implemented ADT following the optimal ADT plan consumes too much testing resources or few accelerated degradation data are obtained during the ADT. To overcome these obstacles, a Bayesian sequential step-down-stress ADT design is proposed in this article. During the sequential ADT, the test under the highest stress level is firstly conducted based on the initial prior information to quickly generate degradation data. Then, the data collected under higher stress levels are employed to construct the prior distributions for the test design under lower stress levels by using the Bayesian inference. In the process of optimization, the inverse Gaussian (IG process is assumed to describe the degradation paths, and the Bayesian D-optimality is selected as the optimal objective. A case study on an electrical connector’s ADT plan is provided to illustrate the application of the proposed Bayesian sequential ADT design method. Compared with the results from a typical static Bayesian ADT plan, the proposed design could guarantee more stable and precise estimations of different reliability measures.

  5. A Bayesian Method for Weighted Sampling

    OpenAIRE

    Lo, Albert Y.

    1993-01-01

    Bayesian statistical inference for sampling from weighted distribution models is studied. Small-sample Bayesian bootstrap clone (BBC) approximations to the posterior distribution are discussed. A second-order property for the BBC in unweighted i.i.d. sampling is given. A consequence is that BBC approximations to a posterior distribution of the mean and to the sampling distribution of the sample average, can be made asymptotically accurate by a proper choice of the random variables that genera...

  6. Clinically-inspired automatic classification of ovarian carcinoma subtypes

    Directory of Open Access Journals (Sweden)

    Aicha BenTaieb

    2016-01-01

    Full Text Available Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists′ workflow, we propose an automatic framework for ovarian carcinoma classification. Materials and Methods: Our method is inspired by pathologists′ workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. Results: This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier′s confusion (confusion matrix between the five different ovarian carcinoma subtypes agrees with clinician′s confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. Conclusions: Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician′s diagnostic procedure by providing a second opinion.

  7. Bayesian Geostatistical Design

    DEFF Research Database (Denmark)

    Diggle, Peter; Lophaven, Søren Nymand

    2006-01-01

    locations to, or deletion of locations from, an existing design, and prospective design, which consists of choosing positions for a new set of sampling locations. We propose a Bayesian design criterion which focuses on the goal of efficient spatial prediction whilst allowing for the fact that model...

  8. Ultrasonographic Features of Papillary Thyroid Carcinomas According to Their Subtypes

    Directory of Open Access Journals (Sweden)

    Hye Jin Baek

    2018-05-01

    Full Text Available BackgroundThe ultrasonographic characteristics and difference for various subtypes of papillary thyroid carcinoma (PTC are still unclear. The aim of this study was to compare the ultrasonographic features of PTC according to its subtype in patients undergoing thyroid surgery.MethodsIn total, 140 patients who underwent preoperative thyroid ultrasonography (US and thyroid surgery between January 2016 and December 2016 were included. The ultrasonographic features and the Korean Thyroid Imaging Reporting and Data System (K-TIRADS category of each thyroid nodule were retrospectively evaluated by a single radiologist, and differences in ultrasonographic features according to the PTC subtype were assessed.ResultsAccording to histopathological analyses, there were 97 classic PTCs (62.2%, 34 follicular variants (21.8%, 5 tall cell variants (3.2%, 2 oncocytic variants (1.3%, 1 Warthin-like variant (0.6%, and 1 diffuse sclerosing variant (0.6%. Most PTCs were classified under K-TIRADS category 5. Among the ultrasonographic features, the nodule margin and the presence of calcification were significantly different among the PTC subtypes. A spiculated/microlobulated margin was the most common type of margin, regardless of the PTC subtype. In particular, all tall cell variants exhibited a spiculated/microlobulated margin. The classic PTC group exhibited the highest prevalence of intranodular calcification, with microcalcification being the most common. The prevalence of multiplicity and nodal metastasis was high in the tall cell variant group.ConclusionThe majority of PTCs in the present study belonged to K-TIRADS category 5, regardless of the subtype. Our findings suggest that ultrasonographic features are not useful for distinguishing PTC subtypes.

  9. Bayesian inference for psychology. Part I : Theoretical advantages and practical ramifications

    NARCIS (Netherlands)

    Wagenmakers, E.-J.; Marsman, M.; Jamil, T.; Ly, A.; Verhagen, J.; Love, J.; Selker, R.; Gronau, Q.F.; Šmíra, M.; Epskamp, S.; Matzke, D.; Rouder, J.N.; Morey, R.D.

    2018-01-01

    Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and p values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete

  10. Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers

    Directory of Open Access Journals (Sweden)

    Xinxin Peng

    2018-04-01

    Full Text Available Summary: Metabolic reprogramming provides critical information for clinical oncology. Using molecular data of 9,125 patient samples from The Cancer Genome Atlas, we identified tumor subtypes in 33 cancer types based on mRNA expression patterns of seven major metabolic processes and assessed their clinical relevance. Our metabolic expression subtypes correlated extensively with clinical outcome: subtypes with upregulated carbohydrate, nucleotide, and vitamin/cofactor metabolism most consistently correlated with worse prognosis, whereas subtypes with upregulated lipid metabolism showed the opposite. Metabolic subtypes correlated with diverse somatic drivers but exhibited effects convergent on cancer hallmark pathways and were modulated by highly recurrent master regulators across cancer types. As a proof-of-concept example, we demonstrated that knockdown of SNAI1 or RUNX1—master regulators of carbohydrate metabolic subtypes—modulates metabolic activity and drug sensitivity. Our study provides a system-level view of metabolic heterogeneity within and across cancer types and identifies pathway cross-talk, suggesting related prognostic, therapeutic, and predictive utility. : Peng et al. analyze a cohort of 9,125 TCGA samples across 33 cancer types to characterize tumor subtypes based on the expression of seven metabolic pathways. They find metabolic expression subtypes are associated with patient survivals and suggest the therapeutic and predictive relevance of subtype-related master regulators. Keywords: The Cancer Genome Atlas, tumor subtypes, prognostic markers, somatic drivers, master regulator, therapeutic targets, drug sensitivity, carbohydrate metabolism

  11. A tutorial introduction to Bayesian models of cognitive development.

    Science.gov (United States)

    Perfors, Amy; Tenenbaum, Joshua B; Griffiths, Thomas L; Xu, Fei

    2011-09-01

    We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations, or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science. Copyright © 2010 Elsevier B.V. All rights reserved.

  12. Approximate Bayesian evaluations of measurement uncertainty

    Science.gov (United States)

    Possolo, Antonio; Bodnar, Olha

    2018-04-01

    The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.

  13. Study on shielded pump system failure analysis method based on Bayesian network

    International Nuclear Information System (INIS)

    Bao Yilan; Huang Gaofeng; Tong Lili; Cao Xuewu

    2012-01-01

    This paper applies Bayesian network to the system failure analysis, with an aim to improve knowledge representation of the uncertainty logic and multi-fault states in system failure analysis. A Bayesian network for shielded pump failure analysis is presented, conducting fault parameter learning, updating Bayesian network parameter based on new samples. Finally, through the Bayesian network inference, vulnerability in this system, the largest possible failure modes, and the fault probability are obtained. The powerful ability of Bayesian network to analyze system fault is illustrated by examples. (authors)

  14. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome.

    Science.gov (United States)

    Tothill, Richard W; Tinker, Anna V; George, Joshy; Brown, Robert; Fox, Stephen B; Lade, Stephen; Johnson, Daryl S; Trivett, Melanie K; Etemadmoghadam, Dariush; Locandro, Bianca; Traficante, Nadia; Fereday, Sian; Hung, Jillian A; Chiew, Yoke-Eng; Haviv, Izhak; Gertig, Dorota; DeFazio, Anna; Bowtell, David D L

    2008-08-15

    The study aim to identify novel molecular subtypes of ovarian cancer by gene expression profiling with linkage to clinical and pathologic features. Microarray gene expression profiling was done on 285 serous and endometrioid tumors of the ovary, peritoneum, and fallopian tube. K-means clustering was applied to identify robust molecular subtypes. Statistical analysis identified differentially expressed genes, pathways, and gene ontologies. Laser capture microdissection, pathology review, and immunohistochemistry validated the array-based findings. Patient survival within k-means groups was evaluated using Cox proportional hazards models. Class prediction validated k-means groups in an independent dataset. A semisupervised survival analysis of the array data was used to compare against unsupervised clustering results. Optimal clustering of array data identified six molecular subtypes. Two subtypes represented predominantly serous low malignant potential and low-grade endometrioid subtypes, respectively. The remaining four subtypes represented higher grade and advanced stage cancers of serous and endometrioid morphology. A novel subtype of high-grade serous cancers reflected a mesenchymal cell type, characterized by overexpression of N-cadherin and P-cadherin and low expression of differentiation markers, including CA125 and MUC1. A poor prognosis subtype was defined by a reactive stroma gene expression signature, correlating with extensive desmoplasia in such samples. A similar poor prognosis signature could be found using a semisupervised analysis. Each subtype displayed distinct levels and patterns of immune cell infiltration. Class prediction identified similar subtypes in an independent ovarian dataset with similar prognostic trends. Gene expression profiling identified molecular subtypes of ovarian cancer of biological and clinical importance.

  15. Bayesian Dark Knowledge

    NARCIS (Netherlands)

    Korattikara, A.; Rathod, V.; Murphy, K.; Welling, M.; Cortes, C.; Lawrence, N.D.; Lee, D.D.; Sugiyama, M.; Garnett, R.

    2015-01-01

    We consider the problem of Bayesian parameter estimation for deep neural networks, which is important in problem settings where we may have little data, and/ or where we need accurate posterior predictive densities p(y|x, D), e.g., for applications involving bandits or active learning. One simple

  16. Bayesian grid matching

    DEFF Research Database (Denmark)

    Hartelius, Karsten; Carstensen, Jens Michael

    2003-01-01

    A method for locating distorted grid structures in images is presented. The method is based on the theories of template matching and Bayesian image restoration. The grid is modeled as a deformable template. Prior knowledge of the grid is described through a Markov random field (MRF) model which r...

  17. An introduction to Bayesian statistics in health psychology

    NARCIS (Netherlands)

    Depaoli, Sarah; Rus, Holly; Clifton, James; van de Schoot, A.G.J.; Tiemensma, Jitske

    2017-01-01

    The aim of the current article is to provide a brief introduction to Bayesian statistics within the field of Health Psychology. Bayesian methods are increasing in prevalence in applied fields, and they have been shown in simulation research to improve the estimation accuracy of structural equation

  18. Bayesian estimation of the discrete coefficient of determination.

    Science.gov (United States)

    Chen, Ting; Braga-Neto, Ulisses M

    2016-12-01

    The discrete coefficient of determination (CoD) measures the nonlinear interaction between discrete predictor and target variables and has had far-reaching applications in Genomic Signal Processing. Previous work has addressed the inference of the discrete CoD using classical parametric and nonparametric approaches. In this paper, we introduce a Bayesian framework for the inference of the discrete CoD. We derive analytically the optimal minimum mean-square error (MMSE) CoD estimator, as well as a CoD estimator based on the Optimal Bayesian Predictor (OBP). For the latter estimator, exact expressions for its bias, variance, and root-mean-square (RMS) are given. The accuracy of both Bayesian CoD estimators with non-informative and informative priors, under fixed or random parameters, is studied via analytical and numerical approaches. We also demonstrate the application of the proposed Bayesian approach in the inference of gene regulatory networks, using gene-expression data from a previously published study on metastatic melanoma.

  19. A Bayesian approach to particle identification in ALICE

    CERN Multimedia

    CERN. Geneva

    2016-01-01

    Among the LHC experiments, ALICE has unique particle identification (PID) capabilities exploiting different types of detectors. During Run 1, a Bayesian approach to PID was developed and intensively tested. It facilitates the combination of information from different sub-systems. The adopted methodology and formalism as well as the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE will be reviewed. Results are presented with PID performed via measurements of specific energy loss (dE/dx) and time-of-flight using information from the TPC and TOF detectors, respectively. Methods to extract priors from data and to compare PID efficiencies and misidentification probabilities in data and Monte Carlo using high-purity samples of identified particles will be presented. Bayesian PID results were found consistent with previous measurements published by ALICE. The Bayesian PID approach gives a higher signal-to-background ratio and a similar or larger statist...

  20. Optimal Detection under the Restricted Bayesian Criterion

    Directory of Open Access Journals (Sweden)

    Shujun Liu

    2017-07-01

    Full Text Available This paper aims to find a suitable decision rule for a binary composite hypothesis-testing problem with a partial or coarse prior distribution. To alleviate the negative impact of the information uncertainty, a constraint is considered that the maximum conditional risk cannot be greater than a predefined value. Therefore, the objective of this paper becomes to find the optimal decision rule to minimize the Bayes risk under the constraint. By applying the Lagrange duality, the constrained optimization problem is transformed to an unconstrained optimization problem. In doing so, the restricted Bayesian decision rule is obtained as a classical Bayesian decision rule corresponding to a modified prior distribution. Based on this transformation, the optimal restricted Bayesian decision rule is analyzed and the corresponding algorithm is developed. Furthermore, the relation between the Bayes risk and the predefined value of the constraint is also discussed. The Bayes risk obtained via the restricted Bayesian decision rule is a strictly decreasing and convex function of the constraint on the maximum conditional risk. Finally, the numerical results including a detection example are presented and agree with the theoretical results.

  1. Bayesian approach and application to operation safety

    International Nuclear Information System (INIS)

    Procaccia, H.; Suhner, M.Ch.

    2003-01-01

    The management of industrial risks requires the development of statistical and probabilistic analyses which use all the available convenient information in order to compensate the insufficient experience feedback in a domain where accidents and incidents remain too scarce to perform a classical statistical frequency analysis. The Bayesian decision approach is well adapted to this problem because it integrates both the expertise and the experience feedback. The domain of knowledge is widen, the forecasting study becomes possible and the decisions-remedial actions are strengthen thanks to risk-cost-benefit optimization analyzes. This book presents the bases of the Bayesian approach and its concrete applications in various industrial domains. After a mathematical presentation of the industrial operation safety concepts and of the Bayesian approach principles, this book treats of some of the problems that can be solved thanks to this approach: softwares reliability, controls linked with the equipments warranty, dynamical updating of databases, expertise modeling and weighting, Bayesian optimization in the domains of maintenance, quality control, tests and design of new equipments. A synthesis of the mathematical formulae used in this approach is given in conclusion. (J.S.)

  2. CRISPR typing and subtyping for improved laboratory surveillance of Salmonella infections.

    Directory of Open Access Journals (Sweden)

    Laëtitia Fabre

    Full Text Available Laboratory surveillance systems for salmonellosis should ideally be based on the rapid serotyping and subtyping of isolates. However, current typing methods are limited in both speed and precision. Using 783 strains and isolates belonging to 130 serotypes, we show here that a new family of DNA repeats named CRISPR (clustered regularly interspaced short palindromic repeats is highly polymorphic in Salmonella. We found that CRISPR polymorphism was strongly correlated with both serotype and multilocus sequence type. Furthermore, spacer microevolution discriminated between subtypes within prevalent serotypes, making it possible to carry out typing and subtyping in a single step. We developed a high-throughput subtyping assay for the most prevalent serotype, Typhimurium. An open web-accessible database was set up, providing a serotype/spacer dictionary and an international tool for strain tracking based on this innovative, powerful typing and subtyping tool.

  3. Stroke subtypes and factors associated with ischemic stroke in ...

    African Journals Online (AJOL)

    Stroke subtypes assessed four OCSP (Oxfordshire Communi-. African Health Sciences Vol 15 Issue 1, March 2015. 68. 69 ty Stroke Project Classification) subtypes classification. 13 was used with lacunar circulation infarct (LACI) and total anterior (TACI), partial anterior (PACI), posterior. (POCI) circulation infarcts as non ...

  4. Interpersonal subtypes in social phobia: diagnostic and treatment implications.

    Science.gov (United States)

    Cain, Nicole M; Pincus, Aaron L; Grosse Holtforth, Martin

    2010-11-01

    Interpersonal assessment may provide a clinically useful way to identify subtypes of social phobia. In this study, we examined evidence for interpersonal subtypes in a sample of 77 socially phobic outpatients. A cluster analysis based on the dimensions of dominance and love on the Inventory of Interpersonal Problems-Circumplex Scales (Alden, Wiggins, & Pincus, 1990) found 2 interpersonal subtypes of socially phobic patients. These subtypes did not differ on pretreatment global symptom severity as measured by the Brief Symptom Inventory (Derogatis, 1993) or diagnostic comorbidity but did exhibit differential responses to outpatient psychotherapy. Overall, friendly-submissive social phobia patients had significantly lower scores on measures of social anxiety and significantly higher scores on measures of well-being and satisfaction at posttreatment than cold-submissive social phobia patients. We discuss the results in terms of interpersonal theory and the clinical relevance of assessment of interpersonal functioning prior to beginning psychotherapy with socially phobic patients.

  5. Towards Bayesian Inference of the Fast-Ion Distribution Function

    DEFF Research Database (Denmark)

    Stagner, L.; Heidbrink, W.W.; Salewski, Mirko

    2012-01-01

    sensitivity of the measurements are incorporated into Bayesian likelihood probabilities, while prior probabilities enforce physical constraints. As an initial step, this poster uses Bayesian statistics to infer the DIII-D electron density profile from multiple diagnostic measurements. Likelihood functions....... However, when theory and experiment disagree (for one or more diagnostics), it is unclear how to proceed. Bayesian statistics provides a framework to infer the DF, quantify errors, and reconcile discrepant diagnostic measurements. Diagnostic errors and ``weight functions" that describe the phase space...

  6. Bayesian Correlation Analysis for Sequence Count Data.

    Directory of Open Access Journals (Sweden)

    Daniel Sánchez-Taltavull

    Full Text Available Evaluating the similarity of different measured variables is a fundamental task of statistics, and a key part of many bioinformatics algorithms. Here we propose a Bayesian scheme for estimating the correlation between different entities' measurements based on high-throughput sequencing data. These entities could be different genes or miRNAs whose expression is measured by RNA-seq, different transcription factors or histone marks whose expression is measured by ChIP-seq, or even combinations of different types of entities. Our Bayesian formulation accounts for both measured signal levels and uncertainty in those levels, due to varying sequencing depth in different experiments and to varying absolute levels of individual entities, both of which affect the precision of the measurements. In comparison with a traditional Pearson correlation analysis, we show that our Bayesian correlation analysis retains high correlations when measurement confidence is high, but suppresses correlations when measurement confidence is low-especially for entities with low signal levels. In addition, we consider the influence of priors on the Bayesian correlation estimate. Perhaps surprisingly, we show that naive, uniform priors on entities' signal levels can lead to highly biased correlation estimates, particularly when different experiments have widely varying sequencing depths. However, we propose two alternative priors that provably mitigate this problem. We also prove that, like traditional Pearson correlation, our Bayesian correlation calculation constitutes a kernel in the machine learning sense, and thus can be used as a similarity measure in any kernel-based machine learning algorithm. We demonstrate our approach on two RNA-seq datasets and one miRNA-seq dataset.

  7. The Prion Protein Preference of Sporadic Creutzfeldt-Jakob Disease Subtypes*

    Science.gov (United States)

    Klemm, Helen M. J.; Welton, Jeremy M.; Masters, Colin L.; Klug, Genevieve M.; Boyd, Alison; Hill, Andrew F.; Collins, Steven J.; Lawson, Victoria A.

    2012-01-01

    Sporadic Creutzfeldt-Jakob disease (CJD) is the most prevalent manifestation of the transmissible spongiform encephalopathies or prion diseases affecting humans. The disease encompasses a spectrum of clinical phenotypes that have been correlated with molecular subtypes that are characterized by the molecular mass of the protease-resistant fragment of the disease-related conformation of the prion protein and a polymorphism at codon 129 of the gene encoding the prion protein. A cell-free assay of prion protein misfolding was used to investigate the ability of these sporadic CJD molecular subtypes to propagate using brain-derived sources of the cellular prion protein (PrPC). This study confirmed the presence of three distinct sporadic CJD molecular subtypes with PrPC substrate requirements that reflected their codon 129 associations in vivo. However, the ability of a sporadic CJD molecular subtype to use a specific PrPC substrate was not determined solely by codon 129 as the efficiency of prion propagation was also influenced by the composition of the brain tissue from which the PrPC substrate was sourced, thus indicating that nuances in PrPC or additional factors may determine sporadic CJD subtype. The results of this study will aid in the design of diagnostic assays that can detect prion disease across the diversity of sporadic CJD subtypes. PMID:22930754

  8. A meta-analysis accounting for sources of variability to estimate heat resistance reference parameters of bacteria using hierarchical Bayesian modeling: Estimation of D at 121.1 °C and pH 7, zT and zpH of Geobacillus stearothermophilus.

    Science.gov (United States)

    Rigaux, Clémence; Denis, Jean-Baptiste; Albert, Isabelle; Carlin, Frédéric

    2013-02-01

    Predicting microbial survival requires reference parameters for each micro-organism of concern. When data are abundant and publicly available, a meta-analysis is a useful approach for assessment of these parameters, which can be performed with hierarchical Bayesian modeling. Geobacillus stearothermophilus is a major agent of microbial spoilage of canned foods and is therefore a persistent problem in the food industry. The thermal inactivation parameters of G. stearothermophilus (D(ref), i.e.the decimal reduction time D at the reference temperature 121.1°C and pH 7.0, z(T) and z(pH)) were estimated from a large set of 430 D values mainly collected from scientific literature. Between-study variability hypotheses on the inactivation parameters D(ref), z(T) and z(pH) were explored, using three different hierarchical Bayesian models. Parameter estimations were made using Bayesian inference and the models were compared with a graphical and a Bayesian criterion. Results show the necessity to account for random effects associated with between-study variability. Assuming variability on D(ref), z(T) and z(pH), the resulting distributions for D(ref), z(T) and z(pH) led to a mean of 3.3 min for D(ref) (95% Credible Interval CI=[0.8; 9.6]), to a mean of 9.1°C for z(T) (CI=[5.4; 13.1]) and to a mean of 4.3 pH units for z(pH) (CI=[2.9; 6.3]), in the range pH 3 to pH 7.5. Results are also given separating variability and uncertainty in these distributions, as well as adjusted parametric distributions to facilitate further use of these results in aqueous canned foods such as canned vegetables. Copyright © 2012 Elsevier B.V. All rights reserved.

  9. A Bayesian approach to meta-analysis of plant pathology studies.

    Science.gov (United States)

    Mila, A L; Ngugi, H K

    2011-01-01

    Bayesian statistical methods are used for meta-analysis in many disciplines, including medicine, molecular biology, and engineering, but have not yet been applied for quantitative synthesis of plant pathology studies. In this paper, we illustrate the key concepts of Bayesian statistics and outline the differences between Bayesian and classical (frequentist) methods in the way parameters describing population attributes are considered. We then describe a Bayesian approach to meta-analysis and present a plant pathological example based on studies evaluating the efficacy of plant protection products that induce systemic acquired resistance for the management of fire blight of apple. In a simple random-effects model assuming a normal distribution of effect sizes and no prior information (i.e., a noninformative prior), the results of the Bayesian meta-analysis are similar to those obtained with classical methods. Implementing the same model with a Student's t distribution and a noninformative prior for the effect sizes, instead of a normal distribution, yields similar results for all but acibenzolar-S-methyl (Actigard) which was evaluated only in seven studies in this example. Whereas both the classical (P = 0.28) and the Bayesian analysis with a noninformative prior (95% credibility interval [CRI] for the log response ratio: -0.63 to 0.08) indicate a nonsignificant effect for Actigard, specifying a t distribution resulted in a significant, albeit variable, effect for this product (CRI: -0.73 to -0.10). These results confirm the sensitivity of the analytical outcome (i.e., the posterior distribution) to the choice of prior in Bayesian meta-analyses involving a limited number of studies. We review some pertinent literature on more advanced topics, including modeling of among-study heterogeneity, publication bias, analyses involving a limited number of studies, and methods for dealing with missing data, and show how these issues can be approached in a Bayesian framework

  10. Inference in hybrid Bayesian networks

    International Nuclear Information System (INIS)

    Langseth, Helge; Nielsen, Thomas D.; Rumi, Rafael; Salmeron, Antonio

    2009-01-01

    Since the 1980s, Bayesian networks (BNs) have become increasingly popular for building statistical models of complex systems. This is particularly true for boolean systems, where BNs often prove to be a more efficient modelling framework than traditional reliability techniques (like fault trees and reliability block diagrams). However, limitations in the BNs' calculation engine have prevented BNs from becoming equally popular for domains containing mixtures of both discrete and continuous variables (the so-called hybrid domains). In this paper we focus on these difficulties, and summarize some of the last decade's research on inference in hybrid Bayesian networks. The discussions are linked to an example model for estimating human reliability.

  11. The image recognition based on neural network and Bayesian decision

    Science.gov (United States)

    Wang, Chugege

    2018-04-01

    The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

  12. Editorial: Bayesian benefits for child psychology and psychiatry researchers.

    Science.gov (United States)

    Oldehinkel, Albertine J

    2016-09-01

    For many scientists, performing statistical tests has become an almost automated routine. However, p-values are frequently used and interpreted incorrectly; and even when used appropriately, p-values tend to provide answers that do not match researchers' questions and hypotheses well. Bayesian statistics present an elegant and often more suitable alternative. The Bayesian approach has rarely been applied in child psychology and psychiatry research so far, but the development of user-friendly software packages and tutorials has placed it well within reach now. Because Bayesian analyses require a more refined definition of hypothesized probabilities of possible outcomes than the classical approach, going Bayesian may offer the additional benefit of sparkling the development and refinement of theoretical models in our field. © 2016 Association for Child and Adolescent Mental Health.

  13. Pulmonary emphysema subtypes on computed tomography: the MESA COPD study.

    Science.gov (United States)

    Smith, Benjamin M; Austin, John H M; Newell, John D; D'Souza, Belinda M; Rozenshtein, Anna; Hoffman, Eric A; Ahmed, Firas; Barr, R Graham

    2014-01-01

    Pulmonary emphysema is divided into 3 major subtypes at autopsy: centrilobular, paraseptal, and panlobular emphysema. These subtypes can be defined by visual assessment on computed tomography (CT); however, clinical characteristics of emphysema subtypes on CT are not well defined. We developed a reliable approach to visual assessment of emphysema subtypes on CT and examined if emphysema subtypes have distinct characteristics. The Multi-Ethnic Study of Atherosclerosis COPD Study recruited smokers with chronic obstructive pulmonary disease (COPD) and controls ages 50-79 years with ≥ 10 pack-years. Participants underwent CT following a standardized protocol. Definitions of centrilobular, paraseptal, and panlobular emphysema were obtained by literature review. Six-minute walk distance and pulmonary function were performed following guidelines. Twenty-seven percent of 318 smokers had emphysema on CT. Interrater reliability of emphysema subtype was substantial (K: 0.70). Compared with participants without emphysema, individuals with centrilobular or panlobular emphysema had greater dyspnea, reduced walk distance, greater hyperinflation, and lower diffusing capacity. In contrast, individuals with paraseptal emphysema were similar to controls, except for male predominance. Centrilobular, but not panlobular or paraseptal, emphysema was associated with greater smoking history (+21 pack-years P emphysema, was associated with reduced body mass index (-5 kg/m(2); P = .01). Other than for dyspnea, these findings were independent of the forced expiratory volume in 1 second. Seventeen percent of smokers without COPD on spirometry had emphysema, which was independently associated with reduced walk distance. Emphysema subtypes on CT are common in smokers with and without COPD. Centrilobular and panlobular emphysema, but not paraseptal emphysema, have considerable symptomatic and physiological consequences. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Gene specific actions of thyroid hormone receptor subtypes.

    Directory of Open Access Journals (Sweden)

    Jean Z Lin

    Full Text Available There are two homologous thyroid hormone (TH receptors (TRs α and β, which are members of the nuclear hormone receptor (NR family. While TRs regulate different processes in vivo and other highly related NRs regulate distinct gene sets, initial studies of TR action revealed near complete overlaps in their actions at the level of individual genes. Here, we assessed the extent that TRα and TRβ differ in target gene regulation by comparing effects of equal levels of stably expressed exogenous TRs +/- T(3 in two cell backgrounds (HepG2 and HeLa. We find that hundreds of genes respond to T(3 or to unliganded TRs in both cell types, but were not able to detect verifiable examples of completely TR subtype-specific gene regulation. TR actions are, however, far from identical and we detect TR subtype-specific effects on global T(3 response kinetics in HepG2 cells and many examples of TR subtype specificity at the level of individual genes, including effects on magnitude of response to TR +/- T(3, TR regulation patterns and T(3 dose response. Cycloheximide (CHX treatment confirms that at least some differential effects involve verifiable direct TR target genes. TR subtype/gene-specific effects emerge in the context of widespread variation in target gene response and we suggest that gene-selective effects on mechanism of TR action highlight differences in TR subtype function that emerge in the environment of specific genes. We propose that differential TR actions could influence physiologic and pharmacologic responses to THs and selective TR modulators (STRMs.

  15. 1 original article diverse genetic subtypes of hiv-1 among female sex

    African Journals Online (AJOL)

    boaz

    Keywords: Diverse, HIV, subtypes, Female Sex workers and Vaccine ... significant probability that infection with this subtype occurred with a short incubation period (p< 0.05). Conclusion: This study .... regression was used to adjust for potential cofounders. .... TABLE 2: DISTRIBUTION OF HIV-1 SUBTYPES AMONG FSWS.

  16. Ischemic stroke subtype is associated with outcome in thrombolyzed patients

    DEFF Research Database (Denmark)

    Schmitz, Marie Louise; Simonsen, Claus Ziegler; Svendsen, M L

    2017-01-01

    OBJECTIVES: The impact of ischemic stroke subtype on clinical outcome in patients treated with intravenous tissue-type plasminogen activator (IV-tPA) is sparsely examined. We studied the association between stroke subtype and clinical outcome in magnetic resonance imaging (MRI)-evaluated patients...... patients were more likely to achieve early neurological improvement and favorable outcome compared with LVD stroke following MRI-based IV-tPA treatment. This finding may reflect a difference in the effect of IV-tPA among stroke subtypes....

  17. Parent stress across molecular subtypes of children with Angelman syndrome.

    Science.gov (United States)

    Miodrag, N; Peters, S

    2015-09-01

    Parenting stress has been consistently reported among parents of children with developmental disabilities. However, to date, no studies have investigated the impact of a molecular subtype of Angelman syndrome (AS) on parent stress, despite distinct phenotypic differences among subtypes. Data for 124 families of children with three subtypes of AS: class I and II deletions (n = 99), imprinting centre defects (IC defects; n = 11) and paternal uniparental disomy (UPD; n = 14) were drawn from the AS Rare Diseases Clinical Research Network (RDCRN) database and collected from five research sites across the Unites States. The AS study at the RDCRN gathered health information to understand how the syndrome develops and how to treat it. Parents completed questionnaires on their perceived psychological stress, the severity of children's aberrant behaviour and children's sleep patterns. Children's adaptive functioning and developmental levels were clinically evaluated. Child-related stress reached clinical levels for 40% of parents of children with deletions, 100% for IC defects and 64.3% for UPD. Sleep difficulties were similar and elevated across subtypes. There were no differences between molecular subtypes for overall child and parent-related stress. However, results showed greater isolation and lack of perceived parenting skills for parents of children with UPD compared with deletions. Better overall cognition for children with deletions was significantly related to more child-related stress while their poorer adaptive functioning was associated with more child-related stress. For all three groups, the severity of children's inappropriate behaviour was positively related to different aspects of stress. How parents react to stress depends, in part, on children's AS molecular subtype. Despite falling under the larger umbrella term of AS, it is important to acknowledge the unique aspects associated with children's molecular subtype. Identifying these factors can

  18. Osteoclastic finger arthrosis - a subtype of polyarthrosis of the hand; Osteoklastische Fingerarthrose - Subtyp der Handpolyarthrose

    Energy Technology Data Exchange (ETDEWEB)

    Dihlmann, W. [Radiologische Praxis, Hamburg-Barmbek (Germany); Dihlmann, A. [Berufsgenossenschaftliches Unfallkrankenhaus Hamburg (Germany)

    1998-02-01

    Aim: Description of a subtype of arthrosis deformans of the hand which is characterised as osteoclastic arthrosis. Patients and methods: Retrospective analysis of radiographs of the hands of 150 women and 100 men with radiological findings of arthrosis deformans. Results: 5% of women and 2% of men showed at least one digital joint with subchondral osteolysis of one or both articulating bones involving at least a third of the phalanx. This subchondral osteolysis far exceeds the cysts which are situated in the epiphyseal part of the articular region. It may develop within a year. Conclusion: Osteoclastic arthrosis of the finger is a subtype of polyarthrosis of the hand. Serial observations suggest that an osteoclast stimulating substance is produced by the cysts or arises directly from the synovial fluid; this enters the subchondral part of the bone through clefts which may or may not be visible radiologically and that this produces osteoclastic activity. The most important differential diagnoses are chronic tophacious gout and a benign tumor. (orig.) [Deutsch] Ziel: Beschreibung eines Subtyps der Arthrosis deformans an der Hand, der als osteoklastische Arthrose bezeichnet wird. Patienten und Methode: Retrospektive Analyse der Handroentgenaufnahmen von 150 Frauen und 100 Maennern mit Roentgenbefunden der Arthrosis deformans. Ergebnisse: 5% der Frauen und 2% der maennlichen Patienten des durchgesehenen Krankenguts zeigten an mindestens einem Fingergelenk eine Arthrose mit subchondralen Osteolysen an einem oder beiden artikulierenden Knochen, die mindestens ein Drittel der Phalanxlaenge erfasst hatten. Diese subchondralen Osteolysen gehen ueber die Groesse und Form der arthrotischen Geroellzysten, die lediglich im knoechernen (epiphysaeren) Gelenksockel sitzen, weit hinaus. Sie koennen innerhalb eines Jahres entstehen. Schlussfolgerung: Die osteoklastische Arthrose der Finger ist ein Subtyp der Handpolyarthrose. Nach Verlaufsbeobachtungen wird vermutet, dass eine

  19. Bayesian Inference Methods for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand

    2013-01-01

    This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

  20. Bayesian-based localization in inhomogeneous transmission media

    DEFF Research Database (Denmark)

    Nadimi, E. S.; Blanes-Vidal, V.; Johansen, P. M.

    2013-01-01

    In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network with heteroge......In this paper, we propose a novel robust probabilistic approach based on the Bayesian inference using received-signal-strength (RSS) measurements with varying path-loss exponent. We derived the probability density function (pdf) of the distance between any two sensors in the network...... with heterogeneous transmission medium as a function of the given RSS measurements and the characteristics of the heterogeneous medium. The results of this study show that the localization mean square error (MSE) of the Bayesian-based method outperformed all other existing localization approaches. © 2013 ACM....

  1. The subtype of VSD and the angiographic differentiation

    International Nuclear Information System (INIS)

    Choe, Kyu Ok; Sul, Jun Hee; Lee, Sung Kyu; Cho, Bum Koo; Hong, Sung Nok

    1985-01-01

    VSD is the most common congenital cardiac malformation and the natural history depends not only on the age of patients and the size of defect but the subtype of VSD as well, important factor in clinical management of those patients. In 110 patients, with surgically repaired VSD in Yonsei Medical Center in 1984, the subtype of VSDs evaluated by surgical observation were correlated with LV angiogram findings to verify the incidence of subtype in Korean and the diagnostic accuracy to predict the subtype by angiogram. 1. 110 patients included 64 boys and 46 girls, the age ranged from 3 months to 14 years (average 4.6 years old). 2. Angiographic findings were interpreted as follows; a. Perimembranous defects were profiled in LAO 60 .deg. LV angiogram and located below the aortic valve. In inlet excavation the shunted blood opacified the recess between septal leaflet of tricuspid valve and interventricular septum in early phase, in infundibular excavation opacified the recess between anterior leaflet of TV and anterior free wall of RV and in trabecular excavation the shunted blood traversed anterior portion of TV ring, opacified trabecular portion of RV cavity. b. Subarterial types were profiled in RAO 30 .deg. LV angiogram, just below aortic valve as well as pulmonic valve. Total infundibular defects were profiled in RAO 30 .deg. and LAO 60 .deg. LV angiogram subaortic in location in both views. c. In muscular VSD the profiled angle was varied according to the subtype but the defects were separated from the aortic valve as muscular septum interposed between the aortic valve and the defect. 3. The incidence of subtype of VSDs evaluated by surgical observation were as follows. Subarterial type : 32 cases (29.1%) Total infundibular defect : 5 cases (4.5%) Perimembranous type : 73 cases (66.3%) Infundibular excavation : 32 cases (29.2%) Trabecular excavation : 28 cases (25.5%) Inlet excavation : 10 cases (9.1%) Mixed : 3 cases (2.7%) Muscular type : 1 cases (0.9%) Total 63

  2. Bayesian modeling of ChIP-chip data using latent variables.

    KAUST Repository

    Wu, Mingqi

    2009-10-26

    BACKGROUND: The ChIP-chip technology has been used in a wide range of biomedical studies, such as identification of human transcription factor binding sites, investigation of DNA methylation, and investigation of histone modifications in animals and plants. Various methods have been proposed in the literature for analyzing the ChIP-chip data, such as the sliding window methods, the hidden Markov model-based methods, and Bayesian methods. Although, due to the integrated consideration of uncertainty of the models and model parameters, Bayesian methods can potentially work better than the other two classes of methods, the existing Bayesian methods do not perform satisfactorily. They usually require multiple replicates or some extra experimental information to parametrize the model, and long CPU time due to involving of MCMC simulations. RESULTS: In this paper, we propose a Bayesian latent model for the ChIP-chip data. The new model mainly differs from the existing Bayesian models, such as the joint deconvolution model, the hierarchical gamma mixture model, and the Bayesian hierarchical model, in two respects. Firstly, it works on the difference between the averaged treatment and control samples. This enables the use of a simple model for the data, which avoids the probe-specific effect and the sample (control/treatment) effect. As a consequence, this enables an efficient MCMC simulation of the posterior distribution of the model, and also makes the model more robust to the outliers. Secondly, it models the neighboring dependence of probes by introducing a latent indicator vector. A truncated Poisson prior distribution is assumed for the latent indicator variable, with the rationale being justified at length. CONCLUSION: The Bayesian latent method is successfully applied to real and ten simulated datasets, with comparisons with some of the existing Bayesian methods, hidden Markov model methods, and sliding window methods. The numerical results indicate that the

  3. The whole-genome landscape of medulloblastoma subtypes

    DEFF Research Database (Denmark)

    Northcott, Paul A.; Buchhalter, Ivo; Morrissy, A. Sorana

    2017-01-01

    actionable targets. Driver mutations were confidently assigned to most patients belonging to Group 3 and Group 4 medulloblastoma subgroups, greatly enhancing previous knowledge. New molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions that target...... KBTBD4 and 'enhancer hijacking' events that activate PRDM6. Thus, the application of integrative genomics to an extensive cohort of clinical samples derived from a single childhood cancer entity revealed a series of cancer genes and biologically relevant subtype diversity that represent attractive...

  4. Subtype and pathway specific responses to anticancer compounds in breast cancer.

    Science.gov (United States)

    Heiser, Laura M; Sadanandam, Anguraj; Kuo, Wen-Lin; Benz, Stephen C; Goldstein, Theodore C; Ng, Sam; Gibb, William J; Wang, Nicholas J; Ziyad, Safiyyah; Tong, Frances; Bayani, Nora; Hu, Zhi; Billig, Jessica I; Dueregger, Andrea; Lewis, Sophia; Jakkula, Lakshmi; Korkola, James E; Durinck, Steffen; Pepin, François; Guan, Yinghui; Purdom, Elizabeth; Neuvial, Pierre; Bengtsson, Henrik; Wood, Kenneth W; Smith, Peter G; Vassilev, Lyubomir T; Hennessy, Bryan T; Greshock, Joel; Bachman, Kurtis E; Hardwicke, Mary Ann; Park, John W; Marton, Laurence J; Wolf, Denise M; Collisson, Eric A; Neve, Richard M; Mills, Gordon B; Speed, Terence P; Feiler, Heidi S; Wooster, Richard F; Haussler, David; Stuart, Joshua M; Gray, Joe W; Spellman, Paul T

    2012-02-21

    Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

  5. Fully probabilistic design of hierarchical Bayesian models

    Czech Academy of Sciences Publication Activity Database

    Quinn, A.; Kárný, Miroslav; Guy, Tatiana Valentine

    2016-01-01

    Roč. 369, č. 1 (2016), s. 532-547 ISSN 0020-0255 R&D Projects: GA ČR GA13-13502S Institutional support: RVO:67985556 Keywords : Fully probabilistic design * Ideal distribution * Minimum cross-entropy principle * Bayesian conditioning * Kullback-Leibler divergence * Bayesian nonparametric modelling Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.832, year: 2016 http://library.utia.cas.cz/separaty/2016/AS/karny-0463052.pdf

  6. Flood quantile estimation at ungauged sites by Bayesian networks

    Science.gov (United States)

    Mediero, L.; Santillán, D.; Garrote, L.

    2012-04-01

    Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a

  7. Breast cancer subtypes: two decades of journey from cell culture to patients.

    Science.gov (United States)

    Zhao, Xiangshan; Gurumurthy, Channabasavaiah Basavaraju; Malhotra, Gautam; Mirza, Sameer; Mohibi, Shakur; Bele, Aditya; Quinn, Meghan G; Band, Hamid; Band, Vimla

    2011-01-01

    Recent molecular profiling has identified six major subtypes of breast cancers that exhibit different survival outcomes for patients. To address the origin of different subtypes of breast cancers, we have now identified, isolated, and immortalized (using hTERT) mammary stem/progenitor cells which maintain their stem/progenitor properties even after immortalization. Our decade long research has shown that these stem/progenitor cells are highly susceptible to oncogenesis. Given the emerging evidence that stem/progenitor cells are precursors of cancers and that distinct subtypes of breast cancer have different survival outcome, these cellular models provide novel tools to understand the oncogenic process leading to various subtypes of breast cancers and for future development of novel therapeutic strategies to treat different subtypes of breast cancers.

  8. Bayesian estimation inherent in a Mexican-hat-type neural network

    Science.gov (United States)

    Takiyama, Ken

    2016-05-01

    Brain functions, such as perception, motor control and learning, and decision making, have been explained based on a Bayesian framework, i.e., to decrease the effects of noise inherent in the human nervous system or external environment, our brain integrates sensory and a priori information in a Bayesian optimal manner. However, it remains unclear how Bayesian computations are implemented in the brain. Herein, I address this issue by analyzing a Mexican-hat-type neural network, which was used as a model of the visual cortex, motor cortex, and prefrontal cortex. I analytically demonstrate that the dynamics of an order parameter in the model corresponds exactly to a variational inference of a linear Gaussian state-space model, a Bayesian estimation, when the strength of recurrent synaptic connectivity is appropriately stronger than that of an external stimulus, a plausible condition in the brain. This exact correspondence can reveal the relationship between the parameters in the Bayesian estimation and those in the neural network, providing insight for understanding brain functions.

  9. Comparison of two motor subtype classifications in de novo Parkinson's disease.

    Science.gov (United States)

    Choi, Seong-Min; Kim, Byeong C; Cho, Bang-Hoon; Kang, Kyung Wook; Choi, Kang-Ho; Kim, Joon-Tae; Lee, Seung-Han; Park, Man-Seok; Kim, Myeong-Kyu; Cho, Ki-Hyun

    2018-04-18

    Clinical subtypes of Parkinson's disease (PD) have been empirically defined based on the prominent motor symptoms. The aim of this study was to compare the prevalence of non-motor symptoms across PD motor subtypes in patients with PD. A total of 192 patients with de novo PD were included. The patients were classified into the tremor-dominant/mixed/akinetic-rigid (TD/mixed/AR) and tremor-dominant/mixed/postural instability and gait disturbance (TD/mixed/PIGD) subtypes, according to previous reports. In the TD/mixed/AR classification, scores for scales related to motor symptoms and activities of daily living (ADL) were significantly different among the groups, and patients with the AR subtype demonstrated more severe scores than patients with the TD subtype. In the TD/mixed/PIGD classification, age, age at symptom onset, scores on motor-related scales, ADL, and non-motor symptoms were significantly different among the groups. Scores including the modified Hoehn and Yahr stages, the motor and ADL subscores of the Unified Parkinson's Disease Rating Scale, the Beck Depression Inventory, and the Non-Motor Symptom Assessment Scale were significantly different after adjustments for age and age at symptom onset, and patients with the PIGD subtype obtained more severe scores than patients with the TD subtype. The TD/mixed/PIGD classification seems to be more suitable for identifying non-motor abnormalities than the TD/mixed/AR classification. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. HIV-1 subtypes D and F are prevalent in Guinea Conakry.

    Science.gov (United States)

    Freimanis, G L; Loua, A; Allain, J P

    2012-04-01

    Limited data is available upon the distribution of different HIV-1/2 genotypes in the blood donor population from Guinea Conakry. To investigate the prevalence of HIV-1/2 subtypes in asymptomatic blood donors in Guinea Conakry, in order to update knowledge of HIV-1/2 epidemiology within this country. Samples from 104 blood donors seropositive for HIV-1/2 were tested for HIV-1 by real-time RT-PCR. Those negative for HIV-1 were tested with HIV-2 nested RT-PCR. Positive samples were further amplified in the HIV-1 gag and pol regions and sequenced. Subtypes were determined by phylogenetic analysis on amplicon sequences. 61 samples were positive by HIV-1 real-time RT-PCR. Of the 43 negative, 2 (4.6%) were positive for HIV-2. 52/61 (85.3%) samples were positive by nested RT-PCR. Of the 52, 43 (70.5%) and 31(59.6%) sequences were obtained in the gag and pol regions, respectively; 23 for both regions. HIV-1 subtype distribution was 1 B (2.1%), 8 F (17%), 8 D (17%) and 28 CRF02_AG (59.6%) with 2 unclassified recombinants (4.3%). Unique clusters for subtype D and F distinguished Guinea from HIV-1 subtype distribution in neighboring countries. Subtype F and subtype D strains, uncommon in West Africa, are a substantial part of HIV-1 epidemiology in Guinea. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...

  12. Identification of Multiple Subtypes of Campylobacter jejuni in Chicken Meat and the Impact on Source Attribution

    Directory of Open Access Journals (Sweden)

    John A. Hudson

    2013-09-01

    Full Text Available Most source attribution studies for Campylobacter use subtyping data based on single isolates from foods and environmental sources in an attempt to draw epidemiological inferences. It has been suggested that subtyping only one Campylobacter isolate per chicken carcass incurs a risk of failing to recognise the presence of clinically relevant, but numerically infrequent, subtypes. To investigate this, between 21 and 25 Campylobacter jejuni isolates from each of ten retail chicken carcasses were subtyped by pulsed-field gel electrophoresis (PFGE using the two restriction enzymes SmaI and KpnI. Among the 227 isolates, thirteen subtypes were identified, the most frequently occurring subtype being isolated from three carcasses. Six carcasses carried a single subtype, three carcasses carried two subtypes each and one carcass carried three subtypes. Some subtypes carried by an individual carcass were shown to be potentially clonally related. Comparison of C. jejuni subtypes from chickens with isolate subtypes from human clinical cases (n = 1248 revealed seven of the thirteen chicken subtypes were indistinguishable from human cases. None of the numerically minor chicken subtypes were identified in the human data. Therefore, typing only one Campylobacter isolate from individual chicken carcasses may be adequate to inform Campylobacter source attribution.

  13. Respiratory panic disorder subtype and sensitivity to the carbon dioxide challenge test

    Directory of Open Access Journals (Sweden)

    A.M. Valença

    2002-07-01

    Full Text Available The aim of the present study was to verify the sensitivity to the carbon dioxide (CO2 challenge test of panic disorder (PD patients with respiratory and nonrespiratory subtypes of the disorder. Our hypothesis is that the respiratory subtype is more sensitive to 35% CO2. Twenty-seven PD subjects with or without agoraphobia were classified into respiratory and nonrespiratory subtypes on the basis of the presence of respiratory symptoms during their panic attacks. The tests were carried out in a double-blind manner using two mixtures: 1 35% CO2 and 65% O2, and 2 100% atmospheric compressed air, 20 min apart. The tests were repeated after 2 weeks during which the participants in the study did not receive any psychotropic drugs. At least 15 of 16 (93.7% respiratory PD subtype patients and 5 of 11 (43.4% nonrespiratory PD patients had a panic attack during one of two CO2 challenges (P = 0.009, Fisher exact test. Respiratory PD subtype patients were more sensitive to the CO2 challenge test. There was agreement between the severity of PD measured by the Clinical Global Impression (CGI Scale and the subtype of PD. Higher CGI scores in the respiratory PD subtype could reflect a greater sensitivity to the CO2 challenge due to a greater severity of PD. Carbon dioxide challenges in PD may define PD subtypes and their underlying mechanisms.

  14. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

    Directory of Open Access Journals (Sweden)

    Hsu Hui-Chi

    2011-04-01

    Full Text Available Abstract Background Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. Methods Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. Results We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was

  15. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization

    International Nuclear Information System (INIS)

    Kao, Kuo-Jang; Chang, Kai-Ming; Hsu, Hui-Chi; Huang, Andrew T

    2011-01-01

    Optimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization. Gene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied. We identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic

  16. Subtypes of attention deficit-hyperactivity disorder (ADHD) and cannabis use.

    Science.gov (United States)

    Loflin, Mallory; Earleywine, Mitch; De Leo, Joseph; Hobkirk, Andrea

    2014-03-01

    The current study examined the association between subtypes of attention-deficit/hyperactivity disorder (ADHD) and cannabis use within a sample of 2811 current users. Data were collected in 2012 from a national U.S. survey of cannabis users. A series of logistic regression equations and chi-squares were assessed for proportional differences between users. When asked about the ADHD symptoms they have experienced when not using cannabis, a higher proportion of daily users met symptom criteria for an ADHD diagnoses of the subtypes that include hyperactive-impulsive symptoms than the inattentive subtype. For nondaily users, the proportions of users meeting symptom criteria did not differ by subtype. These results have implications for identifying which individuals with ADHD might be more likely to self-medicate using cannabis. Furthermore, these findings indirectly support research linking relevant cannabinoid receptors to regulatory control.

  17. Comprehension and computation in Bayesian problem solving

    Directory of Open Access Journals (Sweden)

    Eric D. Johnson

    2015-07-01

    Full Text Available Humans have long been characterized as poor probabilistic reasoners when presented with explicit numerical information. Bayesian word problems provide a well-known example of this, where even highly educated and cognitively skilled individuals fail to adhere to mathematical norms. It is widely agreed that natural frequencies can facilitate Bayesian reasoning relative to normalized formats (e.g. probabilities, percentages, both by clarifying logical set-subset relations and by simplifying numerical calculations. Nevertheless, between-study performance on transparent Bayesian problems varies widely, and generally remains rather unimpressive. We suggest there has been an over-focus on this representational facilitator (i.e. transparent problem structures at the expense of the specific logical and numerical processing requirements and the corresponding individual abilities and skills necessary for providing Bayesian-like output given specific verbal and numerical input. We further suggest that understanding this task-individual pair could benefit from considerations from the literature on mathematical cognition, which emphasizes text comprehension and problem solving, along with contributions of online executive working memory, metacognitive regulation, and relevant stored knowledge and skills. We conclude by offering avenues for future research aimed at identifying the stages in problem solving at which correct versus incorrect reasoners depart, and how individual difference might influence this time point.

  18. Being Bayesian in a quantum world

    International Nuclear Information System (INIS)

    Fuchs, C.

    2005-01-01

    Full text: To be a Bayesian about probability theory is to accept that probabilities represent subjective degrees of belief and nothing more. This is in distinction to the idea that probabilities represent long-term frequencies or objective propensities. But, how can a subjective account of probabilities coexist with the existence of quantum mechanics? To accept quantum mechanics is to accept the calculational apparatus of quantum states and the Born rule for determining probabilities in a quantum measurement. If there ever were a place for probabilities to be objective, it ought to be here. This raises the question of whether Bayesianism and quantum mechanics are compatible at all. For the Bayesian, it only suggests that we should rethink what quantum mechanics is about. Is it 'law of nature' or really more 'law of thought'? From transistors to lasers, the evidence is in that we live in a quantum world. One could infer from this that all the elements in the quantum formalism necessarily mirror nature itself: wave functions are so successful as calculational tools precisely because they represent elements of reality. A more Bayesian-like perspective is that if wave functions generate probabilities, then they too must be Bayesian degrees of belief, with all that such a radical idea entails. In particular, quantum probabilities have no firmer hold on reality than the word 'belief' in 'degrees of belief' already indicates. From this perspective, the only sense in which the quantum formalism mirrors nature is through the constraints it places on gambling agents who would like to better navigate through world. One might think that this is thin information, but it is not insubstantial. To the extent that an agent should use quantum mechanics for his uncertainty accounting rather than some other theory tells us something about the world itself - i.e., the world independent of the agent and his particular beliefs at any moment. In this talk, I will try to shore up these

  19. Bayesian approach for the reliability assessment of corroded interdependent pipe networks

    International Nuclear Information System (INIS)

    Ait Mokhtar, El Hassene; Chateauneuf, Alaa; Laggoune, Radouane

    2016-01-01

    Pipelines under corrosion are subject to various environment conditions, and consequently it becomes difficult to build realistic corrosion models. In the present work, a Bayesian methodology is proposed to allow for updating the corrosion model parameters according to the evolution of environmental conditions. For reliability assessment of dependent structures, Bayesian networks are used to provide interesting qualitative and quantitative description of the information in the system. The qualitative contribution lies in the modeling of complex system, composed by dependent pipelines, as a Bayesian network. The quantitative one lies in the evaluation of the dependencies between pipelines by the use of a new method for the generation of conditional probability tables. The effectiveness of Bayesian updating is illustrated through an application where the new reliability of degraded (corroded) pipe networks is assessed. - Highlights: • A methodology for Bayesian network modeling of pipe networks is proposed. • Bayesian approach based on Metropolis - Hastings algorithm is conducted for corrosion model updating. • The reliability of corroded pipe network is assessed by considering the interdependencies between the pipelines.

  20. Detection and subtyping avian metapneumovirus from turkeys in Iran.

    Science.gov (United States)

    Mayahi, Mansour; Momtaz, Hassan; Jafari, Ramezan Ali; Zamani, Pejman

    2017-01-01

    Avian metapneumovirus (aMPV) causes diseases like rhinotracheitis in turkeys, swollen head syndrome in chickens and avian rhinotracheitis in other birds. Causing respiratory problems, aMPV adversely affects production and inflicts immense economic losses and mortalities, especially in turkey flocks. In recent years, several serological and molecular studies have been conducted on this virus, especially in poultry in Asia and Iran. The purpose of the present study was detecting and subtyping aMPV by reverse transcriptase polymerase chain reaction (RT-PCR) from non-vaccinated, commercial turkey flocks in Iran for the first time. Sixty three meat-type unvaccinated turkey flocks from several provinces of Iran were sampled in major turkey abattoirs. Samples were tested by RT-PCR for detecting and subtyping aMPV. The results showed that 26 samples from three flocks (4.10%) were positive for viral RNA and all of the viruses were found to be subtype B of aMPV. As a result, vaccination especially against subtype B of aMPV should be considered in turkey flocks in Iran to control aMPV infections.

  1. Bayesian target tracking based on particle filter

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    For being able to deal with the nonlinear or non-Gaussian problems, particle filters have been studied by many researchers. Based on particle filter, the extended Kalman filter (EKF) proposal function is applied to Bayesian target tracking. Markov chain Monte Carlo (MCMC) method, the resampling step, etc novel techniques are also introduced into Bayesian target tracking. And the simulation results confirm the improved particle filter with these techniques outperforms the basic one.

  2. Noncausal Bayesian Vector Autoregression

    DEFF Research Database (Denmark)

    Lanne, Markku; Luoto, Jani

    We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...

  3. The Genetic Diversity and Evolution of HIV-1 Subtype B Epidemic in Puerto Rico.

    Science.gov (United States)

    López, Pablo; Rivera-Amill, Vanessa; Rodríguez, Nayra; Vargas, Freddie; Yamamura, Yasuhiro

    2015-12-23

    HIV-1 epidemics in Caribbean countries, including Puerto Rico, have been reported to be almost exclusively associated with the subtype B virus (HIV-1B). However, while HIV infections associated with other clades have been only sporadically reported, no organized data exist to accurately assess the prevalence of non-subtype B HIV-1 infection. We analyzed the nucleotide sequence data of the HIV pol gene associated with HIV isolates from Puerto Rican patients. The sequences (n = 945) were obtained from our "HIV Genotyping" test file, which has been generated over a period of 14 years (2001-2014). REGA subtyping tool found the following subtypes: B (90%), B-like (3%), B/D recombinant (6%), and D/B recombinant (0.6%). Though there were fewer cases, the following subtypes were also found (in the given proportions): A1B (0.3%), BF1 (0.2%), subtype A (01-AE) (0.1%), subtype A (A2) (0.1%), subtype F (12BF) (0.1%), CRF-39 BF-like (0.1%), and others (0.1%). Some of the recombinants were identified as early as 2001. Although the HIV epidemic in Puerto Rico is primarily associated with HIV-1B virus, our analysis uncovered the presence of other subtypes. There was no indication of subtype C, which has been predominantly associated with heterosexual transmission in other parts of the world.

  4. The Genetic Diversity and Evolution of HIV-1 Subtype B Epidemic in Puerto Rico

    Directory of Open Access Journals (Sweden)

    Pablo López

    2015-12-01

    Full Text Available HIV-1 epidemics in Caribbean countries, including Puerto Rico, have been reported to be almost exclusively associated with the subtype B virus (HIV-1B. However, while HIV infections associated with other clades have been only sporadically reported, no organized data exist to accurately assess the prevalence of non-subtype B HIV-1 infection. We analyzed the nucleotide sequence data of the HIV pol gene associated with HIV isolates from Puerto Rican patients. The sequences (n = 945 were obtained from our “HIV Genotyping” test file, which has been generated over a period of 14 years (2001–2014. REGA subtyping tool found the following subtypes: B (90%, B-like (3%, B/D recombinant (6%, and D/B recombinant (0.6%. Though there were fewer cases, the following subtypes were also found (in the given proportions: A1B (0.3%, BF1 (0.2%, subtype A (01-AE (0.1%, subtype A (A2 (0.1%, subtype F (12BF (0.1%, CRF-39 BF-like (0.1%, and others (0.1%. Some of the recombinants were identified as early as 2001. Although the HIV epidemic in Puerto Rico is primarily associated with HIV-1B virus, our analysis uncovered the presence of other subtypes. There was no indication of subtype C, which has been predominantly associated with heterosexual transmission in other parts of the world.

  5. Association Between Imaging Characteristics and Different Molecular Subtypes of Breast Cancer.

    Science.gov (United States)

    Wu, Mingxiang; Ma, Jie

    2017-04-01

    Breast cancer can be divided into four major molecular subtypes based on the expression of hormone receptor (estrogen receptor and progesterone receptor), human epidermal growth factor receptor 2, HER2 status, and molecular proliferation rate (Ki67). In this study, we sought to investigate the association between breast cancer subtype and radiological findings in the Chinese population. Medical records of 300 consecutive invasive breast cancer patients were reviewed from the database: the Breast Imaging Reporting and Data System. The imaging characteristics of the lesions were evaluated. The molecular subtypes of breast cancer were classified into four types: luminal A, luminal B, HER2 overexpressed (HER2), and basal-like breast cancer (BLBC). Univariate and multivariate logistic regression analyses were performed to assess the association between the subtype (dependent variable) and mammography or 15 magnetic resonance imaging (MRI) indicators (independent variables). Luminal A and B subtypes were commonly associated with "clustered calcification distribution," "nipple invasion," or "skin invasion" (P cancers showed association with persistent enhancement in the delayed phase on MRI and "clustered calcification distribution" on mammography (P breast tumor, which are potentially useful tools in the diagnosis and subtyping of breast cancer. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  6. Sleep and daytime function in adults with attention-deficit/hyperactivity disorder: subtype differences.

    Science.gov (United States)

    Yoon, Sun Young Rosalia; Jain, Umesh Ravi; Shapiro, Colin Michael

    2013-07-01

    Although sleep disorders have been reported to affect more than half of adults with attention-deficit/hyperactivity disorder (ADHD), the association between sleep and ADHD is poorly understood. The aims of our study were to investigate sleep-related variables in adults with ADHD and to assess if any differences exist between ADHD of the predominantly inattentive (ADHD-I) and combined (ADHD-C) subtypes. We used the Epworth sleepiness scale (ESS), the Pittsburgh Sleep Quality Index (PSQI), and the fatigue severity scale (FSS) to collect data on daytime sleepiness, sleep quality, and fatigue in 126 subjects (45 ADHD-I and 81 ADHD-C subjects). Approximately 85% of subjects reported excessive daytime sleepiness or poor sleep quality. The most common sleep concerns were initial insomnia, interrupted sleep, and feeling too hot. When examining ADHD subtype differences, ADHD-I subtypes reported poorer sleep quality and more fatigue than ADHD-C subtypes. Partial correlation analyses revealed that interrelationships between sleep quality, daytime sleepiness, and fatigue differ between ADHD subtypes; in ADHD-I subtypes fatigue was associated with sleep quality, while in the ADHD-C subtypes fatigue was associated with both sleep quality and daytime sleepiness. There also appears to be a subtype×gender interaction that affects the perception of fatigue, as subjective fatigue was markedly higher in ADHD-I women than in ADHD-C women. Altogether our data indicate that the interplay of variables associated with daytime function and sleep varies between ADHD subtypes. This finding may have considerable relevance in the management and pathophysiologic understanding of ADHD, and thus lead to tailored treatments for ADHD subtypes. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

  7. Distinguishing subtypes of extrinsic motivation among people with mild to borderline intellectual disability.

    Science.gov (United States)

    Frielink, N; Schuengel, C; Embregts, P

    2017-07-01

    According to self-determination theory, motivation is ordered in types, including amotivation, extrinsic motivation and intrinsic motivation. Self-determination theory defines four subtypes of extrinsic motivation: external motivation, introjected motivation, identified motivation and integrated motivation. Although it has been argued theoretically that the different types of motivation are universally applicable, Reid et al. () proposed a dichotomy of broad subtypes of extrinsic motivation for people with intellectual disability (ID) due to their cognitive limitations. The current study challenges this proposal by testing whether the four subtypes of extrinsic motivation can be differentiated among people with ID as well. The subtypes of extrinsic motivation were measured using two adapted versions of the Self-Regulation Questionnaire, one regarding exercise and one regarding support. In total, 186 adults with mild to borderline ID participated in the study. Results supported the distinction between the four subtypes of extrinsic motivation regarding both exercise and support. In addition, the correlation coefficients supported a quasi-simplex pattern of correlations among the subtypes, indicating that adjacent subtypes were more closely related than non-adjacent subtypes. Moreover, the study showed sufficient Cronbach's alphas and test-retest reliabilities for early stage research. Overall, the results of the current study provide initial evidence for the universality of the four subtypes of extrinsic motivation across populations with and without ID. © 2017 The Authors. Journal of Intellectual Disability Research published by MENCAP and International Association of the Scientific Study of Intellectual and Developmental Disibilities and John Wiley & Sons Ltd.

  8. Bayesian mixture models for assessment of gene differential behaviour and prediction of pCR through the integration of copy number and gene expression data.

    Directory of Open Access Journals (Sweden)

    Filippo Trentini

    Full Text Available We consider modeling jointly microarray RNA expression and DNA copy number data. We propose Bayesian mixture models that define latent Gaussian probit scores for the DNA and RNA, and integrate between the two platforms via a regression of the RNA probit scores on the DNA probit scores. Such a regression conveniently allows us to include additional sample specific covariates such as biological conditions and clinical outcomes. The two developed methods are aimed respectively to make inference on differential behaviour of genes in patients showing different subtypes of breast cancer and to predict the pathological complete response (pCR of patients borrowing strength across the genomic platforms. Posterior inference is carried out via MCMC simulations. We demonstrate the proposed methodology using a published data set consisting of 121 breast cancer patients.

  9. Bayesian statistical inference

    Directory of Open Access Journals (Sweden)

    Bruno De Finetti

    2017-04-01

    Full Text Available This work was translated into English and published in the volume: Bruno De Finetti, Induction and Probability, Biblioteca di Statistica, eds. P. Monari, D. Cocchi, Clueb, Bologna, 1993.Bayesian statistical Inference is one of the last fundamental philosophical papers in which we can find the essential De Finetti's approach to the statistical inference.

  10. Bayesian Exponential Smoothing.

    OpenAIRE

    Forbes, C.S.; Snyder, R.D.; Shami, R.S.

    2000-01-01

    In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by providing both point predictions and measures of the uncertainty surrounding them.

  11. The Complex Subtype-Dependent Role of Connexin 43 (GJA1 in Breast Cancer

    Directory of Open Access Journals (Sweden)

    Mélanie Busby

    2018-02-01

    Full Text Available Gap junction transmembrane channels allow the transfer of small molecules between the cytoplasm of adjacent cells. They are formed by proteins named connexins (Cxs that have long been considered as a tumor suppressor. This widespread view has been challenged by recent studies suggesting that the role of Connexin 43 (Cx43 in cancer is tissue- and stage-specific and can even promote tumor progression. High throughput profiling of invasive breast cancer has allowed for the construction of subtyping schemes that partition patients into at least four distinct intrinsic subtypes. This study characterizes Cx43 expression during cancer progression with each of the tumor subtypes using a compendium of publicly available gene expression data. In particular, we show that Cx43 expression depends greatly on intrinsic subtype. Tumor grade also co-varies with patient subtype, resulting in Cx43 co-expression with grade in a subtype-dependent manner. Better survival was associated with a high expression of Cx43 in unstratified and luminal tumors but with a low expression in Her2e subtype. A better understanding of Cx43 regulation in a subtype-dependent manner is needed to clarify the context in which Cx43 is associated with tumor suppression or cancer progression.

  12. The Complex Subtype-Dependent Role of Connexin 43 (GJA1) in Breast Cancer

    Science.gov (United States)

    Busby, Mélanie; Hallett, Michael T.; Plante, Isabelle

    2018-01-01

    Gap junction transmembrane channels allow the transfer of small molecules between the cytoplasm of adjacent cells. They are formed by proteins named connexins (Cxs) that have long been considered as a tumor suppressor. This widespread view has been challenged by recent studies suggesting that the role of Connexin 43 (Cx43) in cancer is tissue- and stage-specific and can even promote tumor progression. High throughput profiling of invasive breast cancer has allowed for the construction of subtyping schemes that partition patients into at least four distinct intrinsic subtypes. This study characterizes Cx43 expression during cancer progression with each of the tumor subtypes using a compendium of publicly available gene expression data. In particular, we show that Cx43 expression depends greatly on intrinsic subtype. Tumor grade also co-varies with patient subtype, resulting in Cx43 co-expression with grade in a subtype-dependent manner. Better survival was associated with a high expression of Cx43 in unstratified and luminal tumors but with a low expression in Her2e subtype. A better understanding of Cx43 regulation in a subtype-dependent manner is needed to clarify the context in which Cx43 is associated with tumor suppression or cancer progression. PMID:29495625

  13. Power in Bayesian Mediation Analysis for Small Sample Research

    NARCIS (Netherlands)

    Miočević, M.; MacKinnon, David; Levy, Roy

    2017-01-01

    Bayesian methods have the potential for increasing power in mediation analysis (Koopman, Howe, Hollenbeck, & Sin, 2015; Yuan & MacKinnon, 2009). This article compares the power of Bayesian credibility intervals for the mediated effect to the power of normal theory, distribution of the product,

  14. Bayesian analyses of seasonal runoff forecasts

    Science.gov (United States)

    Krzysztofowicz, R.; Reese, S.

    1991-12-01

    Forecasts of seasonal snowmelt runoff volume provide indispensable information for rational decision making by water project operators, irrigation district managers, and farmers in the western United States. Bayesian statistical models and communication frames have been researched in order to enhance the forecast information disseminated to the users, and to characterize forecast skill from the decision maker's point of view. Four products are presented: (i) a Bayesian Processor of Forecasts, which provides a statistical filter for calibrating the forecasts, and a procedure for estimating the posterior probability distribution of the seasonal runoff; (ii) the Bayesian Correlation Score, a new measure of forecast skill, which is related monotonically to the ex ante economic value of forecasts for decision making; (iii) a statistical predictor of monthly cumulative runoffs within the snowmelt season, conditional on the total seasonal runoff forecast; and (iv) a framing of the forecast message that conveys the uncertainty associated with the forecast estimates to the users. All analyses are illustrated with numerical examples of forecasts for six gauging stations from the period 1971 1988.

  15. Bayesian methodology for reliability model acceptance

    International Nuclear Information System (INIS)

    Zhang Ruoxue; Mahadevan, Sankaran

    2003-01-01

    This paper develops a methodology to assess the reliability computation model validity using the concept of Bayesian hypothesis testing, by comparing the model prediction and experimental observation, when there is only one computational model available to evaluate system behavior. Time-independent and time-dependent problems are investigated, with consideration of both cases: with and without statistical uncertainty in the model. The case of time-independent failure probability prediction with no statistical uncertainty is a straightforward application of Bayesian hypothesis testing. However, for the life prediction (time-dependent reliability) problem, a new methodology is developed in this paper to make the same Bayesian hypothesis testing concept applicable. With the existence of statistical uncertainty in the model, in addition to the application of a predictor estimator of the Bayes factor, the uncertainty in the Bayes factor is explicitly quantified through treating it as a random variable and calculating the probability that it exceeds a specified value. The developed method provides a rational criterion to decision-makers for the acceptance or rejection of the computational model

  16. Geographic distribution of hepatitis C virus genotype 6 subtypes in Thailand.

    Science.gov (United States)

    Akkarathamrongsin, Srunthron; Praianantathavorn, Kesmanee; Hacharoen, Nisachol; Theamboonlers, Apiradee; Tangkijvanich, Pisit; Tanaka, Yasuhito; Mizokami, Masashi; Poovorawan, Yong

    2010-02-01

    The nucleotide sequence of hepatitis C virus (HCV) genotype 6 found mostly in south China and south-east Asia, displays profound genetic diversity. The aim of this study to determine the genetic variability of HCV genotype 6 (HCV-6) in Thailand and locate the subtype distribution of genotype 6 in various geographic areas. Four hundred nineteen anti-HCV positive serum samples were collected from patients residing in - the central part of the country. HCV RNA positive samples based on reverse transcriptase- polymerase chain reaction (RT-PCR) of the 5'UTR were amplified with primers specific for the core and NS5B regions. Nucleotide sequences of both regions were analyzed for the genotype by phylogenetic analysis. To determine geographic distribution of HCV-6 subtypes, a search of the international database on subtype distribution in the respective countries was conducted. Among 375 HCV RNA positive samples, 71 had HCV-6 based on phylogenetic analysis of partial core and NS5B regions. The subtype distribution in order of predominance was 6f (56%), 6n (22%), 6i (11%), 6j (10%), and 6e (1%). Among the 13 countries with different subtypes of HCV-6, most sequences have been reported from Vietnam. Subtype 6f was found exclusively in Thailand where five distinct HCV-6 subtypes are circulating. HCV-6, which is endemic in south China and south-east Asia, displays profound genetic diversity and may have evolved over a considerable period of time. (c) 2009 Wiley-Liss, Inc.

  17. A DNA methylation-based definition of biologically distinct breast cancer subtypes.

    Science.gov (United States)

    Stefansson, Olafur A; Moran, Sebastian; Gomez, Antonio; Sayols, Sergi; Arribas-Jorba, Carlos; Sandoval, Juan; Hilmarsdottir, Holmfridur; Olafsdottir, Elinborg; Tryggvadottir, Laufey; Jonasson, Jon G; Eyfjord, Jorunn; Esteller, Manel

    2015-03-01

    In cancer, epigenetic states are deregulated and thought to be of significance in cancer development and progression. We explored DNA methylation-based signatures in association with breast cancer subtypes to assess their impact on clinical presentation and patient prognosis. DNA methylation was analyzed using Infinium 450K arrays in 40 tumors and 17 normal breast samples, together with DNA copy number changes and subtype-specific markers by tissue microarrays. The identified methylation signatures were validated against a cohort of 212 tumors annotated for breast cancer subtypes by the PAM50 method (The Cancer Genome Atlas). Selected markers were pyrosequenced in an independent validation cohort of 310 tumors and analyzed with respect to survival, clinical stage and grade. The results demonstrate that DNA methylation patterns linked to the luminal-B subtype are characterized by CpG island promoter methylation events. In contrast, a large fraction of basal-like tumors are characterized by hypomethylation events occurring within the gene body. Based on these hallmark signatures, we defined two DNA methylation-based subtypes, Epi-LumB and Epi-Basal, and show that they are associated with unfavorable clinical parameters and reduced survival. Our data show that distinct mechanisms leading to changes in CpG methylation states are operative in different breast cancer subtypes. Importantly, we show that a few selected proxy markers can be used to detect the distinct DNA methylation-based subtypes thereby providing valuable information on disease prognosis. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

  18. Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

    Science.gov (United States)

    Li, L.; Xu, C.-Y.; Engeland, K.

    2012-04-01

    With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

  19. The Association between Physical Morbidity and Subtypes of Severe Depression

    DEFF Research Database (Denmark)

    Østergaard, Søren Dinesen; Petrides, Georgio; Dinesen, Peter Thisted

    2013-01-01

    Physical illness and depression are related, but the association between specific physical diseases and diagnostic subtypes of depression remains poorly understood. This study aimed to clarify the relationship between a number of physical diseases and the nonpsychotic and psychotic subtype...... of severe depression....

  20. Ethnic variation of the histological subtypes of renal cell carcinoma ...

    African Journals Online (AJOL)

    E.V. Ezenwa

    The content of the data obtained included the ethnicity categorized as Chinese, Malays, Indians and others (Indonesians, Vietnamese and other minor groups). Other data collected included age, gender and the histological subtype categorized as clear cell, papillary, chromophobe, collecting duct and unclassified subtypes.

  1. Differential affinity of mammalian histone H1 somatic subtypes for DNA and chromatin

    Directory of Open Access Journals (Sweden)

    Mora Xavier

    2007-05-01

    Full Text Available Abstract Background Histone H1 is involved in the formation and maintenance of chromatin higher order structure. H1 has multiple isoforms; the subtypes differ in timing of expression, extent of phosphorylation and turnover rate. In vertebrates, the amino acid substitution rates differ among subtypes by almost one order of magnitude, suggesting that each subtype might have acquired a unique function. We have devised a competitive assay to estimate the relative binding affinities of histone H1 mammalian somatic subtypes H1a-e and H1° for long chromatin fragments (30–35 nucleosomes in physiological salt (0.14 M NaCl at constant stoichiometry. Results The H1 complement of native chromatin was perturbed by adding an additional amount of one of the subtypes. A certain amount of SAR (scaffold-associated region DNA was present in the mixture to avoid precipitation of chromatin by excess H1. SAR DNA also provided a set of reference relative affinities, which were needed to estimate the relative affinities of the subtypes for chromatin from the distribution of the subtypes between the SAR and the chromatin. The amounts of chromatin, SAR and additional H1 were adjusted so as to keep the stoichiometry of perturbed chromatin similar to that of native chromatin. H1 molecules freely exchanged between the chromatin and SAR binding sites. In conditions of free exchange, H1a was the subtype of lowest affinity, H1b and H1c had intermediate affinities and H1d, H1e and H1° the highest affinities. Subtype affinities for chromatin differed by up to 19-fold. The relative affinities of the subtypes for chromatin were equivalent to those estimated for a SAR DNA fragment and a pUC19 fragment of similar length. Avian H5 had an affinity ~12-fold higher than H1e for both DNA and chromatin. Conclusion H1 subtypes freely exchange in vitro between chromatin binding sites in physiological salt (0.14 M NaCl. The large differences in relative affinity of the H1 subtypes for

  2. Applications of Bayesian decision theory to intelligent tutoring systems

    NARCIS (Netherlands)

    Vos, Hendrik J.

    1994-01-01

    Some applications of Bayesian decision theory to intelligent tutoring systems are considered. How the problem of adapting the appropriate amount of instruction to the changing nature of a student's capabilities during the learning process can be situated in the general framework of Bayesian decision

  3. Spatial and spatio-temporal bayesian models with R - INLA

    CERN Document Server

    Blangiardo, Marta

    2015-01-01

    Dedication iiiPreface ix1 Introduction 11.1 Why spatial and spatio-temporal statistics? 11.2 Why do we use Bayesian methods for modelling spatial and spatio-temporal structures? 21.3 Why INLA? 31.4 Datasets 32 Introduction to 212.1 The language 212.2 objects 222.3 Data and session management 342.4 Packages 352.5 Programming in 362.6 Basic statistical analysis with 393 Introduction to Bayesian Methods 533.1 Bayesian Philosophy 533.2 Basic Probability Elements 573.3 Bayes Theorem 623.4 Prior and Posterior Distributions 643.5 Working with the Posterior Distribution 663.6 Choosing the Prior Distr

  4. Global DNA methylation of ischemic stroke subtypes.

    Directory of Open Access Journals (Sweden)

    Carolina Soriano-Tárraga

    Full Text Available Ischemic stroke (IS, a heterogeneous multifactorial disorder, is among the leading causes of mortality and long-term disability in the western world. Epidemiological data provides evidence for a genetic component to the disease, but its epigenetic involvement is still largely unknown. Epigenetic mechanisms, such as DNA methylation, change over time and may be associated with aging processes and with modulation of the risk of various pathologies, such as cardiovascular disease and stroke. We analyzed 2 independent cohorts of IS patients. Global DNA methylation was measured by luminometric methylation assay (LUMA of DNA blood samples. Univariate and multivariate regression analyses were used to assess the methylation differences between the 3 most common IS subtypes, large-artery atherosclerosis (LAA, small-artery disease (SAD, and cardio-aortic embolism (CE. A total of 485 IS patients from 2 independent hospital cohorts (n = 281 and n = 204 were included, distributed across 3 IS subtypes: LAA (78/281, 59/204, SAD (97/281, 53/204, and CE (106/281, 89/204. In univariate analyses, no statistical differences in LUMA levels were observed between the 3 etiologies in either cohort. Multivariate analysis, adjusted by age, sex, hyperlipidemia, and smoking habit, confirmed the lack of differences in methylation levels between the analyzed IS subtypes in both cohorts. Despite differences in pathogenesis, our results showed no global methylation differences between LAA, SAD, and CE subtypes of IS. Further work is required to establish whether the epigenetic mechanism of methylation might play a role in this complex disease.

  5. Imaging features of automated breast volume scanner: Correlation with molecular subtypes of breast cancer

    Energy Technology Data Exchange (ETDEWEB)

    Zheng, Feng-Yang, E-mail: fyzheng16@fudan.edu.cn [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Shanghai Institute of Medical Imaging, Shanghai 200032 (China); Lu, Qing, E-mail: lu.qing@zs-hospital.sh.cn [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Huang, Bei-Jian, E-mail: huang.beijian@zs-hospital.sh.cn [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Shanghai Institute of Medical Imaging, Shanghai 200032 (China); Xia, Han-Sheng, E-mail: zs12036@126.com [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Yan, Li-Xia, E-mail: dndyanlixia@163.com [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Wang, Xi, E-mail: wang.xi@zs-hospital.sh.cn [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Shanghai Institute of Medical Imaging, Shanghai 200032 (China); Yuan, Wei, E-mail: yuan.wei@zs-hospital.sh.cn [Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Wang, Wen-Ping, E-mail: wang.wenping@zs-hospital.sh.cn [Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai 200032 (China); Shanghai Institute of Medical Imaging, Shanghai 200032 (China)

    2017-01-15

    Highlights: • ABVS imaging features have a strong correlation with breast cancer molecular subtypes. • Retraction phenomenon on the coronal planes was the most important predictor for Luminal A and Triple Negative subtypes. • ABVS expand the scope of ultrasound in identifying breast cancer molecular subtypes. - Abstract: Objectives: To investigate the correlation between the imaging features obtained by an automated breast volume scanner (ABVS) and molecular subtypes of breast cancer. Methods: We examined 303 malignant breast tumours by ABVS for specific imaging features and by immunohistochemical analysis to determine the molecular subtype. ABVS imaging features, including retraction phenomenon, shape, margins, echogenicity, post-acoustic features, echogenic halo, and calcifications were analysed by univariate and multivariate logistic regression analyses to determine the significant predictive factors of the molecular subtypes. Results: By univariate logistic regression analysis, the predictive factors of the Luminal-A subtype (n = 128) were retraction phenomenon (odds ratio [OR] = 10.188), post-acoustic shadowing (OR = 5.112), and echogenic halo (OR = 3.263, P < 0.001). The predictive factors of the Human-epidermal-growth-factor-receptor-2-amplified subtype (n = 39) were calcifications (OR = 6.210), absence of retraction phenomenon (OR = 4.375), non-mass lesions (OR = 4.286, P < 0.001), absence of echogenic halo (OR = 3.851, P = 0.035), and post-acoustic enhancement (OR = 3.641, P = 0.008). The predictors for the Triple-Negative subtype (n = 47) were absence of retraction phenomenon (OR = 5.884), post-acoustic enhancement (OR = 5.255, P < 0.001), absence of echogenic halo (OR = 4.138, P = 0.002), and absence of calcifications (OR = 3.363, P = 0.001). Predictors for the Luminal-B subtype (n = 89) had a relatively lower association (OR ≤ 2.328). By multivariate logistic regression analysis, retraction phenomenon was the strongest independent predictor for

  6. Imaging features of automated breast volume scanner: Correlation with molecular subtypes of breast cancer

    International Nuclear Information System (INIS)

    Zheng, Feng-Yang; Lu, Qing; Huang, Bei-Jian; Xia, Han-Sheng; Yan, Li-Xia; Wang, Xi; Yuan, Wei; Wang, Wen-Ping

    2017-01-01

    Highlights: • ABVS imaging features have a strong correlation with breast cancer molecular subtypes. • Retraction phenomenon on the coronal planes was the most important predictor for Luminal A and Triple Negative subtypes. • ABVS expand the scope of ultrasound in identifying breast cancer molecular subtypes. - Abstract: Objectives: To investigate the correlation between the imaging features obtained by an automated breast volume scanner (ABVS) and molecular subtypes of breast cancer. Methods: We examined 303 malignant breast tumours by ABVS for specific imaging features and by immunohistochemical analysis to determine the molecular subtype. ABVS imaging features, including retraction phenomenon, shape, margins, echogenicity, post-acoustic features, echogenic halo, and calcifications were analysed by univariate and multivariate logistic regression analyses to determine the significant predictive factors of the molecular subtypes. Results: By univariate logistic regression analysis, the predictive factors of the Luminal-A subtype (n = 128) were retraction phenomenon (odds ratio [OR] = 10.188), post-acoustic shadowing (OR = 5.112), and echogenic halo (OR = 3.263, P < 0.001). The predictive factors of the Human-epidermal-growth-factor-receptor-2-amplified subtype (n = 39) were calcifications (OR = 6.210), absence of retraction phenomenon (OR = 4.375), non-mass lesions (OR = 4.286, P < 0.001), absence of echogenic halo (OR = 3.851, P = 0.035), and post-acoustic enhancement (OR = 3.641, P = 0.008). The predictors for the Triple-Negative subtype (n = 47) were absence of retraction phenomenon (OR = 5.884), post-acoustic enhancement (OR = 5.255, P < 0.001), absence of echogenic halo (OR = 4.138, P = 0.002), and absence of calcifications (OR = 3.363, P = 0.001). Predictors for the Luminal-B subtype (n = 89) had a relatively lower association (OR ≤ 2.328). By multivariate logistic regression analysis, retraction phenomenon was the strongest independent predictor for

  7. Distribution of human immunodeficiency virus type 1 subtypes in the State of Amazonas, Brazil, and subtype C identification

    International Nuclear Information System (INIS)

    Cunha, L.K.H.; Kashima, S.; Amarante, M.F.C.; Haddad, R.; Rodrigues, E.S.; Silva, K.L.T.; Lima, T.A.; Castro, D.B.; Brito, F.C.; Almeida, E.G.; Covas, D.T.; Malheiro, A.

    2012-01-01

    Few studies have reported the molecular epidemiological characterization of HIV-1 in the Northern region of Brazil. The present study reports the molecular and epidemiological characterization of 31 HIV-1 isolates from blood donors from the State of Amazonas who donated blood between April 2006 and March 2007. Serum/plasma samples from all donors were screened for HIV antibodies by ELISA and the results confirmed by Western blot analysis. Genomic DNA was extracted from the buffy coat using the Super Quik-Gene-DNA Isolation kit. Nested PCR was performed on the env, gag, and pol regions of HIV-1 using the Gene Amp PCR System 9700. Sequencing reactions were performed using the inner PCR primers and the DYEnamic™ ET Dye Terminator Kit, and phylogenetic analysis was performed using the gag, pol, and env gene sequences. We collected samples from 31 blood donors who tested positive for HIV-1 in confirmatory experiments. The male:female ratio of blood donors was 3.4:1, and the mean age was 32.4 years (range: 19 to 61 years). Phylogenetic analysis showed that subtype B is the most prevalent among Northern Brazilian HIV-1-seropositive blood donors. One HIV-1 subtype C and one circulating recombinant form (CRF-BF) of HIV-1 were identified in the State of Amazonas. This is the first study showing the occurrence of a possible “homogenous” subtype C in this region of Brazil. This finding could contribute to a better characterization of the HIV-1 strains that circulate in the country. Key words: HIV-1; Subtypes; Phylogenetic analysis; Blood donors; Molecular and epidemiological characterization

  8. Distribution of human immunodeficiency virus type 1 subtypes in the State of Amazonas, Brazil, and subtype C identification

    Energy Technology Data Exchange (ETDEWEB)

    Cunha, L.K.H. [Departamento de Parasitologia, Universidade Federal do Amazonas, Manaus, AM (Brazil); Kashima, S.; Amarante, M.F.C.; Haddad, R.; Rodrigues, E.S. [Laboratório de Biologia Molecular, Hemocentro de Ribeirão Preto, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP (Brazil); Silva, K.L.T.; Lima, T.A.; Castro, D.B.; Brito, F.C.; Almeida, E.G. [Diretoria de Ensino e Pesquisa,Fundação de Hematologia e Hemoterapia do Amazonas, Manaus, AM (Brazil); Covas, D.T. [Laboratório de Biologia Molecular, Hemocentro de Ribeirão Preto, Faculdade de Medicina de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, SP (Brazil); Malheiro, A. [Departamento de Parasitologia, Universidade Federal do Amazonas, Manaus, AM (Brazil); Diretoria de Ensino e Pesquisa,Fundação de Hematologia e Hemoterapia do Amazonas, Manaus, AM (Brazil)

    2012-01-20

    Few studies have reported the molecular epidemiological characterization of HIV-1 in the Northern region of Brazil. The present study reports the molecular and epidemiological characterization of 31 HIV-1 isolates from blood donors from the State of Amazonas who donated blood between April 2006 and March 2007. Serum/plasma samples from all donors were screened for HIV antibodies by ELISA and the results confirmed by Western blot analysis. Genomic DNA was extracted from the buffy coat using the Super Quik-Gene-DNA Isolation kit. Nested PCR was performed on the env, gag, and pol regions of HIV-1 using the Gene Amp PCR System 9700. Sequencing reactions were performed using the inner PCR primers and the DYEnamic™ ET Dye Terminator Kit, and phylogenetic analysis was performed using the gag, pol, and env gene sequences. We collected samples from 31 blood donors who tested positive for HIV-1 in confirmatory experiments. The male:female ratio of blood donors was 3.4:1, and the mean age was 32.4 years (range: 19 to 61 years). Phylogenetic analysis showed that subtype B is the most prevalent among Northern Brazilian HIV-1-seropositive blood donors. One HIV-1 subtype C and one circulating recombinant form (CRF-BF) of HIV-1 were identified in the State of Amazonas. This is the first study showing the occurrence of a possible “homogenous” subtype C in this region of Brazil. This finding could contribute to a better characterization of the HIV-1 strains that circulate in the country. Key words: HIV-1; Subtypes; Phylogenetic analysis; Blood donors; Molecular and epidemiological characterization.

  9. Hepatitis C virus sequences from different patients confirm the existence and transmissibility of subtype 2q, a rare subtype circulating in the metropolitan area of Barcelona, Spain.

    Science.gov (United States)

    Martró, Elisa; Valero, Ana; Jordana-Lluch, Elena; Saludes, Verónica; Planas, Ramón; González-Candelas, Fernando; Ausina, Vicente; Bracho, Maria Alma

    2011-05-01

    The hepatitis C virus (HCV) has been classified into six genotypes and more than 70 subtypes with distinct geographical and epidemiological distributions. While 18 genotype 2 subtypes have been proposed, only 5 have had their complete sequence determined. The aim of this study was to characterize HCV isolates from three patients from the Barcelona metropolitan area of Spain for whom commercial genotyping methods provided discordant results. Full-length genome sequencing was carried out for 2 of the 3 patients; for the third patient only partial NS5B sequences could be obtained. The generated sequences were subjected to phylogenetic, recombination, and identity analyses. Sequences covering most of the HCV genome (9398 and 9566  nt in length) were obtained and showed a 90.3% identity to each other at the nucleotide level, while both sequences differed by 17.5-22.6% from the other fully sequenced genotype 2 subtypes. No evidence of recombination was found. The NS5B phylogenetic tree showed that sequences from the three patients cluster together with the only representative sequence of the provisionally designed 2q subtype, which also corresponds to a patient from Barcelona. Phylogenetic analysis of the full coding sequence showed that subtype 2q was more closely related to subtype 2k. The results obtained in this study suggest that subtype 2q now meets the requirements for confirmed designation status according to consensus criteria for HCV classification and nomenclature, and its epidemiological value is ensured as it has spread among several patients in the Barcelona metropolitan area. Copyright © 2011 Wiley-Liss, Inc.

  10. A bayesian approach to classification criteria for spectacled eiders

    Science.gov (United States)

    Taylor, B.L.; Wade, P.R.; Stehn, R.A.; Cochrane, J.F.

    1996-01-01

    To facilitate decisions to classify species according to risk of extinction, we used Bayesian methods to analyze trend data for the Spectacled Eider, an arctic sea duck. Trend data from three independent surveys of the Yukon-Kuskokwim Delta were analyzed individually and in combination to yield posterior distributions for population growth rates. We used classification criteria developed by the recovery team for Spectacled Eiders that seek to equalize errors of under- or overprotecting the species. We conducted both a Bayesian decision analysis and a frequentist (classical statistical inference) decision analysis. Bayesian decision analyses are computationally easier, yield basically the same results, and yield results that are easier to explain to nonscientists. With the exception of the aerial survey analysis of the 10 most recent years, both Bayesian and frequentist methods indicated that an endangered classification is warranted. The discrepancy between surveys warrants further research. Although the trend data are abundance indices, we used a preliminary estimate of absolute abundance to demonstrate how to calculate extinction distributions using the joint probability distributions for population growth rate and variance in growth rate generated by the Bayesian analysis. Recent apparent increases in abundance highlight the need for models that apply to declining and then recovering species.

  11. Bayesian Modeling of a Human MMORPG Player

    Science.gov (United States)

    Synnaeve, Gabriel; Bessière, Pierre

    2011-03-01

    This paper describes an application of Bayesian programming to the control of an autonomous avatar in a multiplayer role-playing game (the example is based on World of Warcraft). We model a particular task, which consists of choosing what to do and to select which target in a situation where allies and foes are present. We explain the model in Bayesian programming and show how we could learn the conditional probabilities from data gathered during human-played sessions.

  12. MCMC for parameters estimation by bayesian approach

    International Nuclear Information System (INIS)

    Ait Saadi, H.; Ykhlef, F.; Guessoum, A.

    2011-01-01

    This article discusses the parameter estimation for dynamic system by a Bayesian approach associated with Markov Chain Monte Carlo methods (MCMC). The MCMC methods are powerful for approximating complex integrals, simulating joint distributions, and the estimation of marginal posterior distributions, or posterior means. The MetropolisHastings algorithm has been widely used in Bayesian inference to approximate posterior densities. Calibrating the proposal distribution is one of the main issues of MCMC simulation in order to accelerate the convergence.

  13. Bayesian Methods for Radiation Detection and Dosimetry

    CERN Document Server

    Groer, Peter G

    2002-01-01

    We performed work in three areas: radiation detection, external and internal radiation dosimetry. In radiation detection we developed Bayesian techniques to estimate the net activity of high and low activity radioactive samples. These techniques have the advantage that the remaining uncertainty about the net activity is described by probability densities. Graphs of the densities show the uncertainty in pictorial form. Figure 1 below demonstrates this point. We applied stochastic processes for a method to obtain Bayesian estimates of 222Rn-daughter products from observed counting rates. In external radiation dosimetry we studied and developed Bayesian methods to estimate radiation doses to an individual with radiation induced chromosome aberrations. We analyzed chromosome aberrations after exposure to gammas and neutrons and developed a method for dose-estimation after criticality accidents. The research in internal radiation dosimetry focused on parameter estimation for compartmental models from observed comp...

  14. Bayesian log-periodic model for financial crashes

    DEFF Research Database (Denmark)

    Rodríguez-Caballero, Carlos Vladimir; Knapik, Oskar

    2014-01-01

    This paper introduces a Bayesian approach in econophysics literature about financial bubbles in order to estimate the most probable time for a financial crash to occur. To this end, we propose using noninformative prior distributions to obtain posterior distributions. Since these distributions...... cannot be performed analytically, we develop a Markov Chain Monte Carlo algorithm to draw from posterior distributions. We consider three Bayesian models that involve normal and Student’s t-distributions in the disturbances and an AR(1)-GARCH(1,1) structure only within the first case. In the empirical...... part of the study, we analyze a well-known example of financial bubble – the S&P 500 1987 crash – to show the usefulness of the three methods under consideration and crashes of Merval-94, Bovespa-97, IPCMX-94, Hang Seng-97 using the simplest method. The novelty of this research is that the Bayesian...

  15. Classification and Validation of Behavioral Subtypes of Learning-Disabled Children.

    Science.gov (United States)

    Speece, Deborah L.; And Others

    1985-01-01

    Using the Classroom Behavior Inventory, teachers rated the behaviors of 63 school-identified, learning-disabled first and second graders. Hierarchical cluster analysis techniques identified seven distinct behavioral subtypes. Internal validation techniques indicated that the subtypes were replicable and had profile patterns different from a sample…

  16. Characteristic MRI findings in multiple system atrophy: comparison of the three subtypes

    Energy Technology Data Exchange (ETDEWEB)

    Naka, H.; Ohshita, T.; Murata, Y.; Imon, Y.; Mimori, Y.; Nakamura, S. [Department of Internal Medicine, Hiroshima University School of Medicine, Hiroshima (Japan)

    2002-03-01

    We reviewed MRI findings in 29 patients with probable multiple system atrophy (MSA) to see whether there were common and or less common neuroradiological findings in the various clinical subtypes. We divided the patients into three clinical subtypes according to initial and predominant symptoms: 14 with olivopontocerebellar atrophy (OPCA), eight with the Shy-Drager syndrome (SDS) and seven with striatonigral degeneration (SND). The patients showed atrophy of the brain stem and cerebellum, high signal on T2-weighted images of the base of the pons and middle cerebellar peduncles, high and low signal on T2-weighted images of the putamen and atrophy of frontal and parietal lobes. The degree of atrophy of the middle cerebellar peduncle and cerebellum was greater in OPCA patients and a high-signal lateral rim to the putamen more frequent in SND. However, all findings were observed in all subtypes, and the degrees of atrophy of the putamen and pons and the frequency of high signal in the base of the pons were similar in the subtypes. We also found atrophy of the cerebral hemispheres, especially the frontal and parietal lobes, but its degree was not significantly different in the various subtypes. Our findings suggest that, although MSA can be divided clinically into three subtypes, most of the features on MRI are common and overlap in the subtypes, independently of the clinical presentation. (orig.)

  17. High prevalence of luminal B breast cancer intrinsic subtype in Colombian women.

    Science.gov (United States)

    Serrano-Gomez, Silvia Juliana; Sanabria-Salas, Maria Carolina; Hernández-Suarez, Gustavo; García, Oscar; Silva, Camilo; Romero, Alejandro; Mejía, Juan Carlos; Miele, Lucio; Fejerman, Laura; Zabaleta, Jovanny

    2016-07-01

    Breast cancer is the most frequent malignancy in women worldwide. Distinct intrinsic subtypes of breast cancer have different prognoses, and their relative prevalence varies significantly among ethnic groups. Little is known about the prevalence of breast cancer intrinsic subtypes and their association with clinicopathological data and genetic ancestry in Latin Americans. Immunohistochemistry surrogates from the 2013 St. Gallen International Expert Consensus were used to classify breast cancers in 301 patients from Colombia into intrinsic subtypes. We analyzed the distribution of subtypes by clinicopathological variables. Genetic ancestry was estimated from a panel of 80 ancestry informative markers. Luminal B breast cancer subtype was the most prevalent in our population (37.2%) followed by luminal A (26.3%), non-basal triple negative (NBTN) (11.6%), basal like (9%), human epidermal growth factor receptor 2 (HER2) enriched (8.6%) and unknown (7.3%). We found statistical significant differences in distribution between Colombian region (P = 0.007), age at diagnosis (P = 0.0139), grade (P studies analyzing the molecular profiles of breast cancer in Colombian women will help us understand the molecular basis of this subtype distribution and compare the molecular characteristics of the different intrinsic subtypes in Colombian patients. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  18. Detection of avian metapneumovirus subtypes in turkeys using RT-PCR.

    Science.gov (United States)

    Ongor, H; Karahan, M; Kalin, R; Bulut, H; Cetinkaya, B

    2010-03-20

    This study investigated the prevalence of avian metapneumovirus (aMPV) and the detection of molecular subtypes of field strains of the virus using RT-PCR in clinically healthy turkeys and those showing signs of respiratory disease. In the RT-PCR examination of 624 tracheal tissue samples collected from a local turkey abattoir, 2.9 per cent (18/624) of samples tested positive. In the examination of tracheal swab samples collected from flocks with respiratory problems, 18 of 20 samples tested positive. When the results were assessed at flock level, aMPV infection was detected in only one of the 23 clinically healthy turkey flocks, whereas all four flocks with respiratory problems were infected. Molecular typing using primers specific to the attachment glycoprotein (G) gene showed that all 36 positive samples belonged to subtype B. Partial sequence analysis of DNA samples showed 95 per cent homology between the field types and the reference strain aMPV subtype B. Whereas clinically healthy turkeys had been vaccinated with a subtype A virus vaccine, the flocks with respiratory problems had been vaccinated with a subtype B virus vaccine. Despite four blind passages of RT-PCR-positive samples on Vero and chicken embryo fibroblast cells, no cytopathic effect was detected by microscopic examination.

  19. Preparation of quadri-subtype influenza virus-like particles using bovine immunodeficiency virus gag protein

    Energy Technology Data Exchange (ETDEWEB)

    Tretyakova, Irina; Hidajat, Rachmat; Hamilton, Garrett; Horn, Noah; Nickols, Brian; Prather, Raphael O. [Medigen, Inc., 8420 Gas House Pike, Suite S, Frederick, MD (United States); Tumpey, Terrence M. [Influenza Division, Centers for Disease Control and Prevention, 1600 Clifton Road N.E., Atlanta, GA (United States); Pushko, Peter, E-mail: ppushko@medigen-usa.com [Medigen, Inc., 8420 Gas House Pike, Suite S, Frederick, MD (United States)

    2016-01-15

    Influenza VLPs comprised of hemagglutinin (HA), neuraminidase (NA), and matrix (M1) proteins have been previously used for immunological and virological studies. Here we demonstrated that influenza VLPs can be made in Sf9 cells by using the bovine immunodeficiency virus gag (Bgag) protein in place of M1. We showed that Bgag can be used to prepare VLPs for several influenza subtypes including H1N1 and H10N8. Furthermore, by using Bgag, we prepared quadri-subtype VLPs, which co-expressed within the VLP the four HA subtypes derived from avian-origin H5N1, H7N9, H9N2 and H10N8 viruses. VLPs showed hemagglutination and neuraminidase activities and reacted with specific antisera. The content and co-localization of each HA subtype within the quadri-subtype VLP were evaluated. Electron microscopy showed that Bgag-based VLPs resembled influenza virions with the diameter of 150–200 nm. This is the first report of quadri-subtype design for influenza VLP and the use of Bgag for influenza VLP preparation. - Highlights: • BIV gag protein was configured as influenza VLP core component. • Recombinant influenza VLPs were prepared in Sf9 cells using baculovirus expression system. • Single- and quadri-subtype VLPs were prepared by using BIV gag as a VLP core. • Co-localization of H5, H7, H9, and H10 HA was confirmed within quadri-subtype VLP. • Content of HA subtypes within quadri-subtype VLP was determined. • Potential advantages of quadri-subtype VLPs as influenza vaccine are discussed.

  20. Preparation of quadri-subtype influenza virus-like particles using bovine immunodeficiency virus gag protein

    International Nuclear Information System (INIS)

    Tretyakova, Irina; Hidajat, Rachmat; Hamilton, Garrett; Horn, Noah; Nickols, Brian; Prather, Raphael O.; Tumpey, Terrence M.; Pushko, Peter

    2016-01-01

    Influenza VLPs comprised of hemagglutinin (HA), neuraminidase (NA), and matrix (M1) proteins have been previously used for immunological and virological studies. Here we demonstrated that influenza VLPs can be made in Sf9 cells by using the bovine immunodeficiency virus gag (Bgag) protein in place of M1. We showed that Bgag can be used to prepare VLPs for several influenza subtypes including H1N1 and H10N8. Furthermore, by using Bgag, we prepared quadri-subtype VLPs, which co-expressed within the VLP the four HA subtypes derived from avian-origin H5N1, H7N9, H9N2 and H10N8 viruses. VLPs showed hemagglutination and neuraminidase activities and reacted with specific antisera. The content and co-localization of each HA subtype within the quadri-subtype VLP were evaluated. Electron microscopy showed that Bgag-based VLPs resembled influenza virions with the diameter of 150–200 nm. This is the first report of quadri-subtype design for influenza VLP and the use of Bgag for influenza VLP preparation. - Highlights: • BIV gag protein was configured as influenza VLP core component. • Recombinant influenza VLPs were prepared in Sf9 cells using baculovirus expression system. • Single- and quadri-subtype VLPs were prepared by using BIV gag as a VLP core. • Co-localization of H5, H7, H9, and H10 HA was confirmed within quadri-subtype VLP. • Content of HA subtypes within quadri-subtype VLP was determined. • Potential advantages of quadri-subtype VLPs as influenza vaccine are discussed.

  1. BAYESIAN IMAGE RESTORATION, USING CONFIGURATIONS

    Directory of Open Access Journals (Sweden)

    Thordis Linda Thorarinsdottir

    2011-05-01

    Full Text Available In this paper, we develop a Bayesian procedure for removing noise from images that can be viewed as noisy realisations of random sets in the plane. The procedure utilises recent advances in configuration theory for noise free random sets, where the probabilities of observing the different boundary configurations are expressed in terms of the mean normal measure of the random set. These probabilities are used as prior probabilities in a Bayesian image restoration approach. Estimation of the remaining parameters in the model is outlined for salt and pepper noise. The inference in the model is discussed in detail for 3 X 3 and 5 X 5 configurations and examples of the performance of the procedure are given.

  2. Avian metapneumovirus subtypes circulating in Brazilian vaccinated and nonvaccinated chicken and turkey farms.

    Science.gov (United States)

    Chacón, Jorge Luis; Mizuma, Matheus; Vejarano, Maria P; Toquín, Didier; Eterradossi, Nicolas; Patnayak, Devi P; Goyal, Sagar M; Ferreira, Antonio J Piantino

    2011-03-01

    Avian metapneumovirus (AMPV) causes turkey rhinotracheitis and is associated with swollen head syndrome in chickens, which is usually accompanied by secondary infections that increase mortality. AMPVs circulating in Brazilian vaccinated and nonvaccinated commercial chicken and turkey farms were detected using a universal reverse transcriptase (RT)-PCR assay that can detect the four recognized subtypes of AMPV. The AMPV status of 228 farms with respiratory and reproductive disturbances was investigated. AMPV was detected in broiler, hen, breeder, and turkey farms from six different geographic regions of Brazil. The detected viruses were subtyped using a nested RT-PCR assay and sequence analysis of the G gene. Only subtypes A and B were detected in both vaccinated and nonvaccinated farms. AMPV-A and AMPV-B were detected in 15 and 23 farms, respectively, while both subtypes were simultaneously found in one hen farm. Both vaccine and field viruses were detected in nonvaccinated farms. In five cases, the detected subtype was different than the vaccine subtype. Field subtype B virus was detected mainly during the final years of the survey period. These viruses showed high molecular similarity (more than 96% nucleotide similarity) among themselves and formed a unique phylogenetic group, suggesting that they may have originated from a common strain. These results demonstrate the cocirculation of subtypes A and B in Brazilian commercial farms.

  3. Proteomic maps of breast cancer subtypes

    DEFF Research Database (Denmark)

    Tyanova, Stefka; Albrechtsen, Reidar; Kronqvist, Pauliina

    2016-01-01

    Systems-wide profiling of breast cancer has almost always entailed RNA and DNA analysis by microarray and sequencing techniques. Marked developments in proteomic technologies now enable very deep profiling of clinical samples, with high identification and quantification accuracy. We analysed 40...... oestrogen receptor positive (luminal), Her2 positive and triple negative breast tumours and reached a quantitative depth of >10,000 proteins. These proteomic profiles identified functional differences between breast cancer subtypes, related to energy metabolism, cell growth, mRNA translation and cell......-cell communication. Furthermore, we derived a signature of 19 proteins, which differ between the breast cancer subtypes, through support vector machine (SVM)-based classification and feature selection. Remarkably, only three proteins of the signature were associated with gene copy number variations and eleven were...

  4. ADHD subtype differences in reinforcement sensitivity and visuospatial working memory

    NARCIS (Netherlands)

    Dovis, S.; van der Oord, S.; Wiers, R.W.; Prins, P.J.M.

    2015-01-01

    Both cognitive and motivational deficits are thought to give rise to the problems in the combined (ADHD-C) and inattentive subtype (ADHD-I) of attention-deficit hyperactivity disorder (ADHD). In both subtypes one of the most prominent cognitive weaknesses appears to be in visuospatial working memory

  5. Preoperative subtyping of meningiomas by perfusion MR imaging

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Hao [University Medical Center Groningen, University of Groningen (Netherlands); Shanghai Jiaotong University affiliated First People' s Hospital, Department of Radiology, Shanghai (China); Department of Radiology, University of Groningen (Netherlands); Roediger, Lars A.; Oudkerk, Matthijs [University Medical Center Groningen, University of Groningen (Netherlands); Department of Radiology, University of Groningen (Netherlands); Shen, Tianzhen [Fudan University Huashan Hospital, Department of Radiology, Shanghai (China); Miao, Jingtao [Shanghai Jiaotong University affiliated First People' s Hospital, Department of Radiology, Shanghai (China)

    2008-10-15

    This paper aims to evaluate the value of perfusion magnetic resonance (MR) imaging in the preoperative subtyping of meningiomas by analyzing the relative cerebral blood volume (rCBV) of three benign subtypes and anaplastic meningiomas separately. Thirty-seven meningiomas with peritumoral edema (15 meningothelial, ten fibrous, four angiomatous, and eight anaplastic) underwent perfusion MR imaging by using a gradient echo echo-planar sequence. The maximal rCBV (compared with contralateral normal white matter) in both tumoral parenchyma and peritumoral edema of each tumor was measured. The mean rCBVs of each two histological subtypes were compared using one-way analysis of variance and least significant difference tests. A p value less than 0.05 indicated a statistically significant difference. The mean rCBV of meningothelial, fibrous, angiomatous, and anaplastic meningiomas in tumoral parenchyma were 6.93{+-}3.75, 5.61{+-}4.03, 11.86{+-}1.93, and 5.89{+-}3.85, respectively, and in the peritumoral edema 0.87{+-}0.62, 1.38{+-}1.44, 0.87{+-}0.30, and 3.28{+-}1.39, respectively. The mean rCBV in tumoral parenchyma of angiomatous meningiomas and in the peritumoral edema of anaplastic meningiomas were statistically different (p<0.05) from the other types of meningiomas. Perfusion MR imaging can provide useful functional information on meningiomas and help in the preoperative diagnosis of some subtypes of meningiomas. (orig.)

  6. General and Local: Averaged k-Dependence Bayesian Classifiers

    Directory of Open Access Journals (Sweden)

    Limin Wang

    2015-06-01

    Full Text Available The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB classifier can construct at arbitrary points (values of k along the attribute dependence spectrum, it cannot identify the changes of interdependencies when attributes take different values. Local KDB, which learns in the framework of KDB, is proposed in this study to describe the local dependencies implicated in each test instance. Based on the analysis of functional dependencies, substitution-elimination resolution, a new type of semi-naive Bayesian operation, is proposed to substitute or eliminate generalization to achieve accurate estimation of conditional probability distribution while reducing computational complexity. The final classifier, averaged k-dependence Bayesian (AKDB classifiers, will average the output of KDB and local KDB. Experimental results on the repository of machine learning databases from the University of California Irvine (UCI showed that AKDB has significant advantages in zero-one loss and bias relative to naive Bayes (NB, tree augmented naive Bayes (TAN, Averaged one-dependence estimators (AODE, and KDB. Moreover, KDB and local KDB show mutually complementary characteristics with respect to variance.

  7. A Bayesian Model of the Memory Colour Effect.

    Science.gov (United States)

    Witzel, Christoph; Olkkonen, Maria; Gegenfurtner, Karl R

    2018-01-01

    According to the memory colour effect, the colour of a colour-diagnostic object is not perceived independently of the object itself. Instead, it has been shown through an achromatic adjustment method that colour-diagnostic objects still appear slightly in their typical colour, even when they are colourimetrically grey. Bayesian models provide a promising approach to capture the effect of prior knowledge on colour perception and to link these effects to more general effects of cue integration. Here, we model memory colour effects using prior knowledge about typical colours as priors for the grey adjustments in a Bayesian model. This simple model does not involve any fitting of free parameters. The Bayesian model roughly captured the magnitude of the measured memory colour effect for photographs of objects. To some extent, the model predicted observed differences in memory colour effects across objects. The model could not account for the differences in memory colour effects across different levels of realism in the object images. The Bayesian model provides a particularly simple account of memory colour effects, capturing some of the multiple sources of variation of these effects.

  8. Evolutionary history of HIV-1 subtype B and CRF01_AE transmission clusters among men who have sex with men (MSM) in Kuala Lumpur, Malaysia.

    Science.gov (United States)

    Ng, Kim Tien; Ong, Lai Yee; Lim, Sin How; Takebe, Yutaka; Kamarulzaman, Adeeba; Tee, Kok Keng

    2013-01-01

    HIV-1 epidemics among men who have sex with men (MSM) continue to expand in developed and developing countries. Although HIV infection in MSM is amongst the highest of the key affected populations in many countries in Southeast Asia, comprehensive molecular epidemiological study of HIV-1 among MSM remains inadequate in the region including in Malaysia. Here, we reported the phylodynamic profiles of HIV-1 genotypes circulating among MSM population in Kuala Lumpur, Malaysia. A total of n = 459 newly-diagnosed treatment-naïve consenting subjects were recruited between March 2006 and August 2012, of whom 87 (18.9%) were self-reported MSM. Transmitted drug resistance mutations were absent in these isolates. Cumulatively, phylogenetic reconstructions of the pro-rt gene (HXB2∶2253-3275) showed that HIV-1 subtype B and CRF01_AE were predominant and contributed to approximately 80% of the total HIV-1 infection among MSM. In addition to numerous unique transmission lineages within these genotypes, twelve monophyletic transmission clusters of different sizes (2-7 MSM sequences, supported by posterior probability value of 1) were identified in Malaysia. Bayesian coalescent analysis estimated that the divergence times for these clusters were mainly dated between 1995 and 2005 with four major transmission clusters radiating at least 12 years ago suggesting that active spread of multiple sub-epidemic clusters occurred during this period. The changes in effective population size of subtype B showed an exponential growth within 5 years between 1988 and 1993, while CRF01_AE lineage exhibited similar expansion between 1993 and 2003. Our study provides the first insight of the phylodynamic profile of HIV-1 subtype B and CRF01_AE circulating among MSM population in Kuala Lumpur, Malaysia, unravelling the importance of understanding transmission behaviours as well as evolutionary history of HIV-1 in assessing the risk of outbreak or epidemic expansion.

  9. Bayesian model ensembling using meta-trained recurrent neural networks

    NARCIS (Netherlands)

    Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.

    2017-01-01

    In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian

  10. Dissociative subtype of DSM-5 posttraumatic stress disorder in U.S. veterans.

    Science.gov (United States)

    Tsai, Jack; Armour, Cherie; Southwick, Steven M; Pietrzak, Robert H

    2015-01-01

    The Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5) formally introduced a dissociative subtype of posttraumatic stress disorder (PTSD). This study examined the proportion of U.S. veterans with DSM-5 PTSD that report dissociative symptoms; and compared veterans with PTSD with and without the dissociative subtype and trauma-exposed controls on sociodemographics, clinical characteristics, and quality of life. Multivariable analyses were conducted on a nationally representative sample of 1484 veterans from the National Health and Resilience in Veterans Study (second baseline survey conducted September-October, 2013). Of the 12.0% and 5.2% of veterans who screened positive for lifetime and past-month DSM-5 PTSD, 19.2% and 16.1% screened positive for the dissociative subtype, respectively. Among veterans with PTSD, those with the dissociative subtype reported more severe PTSD symptoms, comorbid depressive and anxiety symptoms, alcohol use problems, and hostility than those without the dissociative subtype. Adjusting for PTSD symptom severity, those with the dissociative subtype continued to report more depression and alcohol use problems. These results underscore the importance of assessing, monitoring, and treating the considerable proportion of veterans with PTSD and dissociative symptoms. Published by Elsevier Ltd.

  11. Clinical Features Associated with Delirium Motor Subtypes in Older Inpatients: Results of a Multicenter Study.

    Science.gov (United States)

    Morandi, Alessandro; Di Santo, Simona G; Cherubini, Antonio; Mossello, Enrico; Meagher, David; Mazzone, Andrea; Bianchetti, Angelo; Ferrara, Nicola; Ferrari, Alberto; Musicco, Massimo; Trabucchi, Marco; Bellelli, Giuseppe

    2017-10-01

    To date motor subtypes of delirium have been evaluated in single-center studies with a limited examination of the relationship between predisposing factors and motor profile of delirium. We sought to report the prevalence and clinical profile of subtypes of delirium in a multicenter study. This is a point prevalence study nested in the "Delirium Day 2015", which included 108 acute and 12 rehabilitation wards in Italy. Delirium was detected using the 4-AT and motor subtypes were measured with the Delirium Motor Subtype Scale (DMSS). A multinomial logistic regression was used to determine the factors associated with delirium subtypes. Of 429 patients with delirium, the DMSS was completed in 275 (64%), classifying 21.5% of the patients with hyperactive delirium, 38.5% with hypoactive, 27.3% with mixed and 12.7% with the non-motor subtype. The 4-AT score was higher in the hyperactive subtype, similar in the hypoactive, mixed subtypes, while it was lowest in the non-motor subtype. Dementia was associated with all three delirium motor subtypes (hyperactive, OR 3.3, 95% CI: 1.2-8.7; hypoactive, OR 2.8, 95% CI: 1.2-6.5; mixed OR 2.6, 95% CI: 1.1-6.2). Atypical antipsychotics were associated with hypoactive delirium (OR 0.23, 95% CI: 0.1-0.7), while intravenous lines were associated with mixed delirium (OR 2.9, 95% CI: 1.2-6.9). The study shows that hypoactive delirium is the most common subtype among hospitalized older patients. Specific clinical features were associated with different delirium subtypes. The use of standardized instruments can help to characterize the phenomenology of different motor subtypes of delirium. Copyright © 2017 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

  12. A Bayesian alternative for multi-objective ecohydrological model specification

    Science.gov (United States)

    Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

    2018-01-01

    Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

  13. The dissociative subtype of posttraumatic stress disorder: rationale, clinical and neurobiological evidence, and implications.

    Science.gov (United States)

    Lanius, Ruth A; Brand, Bethany; Vermetten, Eric; Frewen, Paul A; Spiegel, David

    2012-08-01

    Clinical and neurobiological evidence for a dissociative subtype of posttraumatic stress disorder (PTSD) has recently been documented. A dissociative subtype of PTSD is being considered for inclusion in the forthcoming Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) to address the symptoms of depersonalization and derealization found among a subset of patients with PTSD. This article reviews research related to the dissociative subtype including antecedent, concurrent, and predictive validators as well as the rationale for recommending the dissociative subtype. The relevant literature pertaining to the dissociative subtype of PTSD was reviewed. Latent class analyses point toward a specific subtype of PTSD consisting of symptoms of depersonalization and derealization in both veteran and civilian samples of PTSD. Compared to individuals with PTSD, those with the dissociative subtype of PTSD also exhibit a different pattern of neurobiological response to symptom provocation as well as a differential response to current cognitive behavioral treatment designed for PTSD. We recommend that consideration be given to adding a dissociative subtype of PTSD in the revision of the DSM. This facilitates more accurate analysis of different phenotypes of PTSD, assist in treatment planning that is informed by considering the degree of patients' dissociativity, will improve treatment outcome, and will lead to much-needed research about the prevalence, symptomatology, neurobiology, and treatment of individuals with the dissociative subtype of PTSD. © 2012 Wiley Periodicals, Inc.

  14. MRI and pathological features of different molecular subtypes of breast cancers

    International Nuclear Information System (INIS)

    Yu Yang; Huo Tianlong; Lai Yunyao; Hong Nan

    2014-01-01

    Objective: To investigate the MRI and pathological features of different molecular subtypes of breast cancer. Methods: The data of 202 patients who underwent primary breast cancer resection were retrospectively reviewed. All of the patients had MRI preoperatively. The molecular subtypes of breast cancer defined by immunohistochemistry were classified as basal-like, luminal and HER-2 overexpression. Morphology (including mass or non-mass like enhancement, shape and margin of masses, unifocal or multifocal masses) and enhancement characteristics on MRI, histologic types and grades of tumors were analyzed with Chi-square test, exact test, Fisher exact test, Kruskal-Wallis H test, and Wilcoxon test. Results: Among the 202 patients, 34 were basal-like, 144 were luminal and 24 were HER-2 overexpression. The number of mass cases in each subtype was 29, 133 and 19 respectively,making no significant difference (χ 2 =4.136, P=0.126). As for the shape of basal-like lesions,8 were round,19 were lobular and 2 were irregular, while this distribution was 23, 58, 52 in luminal subtype and 1, 11, 7 in HER-2 overexpression subtype (χ 2 =13.391, P<0.05). The margin was also strikingly different among three groups (smooth, spiculate, irregular): 20, 5, 4 respectively in basal-like, 27, 53, 53 respectively in luminal, and 4, 7, 8 respectively in HER-2 overexpression (χ 2 =28.515, P<0.01). 52.6% (10/19) of HER-2 overexpression cases were multifocal, while only 6.9% (2/29) of luminal and 8.0% (24/133) of basal-like ones were multifocal (χ 2 =16.140, P<0.01). Characteristics in dynamic contrast-enhanced MRI were statistically different, with homogeneous, heterogeneous, and rim enhancement 0, 13, 16 respectively in basal-like cases, 28, 93, 11 respectively in luminal cases and 2, 11, 6 respectively in HER-2 overexpression cases (P<0.01). However, the difference for enhancement curve did not reach significance (P =0.457). Histologic types were significantly different among molecular

  15. Bayesian molecular dating: opening up the black box.

    Science.gov (United States)

    Bromham, Lindell; Duchêne, Sebastián; Hua, Xia; Ritchie, Andrew M; Duchêne, David A; Ho, Simon Y W

    2018-05-01

    Molecular dating analyses allow evolutionary timescales to be estimated from genetic data, offering an unprecedented capacity for investigating the evolutionary past of all species. These methods require us to make assumptions about the relationship between genetic change and evolutionary time, often referred to as a 'molecular clock'. Although initially regarded with scepticism, molecular dating has now been adopted in many areas of biology. This broad uptake has been due partly to the development of Bayesian methods that allow complex aspects of molecular evolution, such as variation in rates of change across lineages, to be taken into account. But in order to do this, Bayesian dating methods rely on a range of assumptions about the evolutionary process, which vary in their degree of biological realism and empirical support. These assumptions can have substantial impacts on the estimates produced by molecular dating analyses. The aim of this review is to open the 'black box' of Bayesian molecular dating and have a look at the machinery inside. We explain the components of these dating methods, the important decisions that researchers must make in their analyses, and the factors that need to be considered when interpreting results. We illustrate the effects that the choices of different models and priors can have on the outcome of the analysis, and suggest ways to explore these impacts. We describe some major research directions that may improve the reliability of Bayesian dating. The goal of our review is to help researchers to make informed choices when using Bayesian phylogenetic methods to estimate evolutionary rates and timescales. © 2017 Cambridge Philosophical Society.

  16. Bayesian logistic regression analysis

    NARCIS (Netherlands)

    Van Erp, H.R.N.; Van Gelder, P.H.A.J.M.

    2012-01-01

    In this paper we present a Bayesian logistic regression analysis. It is found that if one wishes to derive the posterior distribution of the probability of some event, then, together with the traditional Bayes Theorem and the integrating out of nuissance parameters, the Jacobian transformation is an

  17. Bayesian Independent Component Analysis

    DEFF Research Database (Denmark)

    Winther, Ole; Petersen, Kaare Brandt

    2007-01-01

    In this paper we present an empirical Bayesian framework for independent component analysis. The framework provides estimates of the sources, the mixing matrix and the noise parameters, and is flexible with respect to choice of source prior and the number of sources and sensors. Inside the engine...

  18. Particle identification in ALICE: a Bayesian approach

    CERN Document Server

    Adam, Jaroslav; Aggarwal, Madan Mohan; Aglieri Rinella, Gianluca; Agnello, Michelangelo; Agrawal, Neelima; Ahammed, Zubayer; Ahmad, Shakeel; Ahn, Sang Un; Aiola, Salvatore; Akindinov, Alexander; Alam, Sk Noor; Silva De Albuquerque, Danilo; Aleksandrov, Dmitry; Alessandro, Bruno; Alexandre, Didier; Alfaro Molina, Jose Ruben; Alici, Andrea; Alkin, Anton; Millan Almaraz, Jesus Roberto; Alme, Johan; Alt, Torsten; Altinpinar, Sedat; Altsybeev, Igor; Alves Garcia Prado, Caio; Andrei, Cristian; Andronic, Anton; Anguelov, Venelin; Anticic, Tome; Antinori, Federico; Antonioli, Pietro; Aphecetche, Laurent Bernard; Appelshaeuser, Harald; Arcelli, Silvia; Arnaldi, Roberta; Arnold, Oliver Werner; Arsene, Ionut Cristian; Arslandok, Mesut; Audurier, Benjamin; Augustinus, Andre; Averbeck, Ralf Peter; Azmi, Mohd Danish; Badala, Angela; Baek, Yong Wook; Bagnasco, Stefano; Bailhache, Raphaelle Marie; Bala, Renu; Balasubramanian, Supraja; Baldisseri, Alberto; Baral, Rama Chandra; Barbano, Anastasia Maria; Barbera, Roberto; Barile, Francesco; Barnafoldi, Gergely Gabor; Barnby, Lee Stuart; Ramillien Barret, Valerie; Bartalini, Paolo; Barth, Klaus; Bartke, Jerzy Gustaw; Bartsch, Esther; Basile, Maurizio; Bastid, Nicole; Basu, Sumit; Bathen, Bastian; Batigne, Guillaume; Batista Camejo, Arianna; Batyunya, Boris; Batzing, Paul Christoph; Bearden, Ian Gardner; Beck, Hans; Bedda, Cristina; Behera, Nirbhay Kumar; Belikov, Iouri; Bellini, Francesca; Bello Martinez, Hector; Bellwied, Rene; Belmont Iii, Ronald John; Belmont Moreno, Ernesto; Belyaev, Vladimir; Benacek, Pavel; Bencedi, Gyula; Beole, Stefania; Berceanu, Ionela; Bercuci, Alexandru; Berdnikov, Yaroslav; Berenyi, Daniel; Bertens, Redmer Alexander; Berzano, Dario; Betev, Latchezar; Bhasin, Anju; Bhat, Inayat Rasool; Bhati, Ashok Kumar; Bhattacharjee, Buddhadeb; Bhom, Jihyun; Bianchi, Livio; Bianchi, Nicola; Bianchin, Chiara; Bielcik, Jaroslav; Bielcikova, Jana; Bilandzic, Ante; Biro, Gabor; Biswas, Rathijit; Biswas, Saikat; Bjelogrlic, Sandro; Blair, Justin Thomas; Blau, Dmitry; Blume, Christoph; Bock, Friederike; Bogdanov, Alexey; Boggild, Hans; Boldizsar, Laszlo; Bombara, Marek; Book, Julian Heinz; Borel, Herve; Borissov, Alexander; Borri, Marcello; Bossu, Francesco; Botta, Elena; Bourjau, Christian; Braun-Munzinger, Peter; Bregant, Marco; Breitner, Timo Gunther; Broker, Theo Alexander; Browning, Tyler Allen; Broz, Michal; Brucken, Erik Jens; Bruna, Elena; Bruno, Giuseppe Eugenio; Budnikov, Dmitry; Buesching, Henner; Bufalino, Stefania; Buncic, Predrag; Busch, Oliver; Buthelezi, Edith Zinhle; Bashir Butt, Jamila; Buxton, Jesse Thomas; Cabala, Jan; Caffarri, Davide; Cai, Xu; Caines, Helen Louise; Calero Diaz, Liliet; Caliva, Alberto; Calvo Villar, Ernesto; Camerini, Paolo; Carena, Francesco; Carena, Wisla; Carnesecchi, Francesca; Castillo Castellanos, Javier Ernesto; Castro, Andrew John; Casula, Ester Anna Rita; Ceballos Sanchez, Cesar; Cepila, Jan; Cerello, Piergiorgio; Cerkala, Jakub; Chang, Beomsu; Chapeland, Sylvain; Chartier, Marielle; Charvet, Jean-Luc Fernand; Chattopadhyay, Subhasis; Chattopadhyay, Sukalyan; Chauvin, Alex; Chelnokov, Volodymyr; Cherney, Michael Gerard; Cheshkov, Cvetan Valeriev; Cheynis, Brigitte; Chibante Barroso, Vasco Miguel; Dobrigkeit Chinellato, David; Cho, Soyeon; Chochula, Peter; Choi, Kyungeon; Chojnacki, Marek; Choudhury, Subikash; Christakoglou, Panagiotis; Christensen, Christian Holm; Christiansen, Peter; Chujo, Tatsuya; Chung, Suh-Urk; Cicalo, Corrado; Cifarelli, Luisa; Cindolo, Federico; Cleymans, Jean Willy Andre; Colamaria, Fabio Filippo; Colella, Domenico; Collu, Alberto; Colocci, Manuel; Conesa Balbastre, Gustavo; Conesa Del Valle, Zaida; Connors, Megan Elizabeth; Contreras Nuno, Jesus Guillermo; Cormier, Thomas Michael; Corrales Morales, Yasser; Cortes Maldonado, Ismael; Cortese, Pietro; Cosentino, Mauro Rogerio; Costa, Filippo; Crochet, Philippe; Cruz Albino, Rigoberto; Cuautle Flores, Eleazar; Cunqueiro Mendez, Leticia; Dahms, Torsten; Dainese, Andrea; Danisch, Meike Charlotte; Danu, Andrea; Das, Debasish; Das, Indranil; Das, Supriya; Dash, Ajay Kumar; Dash, Sadhana; De, Sudipan; De Caro, Annalisa; De Cataldo, Giacinto; De Conti, Camila; De Cuveland, Jan; De Falco, Alessandro; De Gruttola, Daniele; De Marco, Nora; De Pasquale, Salvatore; Deisting, Alexander; Deloff, Andrzej; Denes, Ervin Sandor; Deplano, Caterina; Dhankher, Preeti; Di Bari, Domenico; Di Mauro, Antonio; Di Nezza, Pasquale; Diaz Corchero, Miguel Angel; Dietel, Thomas; Dillenseger, Pascal; Divia, Roberto; Djuvsland, Oeystein; Dobrin, Alexandru Florin; Domenicis Gimenez, Diogenes; Donigus, Benjamin; Dordic, Olja; Drozhzhova, Tatiana; Dubey, Anand Kumar; Dubla, Andrea; Ducroux, Laurent; Dupieux, Pascal; Ehlers Iii, Raymond James; Elia, Domenico; Endress, Eric; Engel, Heiko; Epple, Eliane; Erazmus, Barbara Ewa; Erdemir, Irem; Erhardt, Filip; Espagnon, Bruno; Estienne, Magali Danielle; Esumi, Shinichi; Eum, Jongsik; Evans, David; Evdokimov, Sergey; Eyyubova, Gyulnara; Fabbietti, Laura; Fabris, Daniela; Faivre, Julien; Fantoni, Alessandra; Fasel, Markus; Feldkamp, Linus; Feliciello, Alessandro; Feofilov, Grigorii; Ferencei, Jozef; Fernandez Tellez, Arturo; Gonzalez Ferreiro, Elena; Ferretti, Alessandro; Festanti, Andrea; Feuillard, Victor Jose Gaston; Figiel, Jan; Araujo Silva Figueredo, Marcel; Filchagin, Sergey; Finogeev, Dmitry; Fionda, Fiorella; Fiore, Enrichetta Maria; Fleck, Martin Gabriel; Floris, Michele; Foertsch, Siegfried Valentin; Foka, Panagiota; Fokin, Sergey; Fragiacomo, Enrico; Francescon, Andrea; Frankenfeld, Ulrich Michael; Fronze, Gabriele Gaetano; Fuchs, Ulrich; Furget, Christophe; Furs, Artur; Fusco Girard, Mario; Gaardhoeje, Jens Joergen; Gagliardi, Martino; Gago Medina, Alberto Martin; Gallio, Mauro; Gangadharan, Dhevan Raja; Ganoti, Paraskevi; Gao, Chaosong; Garabatos Cuadrado, Jose; Garcia-Solis, Edmundo Javier; Gargiulo, Corrado; Gasik, Piotr Jan; Gauger, Erin Frances; Germain, Marie; Gheata, Andrei George; Gheata, Mihaela; Ghosh, Premomoy; Ghosh, Sanjay Kumar; Gianotti, Paola; Giubellino, Paolo; Giubilato, Piero; Gladysz-Dziadus, Ewa; Glassel, Peter; Gomez Coral, Diego Mauricio; Gomez Ramirez, Andres; Sanchez Gonzalez, Andres; Gonzalez, Victor; Gonzalez Zamora, Pedro; Gorbunov, Sergey; Gorlich, Lidia Maria; Gotovac, Sven; Grabski, Varlen; Grachov, Oleg Anatolievich; Graczykowski, Lukasz Kamil; Graham, Katie Leanne; Grelli, Alessandro; Grigoras, Alina Gabriela; Grigoras, Costin; Grigoryev, Vladislav; Grigoryan, Ara; Grigoryan, Smbat; Grynyov, Borys; Grion, Nevio; Gronefeld, Julius Maximilian; Grosse-Oetringhaus, Jan Fiete; Grosso, Raffaele; Guber, Fedor; Guernane, Rachid; Guerzoni, Barbara; Gulbrandsen, Kristjan Herlache; Gunji, Taku; Gupta, Anik; Gupta, Ramni; Haake, Rudiger; Haaland, Oystein Senneset; Hadjidakis, Cynthia Marie; Haiduc, Maria; Hamagaki, Hideki; Hamar, Gergoe; Hamon, Julien Charles; Harris, John William; Harton, Austin Vincent; Hatzifotiadou, Despina; Hayashi, Shinichi; Heckel, Stefan Thomas; Hellbar, Ernst; Helstrup, Haavard; Herghelegiu, Andrei Ionut; Herrera Corral, Gerardo Antonio; Hess, Benjamin Andreas; Hetland, Kristin Fanebust; Hillemanns, Hartmut; Hippolyte, Boris; Horak, David; Hosokawa, Ritsuya; Hristov, Peter Zahariev; Humanic, Thomas; Hussain, Nur; Hussain, Tahir; Hutter, Dirk; Hwang, Dae Sung; Ilkaev, Radiy; Inaba, Motoi; Incani, Elisa; Ippolitov, Mikhail; Irfan, Muhammad; Ivanov, Marian; Ivanov, Vladimir; Izucheev, Vladimir; Jacazio, Nicolo; Jacobs, Peter Martin; Jadhav, Manoj Bhanudas; Jadlovska, Slavka; Jadlovsky, Jan; Jahnke, Cristiane; Jakubowska, Monika Joanna; Jang, Haeng Jin; Janik, Malgorzata Anna; Pahula Hewage, Sandun; Jena, Chitrasen; Jena, Satyajit; Jimenez Bustamante, Raul Tonatiuh; Jones, Peter Graham; Jusko, Anton; Kalinak, Peter; Kalweit, Alexander Philipp; Kamin, Jason Adrian; Kang, Ju Hwan; Kaplin, Vladimir; Kar, Somnath; Karasu Uysal, Ayben; Karavichev, Oleg; Karavicheva, Tatiana; Karayan, Lilit; Karpechev, Evgeny; Kebschull, Udo Wolfgang; Keidel, Ralf; Keijdener, Darius Laurens; Keil, Markus; Khan, Mohammed Mohisin; Khan, Palash; Khan, Shuaib Ahmad; Khanzadeev, Alexei; Kharlov, Yury; Kileng, Bjarte; Kim, Do Won; Kim, Dong Jo; Kim, Daehyeok; Kim, Hyeonjoong; Kim, Jinsook; Kim, Minwoo; Kim, Se Yong; Kim, Taesoo; Kirsch, Stefan; Kisel, Ivan; Kiselev, Sergey; Kisiel, Adam Ryszard; Kiss, Gabor; Klay, Jennifer Lynn; Klein, Carsten; Klein, Jochen; Klein-Boesing, Christian; Klewin, Sebastian; Kluge, Alexander; Knichel, Michael Linus; Knospe, Anders Garritt; Kobdaj, Chinorat; Kofarago, Monika; Kollegger, Thorsten; Kolozhvari, Anatoly; Kondratev, Valerii; Kondratyeva, Natalia; Kondratyuk, Evgeny; Konevskikh, Artem; Kopcik, Michal; Kostarakis, Panagiotis; Kour, Mandeep; Kouzinopoulos, Charalampos; Kovalenko, Oleksandr; Kovalenko, Vladimir; Kowalski, Marek; Koyithatta Meethaleveedu, Greeshma; Kralik, Ivan; Kravcakova, Adela; Krivda, Marian; Krizek, Filip; Kryshen, Evgeny; Krzewicki, Mikolaj; Kubera, Andrew Michael; Kucera, Vit; Kuhn, Christian Claude; Kuijer, Paulus Gerardus; Kumar, Ajay; Kumar, Jitendra; Kumar, Lokesh; Kumar, Shyam; Kurashvili, Podist; Kurepin, Alexander; Kurepin, Alexey; Kuryakin, Alexey; Kweon, Min Jung; Kwon, Youngil; La Pointe, Sarah Louise; La Rocca, Paola; Ladron De Guevara, Pedro; Lagana Fernandes, Caio; Lakomov, Igor; Langoy, Rune; Lara Martinez, Camilo Ernesto; Lardeux, Antoine Xavier; Lattuca, Alessandra; Laudi, Elisa; Lea, Ramona; Leardini, Lucia; Lee, Graham Richard; Lee, Seongjoo; Lehas, Fatiha; Lemmon, Roy Crawford; Lenti, Vito; Leogrande, Emilia; Leon Monzon, Ildefonso; Leon Vargas, Hermes; Leoncino, Marco; Levai, Peter; Li, Shuang; Li, Xiaomei; Lien, Jorgen Andre; Lietava, Roman; Lindal, Svein; Lindenstruth, Volker; Lippmann, Christian; Lisa, Michael Annan; Ljunggren, Hans Martin; Lodato, Davide Francesco; Lonne, Per-Ivar; Loginov, Vitaly; Loizides, Constantinos; Lopez, Xavier Bernard; Lopez Torres, Ernesto; Lowe, Andrew John; Luettig, Philipp Johannes; Lunardon, Marcello; Luparello, Grazia; Lutz, Tyler Harrison; Maevskaya, Alla; Mager, Magnus; Mahajan, Sanjay; Mahmood, Sohail Musa; Maire, Antonin; Majka, Richard Daniel; Malaev, Mikhail; Maldonado Cervantes, Ivonne Alicia; Malinina, Liudmila; Mal'Kevich, Dmitry; Malzacher, Peter; Mamonov, Alexander; Manko, Vladislav; Manso, Franck; Manzari, Vito; Marchisone, Massimiliano; Mares, Jiri; Margagliotti, Giacomo Vito; Margotti, Anselmo; Margutti, Jacopo; Marin, Ana Maria; Markert, Christina; Marquard, Marco; Martin, Nicole Alice; Martin Blanco, Javier; Martinengo, Paolo; Martinez Hernandez, Mario Ivan; Martinez-Garcia, Gines; Martinez Pedreira, Miguel; Mas, Alexis Jean-Michel; Masciocchi, Silvia; Masera, Massimo; Masoni, Alberto; Mastroserio, Annalisa; Matyja, Adam Tomasz; Mayer, Christoph; Mazer, Joel Anthony; Mazzoni, Alessandra Maria; Mcdonald, Daniel; Meddi, Franco; Melikyan, Yuri; Menchaca-Rocha, Arturo Alejandro; Meninno, Elisa; Mercado-Perez, Jorge; Meres, Michal; Miake, Yasuo; Mieskolainen, Matti Mikael; Mikhaylov, Konstantin; Milano, Leonardo; Milosevic, Jovan; Mischke, Andre; Mishra, Aditya Nath; Miskowiec, Dariusz Czeslaw; Mitra, Jubin; Mitu, Ciprian Mihai; Mohammadi, Naghmeh; Mohanty, Bedangadas; Molnar, Levente; Montano Zetina, Luis Manuel; Montes Prado, Esther; Moreira De Godoy, Denise Aparecida; Perez Moreno, Luis Alberto; Moretto, Sandra; Morreale, Astrid; Morsch, Andreas; Muccifora, Valeria; Mudnic, Eugen; Muhlheim, Daniel Michael; Muhuri, Sanjib; Mukherjee, Maitreyee; Mulligan, James Declan; Gameiro Munhoz, Marcelo; Munzer, Robert Helmut; Murakami, Hikari; Murray, Sean; Musa, Luciano; Musinsky, Jan; Naik, Bharati; Nair, Rahul; Nandi, Basanta Kumar; Nania, Rosario; Nappi, Eugenio; Naru, Muhammad Umair; Ferreira Natal Da Luz, Pedro Hugo; Nattrass, Christine; Rosado Navarro, Sebastian; Nayak, Kishora; Nayak, Ranjit; Nayak, Tapan Kumar; Nazarenko, Sergey; Nedosekin, Alexander; Nellen, Lukas; Ng, Fabian; Nicassio, Maria; Niculescu, Mihai; Niedziela, Jeremi; Nielsen, Borge Svane; Nikolaev, Sergey; Nikulin, Sergey; Nikulin, Vladimir; Noferini, Francesco; Nomokonov, Petr; Nooren, Gerardus; Cabanillas Noris, Juan Carlos; Norman, Jaime; Nyanin, Alexander; Nystrand, Joakim Ingemar; Oeschler, Helmut Oskar; Oh, Saehanseul; Oh, Sun Kun; Ohlson, Alice Elisabeth; Okatan, Ali; Okubo, Tsubasa; Olah, Laszlo; Oleniacz, Janusz; Oliveira Da Silva, Antonio Carlos; Oliver, Michael Henry; Onderwaater, Jacobus; Oppedisano, Chiara; Orava, Risto; Oravec, Matej; Ortiz Velasquez, Antonio; Oskarsson, Anders Nils Erik; Otwinowski, Jacek Tomasz; Oyama, Ken; Ozdemir, Mahmut; Pachmayer, Yvonne Chiara; Pagano, Davide; Pagano, Paola; Paic, Guy; Pal, Susanta Kumar; Pan, Jinjin; Pandey, Ashutosh Kumar; Papikyan, Vardanush; Pappalardo, Giuseppe; Pareek, Pooja; Park, Woojin; Parmar, Sonia; Passfeld, Annika; Paticchio, Vincenzo; Patra, Rajendra Nath; Paul, Biswarup; Pei, Hua; Peitzmann, Thomas; Pereira Da Costa, Hugo Denis Antonio; Peresunko, Dmitry Yurevich; Perez Lara, Carlos Eugenio; Perez Lezama, Edgar; Peskov, Vladimir; Pestov, Yury; Petracek, Vojtech; Petrov, Viacheslav; Petrovici, Mihai; Petta, Catia; Piano, Stefano; Pikna, Miroslav; Pillot, Philippe; Ozelin De Lima Pimentel, Lais; Pinazza, Ombretta; Pinsky, Lawrence; Piyarathna, Danthasinghe; Ploskon, Mateusz Andrzej; Planinic, Mirko; Pluta, Jan Marian; Pochybova, Sona; Podesta Lerma, Pedro Luis Manuel; Poghosyan, Martin; Polishchuk, Boris; Poljak, Nikola; Poonsawat, Wanchaloem; Pop, Amalia; Porteboeuf, Sarah Julie; Porter, R Jefferson; Pospisil, Jan; Prasad, Sidharth Kumar; Preghenella, Roberto; Prino, Francesco; Pruneau, Claude Andre; Pshenichnov, Igor; Puccio, Maximiliano; Puddu, Giovanna; Pujahari, Prabhat Ranjan; Punin, Valery; Putschke, Jorn Henning; Qvigstad, Henrik; Rachevski, Alexandre; Raha, Sibaji; Rajput, Sonia; Rak, Jan; Rakotozafindrabe, Andry Malala; Ramello, Luciano; Rami, Fouad; Raniwala, Rashmi; Raniwala, Sudhir; Rasanen, Sami Sakari; Rascanu, Bogdan Theodor; Rathee, Deepika; Read, Kenneth Francis; Redlich, Krzysztof; Reed, Rosi Jan; Rehman, Attiq Ur; Reichelt, Patrick Simon; Reidt, Felix; Ren, Xiaowen; Renfordt, Rainer Arno Ernst; Reolon, Anna Rita; Reshetin, Andrey; Reygers, Klaus Johannes; Riabov, Viktor; Ricci, Renato Angelo; Richert, Tuva Ora Herenui; Richter, Matthias Rudolph; Riedler, Petra; Riegler, Werner; Riggi, Francesco; Ristea, Catalin-Lucian; Rocco, Elena; Rodriguez Cahuantzi, Mario; Rodriguez Manso, Alis; Roeed, Ketil; Rogochaya, Elena; Rohr, David Michael; Roehrich, Dieter; Ronchetti, Federico; Ronflette, Lucile; Rosnet, Philippe; Rossi, Andrea; Roukoutakis, Filimon; Roy, Ankhi; Roy, Christelle Sophie; Roy, Pradip Kumar; Rubio Montero, Antonio Juan; Rui, Rinaldo; Russo, Riccardo; Ryabinkin, Evgeny; Ryabov, Yury; Rybicki, Andrzej; Saarinen, Sampo; Sadhu, Samrangy; Sadovskiy, Sergey; Safarik, Karel; Sahlmuller, Baldo; Sahoo, Pragati; Sahoo, Raghunath; Sahoo, Sarita; Sahu, Pradip Kumar; Saini, Jogender; Sakai, Shingo; Saleh, Mohammad Ahmad; Salzwedel, Jai Samuel Nielsen; Sambyal, Sanjeev Singh; Samsonov, Vladimir; Sandor, Ladislav; Sandoval, Andres; Sano, Masato; Sarkar, Debojit; Sarkar, Nachiketa; Sarma, Pranjal; Scapparone, Eugenio; Scarlassara, Fernando; Schiaua, Claudiu Cornel; Schicker, Rainer Martin; Schmidt, Christian Joachim; Schmidt, Hans Rudolf; Schuchmann, Simone; Schukraft, Jurgen; Schulc, Martin; Schutz, Yves Roland; Schwarz, Kilian Eberhard; Schweda, Kai Oliver; Scioli, Gilda; Scomparin, Enrico; Scott, Rebecca Michelle; Sefcik, Michal; Seger, Janet Elizabeth; Sekiguchi, Yuko; Sekihata, Daiki; Selyuzhenkov, Ilya; Senosi, Kgotlaesele; Senyukov, Serhiy; Serradilla Rodriguez, Eulogio; Sevcenco, Adrian; Shabanov, Arseniy; Shabetai, Alexandre; Shadura, Oksana; Shahoyan, Ruben; Shahzad, Muhammed Ikram; Shangaraev, Artem; Sharma, Ankita; Sharma, Mona; Sharma, Monika; Sharma, Natasha; Sheikh, Ashik Ikbal; Shigaki, Kenta; Shou, Qiye; Shtejer Diaz, Katherin; Sibiryak, Yury; Siddhanta, Sabyasachi; Sielewicz, Krzysztof Marek; Siemiarczuk, Teodor; Silvermyr, David Olle Rickard; Silvestre, Catherine Micaela; Simatovic, Goran; Simonetti, Giuseppe; Singaraju, Rama Narayana; Singh, Ranbir; Singha, Subhash; Singhal, Vikas; Sinha, Bikash; Sarkar - Sinha, Tinku; Sitar, Branislav; Sitta, Mario; Skaali, Bernhard; Slupecki, Maciej; Smirnov, Nikolai; Snellings, Raimond; Snellman, Tomas Wilhelm; Song, Jihye; Song, Myunggeun; Song, Zixuan; Soramel, Francesca; Sorensen, Soren Pontoppidan; Derradi De Souza, Rafael; Sozzi, Federica; Spacek, Michal; Spiriti, Eleuterio; Sputowska, Iwona Anna; Spyropoulou-Stassinaki, Martha; Stachel, Johanna; Stan, Ionel; Stankus, Paul; Stenlund, Evert Anders; Steyn, Gideon Francois; Stiller, Johannes Hendrik; Stocco, Diego; Strmen, Peter; Alarcon Do Passo Suaide, Alexandre; Sugitate, Toru; Suire, Christophe Pierre; Suleymanov, Mais Kazim Oglu; Suljic, Miljenko; Sultanov, Rishat; Sumbera, Michal; Sumowidagdo, Suharyo; Szabo, Alexander; Szanto De Toledo, Alejandro; Szarka, Imrich; Szczepankiewicz, Adam; Szymanski, Maciej Pawel; Tabassam, Uzma; Takahashi, Jun; Tambave, Ganesh Jagannath; Tanaka, Naoto; Tarhini, Mohamad; Tariq, Mohammad; Tarzila, Madalina-Gabriela; Tauro, Arturo; Tejeda Munoz, Guillermo; Telesca, Adriana; Terasaki, Kohei; Terrevoli, Cristina; Teyssier, Boris; Thaeder, Jochen Mathias; Thakur, Dhananjaya; Thomas, Deepa; Tieulent, Raphael Noel; Timmins, Anthony Robert; Toia, Alberica; Trogolo, Stefano; Trombetta, Giuseppe; Trubnikov, Victor; Trzaska, Wladyslaw Henryk; Tsuji, Tomoya; Tumkin, Alexandr; Turrisi, Rosario; Tveter, Trine Spedstad; Ullaland, Kjetil; Uras, Antonio; Usai, Gianluca; Utrobicic, Antonija; Vala, Martin; Valencia Palomo, Lizardo; Vallero, Sara; Van Der Maarel, Jasper; Van Hoorne, Jacobus Willem; Van Leeuwen, Marco; Vanat, Tomas; Vande Vyvre, Pierre; Varga, Dezso; Diozcora Vargas Trevino, Aurora; Vargyas, Marton; Varma, Raghava; Vasileiou, Maria; Vasiliev, Andrey; Vauthier, Astrid; Vechernin, Vladimir; Veen, Annelies Marianne; Veldhoen, Misha; Velure, Arild; Vercellin, Ermanno; Vergara Limon, Sergio; Vernet, Renaud; Verweij, Marta; Vickovic, Linda; Viesti, Giuseppe; Viinikainen, Jussi Samuli; Vilakazi, Zabulon; Villalobos Baillie, Orlando; Villatoro Tello, Abraham; Vinogradov, Alexander; Vinogradov, Leonid; Vinogradov, Yury; Virgili, Tiziano; Vislavicius, Vytautas; Viyogi, Yogendra; Vodopyanov, Alexander; Volkl, Martin Andreas; Voloshin, Kirill; Voloshin, Sergey; Volpe, Giacomo; Von Haller, Barthelemy; Vorobyev, Ivan; Vranic, Danilo; Vrlakova, Janka; Vulpescu, Bogdan; Wagner, Boris; Wagner, Jan; Wang, Hongkai; Wang, Mengliang; Watanabe, Daisuke; Watanabe, Yosuke; Weber, Michael; Weber, Steffen Georg; Weiser, Dennis Franz; Wessels, Johannes Peter; Westerhoff, Uwe; Whitehead, Andile Mothegi; Wiechula, Jens; Wikne, Jon; Wilk, Grzegorz Andrzej; Wilkinson, Jeremy John; Williams, Crispin; Windelband, Bernd Stefan; Winn, Michael Andreas; Yang, Hongyan; Yang, Ping; Yano, Satoshi; Yasin, Zafar; Yin, Zhongbao; Yokoyama, Hiroki; Yoo, In-Kwon; Yoon, Jin Hee; Yurchenko, Volodymyr; Yushmanov, Igor; Zaborowska, Anna; Zaccolo, Valentina; Zaman, Ali; Zampolli, Chiara; Correia Zanoli, Henrique Jose; Zaporozhets, Sergey; Zardoshti, Nima; Zarochentsev, Andrey; Zavada, Petr; Zavyalov, Nikolay; Zbroszczyk, Hanna Paulina; Zgura, Sorin Ion; Zhalov, Mikhail; Zhang, Haitao; Zhang, Xiaoming; Zhang, Yonghong; Chunhui, Zhang; Zhang, Zuman; Zhao, Chengxin; Zhigareva, Natalia; Zhou, Daicui; Zhou, You; Zhou, Zhuo; Zhu, Hongsheng; Zhu, Jianhui; Zichichi, Antonino; Zimmermann, Alice; Zimmermann, Markus Bernhard; Zinovjev, Gennady; Zyzak, Maksym

    2016-05-25

    We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted methodology and formalism, the performance of the Bayesian PID approach for charged pions, kaons and protons in the central barrel of ALICE is studied. PID is performed via measurements of specific energy loss (dE/dx) and time-of-flight. PID efficiencies and misidentification probabilities are extracted and compared with Monte Carlo simulations using high purity samples of identified particles in the decay channels ${\\rm K}_{\\rm S}^{\\rm 0}\\rightarrow \\pi^+\\pi^-$, $\\phi\\rightarrow {\\rm K}^-{\\rm K}^+$ and $\\Lambda\\rightarrow{\\rm p}\\pi^-$ in p–Pb collisions at $\\sqrt{s_{\\rm NN}}= 5.02$TeV. In order to thoroughly assess the validity of the Bayesian approach, this methodology was used to obtain corrected $p_{\\rm T}$ spectra of pions, kaons, protons, and D$^0$ mesons in pp coll...

  19. Risk Based Maintenance of Offshore Wind Turbines Using Bayesian Networks

    DEFF Research Database (Denmark)

    Nielsen, Jannie Jessen; Sørensen, John Dalsgaard

    2010-01-01

    This paper presents how Bayesian networks can be used to make optimal decisions for repairs of offshore wind turbines. The Bayesian network is an efficient tool for updating a deterioration model whenever new information becomes available from inspections/monitoring. The optimal decision is found...... such that the preventive maintenance effort is balanced against the costs to corrective maintenance including indirect costs to reduced production. The basis for the optimization is the risk based Bayesian decision theory. The method is demonstrated through an application example....

  20. Bayesian outcome-based strategy classification.

    Science.gov (United States)

    Lee, Michael D

    2016-03-01

    Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the widely-used "weighted additive" (WADD) model, and show how this model, as well as other important models like "take-the-best" (TTB), can and should be expressed in terms of meaningful priors. Methodologically, they develop an inference approach based on the Minimum Description Length (MDL) principles that balances both the goodness-of-fit and complexity of the decision models they consider. This paper aims to preserve these useful contributions, but provide a complementary Bayesian approach with some theoretical and methodological advantages. We develop a simple graphical model, implemented in JAGS, that allows for fully Bayesian inferences about which models people use to make decisions. To demonstrate the Bayesian approach, we apply it to the models and data considered by Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014), showing how a prior predictive analysis of the models, and posterior inferences about which models people use and the parameter settings at which they use them, can contribute to our understanding of human decision making.

  1. A Bayesian nonparametric approach to causal inference on quantiles.

    Science.gov (United States)

    Xu, Dandan; Daniels, Michael J; Winterstein, Almut G

    2018-02-25

    We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records. © 2018, The International Biometric Society.

  2. Approximate Bayesian computation.

    Directory of Open Access Journals (Sweden)

    Mikael Sunnåker

    Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.

  3. Probability and Bayesian statistics

    CERN Document Server

    1987-01-01

    This book contains selected and refereed contributions to the "Inter­ national Symposium on Probability and Bayesian Statistics" which was orga­ nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa­ pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub­ jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics an...

  4. Balanced sensitivity functions for tuning multi-dimensional Bayesian network classifiers

    NARCIS (Netherlands)

    Bolt, J.H.; van der Gaag, L.C.

    Multi-dimensional Bayesian network classifiers are Bayesian networks of restricted topological structure, which are tailored to classifying data instances into multiple dimensions. Like more traditional classifiers, multi-dimensional classifiers are typically learned from data and may include

  5. Uses and misuses of Bayes' rule and Bayesian classifiers in cybersecurity

    Science.gov (United States)

    Bard, Gregory V.

    2017-12-01

    This paper will discuss the applications of Bayes' Rule and Bayesian Classifiers in Cybersecurity. While the most elementary form of Bayes' rule occurs in undergraduate coursework, there are more complicated forms as well. As an extended example, Bayesian spam filtering is explored, and is in many ways the most triumphant accomplishment of Bayesian reasoning in computer science, as nearly everyone with an email address has a spam folder. Bayesian Classifiers have also been responsible significant cybersecurity research results; yet, because they are not part of the standard curriculum, few in the mathematics or information-technology communities have seen the exact definitions, requirements, and proofs that comprise the subject. Moreover, numerous errors have been made by researchers (described in this paper), due to some mathematical misunderstandings dealing with conditional independence, or other badly chosen assumptions. Finally, to provide instructors and researchers with real-world examples, 25 published cybersecurity papers that use Bayesian reasoning are given, with 2-4 sentence summaries of the focus and contributions of each paper.

  6. HIV-1 epidemiology and circulating subtypes in the countryside of South Brazil

    Directory of Open Access Journals (Sweden)

    Carina Sperotto Librelotto

    2015-06-01

    Full Text Available INTRODUCTION: Human immunodeficiency virus type 1 (HIV-1 has spread worldwide, with several subtypes and circulating recombinant forms. Brazil has an incidence of 20.5 HIV-1/acquired immunodeficiency syndrome (AIDS patients per 100,000 inhabitants; however, the Southernmost State of Rio Grande do Sul (RS has more than twice the number of HIV-1-infected people (41.3/100,000 inhabitants and a different pattern of subtype frequencies, as previously reported in studies conducted in the capital (Porto Alegre and its metropolitan region. This study examined HIV-1/AIDS epidemiological and molecular aspects in the countryside of Rio Grande do Sul. METHODS: Socio-demographic, clinical and risk behavioral characteristics were obtained from HIV-1-positive adult patients using a structured questionnaire. HIV-1 subtypes were determined by nested-polymerase chain reaction (PCR and sequencing of the pol and env genes. RESULTS: The study sample included 149 (55% women patients with a mean age of 41.8 ± 11.9 years. Most (73.8% patients had a low education level and reported heterosexual practices as the most (91.9% probable transmission route. HIV-1 subtypes were detected in 26 patients: 18 (69.2% infected with subtype C, six (23.1% infected with subtype B and two (7.7% infected with BC recombinant forms. CONCLUSIONS: These data highlight the increasing number of HIV-1 subtype C infections in the countryside of South Brazil.

  7. Efficient fuzzy Bayesian inference algorithms for incorporating expert knowledge in parameter estimation

    Science.gov (United States)

    Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad

    2016-05-01

    Bayesian inference has traditionally been conceived as the proper framework for the formal incorporation of expert knowledge in parameter estimation of groundwater models. However, conventional Bayesian inference is incapable of taking into account the imprecision essentially embedded in expert provided information. In order to solve this problem, a number of extensions to conventional Bayesian inference have been introduced in recent years. One of these extensions is 'fuzzy Bayesian inference' which is the result of integrating fuzzy techniques into Bayesian statistics. Fuzzy Bayesian inference has a number of desirable features which makes it an attractive approach for incorporating expert knowledge in the parameter estimation process of groundwater models: (1) it is well adapted to the nature of expert provided information, (2) it allows to distinguishably model both uncertainty and imprecision, and (3) it presents a framework for fusing expert provided information regarding the various inputs of the Bayesian inference algorithm. However an important obstacle in employing fuzzy Bayesian inference in groundwater numerical modeling applications is the computational burden, as the required number of numerical model simulations often becomes extremely exhaustive and often computationally infeasible. In this paper, a novel approach of accelerating the fuzzy Bayesian inference algorithm is proposed which is based on using approximate posterior distributions derived from surrogate modeling, as a screening tool in the computations. The proposed approach is first applied to a synthetic test case of seawater intrusion (SWI) in a coastal aquifer. It is shown that for this synthetic test case, the proposed approach decreases the number of required numerical simulations by an order of magnitude. Then the proposed approach is applied to a real-world test case involving three-dimensional numerical modeling of SWI in Kish Island, located in the Persian Gulf. An expert

  8. Discriminative Bayesian Dictionary Learning for Classification.

    Science.gov (United States)

    Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

    2016-12-01

    We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

  9. Risk-sensitivity in Bayesian sensorimotor integration.

    Directory of Open Access Journals (Sweden)

    Jordi Grau-Moya

    Full Text Available Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.

  10. The Shifting Subtypes of ADHD: Classification Depends on How Symptom Reports Are Combined

    Science.gov (United States)

    Rowland, Andrew S.; Skipper, Betty; Rabiner, David L.; Umbach, David M.; Stallone, Lil; Campbell, Richard A.; Hough, Richard L.; Naftel, A. J.; Sandler, Dale P.

    2008-01-01

    Research on the correlates of ADHD subtypes has yielded inconsistent findings, perhaps because the procedures used to define subtypes vary across studies. We examined this possibility by investigating whether the ADHD subtype distribution in a community sample was sensitive to different methods for combining informant data. We conducted a study to…

  11. Relationship between Self-Typicality and the In-Group Subtypes Effect.

    Science.gov (United States)

    Wallace, David S.; And Others

    1995-01-01

    Examined whether group members differ in the number of in-group subtype distinctions that they draw. Drawing on results of two studies, found that members of groups whose primary function is intragroup interaction (fraternities, sororities, athletic teams) draw more subtype distinctions within their own group than within other groups. (RJM)

  12. Sparse-grid, reduced-basis Bayesian inversion: Nonaffine-parametric nonlinear equations

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Peng, E-mail: peng@ices.utexas.edu [The Institute for Computational Engineering and Sciences, The University of Texas at Austin, 201 East 24th Street, Stop C0200, Austin, TX 78712-1229 (United States); Schwab, Christoph, E-mail: christoph.schwab@sam.math.ethz.ch [Seminar für Angewandte Mathematik, Eidgenössische Technische Hochschule, Römistrasse 101, CH-8092 Zürich (Switzerland)

    2016-07-01

    We extend the reduced basis (RB) accelerated Bayesian inversion methods for affine-parametric, linear operator equations which are considered in [16,17] to non-affine, nonlinear parametric operator equations. We generalize the analysis of sparsity of parametric forward solution maps in [20] and of Bayesian inversion in [48,49] to the fully discrete setting, including Petrov–Galerkin high-fidelity (“HiFi”) discretization of the forward maps. We develop adaptive, stochastic collocation based reduction methods for the efficient computation of reduced bases on the parametric solution manifold. The nonaffinity and nonlinearity with respect to (w.r.t.) the distributed, uncertain parameters and the unknown solution is collocated; specifically, by the so-called Empirical Interpolation Method (EIM). For the corresponding Bayesian inversion problems, computational efficiency is enhanced in two ways: first, expectations w.r.t. the posterior are computed by adaptive quadratures with dimension-independent convergence rates proposed in [49]; the present work generalizes [49] to account for the impact of the PG discretization in the forward maps on the convergence rates of the Quantities of Interest (QoI for short). Second, we propose to perform the Bayesian estimation only w.r.t. a parsimonious, RB approximation of the posterior density. Based on the approximation results in [49], the infinite-dimensional parametric, deterministic forward map and operator admit N-term RB and EIM approximations which converge at rates which depend only on the sparsity of the parametric forward map. In several numerical experiments, the proposed algorithms exhibit dimension-independent convergence rates which equal, at least, the currently known rate estimates for N-term approximation. We propose to accelerate Bayesian estimation by first offline construction of reduced basis surrogates of the Bayesian posterior density. The parsimonious surrogates can then be employed for online data

  13. Space Shuttle RTOS Bayesian Network

    Science.gov (United States)

    Morris, A. Terry; Beling, Peter A.

    2001-01-01

    With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores

  14. Subtyping borderline personality disorder by suicidal behavior.

    Science.gov (United States)

    Soloff, Paul H; Chiappetta, Laurel

    2012-06-01

    Course and outcome of Borderline Personality Disorder (BPD) are favorable for the vast majority of patients; however, up to 10% die by suicide. This discrepancy begs the question of whether there is a high lethality subtype in BPD, defined by recurrent suicidal behavior and increasing attempt lethality over time. In a prospective, longitudinal study, we sought predictors of high lethality among repeat attempters, and defined clinical subtypes by applying trajectory analysis to consecutive lethality scores. Criteria-defined subjects with BPD were assessed using standardized instruments and followed longitudinally. Suicidal behavior was assessed on the Columbia Suicide History, Lethality Rating Scale, and Suicide Intent Scale. Variables discriminating single and repeat attempters were entered into logistic regression models to define predictors of high and low lethality attempts. Trajectory analysis using three attempt and five attempt models identified discrete patterns of Lethality Rating Scale scores. A high lethality trajectory was associated with inpatient recruitment, and poor psychosocial function, a low lethality trajectory with greater Negativism, Substance Use Disorders, Histrionic and/or Narcissistic PD co-morbidity. Illness severity, older age, and poor psychosocial function are characteristics of a poor prognosis subtype related to suicidal behavior.

  15. Bayesian uncertainty analyses of probabilistic risk models

    International Nuclear Information System (INIS)

    Pulkkinen, U.

    1989-01-01

    Applications of Bayesian principles to the uncertainty analyses are discussed in the paper. A short review of the most important uncertainties and their causes is provided. An application of the principle of maximum entropy to the determination of Bayesian prior distributions is described. An approach based on so called probabilistic structures is presented in order to develop a method of quantitative evaluation of modelling uncertainties. The method is applied to a small example case. Ideas for application areas for the proposed method are discussed

  16. Bayesian estimation and tracking a practical guide

    CERN Document Server

    Haug, Anton J

    2012-01-01

    A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation

  17. A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

    Directory of Open Access Journals (Sweden)

    Sho Fukuda

    2014-12-01

    Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

  18. Complete genome of a European hepatitis C virus subtype 1g isolate: phylogenetic and genetic analyses.

    Science.gov (United States)

    Bracho, Maria A; Saludes, Verónica; Martró, Elisa; Bargalló, Ana; González-Candelas, Fernando; Ausina, Vicent

    2008-06-05

    Hepatitis C virus isolates have been classified into six main genotypes and a variable number of subtypes within each genotype, mainly based on phylogenetic analysis. Analyses of the genetic relationship among genotypes and subtypes are more reliable when complete genome sequences (or at least the full coding region) are used; however, so far 31 of 80 confirmed or proposed subtypes have at least one complete genome available. Of these, 20 correspond to confirmed subtypes of epidemic interest. We present and analyse the first complete genome sequence of a HCV subtype 1g isolate. Phylogenetic and genetic distance analyses reveal that HCV-1g is the most divergent subtype among the HCV-1 confirmed subtypes. Potential genomic recombination events between genotypes or subtype 1 genomes were ruled out. We demonstrate phylogenetic congruence of previously deposited partial sequences of HCV-1g with respect to our sequence. In light of this, we propose changing the current status of its subtype-specific designation from provisional to confirmed.

  19. Justifying Objective Bayesianism on Predicate Languages

    Directory of Open Access Journals (Sweden)

    Jürgen Landes

    2015-04-01

    Full Text Available Objective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the maximum entropy principle. In this paper we investigate the extent to which one can provide a unified justification of the objective Bayesian norms in the case in which the background language is a first-order predicate language, with a view to applying the resulting formalism to inductive logic. We show that the maximum entropy principle can be motivated largely in terms of minimising worst-case expected loss.

  20. Structure-based bayesian sparse reconstruction

    KAUST Repository

    Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

    2012-01-01

    Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical

  1. Bayesian programming

    CERN Document Server

    Bessiere, Pierre; Ahuactzin, Juan Manuel; Mekhnacha, Kamel

    2013-01-01

    Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean

  2. Bayesian Approaches to Imputation, Hypothesis Testing, and Parameter Estimation

    Science.gov (United States)

    Ross, Steven J.; Mackey, Beth

    2015-01-01

    This chapter introduces three applications of Bayesian inference to common and novel issues in second language research. After a review of the critiques of conventional hypothesis testing, our focus centers on ways Bayesian inference can be used for dealing with missing data, for testing theory-driven substantive hypotheses without a default null…

  3. Deep Learning and Bayesian Methods

    Directory of Open Access Journals (Sweden)

    Prosper Harrison B.

    2017-01-01

    Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

  4. CRISPR-cas subtype I-Fb in Acinetobacter baumannii: evolution and utilization for strain subtyping.

    Science.gov (United States)

    Karah, Nabil; Samuelsen, Ørjan; Zarrilli, Raffaele; Sahl, Jason W; Wai, Sun Nyunt; Uhlin, Bernt Eric

    2015-01-01

    Clustered regularly interspaced short palindromic repeats (CRISPR) are polymorphic elements found in the genome of some or all strains of particular bacterial species, providing them with a system of acquired immunity against invading bacteriophages and plasmids. Two CRISPR-Cas systems have been identified in Acinetobacter baumannii, an opportunistic pathogen with a remarkable capacity for clonal dissemination. In this study, we investigated the mode of evolution and diversity of spacers of the CRISPR-cas subtype I-Fb locus in a global collection of 76 isolates of A. baumannii obtained from 14 countries and 4 continents. The locus has basically evolved from a common ancestor following two main lineages and several pathways of vertical descent. However, this vertical passage has been interrupted by occasional events of horizontal transfer of the whole locus between distinct isolates. The isolates were assigned into 40 CRISPR-based sequence types (CST). CST1 and CST23-24 comprised 18 and 9 isolates, representing two main sub-clones of international clones CC1 and CC25, respectively. Epidemiological data showed that some of the CST1 isolates were acquired or imported from Iraq, where it has probably been endemic for more than one decade and occasionally been able to spread to USA, Canada, and Europe. CST23-24 has shown a remarkable ability to cause national outbreaks of infections in Sweden, Argentina, UAE, and USA. The three isolates of CST19 were independently imported from Thailand to Sweden and Norway, raising a concern about the prevalence of CST19 in Thailand. Our study highlights the dynamic nature of the CRISPR-cas subtype I-Fb locus in A. baumannii, and demonstrates the possibility of using a CRISPR-based approach for subtyping a significant part of the global population of A. baumannii.

  5. CRISPR-cas Subtype I-Fb in Acinetobacter baumannii: Evolution and Utilization for Strain Subtyping

    Science.gov (United States)

    Karah, Nabil; Samuelsen, Ørjan; Zarrilli, Raffaele; Sahl, Jason W.; Wai, Sun Nyunt; Uhlin, Bernt Eric

    2015-01-01

    Clustered regularly interspaced short palindromic repeats (CRISPR) are polymorphic elements found in the genome of some or all strains of particular bacterial species, providing them with a system of acquired immunity against invading bacteriophages and plasmids. Two CRISPR-Cas systems have been identified in Acinetobacter baumannii, an opportunistic pathogen with a remarkable capacity for clonal dissemination. In this study, we investigated the mode of evolution and diversity of spacers of the CRISPR-cas subtype I-Fb locus in a global collection of 76 isolates of A. baumannii obtained from 14 countries and 4 continents. The locus has basically evolved from a common ancestor following two main lineages and several pathways of vertical descent. However, this vertical passage has been interrupted by occasional events of horizontal transfer of the whole locus between distinct isolates. The isolates were assigned into 40 CRISPR-based sequence types (CST). CST1 and CST23-24 comprised 18 and 9 isolates, representing two main sub-clones of international clones CC1 and CC25, respectively. Epidemiological data showed that some of the CST1 isolates were acquired or imported from Iraq, where it has probably been endemic for more than one decade and occasionally been able to spread to USA, Canada, and Europe. CST23-24 has shown a remarkable ability to cause national outbreaks of infections in Sweden, Argentina, UAE, and USA. The three isolates of CST19 were independently imported from Thailand to Sweden and Norway, raising a concern about the prevalence of CST19 in Thailand. Our study highlights the dynamic nature of the CRISPR-cas subtype I-Fb locus in A. baumannii, and demonstrates the possibility of using a CRISPR-based approach for subtyping a significant part of the global population of A. baumannii. PMID:25706932

  6. Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search

    NARCIS (Netherlands)

    Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.

    2001-01-01

    In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search.

  7. Variation of types of alcoholism: review and subtypes identified in Han Chinese.

    Science.gov (United States)

    Lee, Sheng-Yu; Chen, Shiou-Lan; Chang, Yun-Hsuan; Lu, Ru-Band

    2014-01-03

    Alcoholism, as it has been hypothesized, is caused by a highly heterogeneous genetic load. Since 1960, many reports have used the bio-psycho-social approach to subtype alcoholism; however, no subtypes have been genetically validated. We reviewed and compared the major single-gene, multiple-gene, and gene-to-gene interaction studies on alcoholism published during the past quarter-century, including many recent studies that have made contributions to the subtyping of alcoholism. Four subtypes of alcoholism have been reported: [1] pure alcoholism, [2] anxiety/depression alcoholism, [3] antisocial alcoholism, and [4] mixed alcoholism. Most of the important studies focused on three genes: DRD2, MAOA, and ALDH2. Therefore, our review focuses on these three genes. © 2013.

  8. Bayesian network learning for natural hazard assessments

    Science.gov (United States)

    Vogel, Kristin

    2016-04-01

    Even though quite different in occurrence and consequences, from a modelling perspective many natural hazards share similar properties and challenges. Their complex nature as well as lacking knowledge about their driving forces and potential effects make their analysis demanding. On top of the uncertainty about the modelling framework, inaccurate or incomplete event observations and the intrinsic randomness of the natural phenomenon add up to different interacting layers of uncertainty, which require a careful handling. Thus, for reliable natural hazard assessments it is crucial not only to capture and quantify involved uncertainties, but also to express and communicate uncertainties in an intuitive way. Decision-makers, who often find it difficult to deal with uncertainties, might otherwise return to familiar (mostly deterministic) proceedings. In the scope of the DFG research training group „NatRiskChange" we apply the probabilistic framework of Bayesian networks for diverse natural hazard and vulnerability studies. The great potential of Bayesian networks was already shown in previous natural hazard assessments. Treating each model component as random variable, Bayesian networks aim at capturing the joint distribution of all considered variables. Hence, each conditional distribution of interest (e.g. the effect of precautionary measures on damage reduction) can be inferred. The (in-)dependencies between the considered variables can be learned purely data driven or be given by experts. Even a combination of both is possible. By translating the (in-)dependences into a graph structure, Bayesian networks provide direct insights into the workings of the system and allow to learn about the underlying processes. Besides numerous studies on the topic, learning Bayesian networks from real-world data remains challenging. In previous studies, e.g. on earthquake induced ground motion and flood damage assessments, we tackled the problems arising with continuous variables

  9. Autochthonous and allochthonous contributions of organic carbon to microbial food webs in Svalbard fjords

    KAUST Repository

    Holding, Johnna M.; Duarte, Carlos M.; Delgado-Huertas, Antonio; Soetaert, Karline; Vonk, Jorien E.; Agusti, Susana; Wassmann, Paul; Middelburg, Jack J.

    2017-01-01

    Rising temperatures in the Arctic Ocean are causing sea ice and glaciers to melt at record breaking rates, which has consequences for carbon cycling in the Arctic Ocean that are yet to be fully understood. Microbial carbon cycling is driven by internal processing of in situ produced organic carbon (OC), however recent research suggests that melt water from sea ice and glaciers could introduce an allochthonous source of OC to the microbial food web with ramifications for the metabolic balance of plankton communities. In this study, we characterized autochthonous and allochthonous sources of OC to the Western Svalbard fjord system using stable isotopes of carbon. We quantified δ13C of eukaryotic and prokaryotic planktonic groups using polar lipid-derived fatty acids as biomarkers in addition to measuring δ13C of marine particulate OC and dissolved OC from glacial runoff. δ13C of bacteria (−22.5‰) was higher than that of glacial runoff OC (−28.5‰) and other phytoplankton groups (−24.7 to −29.1‰), which suggests that marine bacteria preferentially use a third source of OC. We present a Bayesian three-source δ13C mixing model whereby ∼ 60% of bacteria carbon is derived from OC in sea ice, and the remaining carbon is derived from autochthonous production and glacial-derived OC. These results suggest that subsidies of OC from melting glaciers will not likely influence microbial carbon cycling in Svalbard fjords in the future and that further research is needed to determine the effects of melting sea ice on microbial carbon cycling in fjord systems and elsewhere in the Arctic Ocean.

  10. Autochthonous and allochthonous contributions of organic carbon to microbial food webs in Svalbard fjords

    KAUST Repository

    Holding, Johnna M.

    2017-03-27

    Rising temperatures in the Arctic Ocean are causing sea ice and glaciers to melt at record breaking rates, which has consequences for carbon cycling in the Arctic Ocean that are yet to be fully understood. Microbial carbon cycling is driven by internal processing of in situ produced organic carbon (OC), however recent research suggests that melt water from sea ice and glaciers could introduce an allochthonous source of OC to the microbial food web with ramifications for the metabolic balance of plankton communities. In this study, we characterized autochthonous and allochthonous sources of OC to the Western Svalbard fjord system using stable isotopes of carbon. We quantified δ13C of eukaryotic and prokaryotic planktonic groups using polar lipid-derived fatty acids as biomarkers in addition to measuring δ13C of marine particulate OC and dissolved OC from glacial runoff. δ13C of bacteria (−22.5‰) was higher than that of glacial runoff OC (−28.5‰) and other phytoplankton groups (−24.7 to −29.1‰), which suggests that marine bacteria preferentially use a third source of OC. We present a Bayesian three-source δ13C mixing model whereby ∼ 60% of bacteria carbon is derived from OC in sea ice, and the remaining carbon is derived from autochthonous production and glacial-derived OC. These results suggest that subsidies of OC from melting glaciers will not likely influence microbial carbon cycling in Svalbard fjords in the future and that further research is needed to determine the effects of melting sea ice on microbial carbon cycling in fjord systems and elsewhere in the Arctic Ocean.

  11. Distinct subtype distribution and somatic mutation spectrum of lymphomas in East Asia.

    Science.gov (United States)

    Ren, Weicheng; Li, Wei; Ye, Xiaofei; Liu, Hui; Pan-Hammarström, Qiang

    2017-07-01

    Here, we give an updated overview of the subtype distribution of lymphomas in East Asia and also present the genome sequencing data on two major subtypes of these tumors. The distribution of lymphoma types/subtypes among East Asian countries is very similar, with a lower proportion of B-cell malignancies and a higher proportion of T/natural killer (NK)-cell lymphomas as compared to Western populations. Extranodal NK/T-cell lymphoma is more frequently observed in East Asia, whereas follicular lymphoma and chronic lymphocytic leukemia, are proportionally lower. The incidence rate of lymphoma subtypes in Asians living in the US was generally intermediate to the general rate in US and Asia, suggesting that both genetic and environmental factors may underlie the geographical variations observed.Key cancer driver mutations have been identified in Asian patients with diffuse large B-cell lymphoma or extranodal NK/T-cell lymphoma through genome sequencing. A distinct somatic mutation profile has also been observed in Chinese diffuse large B-cell lymphoma patients. The incidence and distribution of lymphoma subtypes differed significantly between patients from East Asia and Western countries, suggesting subtype-specific etiologic mechanisms. Further studies on the mechanism underlying these geographical variations may give new insights into our understanding of lymphomagenesis.

  12. Analysis of HIV subtypes and the phylogenetic tree in HIV-positive samples from Saudi Arabia

    International Nuclear Information System (INIS)

    Al-Zahrani, Alhusain J.

    2008-01-01

    Objective was to assess the prevalence of HIV-1 genetic subtypes in Saudi Arabia in samples that are serologically positive for HIV-1 and compare the HIV-1 genetic subtypes prevalent in Saudi Arabia with the subtypes prevalent in other countries. Thirty-nine HIV-1 positive samples were analyzed for HIV-1 subtypes using molecular techniques. The study is retrospective study that was conducted in Dammam, Kingdom of Saudi Arabia and in Abbott laboratories (United States of America) from2004 to 2007. All samples were seropositive for HIV-1 group M. Of the 39 seropositive samples, only 12 were polymerase chain reaction positive. Subtype C is the most common virus strain as it occurred in 58% of these samples; subtype B occurred in 17%; subtypes A, D and G were found in 8% each. The phylogenetic tree was also identified for the isolates. Detection of HIV subtypes is important for epidemiological purposes and may help in tracing the source of HIV infections in the Kingdom of Saudi Arabia. (author)

  13. [Attention characteristics of children with different clinical subtypes of attention deficit hyperactivity disorder].

    Science.gov (United States)

    Liu, Wen-Long; Zhao, Xu; Tan, Jian-Hui; Wang, Juan

    2014-09-01

    To explore the attention characteristics of children with different clinical subtypes of attention deficit hyperactivity disorder (ADHD) and to provide a basis for clinical intervention. A total of 345 children diagnosed with ADHD were selected and the subtypes were identified. Attention assessment was performed by the intermediate visual and auditory continuous performance test at diagnosis, and the visual and auditory attention characteristics were compared between children with different subtypes. A total of 122 normal children were recruited in the control group and their attention characteristics were compared with those of children with ADHD. The scores of full scale attention quotient (AQ) and full scale response control quotient (RCQ) of children with all three subtypes of ADHD were significantly lower than those of normal children (Phyperactive/impulsive subtype (Pattention function of children with ADHD is worse than that of normal children, and the impairment of visual attention function is severer than that of auditory attention function. The degree of functional impairment of visual or auditory attention shows no significant differences between three subtypes of ADHD.

  14. Association between endometriosis and risk of histological subtypes of ovarian cancer

    DEFF Research Database (Denmark)

    Pearce, Celeste Leigh; Templeman, Claire; Rossing, Mary Anne

    2012-01-01

    Endometriosis is a risk factor for epithelial ovarian cancer; however, whether this risk extends to all invasive histological subtypes or borderline tumours is not clear. We undertook an international collaborative study to assess the association between endometriosis and histological subtypes...

  15. Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets.

    Science.gov (United States)

    Agarwal, Rahul; Narayan, Jitendra; Bhattacharyya, Amitava; Saraswat, Mayank; Tomar, Anil Kumar

    2017-10-01

    A very low 5-year survival rate among hepatocellular carcinoma (HCC) patients is mainly due to lack of early stage diagnosis, distant metastasis and high risk of postoperative recurrence. Hence ascertaining novel biomarkers for early diagnosis and patient specific therapeutics is crucial and urgent. Here, we have performed a comprehensive analysis of the expression data of 423 HCC patients (373 tumors and 50 controls) downloaded from The Cancer Genome Atlas (TCGA) followed by pathway enrichment by gene ontology annotations, subtype classification and overall survival analysis. The differential gene expression analysis using non-parametric Wilcoxon test revealed a total of 479 up-regulated and 91 down-regulated genes in HCC compared to controls. The list of top differentially expressed genes mainly consists of tumor/cancer associated genes, such as AFP, THBS4, LCN2, GPC3, NUF2, etc. The genes over-expressed in HCC were mainly associated with cell cycle pathways. In total, 59 kinases associated genes were found over-expressed in HCC, including TTK, MELK, BUB1, NEK2, BUB1B, AURKB, PLK1, CDK1, PKMYT1, PBK, etc. Overall four distinct HCC subtypes were predicted using consensus clustering method. Each subtype was unique in terms of gene expression, pathway enrichment and median survival. Conclusively, this study has exposed a number of interesting genes which can be exploited in future as potential markers of HCC, diagnostic as well as prognostic and subtype classification may guide for improved and specific therapy. Copyright © 2017 Elsevier Inc. All rights reserved.

  16. Testing adaptive toolbox models: a Bayesian hierarchical approach.

    Science.gov (United States)

    Scheibehenne, Benjamin; Rieskamp, Jörg; Wagenmakers, Eric-Jan

    2013-01-01

    Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior.

  17. New hepatitis C virus genotype 1 subtype naturally harbouring resistance-associated mutations to NS5A inhibitors.

    Science.gov (United States)

    Ordeig, Laura; Garcia-Cehic, Damir; Gregori, Josep; Soria, Maria Eugenia; Nieto-Aponte, Leonardo; Perales, Celia; Llorens, Meritxell; Chen, Qian; Riveiro-Barciela, Mar; Buti, Maria; Esteban, Rafael; Esteban, Juan Ignacio; Rodriguez-Frias, Francisco; Quer, Josep

    2018-01-01

    Hepatitis C virus (HCV) is a highly divergent virus currently classified into seven major genotypes and 86 subtypes (ICTV, June 2017), which can have differing responses to therapy. Accurate genotyping/subtyping using high-resolution HCV subtyping enables confident subtype identification, identifies mixed infections and allows detection of new subtypes. During routine genotyping/subtyping, one sample from an Equatorial Guinea patient could not be classified into any of the subtypes. The complete genomic sequence was compared to reference sequences by phylogenetic and sliding window analysis. Resistance-associated substitutions (RASs) were assessed by deep sequencing. The unclassified HCV genome did not belong to any of the existing genotype 1 (G1) subtypes. Sliding window analysis along the complete genome ruled out recombination phenomena suggesting that it belongs to a new HCV G1 subtype. Two NS5A RASs (L31V+Y93H) were found to be naturally combined in the genome which could limit treatment possibilities in patients infected with this subtype.

  18. Spectral analysis of the IntCal98 calibration curve: a Bayesian view

    International Nuclear Information System (INIS)

    Palonen, V.; Tikkanen, P.

    2004-01-01

    Preliminary results from a Bayesian approach to find periodicities in the IntCal98 calibration curve are given. It has been shown in the literature that the discrete Fourier transform (Schuster periodogram) corresponds to the use of an approximate Bayesian model of one harmonic frequency and Gaussian noise. Advantages of the Bayesian approach include the possibility to use models for variable, attenuated and multiple frequencies, the capability to analyze unevenly spaced data and the possibility to assess the significance and uncertainties of spectral estimates. In this work, a new Bayesian model using random walk noise to take care of the trend in the data is developed. Both Bayesian models are described and the first results of the new model are reported and compared with results from straightforward discrete-Fourier-transform and maximum-entropy-method spectral analyses

  19. [Distorted cognition of bodily sensations in subtypes of social anxiety].

    Science.gov (United States)

    Kanai, Yoshihiro; Sasaki, Shoko; Iwanaga, Makoto; Seiwa, Hidetoshi

    2010-02-01

    The purpose of this study was to investigate the relationship between subtypes of social anxiety and distorted cognition of bodily sensations. The package of questionnaires including the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) was administered to 582 undergraduate students. To identify subtypes of social anxiety, cluster analysis was conducted using scores of the SPS and SIAS. Five clusters were identified and labeled as follows: Generalized type characterized by intense anxiety in most social situations, Non-anxious type characterized by low anxiety levels in social situations, Averaged type whose anxiety levels are averaged, Interaction anxiety type who feels anxiety mainly in social interaction situations, and Performance anxiety type who feels anxiety mainly in performance situations. Results of an ANOVA indicated that individuals with interaction type fear the negative evaluation from others regarding their bodily sensations whereas individuals with performance type overestimate the visibility of their bodily sensations to others. Differences in salient aspects of cognitive distortion among social anxiety subtypes may show necessity to select intervention techniques in consideration of subtypes.

  20. Multiple estrogen receptor subtypes influence ingestive behavior in female rodents.

    Science.gov (United States)

    Santollo, Jessica; Daniels, Derek

    2015-12-01

    Postmenopausal women are at an increased risk of obesity and cardiovascular-related diseases. This is attributable, at least in part, to loss of the ovarian hormone estradiol, which inhibits food and fluid intake in humans and laboratory animal models. Although the hypophagic and anti-dipsogenic effects of estradiol have been well documented for decades, the precise mechanisms underlying these effects are not fully understood. An obvious step toward addressing this open question is identifying which estrogen receptor subtypes are involved and what intracellular processes are involved. This question, however, is complicated not only by the variety of estrogen receptor subtypes that exist, but also because many subtypes have multiple locations of action (i.e. in the nucleus or in the plasma membrane). This review will highlight our current understanding of the roles that specific estrogen receptor subtypes play in mediating estradiol's anorexigenic and anti-dipsogenic effects along with highlighting the many open questions that remain. This review will also describe recent work being performed by our laboratory aimed at answering these open questions. Copyright © 2015 Elsevier Inc. All rights reserved.

  1. Combining miRNA and mRNA Expression Profiles in Wilms Tumor Subtypes

    Directory of Open Access Journals (Sweden)

    Nicole Ludwig

    2016-03-01

    Full Text Available Wilms tumor (WT is the most common childhood renal cancer. Recent findings of mutations in microRNA (miRNA processing proteins suggest a pivotal role of miRNAs in WT genesis. We performed miRNA expression profiling of 36 WTs of different subtypes and four normal kidney tissues using microarrays. Additionally, we determined the gene expression profile of 28 of these tumors to identify potentially correlated target genes and affected pathways. We identified 85 miRNAs and 2107 messenger RNAs (mRNA differentially expressed in blastemal WT, and 266 miRNAs and 1267 mRNAs differentially expressed in regressive subtype. The hierarchical clustering of the samples, using either the miRNA or mRNA profile, showed the clear separation of WT from normal kidney samples, but the miRNA pattern yielded better separation of WT subtypes. A correlation analysis of the deregulated miRNA and mRNAs identified 13,026 miRNA/mRNA pairs with inversely correlated expression, of which 2844 are potential interactions of miRNA and their predicted mRNA targets. We found significant upregulation of miRNAs-183, -301a/b and -335 for the blastemal subtype, and miRNAs-181b, -223 and -630 for the regressive subtype. We found marked deregulation of miRNAs regulating epithelial to mesenchymal transition, especially in the blastemal subtype, and miRNAs influencing chemosensitivity, especially in regressive subtypes. Further research is needed to assess the influence of preoperative chemotherapy and tumor infiltrating lymphocytes on the miRNA and mRNA patterns in WT.

  2. A human monoclonal antibody with neutralizing activity against highly divergent influenza subtypes.

    Directory of Open Access Journals (Sweden)

    Nicola Clementi

    Full Text Available The interest in broad-range anti-influenza A monoclonal antibodies (mAbs has recently been strengthened by the identification of anti-hemagglutinin (HA mAbs endowed with heterosubtypic neutralizing activity to be used in the design of "universal" prophylactic or therapeutic tools. However, the majority of the single mAbs described to date do not bind and neutralize viral isolates belonging to highly divergent subtypes clustering into the two different HA-based influenza phylogenetic groups: the group 1 including, among others, subtypes H1, H2, H5 and H9 and the group 2 including, among others, H3 subtype. Here, we describe a human mAb, named PN-SIA28, capable of binding and neutralizing all tested isolates belonging to phylogenetic group 1, including H1N1, H2N2, H5N1 and H9N2 subtypes and several isolates belonging to group 2, including H3N2 isolates from the first period of the 1968 pandemic. Therefore, PN-SIA28 is capable of neutralizing isolates belonging to subtypes responsible of all the reported pandemics, as well as other subtypes with pandemic potential. The region recognized by PN-SIA28 has been identified on the stem region of HA and includes residues highly conserved among the different influenza subtypes. A deep characterization of PN-SIA28 features may represent a useful help in the improvement of available anti-influenza therapeutic strategies and can provide new tools for the development of universal vaccinal strategies.

  3. Molecular subtype classification of urothelial carcinoma in Lynch syndrome.

    Science.gov (United States)

    Therkildsen, Christina; Eriksson, Pontus; Höglund, Mattias; Jönsson, Mats; Sjödahl, Gottfrid; Nilbert, Mef; Liedberg, Fredrik

    2018-05-23

    Lynch syndrome confers an increased risk for urothelial carcinoma (UC). Molecular subtypes may be relevant to prognosis and therapeutic possibilities, but have to date not been defined in Lynch syndrome-associated urothelial cancer. We aimed to provide a molecular description of Lynch syndrome-associated UC. Thus, Lynch syndrome-associated UC of the upper urinary tract and the urinary bladder were identified in the Danish hereditary non-polyposis colorectal cancer (HNPCC) register and were transcriptionally and immunohistochemically profiled and further related to data from 307 sporadic urothelial carcinomas. Whole genome mRNA expression profiles of 41 tumors and immunohistochemical stainings against FGFR3, KRT5, CCNB1, RB1, and CDKN2A (p16) of 37 tumors from Lynch syndrome patients were generated. Pathological data, microsatellite instability, anatomic location, and overall survival data was analyzed and compared with sporadic bladder cancer. The 41 Lynch syndrome-associated UC developed at a mean age of 61 years with 59% women. mRNA expression profiling and immunostaining classified the majority of the Lynch syndrome-associated UC as Urothelial-like tumors with only 20% being Genomically Unstable, Basal/SCC-like or other subtypes. The subtypes were associated with stage, grade, and microsatellite instability. Comparison to larger data sets revealed that Lynch syndrome-associated UC share molecular similarities with sporadic UC. In conclusion, transcriptomic and immunohistochemical profiling identifies a predominance of the Urothelial-like molecular subtype in Lynch syndrome and reveals that the molecular subtypes of sporadic bladder cancer are relevant also within this hereditary, mismatch-repair defective subset. This article is protected by copyright. All rights reserved. Molecular Oncology (2018) © 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

  4. Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC

    NARCIS (Netherlands)

    Ahn, S.; Korattikara, A.; Liu, N.; Rajan, S.; Welling, M.

    2015-01-01

    Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid ovrfitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian

  5. On Bayesian Inference under Sampling from Scale Mixtures of Normals

    NARCIS (Netherlands)

    Fernández, C.; Steel, M.F.J.

    1996-01-01

    This paper considers a Bayesian analysis of the linear regression model under independent sampling from general scale mixtures of Normals.Using a common reference prior, we investigate the validity of Bayesian inference and the existence of posterior moments of the regression and precision

  6. Nonlinear and non-Gaussian Bayesian based handwriting beautification

    Science.gov (United States)

    Shi, Cao; Xiao, Jianguo; Xu, Canhui; Jia, Wenhua

    2013-03-01

    A framework is proposed in this paper to effectively and efficiently beautify handwriting by means of a novel nonlinear and non-Gaussian Bayesian algorithm. In the proposed framework, format and size of handwriting image are firstly normalized, and then typeface in computer system is applied to optimize vision effect of handwriting. The Bayesian statistics is exploited to characterize the handwriting beautification process as a Bayesian dynamic model. The model parameters to translate, rotate and scale typeface in computer system are controlled by state equation, and the matching optimization between handwriting and transformed typeface is employed by measurement equation. Finally, the new typeface, which is transformed from the original one and gains the best nonlinear and non-Gaussian optimization, is the beautification result of handwriting. Experimental results demonstrate the proposed framework provides a creative handwriting beautification methodology to improve visual acceptance.

  7. Identifying Cancer Subtypes from miRNA-TF-mRNA Regulatory Networks and Expression Data.

    Directory of Open Access Journals (Sweden)

    Taosheng Xu

    Full Text Available Identifying cancer subtypes is an important component of the personalised medicine framework. An increasing number of computational methods have been developed to identify cancer subtypes. However, existing methods rarely use information from gene regulatory networks to facilitate the subtype identification. It is widely accepted that gene regulatory networks play crucial roles in understanding the mechanisms of diseases. Different cancer subtypes are likely caused by different regulatory mechanisms. Therefore, there are great opportunities for developing methods that can utilise network information in identifying cancer subtypes.In this paper, we propose a method, weighted similarity network fusion (WSNF, to utilise the information in the complex miRNA-TF-mRNA regulatory network in identifying cancer subtypes. We firstly build the regulatory network where the nodes represent the features, i.e. the microRNAs (miRNAs, transcription factors (TFs and messenger RNAs (mRNAs and the edges indicate the interactions between the features. The interactions are retrieved from various interatomic databases. We then use the network information and the expression data of the miRNAs, TFs and mRNAs to calculate the weight of the features, representing the level of importance of the features. The feature weight is then integrated into a network fusion approach to cluster the samples (patients and thus to identify cancer subtypes. We applied our method to the TCGA breast invasive carcinoma (BRCA and glioblastoma multiforme (GBM datasets. The experimental results show that WSNF performs better than the other commonly used computational methods, and the information from miRNA-TF-mRNA regulatory network contributes to the performance improvement. The WSNF method successfully identified five breast cancer subtypes and three GBM subtypes which show significantly different survival patterns. We observed that the expression patterns of the features in some mi

  8. When mechanism matters: Bayesian forecasting using models of ecological diffusion

    Science.gov (United States)

    Hefley, Trevor J.; Hooten, Mevin B.; Russell, Robin E.; Walsh, Daniel P.; Powell, James A.

    2017-01-01

    Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be implemented for large data sets that are distributed densely across space and time. The hierarchical Bayesian approach is used to understand and forecast the growth and geographic spread in the prevalence of chronic wasting disease in white-tailed deer (Odocoileus virginianus). We compare statistical inference and forecasts from our hierarchical Bayesian model to phenomenological regression-based methods that are commonly used to analyse spatial occurrence data. The mechanistic statistical model based on ecological diffusion led to important ecological insights, obviated a commonly ignored type of collinearity, and was the most accurate method for forecasting.

  9. Bayesian network as a modelling tool for risk management in agriculture

    DEFF Research Database (Denmark)

    Rasmussen, Svend; Madsen, Anders L.; Lund, Mogens

    . In this paper we use Bayesian networks as an integrated modelling approach for representing uncertainty and analysing risk management in agriculture. It is shown how historical farm account data may be efficiently used to estimate conditional probabilities, which are the core elements in Bayesian network models....... We further show how the Bayesian network model RiBay is used for stochastic simulation of farm income, and we demonstrate how RiBay can be used to simulate risk management at the farm level. It is concluded that the key strength of a Bayesian network is the transparency of assumptions......, and that it has the ability to link uncertainty from different external sources to budget figures and to quantify risk at the farm level....

  10. Complete genome of a European hepatitis C virus subtype 1g isolate: phylogenetic and genetic analyses

    Directory of Open Access Journals (Sweden)

    Bargalló Ana

    2008-06-01

    Full Text Available Abstract Background Hepatitis C virus isolates have been classified into six main genotypes and a variable number of subtypes within each genotype, mainly based on phylogenetic analysis. Analyses of the genetic relationship among genotypes and subtypes are more reliable when complete genome sequences (or at least the full coding region are used; however, so far 31 of 80 confirmed or proposed subtypes have at least one complete genome available. Of these, 20 correspond to confirmed subtypes of epidemic interest. Results We present and analyse the first complete genome sequence of a HCV subtype 1g isolate. Phylogenetic and genetic distance analyses reveal that HCV-1g is the most divergent subtype among the HCV-1 confirmed subtypes. Potential genomic recombination events between genotypes or subtype 1 genomes were ruled out. We demonstrate phylogenetic congruence of previously deposited partial sequences of HCV-1g with respect to our sequence. Conclusion In light of this, we propose changing the current status of its subtype-specific designation from provisional to confirmed.

  11. The use of conflicts in searching Bayesian networks

    OpenAIRE

    Poole, David L.

    2013-01-01

    This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorith...

  12. Bootstrap prediction and Bayesian prediction under misspecified models

    OpenAIRE

    Fushiki, Tadayoshi

    2005-01-01

    We consider a statistical prediction problem under misspecified models. In a sense, Bayesian prediction is an optimal prediction method when an assumed model is true. Bootstrap prediction is obtained by applying Breiman's `bagging' method to a plug-in prediction. Bootstrap prediction can be considered to be an approximation to the Bayesian prediction under the assumption that the model is true. However, in applications, there are frequently deviations from the assumed model. In this paper, bo...

  13. Airline Sustainability Modeling: A New Framework with Application of Bayesian Structural Equation Modeling

    Directory of Open Access Journals (Sweden)

    Hashem Salarzadeh Jenatabadi

    2016-11-01

    Full Text Available There are many factors which could influence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM with maximum likelihood and Bayesian predictors. The introduced framework includes economic performance, operational performance, cost performance, and financial performance. Based on both Bayesian SEM (Bayesian-SEM and Classical SEM (Classical-SEM, it was found that economic performance with both operational performance and cost performance are significantly related to the financial performance index. The four mathematical indices employed are root mean square error, coefficient of determination, mean absolute error, and mean absolute percentage error to compare the efficiency of Bayesian-SEM and Classical-SEM in predicting the airline financial performance. The outputs confirmed that the framework with Bayesian prediction delivered a good fit with the data, although the framework predicted with a Classical-SEM approach did not prepare a well-fitting model. The reasons for this discrepancy between Classical and Bayesian predictions, as well as the potential advantages and caveats with the application of Bayesian approach in airline sustainability studies, are debated.

  14. Subjective Bayesian Beliefs

    DEFF Research Database (Denmark)

    Antoniou, Constantinos; Harrison, Glenn W.; Lau, Morten I.

    2015-01-01

    A large literature suggests that many individuals do not apply Bayes’ Rule when making decisions that depend on them correctly pooling prior information and sample data. We replicate and extend a classic experimental study of Bayesian updating from psychology, employing the methods of experimenta...... economics, with careful controls for the confounding effects of risk aversion. Our results show that risk aversion significantly alters inferences on deviations from Bayes’ Rule....

  15. Risk analysis of drinking water microbial contamination versus disinfection by-products (DBPs)

    International Nuclear Information System (INIS)

    Ashbolt, Nicholas John

    2004-01-01

    Managing the provision of safe drinking water has a renewed focus in light of the new World Health Organization (WHO) water safety plans. Risk analysis is a necessary component to assist in selecting priority hazards and identifying hazardous scenarios, be they qualitative to quantitative assessments. For any approach, acute diarrhoeal pathogens are often the higher risk issue for municipal water supplies, no matter how health burden is assessed. Furthermore, potential sequellae (myocarditis, diabetes, reactive arthritis and cancers) only further increase the potential health burden of pathogens; despite the enormous uncertainties in determining pathogen exposures and chemical dose-responses within respective microbial and chemical analyses. These interpretations are currently being improved by Bayesian and bootstrapping approaches to estimate parameters for stochastic assessments. A case example, covering the health benefits of ozonation for Cryptosporidium inactivation versus potential cancers from bromate exposures, illustrated the higher risks from a pathogen than one of the most likely disinfection by-products (DBPs). Such analyses help justify the industries long-held view of the benefits of multiple barriers to hazards and that microbial contamination of water supplies pose a clear public health risk when treatment is inadequate. Therefore, efforts to reduce potential health risks from DBP must not compromise pathogen control, despite socio-political issues

  16. Microbial Forensics: A Scientific Assessment

    Energy Technology Data Exchange (ETDEWEB)

    Keim, Paul

    2003-02-17

    procedures and training to meet these initial challenges so as minimize disturbance of the evidence. While epidemiology and forensics are similar sciences with similar goals when applied to biocrimes, forensics has additional and more stringent requirements. Maintaining a chain of custody on evidentiary samples is one example of an extra requirement imposed on an investigation of a biocrime. Another issue is the intent in microbial forensics to identify a bioattack organism in greatest detail. If possible, forensic investigations will strive to identify the precise strain and substrain, rather than just to the species level, which might be sufficient in an epidemiological investigation. Although multiple groups have developed lists of bioterrorism target pathogens, these lists are too narrow. An expansion of microorganisms relevant to food and water threats should be considered. Computerized networks should be established to track infectious disease outbreaks in real time. These systems could alert public health and agricultural officials to the existence of a potential bioattack earlier than simply waiting for a report of a suspicious cluster of similar patients. Once a biocrime is suspected, a wide variety of methods are available to identify the microorganism used in the bioattack and to analyze features that might lead to the source of the event. A multi-pronged approach to such an investigation may be preferable, using many available methods-ranging from genomics to sequencing to physiology to analysis of substances in the sample. Microbial forensics will be most effective if there is sufficient basic scientific information concerning microbial genetics, evolution, physiology, and ecology. Strain subtyping analysis will be difficult to interpret if we do not understand some of the basic evolutionary mechanisms and population diversity of pathogens. Phenotypic features associated with evidentiary pathogens also may provide investigative leads, but full exploitation of

  17. Bayesian Evaluation of Dynamical Soil Carbon Models Using Soil Carbon Flux Data

    Science.gov (United States)

    Xie, H. W.; Romero-Olivares, A.; Guindani, M.; Allison, S. D.

    2017-12-01

    2016 was Earth's hottest year in the modern temperature record and the third consecutive record-breaking year. As the planet continues to warm, temperature-induced changes in respiration rates of soil microbes could reduce the amount of carbon sequestered in the soil organic carbon (SOC) pool, one of the largest terrestrial stores of carbon. This would accelerate temperature increases. In order to predict the future size of the SOC pool, mathematical soil carbon models (SCMs) describing interactions between the biosphere and atmosphere are needed. SCMs must be validated before they can be chosen for predictive use. In this study, we check two SCMs called CON and AWB for consistency with observed data using Bayesian goodness of fit testing that can be used in the future to compare other models. We compare the fit of the models to longitudinal soil respiration data from a meta-analysis of soil heating experiments using a family of Bayesian goodness of fit metrics called information criteria (IC), including the Widely Applicable Information Criterion (WAIC), the Leave-One-Out Information Criterion (LOOIC), and the Log Pseudo Marginal Likelihood (LPML). These IC's take the entire posterior distribution into account, rather than just one outputted model fit line. A lower WAIC and LOOIC and larger LPML indicate a better fit. We compare AWB and CON with fixed steady state model pool sizes. At equivalent SOC, dissolved organic carbon, and microbial pool sizes, CON always outperforms AWB quantitatively by all three IC's used. AWB monotonically improves in fit as we reduce the SOC steady state pool size while fixing all other pool sizes, and the same is almost true for CON. The AWB model with the lowest SOC is the best performing AWB model, while the CON model with the second lowest SOC is the best performing model. We observe that AWB displays more changes in slope sign and qualitatively displays more adaptive dynamics, which prevents AWB from being fully ruled out for

  18. Cortical hierarchies perform Bayesian causal inference in multisensory perception.

    Directory of Open Access Journals (Sweden)

    Tim Rohe

    2015-02-01

    Full Text Available To form a veridical percept of the environment, the brain needs to integrate sensory signals from a common source but segregate those from independent sources. Thus, perception inherently relies on solving the "causal inference problem." Behaviorally, humans solve this problem optimally as predicted by Bayesian Causal Inference; yet, the underlying neural mechanisms are unexplored. Combining psychophysics, Bayesian modeling, functional magnetic resonance imaging (fMRI, and multivariate decoding in an audiovisual spatial localization task, we demonstrate that Bayesian Causal Inference is performed by a hierarchy of multisensory processes in the human brain. At the bottom of the hierarchy, in auditory and visual areas, location is represented on the basis that the two signals are generated by independent sources (= segregation. At the next stage, in posterior intraparietal sulcus, location is estimated under the assumption that the two signals are from a common source (= forced fusion. Only at the top of the hierarchy, in anterior intraparietal sulcus, the uncertainty about the causal structure of the world is taken into account and sensory signals are combined as predicted by Bayesian Causal Inference. Characterizing the computational operations of signal interactions reveals the hierarchical nature of multisensory perception in human neocortex. It unravels how the brain accomplishes Bayesian Causal Inference, a statistical computation fundamental for perception and cognition. Our results demonstrate how the brain combines information in the face of uncertainty about the underlying causal structure of the world.

  19. Bayesian Plackett-Luce Mixture Models for Partially Ranked Data.

    Science.gov (United States)

    Mollica, Cristina; Tardella, Luca

    2017-06-01

    The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data.

  20. Synthetic microbial ecology and the dynamic interplay between microbial genotypes.

    Science.gov (United States)

    Dolinšek, Jan; Goldschmidt, Felix; Johnson, David R

    2016-11-01

    Assemblages of microbial genotypes growing together can display surprisingly complex and unexpected dynamics and result in community-level functions and behaviors that are not readily expected from analyzing each genotype in isolation. This complexity has, at least in part, inspired a discipline of synthetic microbial ecology. Synthetic microbial ecology focuses on designing, building and analyzing the dynamic behavior of ‘ecological circuits’ (i.e. a set of interacting microbial genotypes) and understanding how community-level properties emerge as a consequence of those interactions. In this review, we discuss typical objectives of synthetic microbial ecology and the main advantages and rationales of using synthetic microbial assemblages. We then summarize recent findings of current synthetic microbial ecology investigations. In particular, we focus on the causes and consequences of the interplay between different microbial genotypes and illustrate how simple interactions can create complex dynamics and promote unexpected community-level properties. We finally propose that distinguishing between active and passive interactions and accounting for the pervasiveness of competition can improve existing frameworks for designing and predicting the dynamics of microbial assemblages.

  1. Testing students' e-learning via Facebook through Bayesian structural equation modeling.

    Science.gov (United States)

    Salarzadeh Jenatabadi, Hashem; Moghavvemi, Sedigheh; Wan Mohamed Radzi, Che Wan Jasimah Bt; Babashamsi, Parastoo; Arashi, Mohammad

    2017-01-01

    Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data) were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

  2. Testing students' e-learning via Facebook through Bayesian structural equation modeling.

    Directory of Open Access Journals (Sweden)

    Hashem Salarzadeh Jenatabadi

    Full Text Available Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a number of means. Although most theories/models developed have been examined through regression or structural equation modeling, Bayesian analysis offers more accurate data analysis results. To address this gap, the unified theory of acceptance and technology use in the context of e-learning via Facebook are re-examined in this study using Bayesian analysis. The data (S1 Data were collected from 170 students enrolled in a business statistics course at University of Malaya, Malaysia, and tested with the maximum likelihood and Bayesian approaches. The difference between the two methods' results indicates that performance expectancy and hedonic motivation are the strongest factors influencing the intention to use e-learning via Facebook. The Bayesian estimation model exhibited better data fit than the maximum likelihood estimator model. The results of the Bayesian and maximum likelihood estimator approaches are compared and the reasons for the result discrepancy are deliberated.

  3. Estimating mental states of a depressed person with bayesian networks

    NARCIS (Netherlands)

    Klein, Michel C.A.; Modena, Gabriele

    2013-01-01

    In this work in progress paper we present an approach based on Bayesian Networks to model the relationship between mental states and empirical observations in a depressed person. We encode relationships and domain expertise as a Hierarchical Bayesian Network. Mental states are represented as latent

  4. A study of finite mixture model: Bayesian approach on financial time series data

    Science.gov (United States)

    Phoong, Seuk-Yen; Ismail, Mohd Tahir

    2014-07-01

    Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.

  5. Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models.

    Science.gov (United States)

    Cruz-Marcelo, Alejandro; Rosner, Gary L; Müller, Peter; Stewart, Clinton F

    2013-04-01

    In biomedical research, it is often of interest to characterize biologic processes giving rise to observations and to make predictions of future observations. Bayesian nonparametric methods provide a means for carrying out Bayesian inference making as few assumptions about restrictive parametric models as possible. There are several proposals in the literature for extending Bayesian nonparametric models to include dependence on covariates. Limited attention, however, has been directed to the following two aspects. In this article, we examine the effect on fitting and predictive performance of incorporating covariates in a class of Bayesian nonparametric models by one of two primary ways: either in the weights or in the locations of a discrete random probability measure. We show that different strategies for incorporating continuous covariates in Bayesian nonparametric models can result in big differences when used for prediction, even though they lead to otherwise similar posterior inferences. When one needs the predictive density, as in optimal design, and this density is a mixture, it is better to make the weights depend on the covariates. We demonstrate these points via a simulated data example and in an application in which one wants to determine the optimal dose of an anticancer drug used in pediatric oncology.

  6. Risk of lymphoma subtypes and dietary habits in a Mediterranean area.

    Science.gov (United States)

    Campagna, Marcello; Cocco, Pierluigi; Zucca, Mariagrazia; Angelucci, Emanuele; Gabbas, Attilio; Latte, Gian Carlo; Uras, Antonella; Rais, Marco; Sanna, Sonia; Ennas, Maria Grazia

    2015-12-01

    Previous studies have suggested that diet might affect risk of lymphoma subtypes. We investigated risk of lymphoma and its major subtypes associated with diet in the Mediterranean island of Sardinia, Italy. In 1998-2004, 322 incident lymphoma cases and 446 randomly selected population controls participated in a case-control study on lymphoma etiology in central-southern Sardinia. Questionnaire interviews included frequency of intake of 112 food items. Risk associated with individual dietary items and groups thereof was explored by unconditional and polytomous logistic regression analysis, adjusting by age, gender and education. We observed an upward trend in risk of lymphoma (all subtypes combined) and B-cell lymphoma with frequency of intake of well done grilled/roasted chicken (p for trend=0.01), and pizza (p for trend=0.047), Neither adherence to Mediterranean diet nor a frequent intake of its individual components conveyed protection. We detected heterogeneity in risk associated with several food items and groups thereof by lymphoma subtypes although we could not rule out chance as responsible for the observed direct or inverse associations. Adherence to a Mediterranean diet does not seem to convey protection against the development of lymphoma. The association with specific food items might vary by lymphoma subtype. Copyright © 2015 Elsevier Ltd. All rights reserved.

  7. Interpersonal Subtypes Within Social Anxiety: The Identification of Distinct Social Features.

    Science.gov (United States)

    Cooper, Danielle; Anderson, Timothy

    2017-10-05

    Although social anxiety disorder is defined by anxiety-related symptoms, little research has focused on the interpersonal features of social anxiety. Prior studies (Cain, Pincus, & Grosse Holtforth, 2010; Kachin, Newman, & Pincus, 2001) identified distinct subgroups of socially anxious individuals' interpersonal circumplex problems that were blends of agency and communion, and yet inconsistencies remain. We predicted 2 distinct interpersonal subtypes would exist for individuals with high social anxiety, and that these social anxiety subtypes would differ on empathetic concern, paranoia, received peer victimization, perspective taking, and emotional suppression. From a sample of 175 undergraduate participants, 51 participants with high social anxiety were selected as above a clinical cutoff on the social phobia scale. Cluster analyses identified 2 interpersonal subtypes of socially anxious individuals: low hostility-high submissiveness (Cluster 1) and high hostility-high submissiveness (Cluster 2). Cluster 1 reported higher levels of empathetic concern, lower paranoia, less peer victimization, and lower emotional suppression compared to Cluster 2. There were no differences between subtypes on perspective taking or cognitive reappraisal. Findings are consistent with an interpersonal conceptualization of social anxiety, and provide evidence of distinct social features between these subtypes. Findings have implications for the etiology, classification, and treatment of social anxiety.

  8. Molecular subtyping of cancer: current status and moving toward clinical applications.

    Science.gov (United States)

    Zhao, Lan; Lee, Victor H F; Ng, Michael K; Yan, Hong; Bijlsma, Maarten F

    2018-04-12

    Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients.

  9. Identifying molecular subtypes in human colon cancer using gene expression and DNA methylation microarray data.

    Science.gov (United States)

    Ren, Zhonglu; Wang, Wenhui; Li, Jinming

    2016-02-01

    Identifying colon cancer subtypes based on molecular signatures may allow for a more rational, patient-specific approach to therapy in the future. Classifications using gene expression data have been attempted before with little concordance between the different studies carried out. In this study we aimed to uncover subtypes of colon cancer that have distinct biological characteristics and identify a set of novel biomarkers which could best reflect the clinical and/or biological characteristics of each subtype. Clustering analysis and discriminant analysis were utilized to discover the subtypes in two different molecular levels on 153 colon cancer samples from The Cancer Genome Atlas (TCGA) Data Portal. At gene expression level, we identified two major subtypes, ECL1 (expression cluster 1) and ECL2 (expression cluster 2) and a list of signature genes. Due to the heterogeneity of colon cancer, the subtype ECL1 can be further subdivided into three nested subclasses, and HOTAIR were found upregulated in subclass 2. At DNA methylation level, we uncovered three major subtypes, MCL1 (methylation cluster 1), MCL2 (methylation cluster 2) and MCL3 (methylation cluster 3). We found only three subtypes of CpG island methylator phenotype (CIMP) in colon cancer instead of the four subtypes in the previous reports, and we found no sufficient evidence to subdivide MCL3 into two distinct subgroups.

  10. Bayesian posterior distributions without Markov chains.

    Science.gov (United States)

    Cole, Stephen R; Chu, Haitao; Greenland, Sander; Hamra, Ghassan; Richardson, David B

    2012-03-01

    Bayesian posterior parameter distributions are often simulated using Markov chain Monte Carlo (MCMC) methods. However, MCMC methods are not always necessary and do not help the uninitiated understand Bayesian inference. As a bridge to understanding Bayesian inference, the authors illustrate a transparent rejection sampling method. In example 1, they illustrate rejection sampling using 36 cases and 198 controls from a case-control study (1976-1983) assessing the relation between residential exposure to magnetic fields and the development of childhood cancer. Results from rejection sampling (odds ratio (OR) = 1.69, 95% posterior interval (PI): 0.57, 5.00) were similar to MCMC results (OR = 1.69, 95% PI: 0.58, 4.95) and approximations from data-augmentation priors (OR = 1.74, 95% PI: 0.60, 5.06). In example 2, the authors apply rejection sampling to a cohort study of 315 human immunodeficiency virus seroconverters (1984-1998) to assess the relation between viral load after infection and 5-year incidence of acquired immunodeficiency syndrome, adjusting for (continuous) age at seroconversion and race. In this more complex example, rejection sampling required a notably longer run time than MCMC sampling but remained feasible and again yielded similar results. The transparency of the proposed approach comes at a price of being less broadly applicable than MCMC.

  11. Cerebral artery alpha-1 AR subtypes: high altitude long-term acclimatization responses.

    Directory of Open Access Journals (Sweden)

    Ravi Goyal

    Full Text Available In response to hypoxia and other stress, the sympathetic (adrenergic nervous system regulates arterial contractility and blood flow, partly through differential activities of the alpha1 (α1 - adrenergic receptor (AR subtypes (α1A-, α1B-, and α1D-AR. Thus, we tested the hypothesis that with acclimatization to long-term hypoxia (LTH, contractility of middle cerebral arteries (MCA is regulated by changes in expression and activation of the specific α1-AR subtypes. We conducted experiments in MCA from adult normoxic sheep maintained near sea level (300 m and those exposed to LTH (110 days at 3801 m. Following acclimatization to LTH, ovine MCA showed a 20% reduction (n = 5; P<0.05 in the maximum tension achieved by 10-5 M phenylephrine (PHE. LTH-acclimatized cerebral arteries also demonstrated a statistically significant (P<0.05 inhibition of PHE-induced contractility in the presence of specific α1-AR subtype antagonists. Importantly, compared to normoxic vessels, there was significantly greater (P<0.05 α1B-AR subtype mRNA and protein levels in LTH acclimatized MCA. Also, our results demonstrate that extracellular regulated kinase 1 and 2 (ERK1/2-mediated negative feedback regulation of PHE-induced contractility is modulated by α1B-AR subtype. Overall, in ovine MCA, LTH produces profound effects on α1-AR subtype expression and function.

  12. Latent constructs underlying sensory subtypes in children with autism: A preliminary study.

    Science.gov (United States)

    Hand, Brittany N; Dennis, Simon; Lane, Alison E

    2017-08-01

    Recent reports identify sensory subtypes in ASD based on shared patterns of responses to daily sensory stimuli [Ausderau et al., 2014; Lane, Molloy, & Bishop, 2014]. Lane et al. propose that two broad sensory dimensions, sensory reactivity and multisensory integration, best explain the differences between subtypes, however this has yet to be tested. The present study tests this hypothesis by examining the latent constructs underlying Lane's sensory subtypes. Participants for this study were caregivers of children with autism spectrum disorder (ASD) aged 2-12 years. Caregiver responses on the Short Sensory Profile (SSP), used to establish Lane's sensory subtypes, were extracted from two existing datasets (total n = 287). Independent component analyses were conducted to test the fit and interpretability of a two-construct structure underlying the SSP, and therefore, the sensory subtypes. The first construct was largely comprised of the taste/smell sensitivity domain, which describes hyper-reactivity to taste and smell stimuli. The second construct had a significant contribution from the low energy/weak domain, which describes behaviors that may be indicative of difficulties with multisensory integration. Findings provide initial support for our hypothesis that sensory reactivity and multisensory integration underlie Lane's sensory subtypes in ASD. Autism Res 2017, 10: 1364-1371. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

  13. Bayesian Estimation of Wave Spectra – Proper Formulation of ABIC

    DEFF Research Database (Denmark)

    Nielsen, Ulrik Dam

    2007-01-01

    It is possible to estimate on-site wave spectra using measured ship responses applied to Bayesian Modelling based on two prior information: the wave spectrum must be smooth both directional-wise and frequency-wise. This paper introduces two hyperparameters into Bayesian Modelling and, hence, a pr...

  14. A default Bayesian hypothesis test for correlations and partial correlations

    NARCIS (Netherlands)

    Wetzels, R.; Wagenmakers, E.J.

    2012-01-01

    We propose a default Bayesian hypothesis test for the presence of a correlation or a partial correlation. The test is a direct application of Bayesian techniques for variable selection in regression models. The test is easy to apply and yields practical advantages that the standard frequentist tests

  15. Systematic search of Bayesian statistics in the field of psychotraumatology

    NARCIS (Netherlands)

    van de Schoot, Rens; Schalken, Naomi; Olff, Miranda

    2017-01-01

    In many different disciplines there is a recent increase in interest of Bayesian analysis. Bayesian methods implement Bayes' theorem, which states that prior beliefs are updated with data, and this process produces updated beliefs about model parameters. The prior is based on how much information we

  16. Rationalizing method of replacement intervals by using Bayesian statistics

    International Nuclear Information System (INIS)

    Kasai, Masao; Notoya, Junichi; Kusakari, Yoshiyuki

    2007-01-01

    This study represents the formulations for rationalizing the replacement intervals of equipments and/or parts taking into account the probability density functions (PDF) of the parameters of failure distribution functions (FDF) and compares the optimized intervals by our formulations with those by conventional formulations which uses only representative values of the parameters of FDF instead of using these PDFs. The failure data are generated by Monte Carlo simulations since the real failure data can not be available for us. The PDF of PDF parameters are obtained by Bayesian method and the representative values are obtained by likelihood estimation and Bayesian method. We found that the method using PDF by Bayesian method brings longer replacement intervals than one using the representative of the parameters. (author)

  17. Bayesian Group Bridge for Bi-level Variable Selection.

    Science.gov (United States)

    Mallick, Himel; Yi, Nengjun

    2017-06-01

    A Bayesian bi-level variable selection method (BAGB: Bayesian Analysis of Group Bridge) is developed for regularized regression and classification. This new development is motivated by grouped data, where generic variables can be divided into multiple groups, with variables in the same group being mechanistically related or statistically correlated. As an alternative to frequentist group variable selection methods, BAGB incorporates structural information among predictors through a group-wise shrinkage prior. Posterior computation proceeds via an efficient MCMC algorithm. In addition to the usual ease-of-interpretation of hierarchical linear models, the Bayesian formulation produces valid standard errors, a feature that is notably absent in the frequentist framework. Empirical evidence of the attractiveness of the method is illustrated by extensive Monte Carlo simulations and real data analysis. Finally, several extensions of this new approach are presented, providing a unified framework for bi-level variable selection in general models with flexible penalties.

  18. Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

    Science.gov (United States)

    Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

    2013-04-01

    SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

  19. Heuristics as Bayesian inference under extreme priors.

    Science.gov (United States)

    Parpart, Paula; Jones, Matt; Love, Bradley C

    2018-05-01

    Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  20. Bayesian Networks and Influence Diagrams

    DEFF Research Database (Denmark)

    Kjærulff, Uffe Bro; Madsen, Anders Læsø

    Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis, Second Edition, provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. This new edition contains six new...

  1. Subtypes of children with attention disabilities.

    NARCIS (Netherlands)

    Brand, E.F.J.M.; Das-Smaal, E.A.; Jong, de P.F.

    1996-01-01

    Subtypes of children with attentional problems were investigated using cluster analysis. Subjects were 9-year-old-elementary school children (N = 443). The test battery administered to these children comprised a comprehensive set of common attention tests, covering different aspects of attentional

  2. Multiple Receptor Subtypes for the CGRP Super-Family

    Directory of Open Access Journals (Sweden)

    R. Quirion

    2001-01-01

    Full Text Available Molecular evidence for the existence of multiple receptors for CGRP has been rather difficult to obtain. Over 10 years after suggesting the existence of at least two classes (CGRP1 and CGRP2 of CGRP receptors on the basis of pharmacological data[1], molecular data on the CGRP2 receptor subtype are still lacking as well as potent and selective antagonists. The situation is somewhat different for the functional CGRP1 subtype which is likely composed of diverse subunits CRLR, RAMP1 and possibly RCP[2]. Moreover, BIBN 4096BS was recently reported as the first nonpeptide highly potent CGRP1 receptor antagonist[3]. However, in situ hybridization and receptor autoradiographic data have clearly shown the existence of major mismatches (e.g., cerebellum between the discrete localization of CRLR, RAMP1, and specific CGRP binding sites supporting the existence of CGRP receptor subtypes. Functional studies have also provided evidence in that regard (for a recent review: [4]. Accordingly, additional studies aiming at cloning additional CGRP receptors are certainly warranted. Similarly, recent evidence from various laboratories including ours suggests the existence of more than one class (CRLR and RAMP2 of adrenomedullin receptors at least in the rat brain. In contrast, most evidence suggests the existence of a single class of amylin receptors. In brief, it appears that multiple receptors or receptor complexes do exist for CGRP and related peptides but their composition is apparently unique among the GPCR super-family and additional data are needed to fully establish the molecular organization of each subtype. Supported by CIHR of Canada.

  3. HLA-B27 subtypes among the Chukotka native groups

    Energy Technology Data Exchange (ETDEWEB)

    Krylov, M.Y.; Alexeeva, L.I.; Erdesz, S.; Benevolenskaya, L.I. [Akademiya Meditsinskikh Nauk SSSR, Moscow (Russian Federation). Inst. Revmatizma; Reveille, J.D.; Arnett, F.C. [Texas Univ., Houston, TX (United States). Health Science Center

    1995-12-31

    The purpose of this study was to assess the relative frequency of the known HLA-B27 subtypes in HLA-B27 positive Chukotka natives, which have higher frequencies of HLA-B27 (to 40%) and spondylarthropathies (to 2%) than the Russian Caucasian population. Using oligotyping of the polymerase-chain reaction amplified second and third exons of the HLA-B27 gene in 86 DNA samples from HLA-B27 positive individuals were successfully typed. All had HLA-B*2705, including 4 patients with Reiter`s syndrome and 5 with ankylosing spondyloarthritis, except one Eskimo who had HLA-B*2702. None had HLA-B*2704, a frequent subtype in Orientals. With respect to HLA-B27 subtypes the indigenous populations from the eastern part of the Chukotka Peninsula are genetically more closely related to Caucasians than to Orientals. (author). 18 refs, 1 fig., 2 tabs.

  4. HLA-B27 subtypes among the Chukotka native groups

    International Nuclear Information System (INIS)

    Krylov, M.Y.; Alexeeva, L.I.; Erdesz, S.; Benevolenskaya, L.I.; Reveille, J.D.; Arnett, F.C.

    1995-01-01

    The purpose of this study was to assess the relative frequency of the known HLA-B27 subtypes in HLA-B27 positive Chukotka natives, which have higher frequencies of HLA-B27 (to 40%) and spondylarthropathies (to 2%) than the Russian Caucasian population. Using oligotyping of the polymerase-chain reaction amplified second and third exons of the HLA-B27 gene in 86 DNA samples from HLA-B27 positive individuals were successfully typed. All had HLA-B*2705, including 4 patients with Reiter's syndrome and 5 with ankylosing spondyloarthritis, except one Eskimo who had HLA-B*2702. None had HLA-B*2704, a frequent subtype in Orientals. With respect to HLA-B27 subtypes the indigenous populations from the eastern part of the Chukotka Peninsula are genetically more closely related to Caucasians than to Orientals. (author). 18 refs, 1 fig., 2 tabs

  5. [The velocity of HCV subtype 6a transmission in southwest China].

    Science.gov (United States)

    Hong, Guo-hu; Tan, Zhao-xia; Guo, Yan; Mao, Qing

    2011-07-01

    To estimate the velocity of HCV subtype 6a transmission in Southwest China. The HCV CE1 region from 61 patients infected with HCV genotype 6 were amplificated by RT-PCR and sequenced. The subtypes were identified, and the period of HCV 6a strains originated in southwest china was estimated by using molecular clock phylogenetic analysis. The velocity of HCV subtype 6a transmission in southwest China was estimated by BEAST v1.6.1 and Tracer v1.5 software theoretically. Most of HCV 6a strains distributed in Southwest China origine around the year 1968 and at last 4 epidemic strains existed. The earlier origine strains could be isolated both in intravenous drug users (IDU) and non-IDU patients. After 1997, the HCV 6a strains transmission in southwest China accelerated and the trend intensified in 2007. HCV 6a strains spread fastly both in IDU and non-IDU patients, which might be the main HCV subtype distributed in Southwest China in the future.

  6. HIV-1 subtypes and response to combination antiretroviral therapy in Europe

    DEFF Research Database (Denmark)

    Bannister, WP; Ruiz, L; Loveday, C

    2006-01-01

    Europe/North America on the basis of the most prevalent subtype, B. However, non-B subtypes are increasingly spreading worldwide. OBJECTIVE: To compare virological and immunological response to cART between patients infected with B and non-B subtypes across Europe. DESIGN: EuroSIDA prospective....../ml and immunological response as a CD4+ T-cell count increase of ³100 cells/mm^3. RESULTS: Forty-five percent of patients were antiretroviral naive at initiation of cART. Virological suppression was achieved by 58% of 689 subtype-B-infected patients and 66% of 102 non-B-infected patients (P=0.159). After adjustment...... for potential confounders, there was no significant difference in odds of achieving virological suppression (non-B compared with B; odds ratio [OR]: 1.05, 95% confidence interval [CI]: 0.58-1.93, P=0.866). An immunological response was achieved by 43% of 753 B-infected patients and 48% of 114 non...

  7. Expert consensus on the classification of subtype in Budd-Chiari syndrome

    Directory of Open Access Journals (Sweden)

    Expert committee on Vane Cava Obstruction,Specialized CommitteeofEndovascology,ChineseMedicalDoctorAssociation

    2017-07-01

    Full Text Available From 2012 to 2015 the Department of Interventional Radiology of the Affiliated Hospital of Xuzhou Medical University undertook the clinical special research subject ”Study on the standardization of interventional diagnosis and treatment of Budd- Chiari syndrome”(No. BL2012021), a program supported by the Department of Science and Technology of Jiangsu Province. Based on the clinical results of three years research and the scientific summary of the experience from more than 2150 cases accumulated in more than 20 years, the Department of Interventional Radiology of the Affiliated Hospital of Xuzhou Medical University presided over a demonstration meeting about “the standardization of interventional diagnosis and treatment of Budd- Chiari syndrome” on January 14, 2016 in Xuzhou City of Jiangsu Province, China. The scholars from the Expert Committee on Vena Cava Obstruction of Specialized Committee of Endovascology, Chinese Medical Doctor Association, as well as the experts from the related medical fields, including interventional radiology, vascular surgery, pathology and diagnostic imaging, who have been engaged in the study of Budd- Chiari syndrome, attended the meeting, and in the meeting the participants made a full and thorough discussion on the classification and subtypes of Budd - Chiari syndrome. The scholars and experts have unanimously reached a consensus on the subtype definition of Budd- Chiari syndrome: the Budd Chiari syndrome is suggested to be classified into the hepatic vein occlusion subtype, the inferior vena cava occlusion subtype and mixed occlusion subtype, including 10 subtype entities in total. The hepatic vein occlusion subtype includes membranous occlusion of hepatic vein/accessory hepatic vein, segmental occlusion of hepatic vein, extensive occlusion of hepatic vein, and hepatic vein occlusion associated with thrombus formation. The inferior vena cava occlusion subtype

  8. Refinement of Triple-Negative Breast Cancer Molecular Subtypes: Implications for Neoadjuvant Chemotherapy Selection.

    Directory of Open Access Journals (Sweden)

    Brian D Lehmann

    Full Text Available Triple-negative breast cancer (TNBC is a heterogeneous disease that can be classified into distinct molecular subtypes by gene expression profiling. Considered a difficult-to-treat cancer, a fraction of TNBC patients benefit significantly from neoadjuvant chemotherapy and have far better overall survival. Outside of BRCA1/2 mutation status, biomarkers do not exist to identify patients most likely to respond to current chemotherapy; and, to date, no FDA-approved targeted therapies are available for TNBC patients. Previously, we developed an approach to identify six molecular subtypes TNBC (TNBCtype, with each subtype displaying unique ontologies and differential response to standard-of-care chemotherapy. Given the complexity of the varying histological landscape of tumor specimens, we used histopathological quantification and laser-capture microdissection to determine that transcripts in the previously described immunomodulatory (IM and mesenchymal stem-like (MSL subtypes were contributed from infiltrating lymphocytes and tumor-associated stromal cells, respectively. Therefore, we refined TNBC molecular subtypes from six (TNBCtype into four (TNBCtype-4 tumor-specific subtypes (BL1, BL2, M and LAR and demonstrate differences in diagnosis age, grade, local and distant disease progression and histopathology. Using five publicly available, neoadjuvant chemotherapy breast cancer gene expression datasets, we retrospectively evaluated chemotherapy response of over 300 TNBC patients from pretreatment biopsies subtyped using either the intrinsic (PAM50 or TNBCtype approaches. Combined analysis of TNBC patients demonstrated that TNBC subtypes significantly differ in response to similar neoadjuvant chemotherapy with 41% of BL1 patients achieving a pathological complete response compared to 18% for BL2 and 29% for LAR with 95% confidence intervals (CIs; [33, 51], [9, 28], [17, 41], respectively. Collectively, we provide pre-clinical data that could inform

  9. Using Alien Coins to Test Whether Simple Inference Is Bayesian

    Science.gov (United States)

    Cassey, Peter; Hawkins, Guy E.; Donkin, Chris; Brown, Scott D.

    2016-01-01

    Reasoning and inference are well-studied aspects of basic cognition that have been explained as statistically optimal Bayesian inference. Using a simplified experimental design, we conducted quantitative comparisons between Bayesian inference and human inference at the level of individuals. In 3 experiments, with more than 13,000 participants, we…

  10. Applying Bayesian statistics to the study of psychological trauma: A suggestion for future research.

    Science.gov (United States)

    Yalch, Matthew M

    2016-03-01

    Several contemporary researchers have noted the virtues of Bayesian methods of data analysis. Although debates continue about whether conventional or Bayesian statistics is the "better" approach for researchers in general, there are reasons why Bayesian methods may be well suited to the study of psychological trauma in particular. This article describes how Bayesian statistics offers practical solutions to the problems of data non-normality, small sample size, and missing data common in research on psychological trauma. After a discussion of these problems and the effects they have on trauma research, this article explains the basic philosophical and statistical foundations of Bayesian statistics and how it provides solutions to these problems using an applied example. Results of the literature review and the accompanying example indicates the utility of Bayesian statistics in addressing problems common in trauma research. Bayesian statistics provides a set of methodological tools and a broader philosophical framework that is useful for trauma researchers. Methodological resources are also provided so that interested readers can learn more. (c) 2016 APA, all rights reserved).

  11. Motion Learning Based on Bayesian Program Learning

    Directory of Open Access Journals (Sweden)

    Cheng Meng-Zhen

    2017-01-01

    Full Text Available The concept of virtual human has been highly anticipated since the 1980s. By using computer technology, Human motion simulation could generate authentic visual effect, which could cheat human eyes visually. Bayesian Program Learning train one or few motion data, generate new motion data by decomposing and combining. And the generated motion will be more realistic and natural than the traditional one.In this paper, Motion learning based on Bayesian program learning allows us to quickly generate new motion data, reduce workload, improve work efficiency, reduce the cost of motion capture, and improve the reusability of data.

  12. Bayesian Inference and Online Learning in Poisson Neuronal Networks.

    Science.gov (United States)

    Huang, Yanping; Rao, Rajesh P N

    2016-08-01

    Motivated by the growing evidence for Bayesian computation in the brain, we show how a two-layer recurrent network of Poisson neurons can perform both approximate Bayesian inference and learning for any hidden Markov model. The lower-layer sensory neurons receive noisy measurements of hidden world states. The higher-layer neurons infer a posterior distribution over world states via Bayesian inference from inputs generated by sensory neurons. We demonstrate how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in a higher-layer neuron represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution over hidden states. In this model, variability in spiking is regarded not as a nuisance but as an integral feature that provides the variability necessary for sampling during inference. We demonstrate how the network can learn the likelihood model, as well as the transition probabilities underlying the dynamics, using a Hebbian learning rule. We present results illustrating the ability of the network to perform inference and learning for arbitrary hidden Markov models.

  13. Hierarchical Bayesian sparse image reconstruction with application to MRFM.

    Science.gov (United States)

    Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

    2009-09-01

    This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

  14. Bayesian inference of chemical kinetic models from proposed reactions

    KAUST Repository

    Galagali, Nikhil

    2015-02-01

    © 2014 Elsevier Ltd. Bayesian inference provides a natural framework for combining experimental data with prior knowledge to develop chemical kinetic models and quantify the associated uncertainties, not only in parameter values but also in model structure. Most existing applications of Bayesian model selection methods to chemical kinetics have been limited to comparisons among a small set of models, however. The significant computational cost of evaluating posterior model probabilities renders traditional Bayesian methods infeasible when the model space becomes large. We present a new framework for tractable Bayesian model inference and uncertainty quantification using a large number of systematically generated model hypotheses. The approach involves imposing point-mass mixture priors over rate constants and exploring the resulting posterior distribution using an adaptive Markov chain Monte Carlo method. The posterior samples are used to identify plausible models, to quantify rate constant uncertainties, and to extract key diagnostic information about model structure-such as the reactions and operating pathways most strongly supported by the data. We provide numerical demonstrations of the proposed framework by inferring kinetic models for catalytic steam and dry reforming of methane using available experimental data.

  15. Empirically Derived Learning Disability Subtypes: A Replication Attempt and Longitudinal Patterns over 15 Years.

    Science.gov (United States)

    Spreen, Otfried; Haaf, Robert G.

    1986-01-01

    Test scores of two groups of learning disabled children (N=63 and N=96) were submitted to cluster analysis in an attempt to replicate previously described subtypes. All three subtypes (visuo-perceptual, linguistic, and articulo-graphomotor types) were identified along with minimally and severely impaired subtypes. Similar clusters in the same…

  16. Peculiarities of diagnostics and clinical course of different immunohistochemical subtypes of breast cancer

    Directory of Open Access Journals (Sweden)

    El Khazhzh M.Kh.

    2014-09-01

    Full Text Available Modern global guidelines in oncology consider treatment of various forms of breast cancer according to molecular tumor subtype. Steroid receptors, epidermal growth factor receptors, p53, Ki67 proliferative activity index and others are the key indicators of aggressiveness of malignant breast tumors. The material for this study was the retrospective study of the standard set of breast cancer immuno¬histochemical markers (estrogen receptors, progesterone, epidermal growth factor type 2 in 8171 patients. 4 groups of patients - luminal A, luminal B, triple negative and HER2-neu positive subtypes of tumors were identified according to immunohistochemical status. We analyzed overall survival without relapse in 491 patients with breast cancer, clinical data and data of immunohistochemical studies were matched. Based on the investigation it was determined that in the early stages of the disease (1-2 luminal A subtype of cancer is often diagnosed. In the late stages the most common subtype is HER2-neu positive breast cancer. Herewith, patients with luminal A subtype of cancer have the best performance of the overall survival (OS (32,91±2,33 months, and the worst results were found in patients with HER2 - neu positive breast cancer (22,58±1,28 months. The data obtained determine HER2 - neu positive subtype as the most aggressive type of breast cancer, and the luminal A subtype – as the least aggressive one.

  17. Breast cancer molecular subtype classifier that incorporates MRI features.

    Science.gov (United States)

    Sutton, Elizabeth J; Dashevsky, Brittany Z; Oh, Jung Hun; Veeraraghavan, Harini; Apte, Aditya P; Thakur, Sunitha B; Morris, Elizabeth A; Deasy, Joseph O

    2016-07-01

    To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes. This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR + (n = 95, 53.4%), ERPR-/HER2 + (n = 35, 19.6%), or triple negative (TN, n = 48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test. Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power. J. Magn. Reson. Imaging 2016;44:122-129. © 2016 Wiley Periodicals, Inc.

  18. Susceptibility of Ae. aegypti (Diptera: Culicidae) to infection with epidemic (subtype IC) and enzootic (subtypes ID, IIIC, IIID) Venezuelan equine encephalitis complex alphaviruses.

    Science.gov (United States)

    Ortiz, Diana I; Kang, Wenli; Weaver, Scoti C

    2008-11-01

    To test the hypothesis that enzootic and epidemic Venezuelan equine encephalitis (VEE) complex alphaviruses can infect and be transmitted by Ae. aegypti, we conducted a series of experimental infection studies. One set of experiments tested the susceptibility of geographic strains of Ae. aegypti from Peru and Texas (U.S.A.) for epidemic (subtype IC) and enzootic (subtype ID) strains from Colombia/Venezuela, whereas the second set of experiments tested the susceptibility of Ae. aegypti from Iquitos, Peru, to enzootic VEE complex strains (subtypes ID, IIIC, and IIID) isolated in the same region, at different infectious doses. Experimental infections using artificial bloodmeals suggested that Ae. aegypti mosquitoes, particularly the strain from Iquitos, Peru, is moderately to highly susceptible to all of these VEE complex alphaviruses. The occurrence of enzootic VEE complex viruses circulating endemically in Iquitos suggests the possibility of a dengue-like transmission cycle among humans in tropical cities.

  19. Bayesian detection of causal rare variants under posterior consistency.

    KAUST Repository

    Liang, Faming

    2013-07-26

    Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.

  20. Bayesian detection of causal rare variants under posterior consistency.

    Directory of Open Access Journals (Sweden)

    Faming Liang

    Full Text Available Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD, to tackle this problem. The new method simultaneously addresses two issues: (i (Global association test Are there any of the variants associated with the disease, and (ii (Causal variant detection Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.

  1. Bayesian detection of causal rare variants under posterior consistency.

    KAUST Repository

    Liang, Faming; Xiong, Momiao

    2013-01-01

    Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small-n-large-P situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small-n-large-P situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.

  2. Bayesian Averaging is Well-Temperated

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2000-01-01

    Bayesian predictions are stochastic just like predictions of any other inference scheme that generalize from a finite sample. While a simple variational argument shows that Bayes averaging is generalization optimal given that the prior matches the teacher parameter distribution the situation is l...

  3. Differentiated Bayesian Conjoint Choice Designs

    NARCIS (Netherlands)

    Z. Sándor (Zsolt); M. Wedel (Michel)

    2003-01-01

    textabstractPrevious conjoint choice design construction procedures have produced a single design that is administered to all subjects. This paper proposes to construct a limited set of different designs. The designs are constructed in a Bayesian fashion, taking into account prior uncertainty about

  4. Bayesian NL interpretation and learning

    NARCIS (Netherlands)

    Zeevat, H.

    2011-01-01

    Everyday natural language communication is normally successful, even though contemporary computational linguistics has shown that NL is characterised by very high degree of ambiguity and the results of stochastic methods are not good enough to explain the high success rate. Bayesian natural language

  5. An elementary introduction to Bayesian computing using WinBUGS.

    Science.gov (United States)

    Fryback, D G; Stout, N K; Rosenberg, M A

    2001-01-01

    Bayesian statistics provides effective techniques for analyzing data and translating the results to inform decision making. This paper provides an elementary tutorial overview of the WinBUGS software for performing Bayesian statistical analysis. Background information on the computational methods used by the software is provided. Two examples drawn from the field of medical decision making are presented to illustrate the features and functionality of the software.

  6. Bayesian network modeling of operator's state recognition process

    International Nuclear Information System (INIS)

    Hatakeyama, Naoki; Furuta, Kazuo

    2000-01-01

    Nowadays we are facing a difficult problem of establishing a good relation between humans and machines. To solve this problem, we suppose that machine system need to have a model of human behavior. In this study we model the state cognition process of a PWR plant operator as an example. We use a Bayesian network as an inference engine. We incorporate the knowledge hierarchy in the Bayesian network and confirm its validity using the example of PWR plant operator. (author)

  7. Prediction consistency and clinical presentations of breast cancer molecular subtypes for Han Chinese population

    Directory of Open Access Journals (Sweden)

    Huang Chi-Cheng

    2012-09-01

    Full Text Available Abstract Background Breast cancer is a heterogeneous disease in terms of transcriptional aberrations; moreover, microarray gene expression profiles had defined 5 molecular subtypes based on certain intrinsic genes. This study aimed to evaluate the prediction consistency of breast cancer molecular subtypes from 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 as well as clinical presentations of each molecualr subtype in Han Chinese population. Methods In all, 169 breast cancer samples (44 from Taiwan and 125 from China of Han Chinese population were gathered, and the gene expression features corresponding to 3 distinct intrinsic gene sets (Sørlie 500, Hu 306 and PAM50 were retrieved for molecular subtype prediction. Results For Sørlie 500 and Hu 306 intrinsic gene set, mean-centring of genes and distance-weighted discrimination (DWD remarkably reduced the number of unclassified cases. Regarding pairwise agreement, the highest predictive consistency was found between Hu 306 and PAM50. In all, 150 and 126 samples were assigned into identical subtypes by both Hu 306 and PAM50 genes, under mean-centring and DWD. Luminal B tended to show a higher nuclear grade and have more HER2 over-expression status than luminal A did. No basal-like breast tumours were ER positive, and most HER2-enriched breast tumours showed HER2 over-expression, whereas, only two-thirds of ER negativity/HER2 over-expression tumros were predicted as HER2-enriched molecular subtype. For 44 Taiwanese breast cancers with survival data, a better prognosis of luminal A than luminal B subtype in ER-postive breast cancers and a better prognosis of basal-like than HER2-enriched subtype in ER-negative breast cancers was observed. Conclusions We suggest that the intrinsic signature Hu 306 or PAM50 be used for breast cancers in the Han Chinese population during molecular subtyping. For the prognostic value and decision making based on intrinsic subtypes, further prospective

  8. Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes

    DEFF Research Database (Denmark)

    Broeks, Annegien; Schmidt, Marjanka K; Sherman, Mark E

    2011-01-01

    Breast cancers demonstrate substantial biological, clinical and etiological heterogeneity. We investigated breast cancer risk associations of eight susceptibility loci identified in GWAS and two putative susceptibility loci in candidate genes in relation to specific breast tumor subtypes. Subtype...... stratification might help in the identification and characterization of novel risk factors for breast cancer subtypes. This may eventually result in further improvements in prevention, early detection and treatment.......Breast cancers demonstrate substantial biological, clinical and etiological heterogeneity. We investigated breast cancer risk associations of eight susceptibility loci identified in GWAS and two putative susceptibility loci in candidate genes in relation to specific breast tumor subtypes. Subtypes...... were defined by five markers (ER, PR, HER2, CK5/6, EGFR) and other pathological and clinical features. Analyses included up to 30 040 invasive breast cancer cases and 53 692 controls from 31 studies within the Breast Cancer Association Consortium. We confirmed previous reports of stronger associations...

  9. Breast cancer molecular subtype classification using deep features: preliminary results

    Science.gov (United States)

    Zhu, Zhe; Albadawy, Ehab; Saha, Ashirbani; Zhang, Jun; Harowicz, Michael R.; Mazurowski, Maciej A.

    2018-02-01

    Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris- tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe- riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

  10. Bayesian estimation methods in metrology

    International Nuclear Information System (INIS)

    Cox, M.G.; Forbes, A.B.; Harris, P.M.

    2004-01-01

    In metrology -- the science of measurement -- a measurement result must be accompanied by a statement of its associated uncertainty. The degree of validity of a measurement result is determined by the validity of the uncertainty statement. In recognition of the importance of uncertainty evaluation, the International Standardization Organization in 1995 published the Guide to the Expression of Uncertainty in Measurement and the Guide has been widely adopted. The validity of uncertainty statements is tested in interlaboratory comparisons in which an artefact is measured by a number of laboratories and their measurement results compared. Since the introduction of the Mutual Recognition Arrangement, key comparisons are being undertaken to determine the degree of equivalence of laboratories for particular measurement tasks. In this paper, we discuss the possible development of the Guide to reflect Bayesian approaches and the evaluation of key comparison data using Bayesian estimation methods

  11. Zebrafish Mnx proteins specify one motoneuron subtype and suppress acquisition of interneuron characteristics

    Directory of Open Access Journals (Sweden)

    Seredick Steve D

    2012-11-01

    Full Text Available Abstract Background Precise matching between motoneuron subtypes and the muscles they innervate is a prerequisite for normal behavior. Motoneuron subtype identity is specified by the combination of transcription factors expressed by the cell during its differentiation. Here we investigate the roles of Mnx family transcription factors in specifying the subtypes of individually identified zebrafish primary motoneurons. Results Zebrafish has three Mnx family members. We show that each of them has a distinct and temporally dynamic expression pattern in each primary motoneuron subtype. We also show that two Mnx family members are expressed in identified VeLD interneurons derived from the same progenitor domain that generates primary motoneurons. Surprisingly, we found that Mnx proteins appear unnecessary for differentiation of VeLD interneurons or the CaP motoneuron subtype. Mnx proteins are, however, required for differentiation of the MiP motoneuron subtype. We previously showed that MiPs require two temporally-distinct phases of Islet1 expression for normal development. Here we show that in the absence of Mnx proteins, the later phase of Islet1 expression is initiated but not sustained, and MiPs become hybrids that co-express morphological and molecular features of motoneurons and V2a interneurons. Unexpectedly, these hybrid MiPs often extend CaP-like axons, and some MiPs appear to be entirely transformed to a CaP morphology. Conclusions Our results suggest that Mnx proteins promote MiP subtype identity by suppressing both interneuron development and CaP axon pathfinding. This is, to our knowledge, the first report of transcription factors that act to distinguish CaP and MiP subtype identities. Our results also suggest that MiP motoneurons are more similar to V2 interneurons than are CaP motoneurons.

  12. Controlling the Regional Identity of hPSC-Derived Neurons to Uncover Neuronal Subtype Specificity of Neurological Disease Phenotypes

    Directory of Open Access Journals (Sweden)

    Kent Imaizumi

    2015-12-01

    Full Text Available The CNS contains many diverse neuronal subtypes, and most neurological diseases target specific subtypes. However, the mechanism of neuronal subtype specificity of disease phenotypes remains elusive. Although in vitro disease models employing human pluripotent stem cells (PSCs have great potential to clarify the association of neuronal subtypes with disease, it is currently difficult to compare various PSC-derived subtypes. This is due to the limited number of subtypes whose induction is established, and different cultivation protocols for each subtype. Here, we report a culture system to control the regional identity of PSC-derived neurons along the anteroposterior (A-P and dorsoventral (D-V axes. This system was successfully used to obtain various neuronal subtypes based on the same protocol. Furthermore, we reproduced subtype-specific phenotypes of amyotrophic lateral sclerosis (ALS and Alzheimer’s disease (AD by comparing the obtained subtypes. Therefore, our culture system provides new opportunities for modeling neurological diseases with PSCs.

  13. [Bayesian approach for the cost-effectiveness evaluation of healthcare technologies].

    Science.gov (United States)

    Berchialla, Paola; Gregori, Dario; Brunello, Franco; Veltri, Andrea; Petrinco, Michele; Pagano, Eva

    2009-01-01

    The development of Bayesian statistical methods for the assessment of the cost-effectiveness of health care technologies is reviewed. Although many studies adopt a frequentist approach, several authors have advocated the use of Bayesian methods in health economics. Emphasis has been placed on the advantages of the Bayesian approach, which include: (i) the ability to make more intuitive and meaningful inferences; (ii) the ability to tackle complex problems, such as allowing for the inclusion of patients who generate no cost, thanks to the availability of powerful computational algorithms; (iii) the importance of a full use of quantitative and structural prior information to produce realistic inferences. Much literature comparing the cost-effectiveness of two treatments is based on the incremental cost-effectiveness ratio. However, new methods are arising with the purpose of decision making. These methods are based on a net benefits approach. In the present context, the cost-effectiveness acceptability curves have been pointed out to be intrinsically Bayesian in their formulation. They plot the probability of a positive net benefit against the threshold cost of a unit increase in efficacy.A case study is presented in order to illustrate the Bayesian statistics in the cost-effectiveness analysis. Emphasis is placed on the cost-effectiveness acceptability curves. Advantages and disadvantages of the method described in this paper have been compared to frequentist methods and discussed.

  14. Numerical methods for Bayesian inference in the face of aging

    International Nuclear Information System (INIS)

    Clarotti, C.A.; Villain, B.; Procaccia, H.

    1996-01-01

    In recent years, much attention has been paid to Bayesian methods for Risk Assessment. Until now, these methods have been studied from a theoretical point of view. Researchers have been mainly interested in: studying the effectiveness of Bayesian methods in handling rare events; debating about the problem of priors and other philosophical issues. An aspect central to the Bayesian approach is numerical computation because any safety/reliability problem, in a Bayesian frame, ends with a problem of numerical integration. This aspect has been neglected until now because most Risk studies assumed the Exponential model as the basic probabilistic model. The existence of conjugate priors makes numerical integration unnecessary in this case. If aging is to be taken into account, no conjugate family is available and the use of numerical integration becomes compulsory. EDF (National Board of Electricity, of France) and ENEA (National Committee for Energy, New Technologies and Environment, of Italy) jointly carried out a research program aimed at developing quadrature methods suitable for Bayesian Interference with underlying Weibull or gamma distributions. The paper will illustrate the main results achieved during the above research program and will discuss, via some sample cases, the performances of the numerical algorithms which on the appearance of stress corrosion cracking in the tubes of Steam Generators of PWR French power plants. (authors)

  15. Latent class analysis of gambling subtypes and impulsive/compulsive associations: Time to rethink diagnostic boundaries for gambling disorder?

    Science.gov (United States)

    Chamberlain, Samuel R; Stochl, Jan; Redden, Sarah A; Odlaug, Brian L; Grant, Jon E

    2017-09-01

    Gambling disorder has been associated with cognitive dysfunction and impaired quality of life. The current definition of non-pathological, problem, and pathological types of gambling is based on total symptom scores, which may overlook nuanced underlying presentations of gambling symptoms. The aims of the current study were (i) to identify subtypes of gambling in young adults, using latent class analysis, based on individual responses from the Structured Clinical Interview for Gambling Disorder (SCI-GD); and (ii) to explore relationships between these gambling subtypes, and clinical/cognitive measures. Total 582 non-treatment seeking young adults were recruited from two US cities, on the basis of gambling five or more times per year. Participants undertook clinical and neurocognitive assessment, including stop-signal, decision-making, and set-shifting tasks. Data from individual items of the Structured Clinical Interview for Gambling Disorder (SCI-GD) were entered into latent class analysis. Optimal number of classes representing gambling subtypes was identified using Bayesian Information Criterion and differences between them were explored using multivariate analysis of variance. Three subtypes of gambling were identified, termed recreational gamblers (60.2% of the sample; reference group), problem gamblers (29.2%), and pathological gamblers (10.5%). Common quality of life impairment, elevated Barratt Impulsivity scores, occurrence of mainstream mental disorders, having a first degree relative with an addiction, and impaired decision-making were evident in both problem and pathological gambling groups. The diagnostic item 'chasing losses' most discriminated recreational from problem gamblers, while endorsement of 'social, financial, or occupational losses due to gambling' most discriminated pathological gambling from both other groups. Significantly higher rates of impulse control disorders occurred in the pathological group, versus the problem group, who in turn

  16. Experiences in applying Bayesian integrative models in interdisciplinary modeling: the computational and human challenges

    DEFF Research Database (Denmark)

    Kuikka, Sakari; Haapasaari, Päivi Elisabet; Helle, Inari

    2011-01-01

    We review the experience obtained in using integrative Bayesian models in interdisciplinary analysis focusing on sustainable use of marine resources and environmental management tasks. We have applied Bayesian models to both fisheries and environmental risk analysis problems. Bayesian belief...... be time consuming and research projects can be difficult to manage due to unpredictable technical problems related to parameter estimation. Biology, sociology and environmental economics have their own scientific traditions. Bayesian models are becoming traditional tools in fisheries biology, where...

  17. Validity of DSM-IV attention deficit/hyperactivity disorder symptom dimensions and subtypes.

    Science.gov (United States)

    Willcutt, Erik G; Nigg, Joel T; Pennington, Bruce F; Solanto, Mary V; Rohde, Luis A; Tannock, Rosemary; Loo, Sandra K; Carlson, Caryn L; McBurnett, Keith; Lahey, Benjamin B

    2012-11-01

    Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria for attention deficit/hyperactivity disorder (ADHD) specify two dimensions of inattention and hyperactivity-impulsivity symptoms that are used to define three nominal subtypes: predominantly hyperactive-impulsive type (ADHD-H), predominantly inattentive type (ADHD-I), and combined type (ADHD-C). To aid decision making for DSM-5 and other future diagnostic systems, a comprehensive literature review and meta-analysis of 546 studies was completed to evaluate the validity of the DSM-IV model of ADHD. Results indicated that DSM-IV criteria identify individuals with significant and persistent impairment in social, academic, occupational, and adaptive functioning when intelligence, demographic factors, and concurrent psychopathology are controlled. Available data overwhelmingly support the concurrent, predictive, and discriminant validity of the distinction between inattention and hyperactivity-impulsivity symptoms, and indicate that nearly all differences among the nominal subtypes are consistent with the relative levels of inattention and hyperactivity-impulsivity symptoms that define the subtypes. In contrast, the DSM-IV subtype model is compromised by weak evidence for the validity of ADHD-H after first grade, minimal support for the distinction between ADHD-I and ADHD-C in studies of etiological influences, academic and cognitive functioning, and treatment response, and the marked longitudinal instability of all three subtypes. Overall, we conclude that the DSM-IV ADHD subtypes provide a convenient clinical shorthand to describe the functional and behavioral correlates of current levels of inattention and hyperactivity-impulsivity symptoms, but do not identify discrete subgroups with sufficient long-term stability to justify the classification of distinct forms of the disorder. Empirical support is stronger for an alternative model that would replace the subtypes with dimensional

  18. Autoradiographic visualization of muscarinic receptor subtypes in human and guinea pig lung

    International Nuclear Information System (INIS)

    Mak, J.C.; Barnes, P.J.

    1990-01-01

    Muscarinic receptor subtypes have been localized in human and guinea pig lung sections by an autoradiographic technique, using [3H](-)quinuclidinyl benzilate [( 3H]QNB) and selective muscarinic antagonists. [3H]QNB was incubated with tissue sections for 90 min at 25 degrees C, and nonspecific binding was determined by incubating adjacent serial sections in the presence of 1 microM atropine. Binding to lung sections had the characterization expected for muscarinic receptors. Autoradiography revealed that muscarinic receptors were widely distributed in human lung, with dense labeling over submucosal glands and airway ganglia, and moderate labeling over nerves in intrapulmonary bronchi and of airway smooth muscle of large and small airways. In addition, alveolar walls were uniformly labeled. In guinea pig lung, labeling of airway smooth muscle was similar, but in contrast to human airways, epithelium was labeled but alveolar walls were not. The muscarinic receptors of human airway smooth muscle from large to small airways were entirely of the M3-subtype, whereas in guinea pig airway smooth muscle, the majority were the M3-subtype with a very small population of the M2-subtype present. In human bronchial submucosal glands, M1- and M3-subtypes appeared to coexist in the proportions of 36 and 64%, respectively. In human alveolar walls the muscarinic receptors were entirely of the M1-subtype, which is absent from the guinea pig lung. No M2-receptors were demonstrated in human lung. The localization of M1-receptors was confirmed by direct labeling with [3H]pirenzepine. With the exception of the alveolar walls in human lung, the localization of muscarinic receptor subtypes on structures in the lung is consistent with known functional studies

  19. Validity of DSM-IV attention–deficit/hyperactivity disorder symptom dimensions and subtypes

    Science.gov (United States)

    Willcutt, Erik G.; Nigg, Joel T.; Pennington, Bruce F.; Solanto, Mary V.; Rohde, Luis A.; Tannock, Rosemary; Loo, Sandra K.; Carlson, Caryn L.; McBurnett, Keith; Lahey, Benjamin B.

    2013-01-01

    DSM-IV criteria for ADHD specify two dimensions of inattention and hyperactivity-impulsivity symptoms that are used to define three nominal subtypes: predominantly hyperactive-impulsive type (ADHD-H), predominantly inattentive type (ADHD-I), and combined type (ADHD-C). To aid decision-making for DSM-5 and other future diagnostic systems, a comprehensive literature review and meta-analysis of 546 studies was completed to evaluate the validity of the DSM-IV model of ADHD. Results indicated that DSM-IV criteria identify individuals with significant and persistent impairment in social, academic, occupational, and adaptive functioning when intelligence, demographic factors, and concurrent psychopathology are controlled. Available data overwhelmingly support the concurrent, predictive, and discriminant validity of the distinction between inattention and hyperactivity-impulsivity symptoms, and indicate that nearly all differences among the nominal subtypes are consistent with the relative levels of inattention and hyperactivity-impulsivity symptoms that define the subtypes. In contrast, the validity of the DSM-IV subtype model is compromised by weak evidence for the validity of ADHD-H after first grade, minimal support for the distinction between ADHD-I and ADHD-C in studies of etiological influences, academic and cognitive functioning, and treatment response, and the marked longitudinal instability of all three subtypes. Overall, it is concluded that the DSM-IV ADHD subtypes provide a convenient clinical shorthand to describe the functional and behavioral correlates of current levels of inattention and hyperactivity-impulsivity symptoms, but do not identify discrete subgroups with sufficient long-term stability to justify the classification of distinct forms of the disorder. Empirical support is stronger for an alternative model that would replace the subtypes with dimensional modifiers that reflect the number of inattention and hyperactivity-impulsivity symptoms at the

  20. Racial Differences in PAM50 Subtypes in the Carolina Breast Cancer Study.

    Science.gov (United States)

    Troester, Melissa A; Sun, Xuezheng; Allott, Emma H; Geradts, Joseph; Cohen, Stephanie M; Tse, Chiu-Kit; Kirk, Erin L; Thorne, Leigh B; Mathews, Michelle; Li, Yan; Hu, Zhiyuan; Robinson, Whitney R; Hoadley, Katherine A; Olopade, Olufunmilayo I; Reeder-Hayes, Katherine E; Earp, H Shelton; Olshan, Andrew F; Carey, Lisa A; Perou, Charles M

    2018-02-01

    African American breast cancer patients have lower frequency of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2 (HER2)-negative disease and higher subtype-specific mortality. Racial differences in molecular subtype within clinically defined subgroups are not well understood. Using data and biospecimens from the population-based Carolina Breast Cancer Study (CBCS) Phase 3 (2008-2013), we classified 980 invasive breast cancers using RNA expression-based PAM50 subtype and recurrence (ROR) score that reflects proliferation and tumor size. Molecular subtypes (Luminal A, Luminal B, HER2-enriched, and Basal-like) and ROR scores (high vs low/medium) were compared by race (blacks vs whites) and age (≤50 years vs > 50 years) using chi-square tests and analysis of variance tests. Black women of all ages had a statistically significantly lower frequency of Luminal A breast cancer (25.4% and 33.6% in blacks vs 42.8% and 52.1% in whites; younger and older, respectively). All other subtype frequencies were higher in black women (case-only odds ratio [OR] = 3.11, 95% confidence interval [CI] = 2.22 to 4.37, for Basal-like; OR = 1.45, 95% CI = 1.02 to 2.06, for Luminal B; OR = 2.04, 95% CI = 1.33 to 3.13, for HER2-enriched). Among clinically HR+/HER2- cases, Luminal A subtype was less common and ROR scores were statistically significantly higher among black women. Multigene assays highlight racial disparities in tumor subtype distribution that persist even in clinically defined subgroups. Differences in tumor biology (eg, HER2-enriched status) may be targetable to reduce disparities among clinically ER+/HER2- cases. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com